From 664bb4ed46a32992ad6a4bd3d251cdc81dddbf94 Mon Sep 17 00:00:00 2001
From: etiennebacher <etiennebacher@users.noreply.github.com>
Date: Fri, 15 Sep 2023 11:02:51 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20easystat?=
 =?UTF-8?q?s/datawizard@faa200093e997a634023396c8cb0f9455b82decf=20?=
 =?UTF-8?q?=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 404.html                                      |   2 +-
 CODE_OF_CONDUCT.html                          |   2 +-
 CONTRIBUTING.html                             |   2 +-
 LICENSE-text.html                             |   2 +-
 LICENSE.html                                  |   2 +-
 SUPPORT.html                                  |   2 +-
 apple-touch-icon-120x120.png                  | Bin 15131 -> 15131 bytes
 apple-touch-icon-152x152.png                  | Bin 20318 -> 20318 bytes
 apple-touch-icon-180x180.png                  | Bin 24596 -> 24596 bytes
 apple-touch-icon-60x60.png                    | Bin 6383 -> 6383 bytes
 apple-touch-icon-76x76.png                    | Bin 8704 -> 8704 bytes
 apple-touch-icon.png                          | Bin 24596 -> 24596 bytes
 articles/index.html                           |   2 +-
 articles/selection_syntax.html                |   2 +-
 articles/standardize_data.html                |  26 +++----
 articles/tidyverse_translation.html           |   2 +-
 authors.html                                  |   2 +-
 favicon-16x16.png                             | Bin 1294 -> 1294 bytes
 favicon-32x32.png                             | Bin 2701 -> 2701 bytes
 index.html                                    |   2 +-
 news/index.html                               |  24 ++++++-
 pkgdown.yml                                   |   2 +-
 reference/adjust-1.png                        | Bin 78659 -> 79459 bytes
 reference/adjust.html                         |  64 +++++++++---------
 reference/assign_labels.html                  |   2 +-
 reference/categorize.html                     |   2 +-
 reference/center.html                         |   2 +-
 reference/coef_var.html                       |   2 +-
 reference/coerce_to_numeric.html              |   2 +-
 reference/colnames.html                       |   2 +-
 reference/contr.deviation.html                |   2 +-
 reference/convert_na_to.html                  |   2 +-
 reference/convert_to_na.html                  |   2 +-
 reference/data_arrange.html                   |   2 +-
 reference/data_codebook.html                  |   2 +-
 reference/data_duplicated.html                |   2 +-
 reference/data_extract.html                   |   2 +-
 reference/data_group.html                     |   2 +-
 reference/data_match.html                     |   2 +-
 reference/data_merge.html                     |   2 +-
 reference/data_modify.html                    |   2 +-
 reference/data_partition.html                 |   2 +-
 reference/data_peek.html                      |   2 +-
 reference/data_read.html                      |   2 +-
 reference/data_relocate.html                  |   2 +-
 reference/data_rename.html                    |   2 +-
 reference/data_restoretype.html               |   2 +-
 reference/data_rotate.html                    |   2 +-
 reference/data_seek.html                      |   2 +-
 reference/data_separate.html                  |   2 +-
 reference/data_tabulate.html                  |   2 +-
 reference/data_to_long.html                   |   2 +-
 reference/data_to_wide.html                   |   2 +-
 reference/data_unique.html                    |   2 +-
 reference/data_unite.html                     |   2 +-
 reference/datawizard-package.html             |   2 +-
 reference/demean.html                         |   2 +-
 reference/describe_distribution.html          |   2 +-
 reference/distribution_mode.html              |   2 +-
 reference/dot-is_deprecated.html              |   2 +-
 reference/efc.html                            |   2 +-
 reference/find_columns.html                   |   2 +-
 reference/index.html                          |   2 +-
 reference/labels_to_levels.html               |   2 +-
 reference/makepredictcall.dw_transformer.html |   2 +-
 reference/mean_sd.html                        |   2 +-
 reference/means_by_group.html                 |   2 +-
 reference/nhanes_sample.html                  |   2 +-
 reference/normalize.html                      |   2 +-
 reference/ranktransform.html                  |   2 +-
 reference/recode_into.html                    |   2 +-
 reference/recode_values.html                  |   2 +-
 reference/reexports.html                      |   2 +-
 reference/remove_empty.html                   |   2 +-
 reference/replace_nan_inf.html                |   2 +-
 reference/rescale.html                        |   2 +-
 reference/rescale_weights.html                |   2 +-
 reference/reshape_ci.html                     |   2 +-
 reference/reverse.html                        |   2 +-
 reference/row_means.html                      |   2 +-
 reference/rownames.html                       |   2 +-
 reference/skewness.html                       |   2 +-
 reference/slide.html                          |   2 +-
 reference/smoothness.html                     |   2 +-
 reference/standardize.default.html            |   2 +-
 reference/standardize.html                    |   2 +-
 reference/text_format.html                    |   2 +-
 reference/to_factor.html                      |   2 +-
 reference/to_numeric.html                     |   2 +-
 reference/visualisation_recipe.html           |   2 +-
 reference/weighted_mean.html                  |   2 +-
 reference/winsorize.html                      |   2 +-
 search.json                                   |   2 +-
 93 files changed, 149 insertions(+), 127 deletions(-)

diff --git a/404.html b/404.html
index 49c8e3f29..8cde7b97b 100644
--- a/404.html
+++ b/404.html
@@ -37,7 +37,7 @@
     
     <a class="navbar-brand me-2" href="https://easystats.github.io/datawizard/index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index eb59fbc82..a2ef5c974 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index 1539cea58..7d8c30cad 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 01c1b94df..3b39f0fb8 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE.html b/LICENSE.html
index 44bc9965c..b8cdb4316 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/SUPPORT.html b/SUPPORT.html
index 3356d467c..f122097cc 100644
--- a/SUPPORT.html
+++ b/SUPPORT.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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diff --git a/apple-touch-icon-152x152.png b/apple-touch-icon-152x152.png
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delta 63
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delta 63
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delta 64
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diff --git a/articles/index.html b/articles/index.html
index 80fbe75c0..725983e32 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/selection_syntax.html b/articles/selection_syntax.html
index 8e8fdbeae..d125d44ea 100644
--- a/articles/selection_syntax.html
+++ b/articles/selection_syntax.html
@@ -39,7 +39,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/standardize_data.html b/articles/standardize_data.html
index 5523e3ca7..fd3363c01 100644
--- a/articles/standardize_data.html
+++ b/articles/standardize_data.html
@@ -39,7 +39,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -660,21 +660,21 @@ <h4 id="compare">Compare<a class="anchor" aria-label="anchor" href="#compare"></
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; # Random Effects: Participant_ID</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Model             | Coefficient</span></span>
-<span><span class="co">#&gt; -------------------------------</span></span>
-<span><span class="co">#&gt; m_raw             |       16.49</span></span>
-<span><span class="co">#&gt; m_VariableWise    |        0.45</span></span>
-<span><span class="co">#&gt; m_ParticipantWise |        0.00</span></span>
-<span><span class="co">#&gt; m_Full            |        0.00</span></span>
+<span><span class="co">#&gt; Model             | Coefficient |   SE |         95% CI</span></span>
+<span><span class="co">#&gt; -------------------------------------------------------</span></span>
+<span><span class="co">#&gt; m_raw             |       16.49 | 3.24 | [11.22, 24.22]</span></span>
+<span><span class="co">#&gt; m_VariableWise    |        0.45 | 0.09 | [ 0.30,  0.65]</span></span>
+<span><span class="co">#&gt; m_ParticipantWise |        0.00 |      |               </span></span>
+<span><span class="co">#&gt; m_Full            |        0.00 |      |               </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; # Random Effects: Residual</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Model             | Coefficient</span></span>
-<span><span class="co">#&gt; -------------------------------</span></span>
-<span><span class="co">#&gt; m_raw             |       33.56</span></span>
-<span><span class="co">#&gt; m_VariableWise    |        0.91</span></span>
-<span><span class="co">#&gt; m_ParticipantWise |        0.98</span></span>
-<span><span class="co">#&gt; m_Full            |        1.00</span></span></code></pre>
+<span><span class="co">#&gt; Model             | Coefficient |   SE |         95% CI</span></span>
+<span><span class="co">#&gt; -------------------------------------------------------</span></span>
+<span><span class="co">#&gt; m_raw             |       33.56 | 1.14 | [31.40, 35.86]</span></span>
+<span><span class="co">#&gt; m_VariableWise    |        0.91 | 0.03 | [ 0.85,  0.97]</span></span>
+<span><span class="co">#&gt; m_ParticipantWise |        0.98 |      |               </span></span>
+<span><span class="co">#&gt; m_Full            |        1.00 |      |</span></span></code></pre>
 <p>As we can see, <strong>variable-wise</strong> standardization only
 affects <strong>the coefficient</strong> (which is expected, as it
 changes the unit), but not the test statistic or statistical
diff --git a/articles/tidyverse_translation.html b/articles/tidyverse_translation.html
index d34e4d308..50984d978 100644
--- a/articles/tidyverse_translation.html
+++ b/articles/tidyverse_translation.html
@@ -39,7 +39,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index 3235a842c..536b47b49 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
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-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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diff --git a/index.html b/index.html
index 1f8ea86eb..6e4fed433 100644
--- a/index.html
+++ b/index.html
@@ -39,7 +39,7 @@
     
     <a class="navbar-brand me-2" href="index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index 30349411e..745789145 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -85,6 +85,28 @@
       <small>Source: <a href="https://github.com/easystats/datawizard/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.9.0" id="datawizard-090">datawizard 0.9.0<a class="anchor" aria-label="anchor" href="#datawizard-090"></a></h2><p class="text-muted">CRAN release: 2023-09-15</p>
+<p>NEW FUNCTIONS</p>
+<ul><li><p><code><a href="../reference/row_means.html">row_means()</a></code>, to compute row means, optionally only for the rows with at least <code>min_valid</code> non-missing values.</p></li>
+<li><p><code><a href="../reference/contr.deviation.html">contr.deviation()</a></code> for sum-deviation contrast coding of factors.</p></li>
+<li><p><code><a href="../reference/means_by_group.html">means_by_group()</a></code>, to compute mean values of variables, grouped by levels of specified factors.</p></li>
+<li><p><code><a href="../reference/data_seek.html">data_seek()</a></code>, to seek for variables in a data frame, based on their column names, variables labels, value labels or factor levels. Searching for labels only works for “labelled” data, i.e. when variables have a <code>label</code> or <code>labels</code> attribute.</p></li>
+</ul><p>CHANGES</p>
+<ul><li><p><code><a href="../reference/recode_into.html">recode_into()</a></code> gains an <code>overwrite</code> argument to skip overwriting already recoded cases when multiple recode patterns apply to the same case.</p></li>
+<li><p><code><a href="../reference/recode_into.html">recode_into()</a></code> gains an <code>preserve_na</code> argument to preserve <code>NA</code> values when recoding.</p></li>
+<li><p><code><a href="../reference/data_read.html">data_read()</a></code> now passes the <code>encoding</code> argument to <code><a href="https://Rdatatable.gitlab.io/data.table/reference/fread.html" class="external-link">data.table::fread()</a></code>. This allows to read files with non-ASCII characters.</p></li>
+<li><p><code>datawizard</code> moves from the GPL-3 license to the MIT license.</p></li>
+<li><p><code><a href="../reference/normalize.html">unnormalize()</a></code> and <code><a href="../reference/standardize.html">unstandardize()</a></code> now work with grouped data (<a href="https://github.com/easystats/datawizard/issues/415" class="external-link">#415</a>).</p></li>
+<li><p><code><a href="../reference/normalize.html">unnormalize()</a></code> now errors instead of emitting a warning if it doesn’t have the necessary info (<a href="https://github.com/easystats/datawizard/issues/415" class="external-link">#415</a>).</p></li>
+</ul><p>BUG FIXES</p>
+<ul><li><p>Fixed issue in <code><a href="../reference/labels_to_levels.html">labels_to_levels()</a></code> when values of labels were not in sorted order and values were not sequentially numbered.</p></li>
+<li><p>Fixed issues in <code><a href="../reference/data_read.html">data_write()</a></code> when writing labelled data into SPSS format and vectors were of different type as value labels.</p></li>
+<li><p>Fixed issue in <code><a href="../reference/recode_into.html">recode_into()</a></code> with probably wrong case number printed in the warning when several recode patterns match to one case.</p></li>
+<li><p>Fixed issue in <code><a href="../reference/recode_into.html">recode_into()</a></code> when original data contained <code>NA</code> values and <code>NA</code> was not included in the recode pattern.</p></li>
+<li><p>Fixed issue in <code><a href="../reference/data_match.html">data_filter()</a></code> where functions containing a <code>=</code> (e.g. when naming arguments, like <code>grepl(pattern, x = a)</code>) were mistakenly seen as faulty syntax.</p></li>
+<li><p>Fixed issue in <code>empty_column()</code> for strings with invalid multibyte strings. For such data frames or files, <code>empty_column()</code> or <code><a href="../reference/data_read.html">data_read()</a></code> no longer fails.</p></li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.8.0" id="datawizard-080">datawizard 0.8.0<a class="anchor" aria-label="anchor" href="#datawizard-080"></a></h2><p class="text-muted">CRAN release: 2023-06-16</p>
 <p>BREAKING CHANGES</p>
diff --git a/pkgdown.yml b/pkgdown.yml
index 8f57e4b9c..ee1c582ed 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   selection_syntax: selection_syntax.html
   standardize_data: standardize_data.html
   tidyverse_translation: tidyverse_translation.html
-last_built: 2023-09-15T10:51Z
+last_built: 2023-09-15T11:00Z
 urls:
   reference: https://easystats.github.io/datawizard/reference
   article: https://easystats.github.io/datawizard/articles
diff --git a/reference/adjust-1.png b/reference/adjust-1.png
index c2c7255ee160da0c56ae848e67fcf68888c2904b..d147e3df8b0cb22884821f725ca9348aba0253e5 100644
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diff --git a/reference/adjust.html b/reference/adjust.html
index c41e9cf0f..700302ec5 100644
--- a/reference/adjust.html
+++ b/reference/adjust.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -273,37 +273,37 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 6 -12.8798594         55         49       44     54       49      34</span>
 <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
 <span class="r-in"><span><span class="fu">adjust</span><span class="op">(</span><span class="va">attitude</span>, effect <span class="op">=</span> <span class="st">"complaints"</span>, select <span class="op">=</span> <span class="st">"rating"</span>, bayesian <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           rating complaints privileges learning raises critical advance</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   -9.883891450         51         30       39     61       92      45</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2    0.299019300         64         51       54     63       73      47</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3    3.768055030         70         68       69     76       86      48</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   -0.945819989         63         45       47     54       84      35</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5    7.726769338         78         56       66     71       83      47</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  -12.904534296         55         49       44     54       49      34</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   -6.966462835         67         42       56     66       68      35</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   -0.007748528         75         50       55     70       66      41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   -4.293873509         82         72       67     71       83      31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   6.564501435         61         45       47     62       80      41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   9.605787127         53         53       58     58       67      34</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   7.319662146         60         47       39     59       74      41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   7.809340723         62         57       42     55       63      25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14  -9.049034220         83         83       45     59       77      35</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   4.481930049         77         54       72     79       77      46</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16  -1.335159201         90         50       72     60       54      36</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17  -4.559355644         85         64       69     79       79      63</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18   5.319662146         60         65       75     55       80      60</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19  -2.231944970         70         46       57     75       85      46</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20  -8.170016431         58         68       54     64       78      52</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21   5.422876378         40         33       34     43       64      33</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22   3.564501435         61         52       62     66       80      41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23 -11.211302123         66         52       50     63       80      37</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24  -2.311641487         37         42       58     50       57      49</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25   7.850626416         54         42       48     66       75      33</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26  -6.518069951         77         66       63     88       76      72</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27   6.992251472         75         58       74     80       78      49</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28  -9.414855719         57         44       45     51       83      38</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29   6.440644356         85         71       71     77       74      55</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30   5.706126491         82         39       59     64       78      39</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          rating complaints privileges learning raises critical advance</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   -9.89339760         51         30       39     61       92      45</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2    0.30196226         64         51       54     63       73      47</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3    3.77674373         70         68       69     76       86      48</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   -0.94383466         63         45       47     54       84      35</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5    7.74311903         78         56       66     71       83      47</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  -12.91020995         55         49       44     54       49      34</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   -6.96064701         67         42       56     66       68      35</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8    0.00572829         75         50       55     70       66      41</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   -4.27369332         82         72       67     71       83      31</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10   6.56457152         61         45       47     62       80      41</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11   9.59819622         53         53       58     58       67      34</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12   7.31877461         60         47       39     59       74      41</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13   7.81036843         62         57       42     55       63      25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14  -9.02789641         83         83       45     59       77      35</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15   4.49732211         77         54       72     79       77      46</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16  -1.30731803         90         50       72     60       54      36</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17  -4.53630259         85         64       69     79       79      63</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18   5.31877461         60         65       75     55       80      60</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19  -2.22325627         70         46       57     75       85      46</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20  -8.17281922         58         68       54     64       78      52</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21   5.40283637         40         33       34     43       64      33</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22   3.56457152         61         52       62     66       80      41</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23 -11.20644392         66         52       50     63       80      37</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24  -2.33455437         37         42       58     50       57      49</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 25   7.84399314         54         42       48     66       75      33</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 26  -6.50267789         77         66       63     88       76      72</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 27   7.00572829         75         58       74     80       78      49</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 28  -9.41861613         57         44       45     51       83      38</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 29   6.46369741         85         71       71     77       74      55</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 30   5.72630668         82         39       59     64       78      39</span>
 <span class="r-in"><span><span class="fu">adjust</span><span class="op">(</span><span class="va">attitude</span>, effect <span class="op">=</span> <span class="st">"complaints"</span>, select <span class="op">=</span> <span class="st">"rating"</span>, additive <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          rating complaints privileges learning raises critical advance</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1   -9.86142016         51         30       39     61       92      45</span>
diff --git a/reference/assign_labels.html b/reference/assign_labels.html
index d42e12772..57f7e1359 100644
--- a/reference/assign_labels.html
+++ b/reference/assign_labels.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/categorize.html b/reference/categorize.html
index c09d1216a..5ee59105a 100644
--- a/reference/categorize.html
+++ b/reference/categorize.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/center.html b/reference/center.html
index 7a5819f2c..b26ede38c 100644
--- a/reference/center.html
+++ b/reference/center.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">datawizard</a>
 
-    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.8.0.14</small>
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
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index 7a939a470..6c0ffef1a 100644
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index 47c00d764..5aa24f450 100644
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index 7b86a91a6..2f3f83bfe 100644
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index e77ab791a..7ecfc723b 100644
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index de414462e..aaec60bf9 100644
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index 49960575b..261d4b5db 100644
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     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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index a498df590..e3c02cddb 100644
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index 05e2cf12a..9e586d3b8 100644
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index d008c8fec..9bc5f81be 100644
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index 86fe62c08..fbc9d0ae8 100644
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index 5c26d6f24..4b47c86f9 100644
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index 07816c6d0..833b388e2 100644
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index 0fef85e63..9250ae863 100644
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index e8c672dc3..def017b80 100644
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index 87a1ba842..186371169 100644
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index 38fafd998..33befe89c 100644
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index b0358770e..1169b434b 100644
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diff --git a/reference/winsorize.html b/reference/winsorize.html
index a77855ed6..84a639f2a 100644
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@@ -10,7 +10,7 @@
     
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+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
 
     
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diff --git a/search.json b/search.json
index 1ae4e0fa3..dcea4945a 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement patilindrajeet.science@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to datawizard","title":"Contributing to datawizard","text":"outlines propose change datawizard.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to datawizard","text":"Small typos grammatical errors documentation may edited directly using GitHub web interface, long changes made source file. want fix typos documentation, please edit related .R file R/ folder. edit .Rd file man/.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"filing-an-issue","dir":"","previous_headings":"","what":"Filing an issue","title":"Contributing to datawizard","text":"easiest way propose change new feature file issue. ’ve found bug, may also create associated issue. possible, try illustrate proposal bug minimal reproducible example.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"pull-requests","dir":"","previous_headings":"","what":"Pull requests","title":"Contributing to datawizard","text":"Please create Git branch pull request (PR). contributed code roughly follow R style guide, particular easystats convention code-style. datawizard uses roxygen2, Markdown syntax, documentation. datawizard uses testthat. Adding tests PR makes easier merge PR code base. PR user-visible change, may add bullet top NEWS.md describing changes made. may optionally add GitHub username, links relevant issue(s)/PR(s).","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to datawizard","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://easystats.github.io/datawizard/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 datawizard authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://easystats.github.io/datawizard/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with {datawizard}","title":"Getting help with {datawizard}","text":"Thanks using datawizard. filing issue, places explore pieces put together make process smooth possible. Start making minimal reproducible example using reprex package. haven’t heard used reprex , ’re treat! Seriously, reprex make R-question-asking endeavors easier (pretty insane ROI five ten minutes ’ll take learn ’s ). additional reprex pointers, check Get help! resource used tidyverse team. Armed reprex, next step figure ask: ’s question: start StackOverflow. people answer questions. ’s bug: ’re right place, file issue. ’re sure: let community help figure ! problem bug feature request, can easily return report . opening new issue, sure search issues pull requests make sure bug hasn’t reported /already fixed development version. default, search pre-populated :issue :open. can edit qualifiers (e.g. :pr, :closed) needed. example, ’d simply remove :open search issues repo, open closed. Thanks help!","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"quoted-names","dir":"Articles","previous_headings":"Selecting variables","what":"Quoted names","title":"A quick summary of selection syntax in `{datawizard}`","text":"simple way select one several variables. Just use character vector containing variables names, like base R.","code":"data_select(iris, c(\"Sepal.Length\", \"Petal.Width\")) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"unquoted-names","dir":"Articles","previous_headings":"Selecting variables","what":"Unquoted names","title":"A quick summary of selection syntax in `{datawizard}`","text":"also possible use unquoted names. useful use tidyverse want consistent way variable names passed.","code":"iris %>%   group_by(Species) %>%   standardise(Petal.Length) %>%   ungroup() #> # A tibble: 10 × 5 #>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species    #>           <dbl>       <dbl>        <dbl>       <dbl> <fct>      #>  1          4.3         3         -1.09          0.1 setosa     #>  2          5           3.3        0.873         0.2 setosa     #>  3          7.7         3.8        1.50          2.2 virginica  #>  4          4.4         3.2        0.218         0.2 setosa     #>  5          5.9         3         -0.542         1.8 virginica  #>  6          6.5         3         -0.414         2   virginica  #>  7          5.5         2.5       -1.09          1.3 versicolor #>  8          5.5         2.6        0.218         1.2 versicolor #>  9          5.8         2.7       -0.542         1.9 virginica  #> 10          6.1         3          0.873         1.4 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"positions","dir":"Articles","previous_headings":"Selecting variables","what":"Positions","title":"A quick summary of selection syntax in `{datawizard}`","text":"addition variable names, select can also take indices variables select dataframe.","code":"data_select(iris, c(1, 2, 5)) #>    Sepal.Length Sepal.Width    Species #> 1           4.3         3.0     setosa #> 2           5.0         3.3     setosa #> 3           7.7         3.8  virginica #> 4           4.4         3.2     setosa #> 5           5.9         3.0  virginica #> 6           6.5         3.0  virginica #> 7           5.5         2.5 versicolor #> 8           5.5         2.6 versicolor #> 9           5.8         2.7  virginica #> 10          6.1         3.0 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"functions","dir":"Articles","previous_headings":"Selecting variables","what":"Functions","title":"A quick summary of selection syntax in `{datawizard}`","text":"can also pass function select argument. function applied columns return TRUE FALSE. example, want keep numeric columns, can use .numeric. Note can provide custom function select, provided returns TRUE FALSE applied column.","code":"data_select(iris, is.numeric) #>    Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1           4.3         3.0          1.1         0.1 #> 2           5.0         3.3          1.4         0.2 #> 3           7.7         3.8          6.7         2.2 #> 4           4.4         3.2          1.3         0.2 #> 5           5.9         3.0          5.1         1.8 #> 6           6.5         3.0          5.2         2.0 #> 7           5.5         2.5          4.0         1.3 #> 8           5.5         2.6          4.4         1.2 #> 9           5.8         2.7          5.1         1.9 #> 10          6.1         3.0          4.6         1.4 my_function <- function(i) {   is.numeric(i) && mean(i, na.rm = TRUE) > 3.5 }  data_select(iris, my_function) #>    Sepal.Length Petal.Length #> 1           4.3          1.1 #> 2           5.0          1.4 #> 3           7.7          6.7 #> 4           4.4          1.3 #> 5           5.9          5.1 #> 6           6.5          5.2 #> 7           5.5          4.0 #> 8           5.5          4.4 #> 9           5.8          5.1 #> 10          6.1          4.6"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"patterns","dir":"Articles","previous_headings":"Selecting variables","what":"Patterns","title":"A quick summary of selection syntax in `{datawizard}`","text":"larger datasets, tedious write names variables select, fragile rely variable positions may change later. end, can use four select helpers: starts_with(), ends_with(), contains(), regex(). first three can take several patterns, regex() takes single regular expression.","code":"data_select(iris, starts_with(\"Sep\", \"Peta\")) #>    Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1           4.3         3.0          1.1         0.1 #> 2           5.0         3.3          1.4         0.2 #> 3           7.7         3.8          6.7         2.2 #> 4           4.4         3.2          1.3         0.2 #> 5           5.9         3.0          5.1         1.8 #> 6           6.5         3.0          5.2         2.0 #> 7           5.5         2.5          4.0         1.3 #> 8           5.5         2.6          4.4         1.2 #> 9           5.8         2.7          5.1         1.9 #> 10          6.1         3.0          4.6         1.4  data_select(iris, ends_with(\"dth\", \"ies\")) #>    Sepal.Width Petal.Width    Species #> 1          3.0         0.1     setosa #> 2          3.3         0.2     setosa #> 3          3.8         2.2  virginica #> 4          3.2         0.2     setosa #> 5          3.0         1.8  virginica #> 6          3.0         2.0  virginica #> 7          2.5         1.3 versicolor #> 8          2.6         1.2 versicolor #> 9          2.7         1.9  virginica #> 10         3.0         1.4 versicolor  data_select(iris, contains(\"pal\", \"ec\")) #>    Sepal.Length Sepal.Width    Species #> 1           4.3         3.0     setosa #> 2           5.0         3.3     setosa #> 3           7.7         3.8  virginica #> 4           4.4         3.2     setosa #> 5           5.9         3.0  virginica #> 6           6.5         3.0  virginica #> 7           5.5         2.5 versicolor #> 8           5.5         2.6 versicolor #> 9           5.8         2.7  virginica #> 10          6.1         3.0 versicolor  data_select(iris, regex(\"^Sep|ies\")) #>    Sepal.Length Sepal.Width    Species #> 1           4.3         3.0     setosa #> 2           5.0         3.3     setosa #> 3           7.7         3.8  virginica #> 4           4.4         3.2     setosa #> 5           5.9         3.0  virginica #> 6           6.5         3.0  virginica #> 7           5.5         2.5 versicolor #> 8           5.5         2.6 versicolor #> 9           5.8         2.7  virginica #> 10          6.1         3.0 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"excluding-variables","dir":"Articles","previous_headings":"","what":"Excluding variables","title":"A quick summary of selection syntax in `{datawizard}`","text":"want keep variables except ones? two ways can invert selection. first way put minus sign \"-\" front select argument. Note use numeric indices, can’t mix negative positive values. means use select = -(1:2) want exclude first two columns; select = -1:2 work: thing variable names: second way use argument exclude. argument possibilities select. Although may required contexts, wanted , use select exclude arguments time.","code":"data_select(iris, -c(\"Sepal.Length\", \"Petal.Width\")) #>    Sepal.Width Petal.Length    Species #> 1          3.0          1.1     setosa #> 2          3.3          1.4     setosa #> 3          3.8          6.7  virginica #> 4          3.2          1.3     setosa #> 5          3.0          5.1  virginica #> 6          3.0          5.2  virginica #> 7          2.5          4.0 versicolor #> 8          2.6          4.4 versicolor #> 9          2.7          5.1  virginica #> 10         3.0          4.6 versicolor  data_select(iris, -starts_with(\"Sep\", \"Peta\")) #>       Species #> 1      setosa #> 2      setosa #> 3   virginica #> 4      setosa #> 5   virginica #> 6   virginica #> 7  versicolor #> 8  versicolor #> 9   virginica #> 10 versicolor  data_select(iris, -is.numeric) #>       Species #> 1      setosa #> 2      setosa #> 3   virginica #> 4      setosa #> 5   virginica #> 6   virginica #> 7  versicolor #> 8  versicolor #> 9   virginica #> 10 versicolor data_select(iris, -(1:2)) #>    Petal.Length Petal.Width    Species #> 1           1.1         0.1     setosa #> 2           1.4         0.2     setosa #> 3           6.7         2.2  virginica #> 4           1.3         0.2     setosa #> 5           5.1         1.8  virginica #> 6           5.2         2.0  virginica #> 7           4.0         1.3 versicolor #> 8           4.4         1.2 versicolor #> 9           5.1         1.9  virginica #> 10          4.6         1.4 versicolor data_select(iris, -(Petal.Length:Species)) #>    Sepal.Length Sepal.Width #> 1           4.3         3.0 #> 2           5.0         3.3 #> 3           7.7         3.8 #> 4           4.4         3.2 #> 5           5.9         3.0 #> 6           6.5         3.0 #> 7           5.5         2.5 #> 8           5.5         2.6 #> 9           5.8         2.7 #> 10          6.1         3.0 data_select(iris, exclude = c(\"Sepal.Length\", \"Petal.Width\")) #>    Sepal.Width Petal.Length    Species #> 1          3.0          1.1     setosa #> 2          3.3          1.4     setosa #> 3          3.8          6.7  virginica #> 4          3.2          1.3     setosa #> 5          3.0          5.1  virginica #> 6          3.0          5.2  virginica #> 7          2.5          4.0 versicolor #> 8          2.6          4.4 versicolor #> 9          2.7          5.1  virginica #> 10         3.0          4.6 versicolor  data_select(iris, exclude = starts_with(\"Sep\", \"Peta\")) #>       Species #> 1      setosa #> 2      setosa #> 3   virginica #> 4      setosa #> 5   virginica #> 6   virginica #> 7  versicolor #> 8  versicolor #> 9   virginica #> 10 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"programming-with-selections","dir":"Articles","previous_headings":"","what":"Programming with selections","title":"A quick summary of selection syntax in `{datawizard}`","text":"Since datawizard 0.6.0, possible pass function arguments loop indices select exclude arguments. makes easier program datawizard. example, want let user decide selection want use: also possible pass values loops, example list patterns want relocate columns based patterns, one one: loop , columns starting \"Sep\" moved end data frame, thing made columns starting \"Pet\".","code":"my_function <- function(data, selection) {   find_columns(data, select = selection) } my_function(iris, \"Sepal.Length\") #> [1] \"Sepal.Length\" my_function(iris, starts_with(\"Sep\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  my_function_2 <- function(data, pattern) {   find_columns(data, select = starts_with(pattern)) } my_function_2(iris, \"Sep\") #> [1] \"Sepal.Length\" \"Sepal.Width\" new_iris <- iris for (i in c(\"Sep\", \"Pet\")) {   new_iris <- new_iris %>%     data_relocate(select = starts_with(i), after = -1) } new_iris #>       Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1      setosa          4.3         3.0          1.1         0.1 #> 2      setosa          5.0         3.3          1.4         0.2 #> 3   virginica          7.7         3.8          6.7         2.2 #> 4      setosa          4.4         3.2          1.3         0.2 #> 5   virginica          5.9         3.0          5.1         1.8 #> 6   virginica          6.5         3.0          5.2         2.0 #> 7  versicolor          5.5         2.5          4.0         1.3 #> 8  versicolor          5.5         2.6          4.4         1.2 #> 9   virginica          5.8         2.7          5.1         1.9 #> 10 versicolor          6.1         3.0          4.6         1.4"},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"ignore-the-case","dir":"Articles","previous_headings":"Useful to know","what":"Ignore the case","title":"A quick summary of selection syntax in `{datawizard}`","text":"every selection uses variable names, can ignore case selection applying ignore_case = TRUE.","code":"data_select(iris, c(\"sepal.length\", \"petal.width\"), ignore_case = TRUE) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4  data_select(iris, ~ Sepal.length + petal.Width, ignore_case = TRUE) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4  data_select(iris, starts_with(\"sep\", \"peta\"), ignore_case = TRUE) #>    Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1           4.3         3.0          1.1         0.1 #> 2           5.0         3.3          1.4         0.2 #> 3           7.7         3.8          6.7         2.2 #> 4           4.4         3.2          1.3         0.2 #> 5           5.9         3.0          5.1         1.8 #> 6           6.5         3.0          5.2         2.0 #> 7           5.5         2.5          4.0         1.3 #> 8           5.5         2.6          4.4         1.2 #> 9           5.8         2.7          5.1         1.9 #> 10          6.1         3.0          4.6         1.4"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"formulas","dir":"Articles","previous_headings":"Useful to know","what":"Formulas","title":"A quick summary of selection syntax in `{datawizard}`","text":"also possible use formulas select variables: made easier use selection custom functions datawizard 0.6.0, kept available backward compatibility.","code":"data_select(iris, ~ Sepal.Length + Petal.Width) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data Standardization","text":"make sense data effects, scientists might want standardize (Z-score) variables. makes data unitless, expressed terms deviation index centrality (e.g., mean median). However, aside benefits, standardization also comes challenges issues, scientist aware .","code":""},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"methods-of-standardization","dir":"Articles","previous_headings":"Introduction","what":"Methods of Standardization","title":"Data Standardization","text":"datawizard package offers two methods standardization via standardize() function: Normal standardization: center around mean, SD units (default). Robust standardization: center around median, MAD (median absolute deviation) units (robust = TRUE). Let’s look following example: can see different methods give different central variation values: standardize() can also used standardize full data frame - numeric variable standardized separately: Weighted standardization also supported via weights argument, factors can also standardized (’re kind thing) setting force = TRUE, converts factors treatment-coded dummy variables standardizing.","code":"library(datawizard) library(effectsize) # for data  # let's have a look at what the data look like data(\"hardlyworking\", package = \"effectsize\") head(hardlyworking) #>     salary xtra_hours n_comps age seniority is_senior #> 1 19744.65       4.16       1  32         3     FALSE #> 2 11301.95       1.62       0  34         3     FALSE #> 3 20635.62       1.19       3  33         5      TRUE #> 4 23047.16       7.19       1  35         3     FALSE #> 5 27342.15      11.26       0  33         4     FALSE #> 6 25656.63       3.63       2  30         5      TRUE # let's use both methods of standardization hardlyworking$xtra_hours_z <- standardize(hardlyworking$xtra_hours) hardlyworking$xtra_hours_zr <- standardize(hardlyworking$xtra_hours, robust = TRUE) library(dplyr)  hardlyworking %>%   select(starts_with(\"xtra_hours\")) %>%   data_to_long() %>%   group_by(Name) %>%   summarise(     mean = mean(Value),     sd = sd(Value),     median = median(Value),     mad = mad(Value)   ) hardlyworking_z <- standardize(hardlyworking) hardlyworking_z %>%   select(-xtra_hours_z, -xtra_hours_zr) %>%   data_to_long() %>%   group_by(Name) %>%   summarise(     mean = mean(Value),     sd = sd(Value),     median = median(Value),     mad = mad(Value)   )"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"variable-wise-vs--participant-wise","dir":"Articles","previous_headings":"Introduction","what":"Variable-wise vs. Participant-wise","title":"Data Standardization","text":"Standardization important step extra caution required repeated-measures designs, three ways standardizing data: Variable-wise: common method. simple scaling column. Participant-wise: Variables standardized “within” participant, .e., participant, participant’s mean SD. Full: Participant-wise first re-standardizing variable-wise. Unfortunately, method used often explicitly stated. issue methods can generate important discrepancies (can turn contribute reproducibility crisis). Let’s investigate 3 methods.","code":""},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"the-data","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"The Data","title":"Data Standardization","text":"take emotion dataset participants exposed negative pictures rate emotions (valence) amount memories associated picture (autobiographical link). One make hypothesis young participants context war violence, negative pictures (mutilations) less related memories less negative pictures (involving example car crashes sick people). words, expect positive relationship valence (high values corresponding less negativity) autobiographical link. Let’s look data, averaged participants: can see means SDs, lot variability participants means individual within-participant SD.","code":"# Download the 'emotion' dataset load(url(\"https://raw.githubusercontent.com/neuropsychology/psycho.R/master/data/emotion.rda\"))  # Discard neutral pictures (keep only negative) emotion <- emotion %>% filter(Emotion_Condition == \"Negative\")  # Summary emotion %>%   drop_na(Subjective_Valence, Autobiographical_Link) %>%   group_by(Participant_ID) %>%   summarise(     n_Trials = n(),     Valence_Mean = mean(Subjective_Valence),     Valence_SD = sd(Subjective_Valence)   ) #> # A tibble: 19 × 4 #> # Groups:   Participant_ID [19] #>    Participant_ID n_Trials Valence_Mean Valence_SD #>    <fct>             <int>        <dbl>      <dbl> #>  1 10S                  24       -58.1       42.6  #>  2 11S                  24       -73.2       37.0  #>  3 12S                  24       -57.5       26.6  #>  4 13S                  24       -63.2       23.7  #>  5 14S                  24       -56.6       26.5  #>  6 15S                  24       -60.6       33.7  #>  7 16S                  24       -46.1       24.9  #>  8 17S                  24        -1.54       4.98 #>  9 18S                  24       -67.2       35.0  #> 10 19S                  24       -59.6       33.2  #> 11 1S                   24       -53.0       42.9  #> 12 2S                   23       -43.0       39.2  #> 13 3S                   24       -64.3       34.4  #> 14 4S                   24       -81.6       27.6  #> 15 5S                   24       -58.1       25.3  #> 16 6S                   24       -74.7       29.2  #> 17 7S                   24       -62.3       39.7  #> 18 8S                   24       -56.9       32.7  #> 19 9S                   24       -31.5       52.7"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"effect-of-standardization","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Effect of Standardization","title":"Data Standardization","text":"create three data frames standardized three techniques. Let’s see three standardization techniques affected Valence variable.","code":"Z_VariableWise <- emotion %>%   standardize()  Z_ParticipantWise <- emotion %>%   group_by(Participant_ID) %>%   standardize()  Z_Full <- emotion %>%   group_by(Participant_ID) %>%   standardize() %>%   ungroup() %>%   standardize()"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"across-participants","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Across Participants","title":"Data Standardization","text":"can calculate mean SD Valence across participants: means SD appear fairly similar (0 1)…  marginal distributions…","code":"# Create a convenient function to print summarise_Subjective_Valence <- function(data) {   df_name <- deparse(substitute(data))   data %>%     ungroup() %>%     summarise(       DF = df_name,       Mean = mean(Subjective_Valence),       SD = sd(Subjective_Valence)     ) } # Check the results rbind(   summarise_Subjective_Valence(Z_VariableWise),   summarise_Subjective_Valence(Z_ParticipantWise),   summarise_Subjective_Valence(Z_Full) ) library(see) library(ggplot2)  ggplot() +   geom_density(aes(Z_VariableWise$Subjective_Valence,     color = \"Z_VariableWise\"   ), linewidth = 1) +   geom_density(aes(Z_ParticipantWise$Subjective_Valence,     color = \"Z_ParticipantWise\"   ), linewidth = 1) +   geom_density(aes(Z_Full$Subjective_Valence,     color = \"Z_Full\"   ), linewidth = 1) +   see::theme_modern() +   labs(color = \"\")"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"at-the-participant-level","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"At the Participant Level","title":"Data Standardization","text":"However, can also look happens participant level. Let’s look first 5 participants: Seems like full participant-wise standardization give similar results, different ones variable-wise standardization.","code":"# Create convenient function print_participants <- function(data) {   df_name <- deparse(substitute(data))   data %>%     group_by(Participant_ID) %>%     summarise(       DF = df_name,       Mean = mean(Subjective_Valence),       SD = sd(Subjective_Valence)     ) %>%     head(5) %>%     select(DF, everything()) }  # Check the results rbind(   print_participants(Z_VariableWise),   print_participants(Z_ParticipantWise),   print_participants(Z_Full) )"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"compare","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Compare","title":"Data Standardization","text":"Let’s correlation variable-wise participant-wise methods.  three standardization methods roughly present characteristics general level (mean 0 SD 1) similar distribution, values exactly ! Let’s now answer original question investigating linear relationship valence autobiographical link. can running mixed-effects model participants entered random effects. can extract parameters interest model, find: can see, variable-wise standardization affects coefficient (expected, changes unit), test statistic statistical significance. However, using participant-wise standardization affect coefficient significance. method better justified, choice depends specific case, context, data goal.","code":"r <- cor.test(   Z_VariableWise$Subjective_Valence,   Z_ParticipantWise$Subjective_Valence )  data.frame(   Original = emotion$Subjective_Valence,   VariableWise = Z_VariableWise$Subjective_Valence,   ParticipantWise = Z_ParticipantWise$Subjective_Valence ) %>%   ggplot(aes(x = VariableWise, y = ParticipantWise, colour = Original)) +   geom_point(alpha = 0.75, shape = 16) +   geom_smooth(method = \"lm\", color = \"black\") +   scale_color_distiller(palette = 1) +   ggtitle(paste0(\"r = \", round(r$estimate, 2))) +   see::theme_modern() library(lme4) m_raw <- lmer(   formula = Subjective_Valence ~ Autobiographical_Link + (1 | Participant_ID),   data = emotion ) m_VariableWise <- update(m_raw, data = Z_VariableWise) m_ParticipantWise <- update(m_raw, data = Z_ParticipantWise) m_Full <- update(m_raw, data = Z_Full) # Convenient function get_par <- function(model) {   mod_name <- deparse(substitute(model))   parameters::model_parameters(model) %>%     mutate(Model = mod_name) %>%     select(-Parameter) %>%     select(Model, everything()) %>%     .[-1, ] }  # Run the model on all datasets rbind(   get_par(m_raw),   get_par(m_VariableWise),   get_par(m_ParticipantWise),   get_par(m_Full) ) #> # Fixed Effects #>  #> Model             | Coefficient |   SE |        95% CI | t(451) |     p #> ----------------------------------------------------------------------- #> m_raw             |        0.09 | 0.07 | [-0.04, 0.22] |   1.36 | 0.174 #> m_VariableWise    |        0.07 | 0.05 | [-0.03, 0.17] |   1.36 | 0.174 #> m_ParticipantWise |        0.08 | 0.05 | [-0.01, 0.17] |   1.75 | 0.080 #> m_Full            |        0.08 | 0.05 | [-0.01, 0.17] |   1.75 | 0.080 #>  #> # Random Effects: Participant_ID #>  #> Model             | Coefficient #> ------------------------------- #> m_raw             |       16.49 #> m_VariableWise    |        0.45 #> m_ParticipantWise |        0.00 #> m_Full            |        0.00 #>  #> # Random Effects: Residual #>  #> Model             | Coefficient #> ------------------------------- #> m_raw             |       33.56 #> m_VariableWise    |        0.91 #> m_ParticipantWise |        0.98 #> m_Full            |        1.00"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"conclusion","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Conclusion","title":"Data Standardization","text":"Standardization can useful cases justified. Variable Participant-wise standardization methods appear produce similar data. Variable Participant-wise standardization can lead different results. chosen method can strongly influence results therefore explicitly stated justified enhance reproducibility results. showed yet another way sneakily tweaking data can change results. prevent use bad practice, can highlight importance open data, open analysis/scripts, preregistration.","code":""},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"see-also","dir":"Articles","previous_headings":"","what":"See also","title":"Data Standardization","text":"datawizard::demean(): https://easystats.github.io/datawizard/reference/demean.html standardize_parameters(method = \"pseudo\") mixed-effects models https://easystats.github.io/parameters/articles/standardize_parameters_effsize.html","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Coming from 'tidyverse'","text":"datawizard package aims make basic data wrangling easier base R. data wrangling workflow supports similar one supported tidyverse package combination dplyr tidyr. However, one main features dependencies: {stats} {utils} (included base R) insight, core package easystats ecosystem. package grew organically simultaneously satisfy “0 non-base hard dependency” principle easystats data wrangling needs constituent packages ecosystem. One drawback genesis features tidyverse packages supported since features necessary easystats ecosystem implemented. missing features (summarize pipe operator %>%) made available dependency-free packages, {poorman}. also important note datawizard designed avoid namespace collisions tidyverse packages. article, see go basic data wrangling steps datawizard. also compare tidyverse syntax achieving . way, decide make switch, can easily find translations . vignette largely inspired dplyr’s Getting started vignette.","code":"library(dplyr) library(tidyr) library(datawizard)  data(efc) efc <- head(efc)"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"workhorses","dir":"Articles","previous_headings":"","what":"Workhorses","title":"Coming from 'tidyverse'","text":"look tidyverse equivalents, can first look datawizard’s key functions data wrangling: Note functions datawizard strict equivalent dplyr tidyr (e.g data_rotate()), won’t discuss next section.","code":""},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"equivalence-with-dplyr-tidyr","dir":"Articles","previous_headings":"","what":"Equivalence with {dplyr} / {tidyr}","title":"Coming from 'tidyverse'","text":"look individually, let’s first look summary table equivalence.","code":""},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"filtering","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Filtering","title":"Coming from 'tidyverse'","text":"data_filter() wrapper around subset(). However, want several filtering conditions, can either use & (subset()) , (dplyr::filter()).","code":"# ---------- datawizard ----------- starwars %>%   data_filter(     skin_color == \"light\",     eye_color == \"brown\"   )  # or starwars %>%   data_filter(     skin_color == \"light\" &       eye_color == \"brown\"   ) # ---------- tidyverse ----------- starwars %>%   filter(     skin_color == \"light\",     eye_color == \"brown\"   ) ## # A tibble: 7 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Leia Org…    150    49 brown      light      brown             19 fema… femin… ## 2 Biggs Da…    183    84 black      light      brown             24 male  mascu… ## 3 Cordé        157    NA brown      light      brown             NA fema… femin… ## 4 Dormé        165    NA brown      light      brown             NA fema… femin… ## 5 Raymus A…    188    79 brown      light      brown             NA male  mascu… ## 6 Poe Dame…     NA    NA brown      light      brown             NA male  mascu… ## 7 Padmé Am…    165    45 brown      light      brown             46 fema… femin… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> ## # A tibble: 7 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Leia Org…    150    49 brown      light      brown             19 fema… femin… ## 2 Biggs Da…    183    84 black      light      brown             24 male  mascu… ## 3 Cordé        157    NA brown      light      brown             NA fema… femin… ## 4 Dormé        165    NA brown      light      brown             NA fema… femin… ## 5 Raymus A…    188    79 brown      light      brown             NA male  mascu… ## 6 Poe Dame…     NA    NA brown      light      brown             NA male  mascu… ## 7 Padmé Am…    165    45 brown      light      brown             46 fema… femin… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list>"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"selecting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Selecting","title":"Coming from 'tidyverse'","text":"data_select() equivalent dplyr::select(). main difference two functions data_select() uses two arguments (select exclude) requires quoted column names want select several variables, dplyr::select() accepts unquoted column names. can find list select helpers ?data_select.","code":"# ---------- datawizard ----------- starwars %>%   data_select(select = c(\"hair_color\", \"skin_color\", \"eye_color\")) # ---------- tidyverse ----------- starwars %>%   select(hair_color, skin_color, eye_color) ## # A tibble: 6 × 3 ##   hair_color  skin_color  eye_color ##   <chr>       <chr>       <chr>     ## 1 blond       fair        blue      ## 2 NA          gold        yellow    ## 3 NA          white, blue red       ## 4 none        white       yellow    ## 5 brown       light       brown     ## 6 brown, grey light       blue # ---------- datawizard ----------- starwars %>%   data_select(select = -ends_with(\"color\")) # ---------- tidyverse ----------- starwars %>%   select(-ends_with(\"color\")) ## # A tibble: 6 × 11 ##   name     height  mass birth_year sex   gender homeworld species films vehicles ##   <chr>     <int> <dbl>      <dbl> <chr> <chr>  <chr>     <chr>   <lis> <list>   ## 1 Luke Sk…    172    77       19   male  mascu… Tatooine  Human   <chr> <chr>    ## 2 C-3PO       167    75      112   none  mascu… Tatooine  Droid   <chr> <chr>    ## 3 R2-D2        96    32       33   none  mascu… Naboo     Droid   <chr> <chr>    ## 4 Darth V…    202   136       41.9 male  mascu… Tatooine  Human   <chr> <chr>    ## 5 Leia Or…    150    49       19   fema… femin… Alderaan  Human   <chr> <chr>    ## 6 Owen La…    178   120       52   male  mascu… Tatooine  Human   <chr> <chr>    ## # ℹ 1 more variable: starships <list> # ---------- datawizard ----------- starwars %>%   data_select(select = -(hair_color:eye_color)) # ---------- tidyverse ----------- starwars %>%   select(!(hair_color:eye_color)) ## # A tibble: 6 × 11 ##   name     height  mass birth_year sex   gender homeworld species films vehicles ##   <chr>     <int> <dbl>      <dbl> <chr> <chr>  <chr>     <chr>   <lis> <list>   ## 1 Luke Sk…    172    77       19   male  mascu… Tatooine  Human   <chr> <chr>    ## 2 C-3PO       167    75      112   none  mascu… Tatooine  Droid   <chr> <chr>    ## 3 R2-D2        96    32       33   none  mascu… Naboo     Droid   <chr> <chr>    ## 4 Darth V…    202   136       41.9 male  mascu… Tatooine  Human   <chr> <chr>    ## 5 Leia Or…    150    49       19   fema… femin… Alderaan  Human   <chr> <chr>    ## 6 Owen La…    178   120       52   male  mascu… Tatooine  Human   <chr> <chr>    ## # ℹ 1 more variable: starships <list> # ---------- datawizard ----------- starwars %>%   data_select(exclude = regex(\"color$\")) # ---------- tidyverse ----------- starwars %>%   select(-contains(\"color$\")) ## # A tibble: 6 × 11 ##   name     height  mass birth_year sex   gender homeworld species films vehicles ##   <chr>     <int> <dbl>      <dbl> <chr> <chr>  <chr>     <chr>   <lis> <list>   ## 1 Luke Sk…    172    77       19   male  mascu… Tatooine  Human   <chr> <chr>    ## 2 C-3PO       167    75      112   none  mascu… Tatooine  Droid   <chr> <chr>    ## 3 R2-D2        96    32       33   none  mascu… Naboo     Droid   <chr> <chr>    ## 4 Darth V…    202   136       41.9 male  mascu… Tatooine  Human   <chr> <chr>    ## 5 Leia Or…    150    49       19   fema… femin… Alderaan  Human   <chr> <chr>    ## 6 Owen La…    178   120       52   male  mascu… Tatooine  Human   <chr> <chr>    ## # ℹ 1 more variable: starships <list> # ---------- datawizard ----------- starwars %>%   data_select(select = is.numeric) # ---------- tidyverse ----------- starwars %>%   select(where(is.numeric)) ## # A tibble: 6 × 3 ##   height  mass birth_year ##    <int> <dbl>      <dbl> ## 1    172    77       19   ## 2    167    75      112   ## 3     96    32       33   ## 4    202   136       41.9 ## 5    150    49       19   ## 6    178   120       52"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"modifying","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Modifying","title":"Coming from 'tidyverse'","text":"data_modify() wrapper around base::transform() several additional benefits: allows us use newly created variables following expressions; works grouped data; preserves variable attributes labels; accepts expressions character vectors easy program last point also main difference data_modify() dplyr::mutate(). data_modify() accepts expressions strings: makes easy use custom functions:","code":"# ---------- datawizard ----------- efc %>%   data_modify(     c12hour_c = center(c12hour),     c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),     c12hour_z2 = standardize(c12hour)   ) # ---------- tidyverse ----------- efc %>%   mutate(     c12hour_c = center(c12hour),     c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),     c12hour_z2 = standardize(c12hour)   ) ##   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z c12hour_z2 ## 1      16      2      3        2      12     -67.6 -0.9420928 -0.9420928 ## 2     148      2      3        2      20      64.4  0.8974967  0.8974967 ## 3      70      2      3        1      11     -13.6 -0.1895335 -0.1895335 ## 4      NA      2   <NA>        2      10        NA         NA         NA ## 5     168      2      4        2      12      84.4  1.1762224  1.1762224 ## 6      16      2      4        2      19     -67.6 -0.9420928 -0.9420928 new_exp <- c(   \"c12hour_c = center(c12hour)\",   \"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)\" ) data_modify(efc, new_exp) ##   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z ## 1      16      2      3        2      12     -67.6 -0.9420928 ## 2     148      2      3        2      20      64.4  0.8974967 ## 3      70      2      3        1      11     -13.6 -0.1895335 ## 4      NA      2   <NA>        2      10        NA         NA ## 5     168      2      4        2      12      84.4  1.1762224 ## 6      16      2      4        2      19     -67.6 -0.9420928 miles_to_km <- function(data, var) {   data_modify(     data,     paste0(\"km = \", var, \"* 1.609344\")   ) }  distance <- data.frame(miles = c(1, 8, 233, 88, 9)) distance ##   miles ## 1     1 ## 2     8 ## 3   233 ## 4    88 ## 5     9 miles_to_km(distance, \"miles\") ##   miles         km ## 1     1   1.609344 ## 2     8  12.874752 ## 3   233 374.977152 ## 4    88 141.622272 ## 5     9  14.484096"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"sorting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Sorting","title":"Coming from 'tidyverse'","text":"data_arrange() equivalent dplyr::arrange(). takes two arguments: data frame, vector column names used sort rows. Note contrary functions datawizard, possible use select helpers starts_with() data_arrange(). can also sort variables descending order putting \"-\" front name, like :","code":"# ---------- datawizard ----------- starwars %>%   data_arrange(c(\"hair_color\", \"height\")) # ---------- tidyverse ----------- starwars %>%   arrange(hair_color, height) ## # A tibble: 6 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu… ## 2 Leia Org…    150    49 brown      light      brown           19   fema… femin… ## 3 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu… ## 4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu… ## 5 R2-D2         96    32 NA         white, bl… red             33   none  mascu… ## 6 C-3PO        167    75 NA         gold       yellow         112   none  mascu… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> # ---------- datawizard ----------- starwars %>%   data_arrange(c(\"-hair_color\", \"-height\")) # ---------- tidyverse ----------- starwars %>%   arrange(desc(hair_color), -height) ## # A tibble: 6 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Darth Va…    202   136 none       white      yellow          41.9 male  mascu… ## 2 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu… ## 3 Leia Org…    150    49 brown      light      brown           19   fema… femin… ## 4 Luke Sky…    172    77 blond      fair       blue            19   male  mascu… ## 5 C-3PO        167    75 NA         gold       yellow         112   none  mascu… ## 6 R2-D2         96    32 NA         white, bl… red             33   none  mascu… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list>"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"extracting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Extracting","title":"Coming from 'tidyverse'","text":"Although mostly work data frames, sometimes useful extract single column vector. can done data_extract(), reproduces behavior dplyr::pull(): can also specify several variables select. case, data_extract() equivalent data_select():","code":"# ---------- datawizard ----------- starwars %>%   data_extract(gender) # ---------- tidyverse ----------- starwars %>%   pull(gender) ## [1] \"masculine\" \"masculine\" \"masculine\" \"masculine\" \"feminine\"  \"masculine\" starwars %>%   data_extract(select = contains(\"color\")) ## # A tibble: 6 × 3 ##   hair_color  skin_color  eye_color ##   <chr>       <chr>       <chr>     ## 1 blond       fair        blue      ## 2 NA          gold        yellow    ## 3 NA          white, blue red       ## 4 none        white       yellow    ## 5 brown       light       brown     ## 6 brown, grey light       blue"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"renaming","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Renaming","title":"Coming from 'tidyverse'","text":"data_rename() equivalent dplyr::rename() syntax two different. dplyr::rename() takes new-old pairs column names, data_rename() requires vector column names rename, vector new names columns must length. way data_rename() designed makes easy apply modifications vector column names. example, can remove underscores use TitleCase following code: also possible add prefix suffix subset variables data_addprefix() data_addsuffix(). argument select accepts select helpers saw data_select():","code":"# ---------- datawizard ----------- starwars %>%   data_rename(     pattern = c(\"sex\", \"hair_color\"),     replacement = c(\"Sex\", \"Hair Color\")   ) # ---------- tidyverse ----------- starwars %>%   rename(     Sex = sex,     \"Hair Color\" = hair_color   ) ## # A tibble: 6 × 14 ##   name    height  mass `Hair Color` skin_color eye_color birth_year Sex   gender ##   <chr>    <int> <dbl> <chr>        <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Luke S…    172    77 blond        fair       blue            19   male  mascu… ## 2 C-3PO      167    75 NA           gold       yellow         112   none  mascu… ## 3 R2-D2       96    32 NA           white, bl… red             33   none  mascu… ## 4 Darth …    202   136 none         white      yellow          41.9 male  mascu… ## 5 Leia O…    150    49 brown        light      brown           19   fema… femin… ## 6 Owen L…    178   120 brown, grey  light      blue            52   male  mascu… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> to_rename <- names(starwars)  starwars %>%   data_rename(     pattern = to_rename,     replacement = tools::toTitleCase(gsub(\"_\", \" \", to_rename, fixed = TRUE))   ) ## # A tibble: 6 × 14 ##   Name     Height  Mass `Hair Color` `Skin Color` `Eye Color` `Birth Year` Sex   ##   <chr>     <int> <dbl> <chr>        <chr>        <chr>              <dbl> <chr> ## 1 Luke Sk…    172    77 blond        fair         blue                19   male  ## 2 C-3PO       167    75 NA           gold         yellow             112   none  ## 3 R2-D2        96    32 NA           white, blue  red                 33   none  ## 4 Darth V…    202   136 none         white        yellow              41.9 male  ## 5 Leia Or…    150    49 brown        light        brown               19   fema… ## 6 Owen La…    178   120 brown, grey  light        blue                52   male  ## # ℹ 6 more variables: Gender <chr>, Homeworld <chr>, Species <chr>, ## #   Films <list>, Vehicles <list>, Starships <list> starwars %>%   data_addprefix(     pattern = \"OLD.\",     select = contains(\"color\")   ) %>%   data_addsuffix(     pattern = \".NEW\",     select = -contains(\"color\")   ) ## # A tibble: 6 × 14 ##   name.NEW       height.NEW mass.NEW OLD.hair_color OLD.skin_color OLD.eye_color ##   <chr>               <int>    <dbl> <chr>          <chr>          <chr>         ## 1 Luke Skywalker        172       77 blond          fair           blue          ## 2 C-3PO                 167       75 NA             gold           yellow        ## 3 R2-D2                  96       32 NA             white, blue    red           ## 4 Darth Vader           202      136 none           white          yellow        ## 5 Leia Organa           150       49 brown          light          brown         ## 6 Owen Lars             178      120 brown, grey    light          blue          ## # ℹ 8 more variables: birth_year.NEW <dbl>, sex.NEW <chr>, gender.NEW <chr>, ## #   homeworld.NEW <chr>, species.NEW <chr>, films.NEW <list>, ## #   vehicles.NEW <list>, starships.NEW <list>"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"relocating","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Relocating","title":"Coming from 'tidyverse'","text":"Sometimes, want relocate one small subset columns dataset. Rather typing many names data_select(), can use data_relocate(), equivalent dplyr::relocate(). Just like data_select(), can specify list variables want relocate select exclude. , arguments after1 specify selected columns relocated: addition column names, accept column indices. Finally, one can use = -1 relocate selected columns just last column, = -1 relocate last column.","code":"# ---------- datawizard ----------- starwars %>%   data_relocate(sex:homeworld, before = \"height\") # ---------- tidyverse ----------- starwars %>%   relocate(sex:homeworld, .before = height) ## # A tibble: 6 × 14 ##   name       sex   gender homeworld height  mass hair_color skin_color eye_color ##   <chr>      <chr> <chr>  <chr>      <int> <dbl> <chr>      <chr>      <chr>     ## 1 Luke Skyw… male  mascu… Tatooine     172    77 blond      fair       blue      ## 2 C-3PO      none  mascu… Tatooine     167    75 NA         gold       yellow    ## 3 R2-D2      none  mascu… Naboo         96    32 NA         white, bl… red       ## 4 Darth Vad… male  mascu… Tatooine     202   136 none       white      yellow    ## 5 Leia Orga… fema… femin… Alderaan     150    49 brown      light      brown     ## 6 Owen Lars  male  mascu… Tatooine     178   120 brown, gr… light      blue      ## # ℹ 5 more variables: birth_year <dbl>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> # ---------- datawizard ----------- starwars %>%   data_relocate(sex:homeworld, after = -1) ## # A tibble: 6 × 14 ##   name     height  mass hair_color skin_color eye_color birth_year species films ##   <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>   <lis> ## 1 Luke Sk…    172    77 blond      fair       blue            19   Human   <chr> ## 2 C-3PO       167    75 NA         gold       yellow         112   Droid   <chr> ## 3 R2-D2        96    32 NA         white, bl… red             33   Droid   <chr> ## 4 Darth V…    202   136 none       white      yellow          41.9 Human   <chr> ## 5 Leia Or…    150    49 brown      light      brown           19   Human   <chr> ## 6 Owen La…    178   120 brown, gr… light      blue            52   Human   <chr> ## # ℹ 5 more variables: vehicles <list>, starships <list>, sex <chr>, ## #   gender <chr>, homeworld <chr>"},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"longer","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Reshaping","what":"Longer","title":"Coming from 'tidyverse'","text":"Reshaping data wide long long wide format can done data_to_long() data_to_wide(). functions designed match tidyr::pivot_longer() tidyr::pivot_wider() arguments, thing change function name. However, tidyr::pivot_longer() tidyr::pivot_wider() features available yet. use relig_income dataset, {tidyr} vignette. like reshape dataset 3 columns: religion, count, income. column “religion” doesn’t need change, exclude -religion. , remaining column corresponds income category. Therefore, want move column names single column called “income”. Finally, values corresponding columns reshaped single new column, called “count”. explore bit arguments data_to_long(), use another dataset: billboard dataset.","code":"relig_income ## # A tibble: 18 × 11 ##    religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` `$75-100k` ##    <chr>      <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>      <dbl> ##  1 Agnostic      27        34        60        81        76       137        122 ##  2 Atheist       12        27        37        52        35        70         73 ##  3 Buddhist      27        21        30        34        33        58         62 ##  4 Catholic     418       617       732       670       638      1116        949 ##  5 Don’t k…      15        14        15        11        10        35         21 ##  6 Evangel…     575       869      1064       982       881      1486        949 ##  7 Hindu          1         9         7         9        11        34         47 ##  8 Histori…     228       244       236       238       197       223        131 ##  9 Jehovah…      20        27        24        24        21        30         15 ## 10 Jewish        19        19        25        25        30        95         69 ## 11 Mainlin…     289       495       619       655       651      1107        939 ## 12 Mormon        29        40        48        51        56       112         85 ## 13 Muslim         6         7         9        10         9        23         16 ## 14 Orthodox      13        17        23        32        32        47         38 ## 15 Other C…       9         7        11        13        13        14         18 ## 16 Other F…      20        33        40        46        49        63         46 ## 17 Other W…       5         2         3         4         2         7          3 ## 18 Unaffil…     217       299       374       365       341       528        407 ## # ℹ 3 more variables: `$100-150k` <dbl>, `>150k` <dbl>, ## #   `Don't know/refused` <dbl> # ---------- datawizard ----------- relig_income %>%   data_to_long(     -religion,     names_to = \"income\",     values_to = \"count\"   ) # ---------- tidyverse ----------- relig_income %>%   pivot_longer(     !religion,     names_to = \"income\",     values_to = \"count\"   ) ## # A tibble: 180 × 3 ##    religion income             count ##    <chr>    <chr>              <dbl> ##  1 Agnostic <$10k                 27 ##  2 Agnostic $10-20k               34 ##  3 Agnostic $20-30k               60 ##  4 Agnostic $30-40k               81 ##  5 Agnostic $40-50k               76 ##  6 Agnostic $50-75k              137 ##  7 Agnostic $75-100k             122 ##  8 Agnostic $100-150k            109 ##  9 Agnostic >150k                 84 ## 10 Agnostic Don't know/refused    96 ## # ℹ 170 more rows billboard ## # A tibble: 317 × 79 ##    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7   wk8 ##    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ##  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99    NA ##  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA    NA ##  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54    53 ##  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55    59 ##  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36    49 ##  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2     2 ##  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA    NA ##  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35    38 ##  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16    14 ## 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61    58 ## # ℹ 307 more rows ## # ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>, ## #   wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>, ## #   wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, ## #   wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, ## #   wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, ## #   wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, … # ---------- datawizard ----------- billboard %>%   data_to_long(     cols = starts_with(\"wk\"),     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) # ---------- tidyverse ----------- billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) ## # A tibble: 5,307 × 5 ##    artist  track                   date.entered week   rank ##    <chr>   <chr>                   <date>       <chr> <dbl> ##  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87 ##  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82 ##  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72 ##  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77 ##  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87 ##  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94 ##  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99 ##  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91 ##  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87 ## 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92 ## # ℹ 5,297 more rows"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"wider","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Reshaping","what":"Wider","title":"Coming from 'tidyverse'","text":", use example tidyr vignette show close data_to_wide() pivot_wider() :","code":"fish_encounters ## # A tibble: 114 × 3 ##    fish  station  seen ##    <fct> <fct>   <int> ##  1 4842  Release     1 ##  2 4842  I80_1       1 ##  3 4842  Lisbon      1 ##  4 4842  Rstr        1 ##  5 4842  Base_TD     1 ##  6 4842  BCE         1 ##  7 4842  BCW         1 ##  8 4842  BCE2        1 ##  9 4842  BCW2        1 ## 10 4842  MAE         1 ## # ℹ 104 more rows # ---------- datawizard ----------- fish_encounters %>%   data_to_wide(     names_from = \"station\",     values_from = \"seen\",     values_fill = 0   ) # ---------- tidyverse ----------- fish_encounters %>%   pivot_wider(     names_from = station,     values_from = seen,     values_fill = 0   ) ## # A tibble: 19 × 12 ##    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE   MAW ##    <fct>   <dbl> <dbl>  <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ##  1 4842        1     1      1     1       1     1     1     1     1     1     1 ##  2 4843        1     1      1     1       1     1     1     1     1     1     1 ##  3 4844        1     1      1     1       1     1     1     1     1     1     1 ##  4 4845        1     1      1     1       1     0     0     0     0     0     0 ##  5 4847        1     1      1     0       0     0     0     0     0     0     0 ##  6 4848        1     1      1     1       0     0     0     0     0     0     0 ##  7 4849        1     1      0     0       0     0     0     0     0     0     0 ##  8 4850        1     1      0     1       1     1     1     0     0     0     0 ##  9 4851        1     1      0     0       0     0     0     0     0     0     0 ## 10 4854        1     1      0     0       0     0     0     0     0     0     0 ## 11 4855        1     1      1     1       1     0     0     0     0     0     0 ## 12 4857        1     1      1     1       1     1     1     1     1     0     0 ## 13 4858        1     1      1     1       1     1     1     1     1     1     1 ## 14 4859        1     1      1     1       1     0     0     0     0     0     0 ## 15 4861        1     1      1     1       1     1     1     1     1     1     1 ## 16 4862        1     1      1     1       1     1     1     1     1     0     0 ## 17 4863        1     1      0     0       0     0     0     0     0     0     0 ## 18 4864        1     1      0     0       0     0     0     0     0     0     0 ## 19 4865        1     1      1     0       0     0     0     0     0     0     0"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"joining","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Joining","title":"Coming from 'tidyverse'","text":"datawizard, joining datasets done data_join() (alias data_merge()). Contrary dplyr, unique function takes care types join, specified inside function argument join (default, join = \"left\"). , show perform four common joins: full, left, right inner. use datasets band_membersand band_instruments provided dplyr:","code":"band_members ## # A tibble: 3 × 2 ##   name  band    ##   <chr> <chr>   ## 1 Mick  Stones  ## 2 John  Beatles ## 3 Paul  Beatles band_instruments ## # A tibble: 3 × 2 ##   name  plays  ##   <chr> <chr>  ## 1 John  guitar ## 2 Paul  bass   ## 3 Keith guitar"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"full-join","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Joining","what":"Full join","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"full\") # ---------- tidyverse ----------- band_members %>%   full_join(band_instruments) ## # A tibble: 4 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 Mick  Stones  NA     ## 2 John  Beatles guitar ## 3 Paul  Beatles bass   ## 4 Keith NA      guitar"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"left-and-right-joins","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Joining","what":"Left and right joins","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"left\") # ---------- tidyverse ----------- band_members %>%   left_join(band_instruments) ## # A tibble: 3 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 Mick  Stones  NA     ## 2 John  Beatles guitar ## 3 Paul  Beatles bass # ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"right\") # ---------- tidyverse ----------- band_members %>%   right_join(band_instruments) ## # A tibble: 3 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 John  Beatles guitar ## 2 Paul  Beatles bass   ## 3 Keith NA      guitar"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"inner-join","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Joining","what":"Inner join","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"inner\") # ---------- tidyverse ----------- band_members %>%   inner_join(band_instruments) ## # A tibble: 2 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 John  Beatles guitar ## 2 Paul  Beatles bass"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"uniting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Uniting","title":"Coming from 'tidyverse'","text":"Uniting variables useful e.g create unique indices combining several variables gather years, months, days single date. data_unite() offers interface close tidyr::unite():","code":"test <- data.frame(   year = 2002:2004,   month = c(\"02\", \"03\", \"09\"),   day = c(\"11\", \"22\", \"28\"),   stringsAsFactors = FALSE ) test ##   year month day ## 1 2002    02  11 ## 2 2003    03  22 ## 3 2004    09  28 # ---------- datawizard ----------- test %>%   data_unite(     new_column = \"date\",     select = c(\"year\", \"month\", \"day\"),     separator = \"-\"   ) # ---------- tidyverse ----------- test %>%   unite(     col = \"date\",     year, month, day,     sep = \"-\"   ) ##         date ## 1 2002-02-11 ## 2 2003-03-22 ## 3 2004-09-28 # ---------- datawizard ----------- test %>%   data_unite(     new_column = \"date\",     select = c(\"year\", \"month\", \"day\"),     separator = \"-\",     append = TRUE   ) # ---------- tidyverse ----------- test %>%   unite(     col = \"date\",     year, month, day,     sep = \"-\",     remove = FALSE   ) ##   year month day       date ## 1 2002    02  11 2002-02-11 ## 2 2003    03  22 2003-03-22 ## 3 2004    09  28 2004-09-28"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"separating","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Separating","title":"Coming from 'tidyverse'","text":"Separating variables counterpart uniting variables useful split values multiple columns, e.g. splitting date values years, months days. data_separate() offers interface close tidyr::separate(): Unlike tidyr::separate(), can separate multiple columns one step data_separate().","code":"test <- data.frame(   date_arrival = c(\"2002-02-11\", \"2003-03-22\", \"2004-09-28\"),   date_departure = c(\"2002-03-15\", \"2003-03-28\", \"2004-09-30\"),   stringsAsFactors = FALSE ) test ##   date_arrival date_departure ## 1   2002-02-11     2002-03-15 ## 2   2003-03-22     2003-03-28 ## 3   2004-09-28     2004-09-30 # ---------- datawizard ----------- test %>%   data_separate(     select = \"date_arrival\",     new_columns = c(\"Year\", \"Month\", \"Day\")   ) # ---------- tidyverse ----------- test %>%   separate(     date_arrival,     into = c(\"Year\", \"Month\", \"Day\")   ) ##   date_departure Year Month Day ## 1     2002-03-15 2002    02  11 ## 2     2003-03-28 2003    03  22 ## 3     2004-09-30 2004    09  28 test %>%   data_separate(     new_columns = list(       date_arrival = c(\"Arr_Year\", \"Arr_Month\", \"Arr_Day\"),       date_departure = c(\"Dep_Year\", \"Dep_Month\", \"Dep_Day\")     )   ) ##   Arr_Year Arr_Month Arr_Day Dep_Year Dep_Month Dep_Day ## 1     2002        02      11     2002        03      15 ## 2     2003        03      22     2003        03      28 ## 3     2004        09      28     2004        09      30"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"other-useful-functions","dir":"Articles","previous_headings":"","what":"Other useful functions","title":"Coming from 'tidyverse'","text":"datawizard contains functions necessarily included dplyr tidyr directly modify data. inspired package janitor.","code":""},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"work-with-rownames","dir":"Articles","previous_headings":"Other useful functions","what":"Work with rownames","title":"Coming from 'tidyverse'","text":"can convert column rownames move rownames new column rownames_as_column() column_as_rownames():","code":"mtcars <- head(mtcars) mtcars ##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb ## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 ## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 ## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 ## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 ## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 ## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 mtcars2 <- mtcars %>%   rownames_as_column(var = \"model\")  mtcars2 ##               model  mpg cyl disp  hp drat    wt  qsec vs am gear carb ## 1         Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 ## 2     Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 ## 3        Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 ## 4    Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 ## 5 Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 ## 6           Valiant 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 mtcars2 %>%   column_as_rownames(var = \"model\") ##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb ## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 ## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 ## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 ## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 ## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 ## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"work-with-row-ids","dir":"Articles","previous_headings":"Other useful functions","what":"Work with row ids","title":"Coming from 'tidyverse'","text":"rowid_as_column() close identical tibble::rowid_to_column(). main difference use grouped data. tibble::rowid_to_column() uses one distinct rowid every row dataset, rowid_as_column() creates one id every row group. Therefore, two rows different groups can row id. means rowid_as_column() closer using n() mutate(), like following:","code":"test <- data.frame(   group = c(\"A\", \"A\", \"B\", \"B\"),   value = c(3, 5, 8, 1),   stringsAsFactors = FALSE ) test ##   group value ## 1     A     3 ## 2     A     5 ## 3     B     8 ## 4     B     1 test %>%   data_group(group) %>%   tibble::rowid_to_column() ##   rowid group value ## 1     1     A     3 ## 2     2     A     5 ## 3     3     B     8 ## 4     4     B     1 test %>%   data_group(group) %>%   rowid_as_column() ## # A tibble: 4 × 3 ## # Groups:   group [2] ##   rowid group value ##   <int> <chr> <dbl> ## 1     1 A         3 ## 2     2 A         5 ## 3     1 B         8 ## 4     2 B         1 test %>%   data_group(group) %>%   mutate(id = seq_len(n())) ## # A tibble: 4 × 3 ## # Groups:   group [2] ##   group value    id ##   <chr> <dbl> <int> ## 1 A         3     1 ## 2 A         5     2 ## 3 B         8     1 ## 4 B         1     2"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"work-with-column-names","dir":"Articles","previous_headings":"Other useful functions","what":"Work with column names","title":"Coming from 'tidyverse'","text":"dealing messy data, sometimes useful use row column names, vice versa. can done row_to_colnames() colnames_to_row().","code":"x <- data.frame(   X_1 = c(NA, \"Title\", 1:3),   X_2 = c(NA, \"Title2\", 4:6) ) x ##     X_1    X_2 ## 1  <NA>   <NA> ## 2 Title Title2 ## 3     1      4 ## 4     2      5 ## 5     3      6 x2 <- x %>%   row_to_colnames(row = 2) x2 ##   Title Title2 ## 1  <NA>   <NA> ## 3     1      4 ## 4     2      5 ## 5     3      6 x2 %>%   colnames_to_row() ##       x1     x2 ## 1  Title Title2 ## 11  <NA>   <NA> ## 3      1      4 ## 4      2      5 ## 5      3      6"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"take-a-quick-look-at-the-data","dir":"Articles","previous_headings":"Other useful functions","what":"Take a quick look at the data","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- data_peek(iris) # ---------- tidyverse ----------- glimpse(iris) ## Data frame with 150 rows and 5 variables ##  ## Variable     | Type    | Values                                         ## ----------------------------------------------------------------------- ## Sepal.Length | numeric | 5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5, 4.4, ...   ## Sepal.Width  | numeric | 3.5, 3, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, ... ## Petal.Length | numeric | 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, ...    ## Petal.Width  | numeric | 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, ...    ## Species      | factor  | setosa, setosa, setosa, setosa, setosa, ..."},{"path":"https://easystats.github.io/datawizard/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Indrajeet Patil. Author.            @patilindrajeets Etienne Bacher. Author, maintainer. Dominique Makowski. Author.            @Dom_Makowski Daniel Lüdecke. Author.            @strengejacke Mattan S. Ben-Shachar. Author. Brenton M. Wiernik. Author.            @bmwiernik Rémi Thériault. Contributor.            @rempsyc Thomas J. Faulkenberry. Reviewer. Robert Garrett. Reviewer.","code":""},{"path":"https://easystats.github.io/datawizard/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Patil et al., (2022). datawizard: R Package Easy Data Preparation Statistical Transformations. Journal Open Source Software, 7(78), 4684, https://doi.org/10.21105/joss.04684","code":"@Article{,   title = {{datawizard}: An {R} Package for Easy Data Preparation and Statistical Transformations},   author = {Indrajeet Patil and Dominique Makowski and Mattan S. Ben-Shachar and Brenton M. Wiernik and Etienne Bacher and Daniel Lüdecke},   journal = {Journal of Open Source Software},   year = {2022},   volume = {7},   number = {78},   pages = {4684},   doi = {10.21105/joss.04684}, }"},{"path":"https://easystats.github.io/datawizard/index.html","id":"datawizard-easy-data-wrangling-and-statistical-transformations-","dir":"","previous_headings":"","what":"Easy Data Wrangling and Statistical Transformations","title":"Easy Data Wrangling and Statistical Transformations","text":"datawizard lightweight package easily manipulate, clean, transform, prepare data analysis. part easystats ecosystem, suite R packages deal entire statistical analysis, cleaning data reporting results. covers two aspects data preparation: Data manipulation: datawizard offers similar set functions tidyverse packages, dplyr tidyr, select, filter reshape data, key differences. 1) data manipulation functions start prefix data_* (makes easy identify). 2) Although functions can used exactly tidyverse equivalents, also string-friendly (makes easy program use inside functions). Finally, datawizard super lightweight (dependencies, similar poorman), makes awesome developers use packages. Statistical transformations: datawizard also powerful functions easily apply common data transformations, including standardization, normalization, rescaling, rank-transformation, scale reversing, recoding, binning, etc.","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Easy Data Wrangling and Statistical Transformations","text":"Tip Instead library(datawizard), use library(easystats). make features easystats-ecosystem available. stay updated, use easystats::install_latest().","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Easy Data Wrangling and Statistical Transformations","text":"cite package, run following command:","code":"citation(\"datawizard\") To cite package 'datawizard' in publications use:    Patil et al., (2022). datawizard: An R Package for Easy Data   Preparation and Statistical Transformations. Journal of Open Source   Software, 7(78), 4684, https://doi.org/10.21105/joss.04684  A BibTeX entry for LaTeX users is    @Article{,     title = {{datawizard}: An {R} Package for Easy Data Preparation and Statistical Transformations},     author = {Indrajeet Patil and Dominique Makowski and Mattan S. Ben-Shachar and Brenton M. Wiernik and Etienne Bacher and Daniel Lüdecke},     journal = {Journal of Open Source Software},     year = {2022},     volume = {7},     number = {78},     pages = {4684},     doi = {10.21105/joss.04684},   }"},{"path":"https://easystats.github.io/datawizard/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"Easy Data Wrangling and Statistical Transformations","text":"courses tutorials statistical modeling assume working clean tidy dataset. practice, however, major part statistical modeling preparing data–cleaning values, creating new columns, reshaping dataset, transforming variables. datawizard provides easy use tools perform common, critical, sometimes tedious data preparation tasks.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/index.html","id":"select-filter-and-remove-variables","dir":"","previous_headings":"Data wrangling","what":"Select, filter and remove variables","title":"Easy Data Wrangling and Statistical Transformations","text":"package provides helpers filter rows meeting certain conditions… … logical expressions: Finding columns data frame, retrieving data selected columns, can achieved using find_columns() get_columns(): also possible extract one variables: Due consistent API, removing variables just simple:","code":"data_match(mtcars, data.frame(vs = 0, am = 1)) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 data_filter(mtcars, vs == 0 & am == 1) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 # find column names matching a pattern find_columns(iris, starts_with(\"Sepal\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  # return data columns matching a pattern get_columns(iris, starts_with(\"Sepal\")) |> head() #>   Sepal.Length Sepal.Width #> 1          5.1         3.5 #> 2          4.9         3.0 #> 3          4.7         3.2 #> 4          4.6         3.1 #> 5          5.0         3.6 #> 6          5.4         3.9 # single variable data_extract(mtcars, \"gear\") #>  [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4  # more variables head(data_extract(iris, ends_with(\"Width\"))) #>   Sepal.Width Petal.Width #> 1         3.5         0.2 #> 2         3.0         0.2 #> 3         3.2         0.2 #> 4         3.1         0.2 #> 5         3.6         0.2 #> 6         3.9         0.4 head(data_remove(iris, starts_with(\"Sepal\"))) #>   Petal.Length Petal.Width Species #> 1          1.4         0.2  setosa #> 2          1.4         0.2  setosa #> 3          1.3         0.2  setosa #> 4          1.5         0.2  setosa #> 5          1.4         0.2  setosa #> 6          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/index.html","id":"reorder-or-rename","dir":"","previous_headings":"Data wrangling","what":"Reorder or rename","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"head(data_relocate(iris, select = \"Species\", before = \"Sepal.Length\")) #>   Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1  setosa          5.1         3.5          1.4         0.2 #> 2  setosa          4.9         3.0          1.4         0.2 #> 3  setosa          4.7         3.2          1.3         0.2 #> 4  setosa          4.6         3.1          1.5         0.2 #> 5  setosa          5.0         3.6          1.4         0.2 #> 6  setosa          5.4         3.9          1.7         0.4 head(data_rename(iris, c(\"Sepal.Length\", \"Sepal.Width\"), c(\"length\", \"width\"))) #>   length width Petal.Length Petal.Width Species #> 1    5.1   3.5          1.4         0.2  setosa #> 2    4.9   3.0          1.4         0.2  setosa #> 3    4.7   3.2          1.3         0.2  setosa #> 4    4.6   3.1          1.5         0.2  setosa #> 5    5.0   3.6          1.4         0.2  setosa #> 6    5.4   3.9          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/index.html","id":"merge","dir":"","previous_headings":"Data wrangling","what":"Merge","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"x <- data.frame(a = 1:3, b = c(\"a\", \"b\", \"c\"), c = 5:7, id = 1:3) y <- data.frame(c = 6:8, d = c(\"f\", \"g\", \"h\"), e = 100:102, id = 2:4)  x #>   a b c id #> 1 1 a 5  1 #> 2 2 b 6  2 #> 3 3 c 7  3 y #>   c d   e id #> 1 6 f 100  2 #> 2 7 g 101  3 #> 3 8 h 102  4  data_merge(x, y, join = \"full\") #>    a    b c id    d   e #> 3  1    a 5  1 <NA>  NA #> 1  2    b 6  2    f 100 #> 2  3    c 7  3    g 101 #> 4 NA <NA> 8  4    h 102  data_merge(x, y, join = \"left\") #>   a b c id    d   e #> 3 1 a 5  1 <NA>  NA #> 1 2 b 6  2    f 100 #> 2 3 c 7  3    g 101  data_merge(x, y, join = \"right\") #>    a    b c id d   e #> 1  2    b 6  2 f 100 #> 2  3    c 7  3 g 101 #> 3 NA <NA> 8  4 h 102  data_merge(x, y, join = \"semi\", by = \"c\") #>   a b c id #> 2 2 b 6  2 #> 3 3 c 7  3  data_merge(x, y, join = \"anti\", by = \"c\") #>   a b c id #> 1 1 a 5  1  data_merge(x, y, join = \"inner\") #>   a b c id d   e #> 1 2 b 6  2 f 100 #> 2 3 c 7  3 g 101  data_merge(x, y, join = \"bind\") #>    a    b c id    d   e #> 1  1    a 5  1 <NA>  NA #> 2  2    b 6  2 <NA>  NA #> 3  3    c 7  3 <NA>  NA #> 4 NA <NA> 6  2    f 100 #> 5 NA <NA> 7  3    g 101 #> 6 NA <NA> 8  4    h 102"},{"path":"https://easystats.github.io/datawizard/index.html","id":"reshape","dir":"","previous_headings":"Data wrangling","what":"Reshape","title":"Easy Data Wrangling and Statistical Transformations","text":"common data wrangling task reshape data. Either go wide/Cartesian long/tidy format way","code":"wide_data <- data.frame(replicate(5, rnorm(10)))  head(data_to_long(wide_data)) #>   name       value #> 1   X1 -0.08281164 #> 2   X2 -1.12490028 #> 3   X3 -0.70632036 #> 4   X4 -0.70278946 #> 5   X5  0.07633326 #> 6   X1  1.93468099 long_data <- data_to_long(wide_data, rows_to = \"Row_ID\") # Save row number  data_to_wide(long_data,   names_from = \"name\",   values_from = \"value\",   id_cols = \"Row_ID\" ) #>    Row_ID          X1          X2          X3         X4          X5 #> 1       1 -0.08281164 -1.12490028 -0.70632036 -0.7027895  0.07633326 #> 2       2  1.93468099 -0.87430362  0.96687656  0.2998642 -0.23035595 #> 3       3 -2.05128979  0.04386162 -0.71016648  1.1494697  0.31746484 #> 4       4  0.27773897 -0.58397514 -0.05917365 -0.3016415 -1.59268440 #> 5       5 -1.52596060 -0.82329858 -0.23094342 -0.5473394 -0.18194062 #> 6       6 -0.26916362  0.11059280  0.69200045 -0.3854041  1.75614174 #> 7       7  1.23305388  0.36472778  1.35682290  0.2763720  0.11394932 #> 8       8  0.63360774  0.05370100  1.78872284  0.1518608 -0.29216508 #> 9       9  0.35271746  1.36867235  0.41071582 -0.4313808  1.75409316 #> 10     10 -0.56048248 -0.38045724 -2.18785470 -1.8705001  1.80958455"},{"path":"https://easystats.github.io/datawizard/index.html","id":"empty-rows-and-columns","dir":"","previous_headings":"Data wrangling","what":"Empty rows and columns","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"tmp <- data.frame(   a = c(1, 2, 3, NA, 5),   b = c(1, NA, 3, NA, 5),   c = c(NA, NA, NA, NA, NA),   d = c(1, NA, 3, NA, 5) )  tmp #>    a  b  c  d #> 1  1  1 NA  1 #> 2  2 NA NA NA #> 3  3  3 NA  3 #> 4 NA NA NA NA #> 5  5  5 NA  5  # indices of empty columns or rows empty_columns(tmp) #> c  #> 3 empty_rows(tmp) #> [1] 4  # remove empty columns or rows remove_empty_columns(tmp) #>    a  b  d #> 1  1  1  1 #> 2  2 NA NA #> 3  3  3  3 #> 4 NA NA NA #> 5  5  5  5 remove_empty_rows(tmp) #>   a  b  c  d #> 1 1  1 NA  1 #> 2 2 NA NA NA #> 3 3  3 NA  3 #> 5 5  5 NA  5  # remove empty columns and rows remove_empty(tmp) #>   a  b  d #> 1 1  1  1 #> 2 2 NA NA #> 3 3  3  3 #> 5 5  5  5"},{"path":"https://easystats.github.io/datawizard/index.html","id":"recode-or-cut-dataframe","dir":"","previous_headings":"Data wrangling","what":"Recode or cut dataframe","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"set.seed(123) x <- sample(1:10, size = 50, replace = TRUE)  table(x) #> x #>  1  2  3  4  5  6  7  8  9 10  #>  2  3  5  3  7  5  5  2 11  7  # cut into 3 groups, based on distribution (quantiles) table(categorize(x, split = \"quantile\", n_groups = 3)) #>  #>  1  2  3  #> 13 19 18"},{"path":"https://easystats.github.io/datawizard/index.html","id":"data-transformations","dir":"","previous_headings":"","what":"Data Transformations","title":"Easy Data Wrangling and Statistical Transformations","text":"packages also contains multiple functions help transform data.","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"standardize","dir":"","previous_headings":"Data Transformations","what":"Standardize","title":"Easy Data Wrangling and Statistical Transformations","text":"example, standardize (z-score) data:","code":"# before summary(swiss) #>    Fertility      Agriculture     Examination      Education     #>  Min.   :35.00   Min.   : 1.20   Min.   : 3.00   Min.   : 1.00   #>  1st Qu.:64.70   1st Qu.:35.90   1st Qu.:12.00   1st Qu.: 6.00   #>  Median :70.40   Median :54.10   Median :16.00   Median : 8.00   #>  Mean   :70.14   Mean   :50.66   Mean   :16.49   Mean   :10.98   #>  3rd Qu.:78.45   3rd Qu.:67.65   3rd Qu.:22.00   3rd Qu.:12.00   #>  Max.   :92.50   Max.   :89.70   Max.   :37.00   Max.   :53.00   #>     Catholic       Infant.Mortality #>  Min.   :  2.150   Min.   :10.80    #>  1st Qu.:  5.195   1st Qu.:18.15    #>  Median : 15.140   Median :20.00    #>  Mean   : 41.144   Mean   :19.94    #>  3rd Qu.: 93.125   3rd Qu.:21.70    #>  Max.   :100.000   Max.   :26.60  # after summary(standardize(swiss)) #>    Fertility         Agriculture       Examination         Education       #>  Min.   :-2.81327   Min.   :-2.1778   Min.   :-1.69084   Min.   :-1.0378   #>  1st Qu.:-0.43569   1st Qu.:-0.6499   1st Qu.:-0.56273   1st Qu.:-0.5178   #>  Median : 0.02061   Median : 0.1515   Median :-0.06134   Median :-0.3098   #>  Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.0000   #>  3rd Qu.: 0.66504   3rd Qu.: 0.7481   3rd Qu.: 0.69074   3rd Qu.: 0.1062   #>  Max.   : 1.78978   Max.   : 1.7190   Max.   : 2.57094   Max.   : 4.3702   #>     Catholic       Infant.Mortality   #>  Min.   :-0.9350   Min.   :-3.13886   #>  1st Qu.:-0.8620   1st Qu.:-0.61543   #>  Median :-0.6235   Median : 0.01972   #>  Mean   : 0.0000   Mean   : 0.00000   #>  3rd Qu.: 1.2464   3rd Qu.: 0.60337   #>  Max.   : 1.4113   Max.   : 2.28566"},{"path":"https://easystats.github.io/datawizard/index.html","id":"winsorize","dir":"","previous_headings":"Data Transformations","what":"Winsorize","title":"Easy Data Wrangling and Statistical Transformations","text":"winsorize data:","code":"# before anscombe #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1  10 10 10  8  8.04 9.14  7.46  6.58 #> 2   8  8  8  8  6.95 8.14  6.77  5.76 #> 3  13 13 13  8  7.58 8.74 12.74  7.71 #> 4   9  9  9  8  8.81 8.77  7.11  8.84 #> 5  11 11 11  8  8.33 9.26  7.81  8.47 #> 6  14 14 14  8  9.96 8.10  8.84  7.04 #> 7   6  6  6  8  7.24 6.13  6.08  5.25 #> 8   4  4  4 19  4.26 3.10  5.39 12.50 #> 9  12 12 12  8 10.84 9.13  8.15  5.56 #> 10  7  7  7  8  4.82 7.26  6.42  7.91 #> 11  5  5  5  8  5.68 4.74  5.73  6.89  # after winsorize(anscombe) #>    x1 x2 x3 x4   y1   y2   y3   y4 #> 1  10 10 10  8 8.04 9.13 7.46 6.58 #> 2   8  8  8  8 6.95 8.14 6.77 5.76 #> 3  12 12 12  8 7.58 8.74 8.15 7.71 #> 4   9  9  9  8 8.81 8.77 7.11 8.47 #> 5  11 11 11  8 8.33 9.13 7.81 8.47 #> 6  12 12 12  8 8.81 8.10 8.15 7.04 #> 7   6  6  6  8 7.24 6.13 6.08 5.76 #> 8   6  6  6  8 5.68 6.13 6.08 8.47 #> 9  12 12 12  8 8.81 9.13 8.15 5.76 #> 10  7  7  7  8 5.68 7.26 6.42 7.91 #> 11  6  6  6  8 5.68 6.13 6.08 6.89"},{"path":"https://easystats.github.io/datawizard/index.html","id":"center","dir":"","previous_headings":"Data Transformations","what":"Center","title":"Easy Data Wrangling and Statistical Transformations","text":"grand-mean center data","code":"center(anscombe) #>    x1 x2 x3 x4          y1         y2    y3         y4 #> 1   1  1  1 -1  0.53909091  1.6390909 -0.04 -0.9209091 #> 2  -1 -1 -1 -1 -0.55090909  0.6390909 -0.73 -1.7409091 #> 3   4  4  4 -1  0.07909091  1.2390909  5.24  0.2090909 #> 4   0  0  0 -1  1.30909091  1.2690909 -0.39  1.3390909 #> 5   2  2  2 -1  0.82909091  1.7590909  0.31  0.9690909 #> 6   5  5  5 -1  2.45909091  0.5990909  1.34 -0.4609091 #> 7  -3 -3 -3 -1 -0.26090909 -1.3709091 -1.42 -2.2509091 #> 8  -5 -5 -5 10 -3.24090909 -4.4009091 -2.11  4.9990909 #> 9   3  3  3 -1  3.33909091  1.6290909  0.65 -1.9409091 #> 10 -2 -2 -2 -1 -2.68090909 -0.2409091 -1.08  0.4090909 #> 11 -4 -4 -4 -1 -1.82090909 -2.7609091 -1.77 -0.6109091"},{"path":"https://easystats.github.io/datawizard/index.html","id":"ranktransform","dir":"","previous_headings":"Data Transformations","what":"Ranktransform","title":"Easy Data Wrangling and Statistical Transformations","text":"rank-transform data:","code":"# before head(trees) #>   Girth Height Volume #> 1   8.3     70   10.3 #> 2   8.6     65   10.3 #> 3   8.8     63   10.2 #> 4  10.5     72   16.4 #> 5  10.7     81   18.8 #> 6  10.8     83   19.7  # after head(ranktransform(trees)) #>   Girth Height Volume #> 1     1    6.0    2.5 #> 2     2    3.0    2.5 #> 3     3    1.0    1.0 #> 4     4    8.5    5.0 #> 5     5   25.5    7.0 #> 6     6   28.0    9.0"},{"path":"https://easystats.github.io/datawizard/index.html","id":"rescale","dir":"","previous_headings":"Data Transformations","what":"Rescale","title":"Easy Data Wrangling and Statistical Transformations","text":"rescale numeric variable new range:","code":"change_scale(c(0, 1, 5, -5, -2)) #> [1]  50  60 100   0  30 #> attr(,\"min_value\") #> [1] -5 #> attr(,\"max_value\") #> [1] 5 #> attr(,\"new_min\") #> [1] 0 #> attr(,\"new_max\") #> [1] 100 #> attr(,\"range_difference\") #> [1] 10 #> attr(,\"to_range\") #> [1]   0 100 #> attr(,\"class\") #> [1] \"dw_transformer\" \"numeric\""},{"path":"https://easystats.github.io/datawizard/index.html","id":"rotate-or-transpose","dir":"","previous_headings":"Data Transformations","what":"Rotate or transpose","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"x <- mtcars[1:3, 1:4]  x #>                mpg cyl disp  hp #> Mazda RX4     21.0   6  160 110 #> Mazda RX4 Wag 21.0   6  160 110 #> Datsun 710    22.8   4  108  93  data_rotate(x) #>      Mazda RX4 Mazda RX4 Wag Datsun 710 #> mpg         21            21       22.8 #> cyl          6             6        4.0 #> disp       160           160      108.0 #> hp         110           110       93.0"},{"path":"https://easystats.github.io/datawizard/index.html","id":"data-properties","dir":"","previous_headings":"","what":"Data properties","title":"Easy Data Wrangling and Statistical Transformations","text":"datawizard provides way provide comprehensive descriptive summary variables dataframe: even just variable also additional data properties can computed using package.","code":"data(iris) describe_distribution(iris) #> Variable     | Mean |   SD |  IQR |        Range | Skewness | Kurtosis |   n | n_Missing #> ---------------------------------------------------------------------------------------- #> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] |     0.31 |    -0.55 | 150 |         0 #> Sepal.Width  | 3.06 | 0.44 | 0.52 | [2.00, 4.40] |     0.32 |     0.23 | 150 |         0 #> Petal.Length | 3.76 | 1.77 | 3.52 | [1.00, 6.90] |    -0.27 |    -1.40 | 150 |         0 #> Petal.Width  | 1.20 | 0.76 | 1.50 | [0.10, 2.50] |    -0.10 |    -1.34 | 150 |         0 describe_distribution(mtcars$wt) #> Mean |   SD |  IQR |        Range | Skewness | Kurtosis |  n | n_Missing #> ------------------------------------------------------------------------ #> 3.22 | 0.98 | 1.19 | [1.51, 5.42] |     0.47 |     0.42 | 32 |         0 x <- (-10:10)^3 + rnorm(21, 0, 100) smoothness(x, method = \"diff\") #> [1] 1.791243 #> attr(,\"class\") #> [1] \"parameters_smoothness\" \"numeric\""},{"path":"https://easystats.github.io/datawizard/index.html","id":"function-design-and-pipe-workflow","dir":"","previous_headings":"","what":"Function design and pipe-workflow","title":"Easy Data Wrangling and Statistical Transformations","text":"design datawizard functions follows design principle makes easy user understand remember functions work: first argument data methods work data frames, two arguments following select exclude variables following arguments arguments related specific tasks functions important, functions accept data frames usually first argument, also return (modified) data frame . Thus, datawizard integrates smoothly “pipe-workflow”.","code":"iris |>   # all rows where Species is \"versicolor\" or \"virginica\"   data_filter(Species %in% c(\"versicolor\", \"virginica\")) |>   # select only columns with \".\" in names (i.e. drop Species)   get_columns(contains(\"\\\\.\")) |>   # move columns that ends with \"Length\" to start of data frame   data_relocate(ends_with(\"Length\")) |>   # remove fourth column   data_remove(4) |>   head() #>    Sepal.Length Petal.Length Sepal.Width #> 51          7.0          4.7         3.2 #> 52          6.4          4.5         3.2 #> 53          6.9          4.9         3.1 #> 54          5.5          4.0         2.3 #> 55          6.5          4.6         2.8 #> 56          5.7          4.5         2.8"},{"path":"https://easystats.github.io/datawizard/index.html","id":"contributing-and-support","dir":"","previous_headings":"","what":"Contributing and Support","title":"Easy Data Wrangling and Statistical Transformations","text":"case want file issue contribute another way package, please follow guide. questions functionality, may either contact us via email also file issue.","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Easy Data Wrangling and Statistical Transformations","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust data for the effect of other variable(s) — adjust","title":"Adjust data for the effect of other variable(s) — adjust","text":"function can used adjust data effect variables present dataset. based underlying fitting regressions models, allowing quite flexibility, including factors random effects mixed models (multilevel partialization), continuous variables smooth terms general additive models (non-linear partialization) /fitting models Bayesian framework. values returned function residuals regression models. Note regular correlation two \"adjusted\" variables equivalent partial correlation .","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust data for the effect of other variable(s) — adjust","text":"","code":"adjust(   data,   effect = NULL,   select = is.numeric,   exclude = NULL,   multilevel = FALSE,   additive = FALSE,   bayesian = FALSE,   keep_intercept = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE )  data_adjust(   data,   effect = NULL,   select = is.numeric,   exclude = NULL,   multilevel = FALSE,   additive = FALSE,   bayesian = FALSE,   keep_intercept = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE )"},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust data for the effect of other variable(s) — adjust","text":"data data frame. effect Character vector column names adjusted (regressed ). NULL (default), variables selected. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. multilevel TRUE, factors included random factors. Else, FALSE (default), included fixed effects simple regression model. additive TRUE, continuous variables included smooth terms additive models. goal regress-potential non-linear effects. bayesian TRUE, models fitted Bayesian framework using rstanarm. keep_intercept FALSE (default), intercept model re-added. avoids centering around 0 happens default regressing another variable (see examples visual representation ). ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust data for the effect of other variable(s) — adjust","text":"data frame comparable data, adjusted variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust data for the effect of other variable(s) — adjust","text":"","code":"adjusted_all <- adjust(attitude) head(adjusted_all) #>        rating complaints privileges    learning     raises   critical #> 1  -8.1102953  5.5583770 -15.848949 -2.75102306  0.5742664  15.605502 #> 2   1.6472337  0.0646564  -1.422592 -3.06207012 -1.5567655  -2.315781 #> 3   1.0605589 -7.5116953  11.174609  5.59808033  4.8603132   8.061801 #> 4  -0.2268416  3.8345277  -4.567441  0.03866933 -7.1185324  13.002574 #> 5   6.5462010 -1.2420122  -3.051098  0.87312095 -2.7131349   6.500353 #> 6 -10.9418499  5.2030745   2.664156 -1.24552098  4.1370346 -21.678382 #>      advance #> 1  2.8684130 #> 2  5.3937097 #> 3 -6.4236221 #> 4 -0.3951046 #> 5  2.1988621 #> 6 -3.1912418 adjusted_one <- adjust(attitude, effect = \"complaints\", select = \"rating\") head(adjusted_one) #>        rating complaints privileges learning raises critical advance #> 1  -9.8614202         51         30       39     61       92      45 #> 2   0.3286522         64         51       54     63       73      47 #> 3   3.8009933         70         68       69     76       86      48 #> 4  -0.9167380         63         45       47     54       84      35 #> 5   7.7641147         78         56       66     71       83      47 #> 6 -12.8798594         55         49       44     54       49      34 # \\donttest{ adjust(attitude, effect = \"complaints\", select = \"rating\", bayesian = TRUE) #>           rating complaints privileges learning raises critical advance #> 1   -9.883891450         51         30       39     61       92      45 #> 2    0.299019300         64         51       54     63       73      47 #> 3    3.768055030         70         68       69     76       86      48 #> 4   -0.945819989         63         45       47     54       84      35 #> 5    7.726769338         78         56       66     71       83      47 #> 6  -12.904534296         55         49       44     54       49      34 #> 7   -6.966462835         67         42       56     66       68      35 #> 8   -0.007748528         75         50       55     70       66      41 #> 9   -4.293873509         82         72       67     71       83      31 #> 10   6.564501435         61         45       47     62       80      41 #> 11   9.605787127         53         53       58     58       67      34 #> 12   7.319662146         60         47       39     59       74      41 #> 13   7.809340723         62         57       42     55       63      25 #> 14  -9.049034220         83         83       45     59       77      35 #> 15   4.481930049         77         54       72     79       77      46 #> 16  -1.335159201         90         50       72     60       54      36 #> 17  -4.559355644         85         64       69     79       79      63 #> 18   5.319662146         60         65       75     55       80      60 #> 19  -2.231944970         70         46       57     75       85      46 #> 20  -8.170016431         58         68       54     64       78      52 #> 21   5.422876378         40         33       34     43       64      33 #> 22   3.564501435         61         52       62     66       80      41 #> 23 -11.211302123         66         52       50     63       80      37 #> 24  -2.311641487         37         42       58     50       57      49 #> 25   7.850626416         54         42       48     66       75      33 #> 26  -6.518069951         77         66       63     88       76      72 #> 27   6.992251472         75         58       74     80       78      49 #> 28  -9.414855719         57         44       45     51       83      38 #> 29   6.440644356         85         71       71     77       74      55 #> 30   5.706126491         82         39       59     64       78      39 adjust(attitude, effect = \"complaints\", select = \"rating\", additive = TRUE) #>          rating complaints privileges learning raises critical advance #> 1   -9.86142016         51         30       39     61       92      45 #> 2    0.32865220         64         51       54     63       73      47 #> 3    3.80099328         70         68       69     76       86      48 #> 4   -0.91673799         63         45       47     54       84      35 #> 5    7.76411473         78         56       66     71       83      47 #> 6  -12.87985944         55         49       44     54       49      34 #> 7   -6.93517726         67         42       56     66       68      35 #> 8    0.02794419         75         50       55     70       66      41 #> 9   -4.25432454         82         72       67     71       83      31 #> 10   6.59248165         61         45       47     62       80      41 #> 11   9.62936020         53         53       58     58       67      34 #> 12   7.34709147         60         47       39     59       74      41 #> 13   7.83787183         62         57       42     55       63      25 #> 14  -9.00893436         83         83       45     59       77      35 #> 15   4.51872455         77         54       72     79       77      46 #> 16  -1.29120309         90         50       72     60       54      36 #> 17  -4.51815400         85         64       69     79       79      63 #> 18   5.34709147         60         65       75     55       80      60 #> 19  -2.19900672         70         46       57     75       85      46 #> 20  -8.14368889         58         68       54     64       78      52 #> 21   5.43928784         40         33       34     43       64      33 #> 22   3.59248165         61         52       62     66       80      41 #> 23 -11.18056744         66         52       50     63       80      37 #> 24  -2.29688270         37         42       58     50       57      49 #> 25   7.87475038         54         42       48     66       75      33 #> 26  -6.48127545         77         66       63     88       76      72 #> 27   7.02794419         75         58       74     80       78      49 #> 28  -9.38907907         57         44       45     51       83      38 #> 29   6.48184600         85         71       71     77       74      55 #> 30   5.74567546         82         39       59     64       78      39 attitude$complaints_LMH <- cut(attitude$complaints, 3) adjust(attitude, effect = \"complaints_LMH\", select = \"rating\", multilevel = TRUE) #>         rating complaints privileges learning raises critical advance #> 1   -9.9809282         51         30       39     61       92      45 #> 2    2.6250549         64         51       54     63       73      47 #> 3   10.6250549         70         68       69     76       86      48 #> 4    0.6250549         63         45       47     54       84      35 #> 5    5.6503521         78         56       66     71       83      47 #> 6  -17.3749451         55         49       44     54       49      34 #> 7   -2.3749451         67         42       56     66       68      35 #> 8   -4.3496479         75         50       55     70       66      41 #> 9   -3.3496479         82         72       67     71       83      31 #> 10   6.6250549         61         45       47     62       80      41 #> 11  11.0190718         53         53       58     58       67      34 #> 12   6.6250549         60         47       39     59       74      41 #> 13   8.6250549         62         57       42     55       63      25 #> 14  -7.3496479         83         83       45     59       77      35 #> 15   1.6503521         77         54       72     79       77      46 #> 16   5.6503521         90         50       72     60       54      36 #> 17  -1.3496479         85         64       69     79       79      63 #> 18   4.6250549         60         65       75     55       80      60 #> 19   4.6250549         70         46       57     75       85      46 #> 20 -10.3749451         58         68       54     64       78      52 #> 21  -2.9809282         40         33       34     43       64      33 #> 22   3.6250549         61         52       62     66       80      41 #> 23  -7.3749451         66         52       50     63       80      37 #> 24 -12.9809282         37         42       58     50       57      49 #> 25  10.0190718         54         42       48     66       75      33 #> 26  -9.3496479         77         66       63     88       76      72 #> 27   2.6503521         75         58       74     80       78      49 #> 28 -12.3749451         57         44       45     51       83      38 #> 29   9.6503521         85         71       71     77       74      55 #> 30   6.6503521         82         39       59     64       78      39 #>    complaints_LMH #> 1     (36.9,54.7] #> 2     (54.7,72.3] #> 3     (54.7,72.3] #> 4     (54.7,72.3] #> 5     (72.3,90.1] #> 6     (54.7,72.3] #> 7     (54.7,72.3] #> 8     (72.3,90.1] #> 9     (72.3,90.1] #> 10    (54.7,72.3] #> 11    (36.9,54.7] #> 12    (54.7,72.3] #> 13    (54.7,72.3] #> 14    (72.3,90.1] #> 15    (72.3,90.1] #> 16    (72.3,90.1] #> 17    (72.3,90.1] #> 18    (54.7,72.3] #> 19    (54.7,72.3] #> 20    (54.7,72.3] #> 21    (36.9,54.7] #> 22    (54.7,72.3] #> 23    (54.7,72.3] #> 24    (36.9,54.7] #> 25    (36.9,54.7] #> 26    (72.3,90.1] #> 27    (72.3,90.1] #> 28    (54.7,72.3] #> 29    (72.3,90.1] #> 30    (72.3,90.1] # }  # Generate data data <- simulate_correlation(n = 100, r = 0.7) data$V2 <- (5 * data$V2) + 20 # Add intercept  # Adjust adjusted <- adjust(data, effect = \"V1\", select = \"V2\") adjusted_icpt <- adjust(data, effect = \"V1\", select = \"V2\", keep_intercept = TRUE)  # Visualize plot(data$V1, data$V2,   pch = 19, col = \"blue\",   ylim = c(min(adjusted$V2), max(data$V2)),   main = \"Original (blue), adjusted (green), and adjusted - intercept kept (red) data\" ) abline(lm(V2 ~ V1, data = data), col = \"blue\") points(adjusted$V1, adjusted$V2, pch = 19, col = \"green\") abline(lm(V2 ~ V1, data = adjusted), col = \"green\") points(adjusted_icpt$V1, adjusted_icpt$V2, pch = 19, col = \"red\") abline(lm(V2 ~ V1, data = adjusted_icpt), col = \"red\")"},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign variable and value labels — assign_labels","title":"Assign variable and value labels — assign_labels","text":"Assign variable values labels variable variables data frame. Labels stored attributes (\"label\" variable labels \"labels\") value labels.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign variable and value labels — assign_labels","text":"","code":"assign_labels(x, ...)  # S3 method for numeric assign_labels(x, variable = NULL, values = NULL, ...)  # S3 method for data.frame assign_labels(   x,   select = NULL,   exclude = NULL,   values = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assign variable and value labels — assign_labels","text":"x data frame, factor vector. ... Currently used. variable variable label string. values value labels (named) character vector. values named vector, length labels must equal length unique values. named vector, left-hand side (LHS) value x, right-hand side (RHS) associated value label. Non-matching labels omitted. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign variable and value labels — assign_labels","text":"labelled variable, data frame labelled variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Assign variable and value labels — assign_labels","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assign variable and value labels — assign_labels","text":"","code":"x <- 1:3 # labelling by providing required number of labels assign_labels(   x,   variable = \"My x\",   values = c(\"one\", \"two\", \"three\") ) #> [1] 1 2 3 #> attr(,\"label\") #> [1] \"My x\" #> attr(,\"labels\") #>   one   two three  #>     1     2     3   # labelling using named vectors data(iris) out <- assign_labels(   iris$Species,   variable = \"Labelled Species\",   values = c(`setosa` = \"Spec1\", `versicolor` = \"Spec2\", `virginica` = \"Spec3\") ) str(out) #>  Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  - attr(*, \"label\")= chr \"Labelled Species\" #>  - attr(*, \"labels\")= Named chr [1:3] \"setosa\" \"versicolor\" \"virginica\" #>   ..- attr(*, \"names\")= chr [1:3] \"Spec1\" \"Spec2\" \"Spec3\"  # data frame example out <- assign_labels(   iris,   select = \"Species\",   variable = \"Labelled Species\",   values = c(`setosa` = \"Spec1\", `versicolor` = \"Spec2\", `virginica` = \"Spec3\") ) str(out$Species) #>  Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  - attr(*, \"label\")= chr \"Labelled Species\" #>  - attr(*, \"labels\")= Named chr [1:3] \"setosa\" \"versicolor\" \"virginica\" #>   ..- attr(*, \"names\")= chr [1:3] \"Spec1\" \"Spec2\" \"Spec3\"  # Partial labelling x <- 1:5 assign_labels(   x,   variable = \"My x\",   values = c(`1` = \"lowest\", `5` = \"highest\") ) #> [1] 1 2 3 4 5 #> attr(,\"label\") #> [1] \"My x\" #> attr(,\"labels\") #>  lowest highest  #>       1       5"},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode (or ","title":"Recode (or ","text":"functions divides range variables intervals recodes values inside intervals according related interval. basically wrapper around base R's cut(), providing simplified accessible way define interval breaks (cut-values).","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode (or ","text":"","code":"categorize(x, ...)  # S3 method for numeric categorize(   x,   split = \"median\",   n_groups = NULL,   range = NULL,   lowest = 1,   labels = NULL,   verbose = TRUE,   ... )  # S3 method for data.frame categorize(   x,   select = NULL,   exclude = NULL,   split = \"median\",   n_groups = NULL,   range = NULL,   lowest = 1,   labels = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode (or ","text":"x (grouped) data frame, numeric vector factor. ... used. split Character vector, indicating breaks split variables, numeric values values indicating breaks. character, may one \"median\", \"mean\", \"quantile\", \"equal_length\", \"equal_range\". \"median\" \"mean\" return dichotomous variables, split mean median, respectively. \"quantile\" \"equal_length\" split variable n_groups groups, group refers interval specific range values. Thus, length interval based number groups. \"equal_range\" also splits variable multiple groups, however, length interval given, number resulting groups (hence, number breaks) determined many intervals can generated, based full range variable. n_groups split \"quantile\" \"equal_length\", defines number requested groups (.e. resulting number levels values) recoded variable(s). \"quantile\" define intervals based distribution variable, \"equal_length\" tries divide range variable pieces equal length. range split = \"equal_range\", defines range values recoded new value. lowest Minimum value recoded variable(s). NULL (default), numeric variables, minimum original input preserved. factors, default minimum 1. split = \"equal_range\", default minimum always 1, unless specified otherwise lowest. labels Character vector value labels. NULL, categorize() returns factors instead numeric variables, labels used labelling factor levels. Can also \"mean\" \"median\" factor labels mean/median groups. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode (or ","text":"x, recoded groups. default x numeric, unless labels specified. case, factor returned, factor levels (.e. recoded groups labelled accordingly.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"splits-and-breaks-cut-off-values-","dir":"Reference","previous_headings":"","what":"Splits and breaks (cut-off values)","title":"Recode (or ","text":"Breaks general exclusive, means values indicate lower bound next group interval begin. Take simple example, numeric variable values 1 9. median 5, thus first interval ranges 1-4 recoded 1, 5-9 turn 2 (compare cbind(1:9, categorize(1:9))). variable, using split = \"quantile\" n_groups = 3 define breaks 3.67 6.33 (see quantile(1:9, probs = c(1/3, 2/3))), means values 1 3 belong first interval recoded 1 (next interval starts 3.67), 4 6 2 7 9 3.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"recoding-into-groups-with-equal-size-or-range","dir":"Reference","previous_headings":"","what":"Recoding into groups with equal size or range","title":"Recode (or ","text":"split = \"equal_length\" split = \"equal_range\" try divide range x intervals similar () length. difference split = \"equal_length\" divide range x n_groups pieces thereby defining intervals used breaks (hence, equivalent cut(x, breaks = n_groups)),  split = \"equal_range\" cut x intervals length range, first interval defaults starts 1. lowest (starting) value interval can defined using lowest argument.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Recode (or ","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode (or ","text":"","code":"set.seed(123) x <- sample(1:10, size = 50, replace = TRUE)  table(x) #> x #>  1  2  3  4  5  6  7  8  9 10  #>  2  3  5  3  7  5  5  2 11  7   # by default, at median table(categorize(x)) #>  #>  1  2  #> 25 25   # into 3 groups, based on distribution (quantiles) table(categorize(x, split = \"quantile\", n_groups = 3)) #>  #>  1  2  3  #> 13 19 18   # into 3 groups, user-defined break table(categorize(x, split = c(3, 5))) #>  #>  1  2  3  #>  5  8 37   set.seed(123) x <- sample(1:100, size = 500, replace = TRUE)  # into 5 groups, try to recode into intervals of similar length, # i.e. the range within groups is the same for all groups table(categorize(x, split = \"equal_length\", n_groups = 5)) #>  #>   1   2   3   4   5  #>  89 116  96  94 105   # into 5 groups, try to return same range within groups # i.e. 1-20, 21-40, 41-60, etc. Since the range of \"x\" is # 1-100, and we have a range of 20, this results into 5 # groups, and thus is for this particular case identical # to the previous result. table(categorize(x, split = \"equal_range\", range = 20)) #>  #>   1   2   3   4   5  #>  89 116  96  94 105   # return factor with value labels instead of numeric value set.seed(123) x <- sample(1:10, size = 30, replace = TRUE) categorize(x, \"equal_length\", n_groups = 3) #>  [1] 1 1 3 1 2 2 2 2 3 3 2 1 3 3 3 1 3 3 3 3 3 1 2 1 3 2 3 3 3 3 categorize(x, \"equal_length\", n_groups = 3, labels = c(\"low\", \"mid\", \"high\")) #>  [1] low  low  high low  mid  mid  mid  mid  high high mid  low  high high high #> [16] low  high high high high high low  mid  low  high mid  high high high high #> Levels: low mid high  # cut numeric into groups with the mean or median as a label name x <- sample(1:10, size = 30, replace = TRUE) categorize(x, \"equal_length\", n_groups = 3, labels = \"mean\") #>  [1] 8.45 8.45 5.33 8.45 5.33 5.33 8.45 1.57 5.33 8.45 1.57 1.57 8.45 8.45 5.33 #> [16] 5.33 8.45 8.45 5.33 5.33 8.45 5.33 5.33 8.45 1.57 5.33 1.57 1.57 1.57 5.33 #> Levels: 1.57 5.33 8.45 categorize(x, \"equal_length\", n_groups = 3, labels = \"median\") #>  [1] 9.00 9.00 5.50 9.00 5.50 5.50 9.00 2.00 5.50 9.00 2.00 2.00 9.00 9.00 5.50 #> [16] 5.50 9.00 9.00 5.50 5.50 9.00 5.50 5.50 9.00 2.00 5.50 2.00 2.00 2.00 5.50 #> Levels: 2.00 5.50 9.00"},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":null,"dir":"Reference","previous_headings":"","what":"Centering (Grand-Mean Centering) — center","title":"Centering (Grand-Mean Centering) — center","text":"Performs grand-mean centering data.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centering (Grand-Mean Centering) — center","text":"","code":"center(x, ...)  centre(x, ...)  # S3 method for numeric center(   x,   robust = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   verbose = TRUE,   ... )  # S3 method for data.frame center(   x,   select = NULL,   exclude = NULL,   robust = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   force = FALSE,   remove_na = c(\"none\", \"selected\", \"all\"),   append = FALSE,   ignore_case = FALSE,   verbose = TRUE,   regex = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centering (Grand-Mean Centering) — center","text":"x (grouped) data frame, (numeric character) vector factor. ... Currently used. robust Logical, TRUE, centering done subtracting median variables. FALSE, variables centered subtracting mean. weights Can NULL (weighting), : data frames: numeric vector weights, character name column data.frame contains weights. numeric vectors: numeric vector weights. reference data frame variable centrality deviation computed instead input variable. Useful standardizing subset new data according another data frame. center Numeric value, can used alternative reference define reference centrality. center length 1, recycled match length selected variables centering. Else, center must length number selected variables. Values center matched selected variables provided order, unless named vector given. case, names matched names selected variables. verbose Toggle warnings messages. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. force Logical, TRUE, forces centering factors well. Factors converted numerical values, lowest level value 1 (unless factor numeric levels, converted corresponding numeric value). remove_na missing values (NA) treated: \"none\" (default): column's standardization done separately, ignoring NAs. Else, rows NA columns selected select / exclude (\"selected\") columns (\"\") dropped standardization, resulting data frame include cases. append Logical string. TRUE, centered variables get new column names (suffix \"_c\") appended (column bind) x, thus returning original centered variables. FALSE, original variables x overwritten centered versions. character value, centered variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centering (Grand-Mean Centering) — center","text":"centered variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Centering (Grand-Mean Centering) — center","text":"Difference centering standardizing: Standardized variables computed subtracting mean variable dividing standard deviation, centering variables involves subtraction.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Centering (Grand-Mean Centering) — center","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centering (Grand-Mean Centering) — center","text":"","code":"data(iris)  # entire data frame or a vector head(iris$Sepal.Width) #> [1] 3.5 3.0 3.2 3.1 3.6 3.9 head(center(iris$Sepal.Width)) #> [1]  0.44266667 -0.05733333  0.14266667  0.04266667  0.54266667  0.84266667 head(center(iris)) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   -0.7433333  0.44266667       -2.358  -0.9993333  setosa #> 2   -0.9433333 -0.05733333       -2.358  -0.9993333  setosa #> 3   -1.1433333  0.14266667       -2.458  -0.9993333  setosa #> 4   -1.2433333  0.04266667       -2.258  -0.9993333  setosa #> 5   -0.8433333  0.54266667       -2.358  -0.9993333  setosa #> 6   -0.4433333  0.84266667       -2.058  -0.7993333  setosa head(center(iris, force = TRUE)) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   -0.7433333  0.44266667       -2.358  -0.9993333      -1 #> 2   -0.9433333 -0.05733333       -2.358  -0.9993333      -1 #> 3   -1.1433333  0.14266667       -2.458  -0.9993333      -1 #> 4   -1.2433333  0.04266667       -2.258  -0.9993333      -1 #> 5   -0.8433333  0.54266667       -2.358  -0.9993333      -1 #> 6   -0.4433333  0.84266667       -2.058  -0.7993333      -1  # only the selected columns from a data frame center(anscombe, select = c(\"x1\", \"x3\")) #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1   1 10  1  8  8.04 9.14  7.46  6.58 #> 2  -1  8 -1  8  6.95 8.14  6.77  5.76 #> 3   4 13  4  8  7.58 8.74 12.74  7.71 #> 4   0  9  0  8  8.81 8.77  7.11  8.84 #> 5   2 11  2  8  8.33 9.26  7.81  8.47 #> 6   5 14  5  8  9.96 8.10  8.84  7.04 #> 7  -3  6 -3  8  7.24 6.13  6.08  5.25 #> 8  -5  4 -5 19  4.26 3.10  5.39 12.50 #> 9   3 12  3  8 10.84 9.13  8.15  5.56 #> 10 -2  7 -2  8  4.82 7.26  6.42  7.91 #> 11 -4  5 -4  8  5.68 4.74  5.73  6.89 center(anscombe, exclude = c(\"x1\", \"x3\")) #>    x1 x2 x3 x4          y1         y2    y3         y4 #> 1  10  1 10 -1  0.53909091  1.6390909 -0.04 -0.9209091 #> 2   8 -1  8 -1 -0.55090909  0.6390909 -0.73 -1.7409091 #> 3  13  4 13 -1  0.07909091  1.2390909  5.24  0.2090909 #> 4   9  0  9 -1  1.30909091  1.2690909 -0.39  1.3390909 #> 5  11  2 11 -1  0.82909091  1.7590909  0.31  0.9690909 #> 6  14  5 14 -1  2.45909091  0.5990909  1.34 -0.4609091 #> 7   6 -3  6 -1 -0.26090909 -1.3709091 -1.42 -2.2509091 #> 8   4 -5  4 10 -3.24090909 -4.4009091 -2.11  4.9990909 #> 9  12  3 12 -1  3.33909091  1.6290909  0.65 -1.9409091 #> 10  7 -2  7 -1 -2.68090909 -0.2409091 -1.08  0.4090909 #> 11  5 -4  5 -1 -1.82090909 -2.7609091 -1.77 -0.6109091  # centering with reference center and scale d <- data.frame(   a = c(-2, -1, 0, 1, 2),   b = c(3, 4, 5, 6, 7) )  # default centering at mean center(d) #>    a  b #> 1 -2 -2 #> 2 -1 -1 #> 3  0  0 #> 4  1  1 #> 5  2  2  # centering, using 0 as mean center(d, center = 0) #>    a b #> 1 -2 3 #> 2 -1 4 #> 3  0 5 #> 4  1 6 #> 5  2 7  # centering, using -5 as mean center(d, center = -5) #>   a  b #> 1 3  8 #> 2 4  9 #> 3 5 10 #> 4 6 11 #> 5 7 12"},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the coefficient of variation — coef_var","title":"Compute the coefficient of variation — coef_var","text":"Compute coefficient variation (CV, ratio standard deviation mean, \\(\\sigma/\\mu\\)) set numeric values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the coefficient of variation — coef_var","text":"","code":"coef_var(x, ...)  distribution_coef_var(x, ...)  # S3 method for numeric coef_var(   x,   mu = NULL,   sigma = NULL,   method = c(\"standard\", \"unbiased\", \"median_mad\", \"qcd\"),   trim = 0,   remove_na = FALSE,   n = NULL,   na.rm = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the coefficient of variation — coef_var","text":"x numeric vector ratio scale (see details), vector values can coerced one. ... arguments passed computation functions. mu numeric vector mean values use compute coefficient variation. supplied, x used compute mean. sigma numeric vector standard deviation values use compute coefficient variation. supplied, x used compute SD. method Method use compute CV. Can \"standard\" compute dividing standard deviation mean, \"unbiased\" unbiased estimator normally distributed data, one two robust alternatives: \"median_mad\" divide median stats::mad(), \"qcd\" (quartile coefficient dispersion, interquartile range divided sum quartiles [twice midhinge]: \\((Q_3 - Q_1)/(Q_3 + Q_1)\\). trim fraction (0 0.5) values trimmed end x mean standard deviation (measures) computed. Values trim outside range (0 0.5) taken nearest endpoint. remove_na Logical. NA values removed computing (TRUE) (FALSE, default)? n method = \"unbiased\" mu sigma provided (computed x), sample size use adjust computed CV small-sample bias? na.rm Deprecated. Please use remove_na instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the coefficient of variation — coef_var","text":"computed coefficient variation x.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute the coefficient of variation — coef_var","text":"CV applicable values taken ratio scale: values fixed meaningfully defined 0 (either lowest highest possible value), ratios interpretable example, many sandwiches eaten week? 0 means \"none\" 20 sandwiches 4 times 5 sandwiches. center number sandwiches, longer ratio scale (0 \"none\" mean, ratio 4 -2 meaningful). Scaling ratio scale still results ratio scale. can re define \"many half sandwiches eat week ( = sandwiches * 0.5) 0 still mean \"none\", 20 half-sandwiches still 4 times 5 half-sandwiches. means CV invariant shifting, scaling:","code":"sandwiches <- c(0, 4, 15, 0, 0, 5, 2, 7) coef_var(sandwiches) #> [1] 1.239094  coef_var(sandwiches / 2) # same #> [1] 1.239094  coef_var(sandwiches + 4) # different! 0 is no longer meaningful! #> [1] 0.6290784"},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the coefficient of variation — coef_var","text":"","code":"coef_var(1:10) #> [1] 0.5504819 coef_var(c(1:10, 100), method = \"median_mad\") #> [1] 0.7413 coef_var(c(1:10, 100), method = \"qcd\") #> [1] 0.4166667 coef_var(mu = 10, sigma = 20) #> [1] 2 coef_var(mu = 10, sigma = 20, method = \"unbiased\", n = 30) #> [1] 2.250614"},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to Numeric (if possible) — coerce_to_numeric","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"Tries convert vector numeric possible (warnings errors). Otherwise, leaves .","code":""},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"","code":"coerce_to_numeric(x)"},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"x vector converted.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"Numeric vector (possible)","code":""},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"","code":"coerce_to_numeric(c(\"1\", \"2\")) #> [1] 1 2 coerce_to_numeric(c(\"1\", \"2\", \"A\")) #> [1] \"1\" \"2\" \"A\""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for working with column names — row_to_colnames","title":"Tools for working with column names — row_to_colnames","text":"Tools working column names","code":""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for working with column names — row_to_colnames","text":"","code":"row_to_colnames(x, row = 1, na_prefix = \"x\", verbose = TRUE)  colnames_to_row(x, prefix = \"x\")"},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for working with column names — row_to_colnames","text":"x data frame. row Row use column names. na_prefix Prefix give column name row NA. Default 'x', incremented NA (x1, x2, etc.). verbose Toggle warnings. prefix Prefix give column name. Default 'x', incremented column (x1, x2, etc.).","code":""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tools for working with column names — row_to_colnames","text":"row_to_colnames() colnames_to_row() return data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for working with column names — row_to_colnames","text":"","code":"# Convert a row to column names -------------------------------- test <- data.frame(   a = c(\"iso\", 2, 5),   b = c(\"year\", 3, 6),   c = c(\"value\", 5, 7) ) test #>     a    b     c #> 1 iso year value #> 2   2    3     5 #> 3   5    6     7 row_to_colnames(test) #>   iso year value #> 2   2    3     5 #> 3   5    6     7  # Convert column names to row -------------------------------- test <- data.frame(   ARG = c(\"BRA\", \"FRA\"),   `1960` = c(1960, 1960),   `2000` = c(2000, 2000) ) test #>   ARG X1960 X2000 #> 1 BRA  1960  2000 #> 2 FRA  1960  2000 colnames_to_row(test) #>    x1    x2    x3 #> 1 ARG X1960 X2000 #> 2 BRA  1960  2000 #> 3 FRA  1960  2000"},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":null,"dir":"Reference","previous_headings":"","what":"Deviation Contrast Matrix — contr.deviation","title":"Deviation Contrast Matrix — contr.deviation","text":"Build deviation contrast matrix, type effects contrast matrix.","code":""},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deviation Contrast Matrix — contr.deviation","text":"","code":"contr.deviation(n, base = 1, contrasts = TRUE, sparse = FALSE)"},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deviation Contrast Matrix — contr.deviation","text":"n vector levels factor, number levels. base integer specifying group considered     baseline group. Ignored contrasts FALSE. contrasts logical indicating whether contrasts     computed. sparse logical indicating result sparse     (class dgCMatrix), using     package Matrix.","code":""},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Deviation Contrast Matrix — contr.deviation","text":"effects coding, unlike treatment/dummy coding (stats::contr.treatment()), contrast sums 0. regressions models, results intercept represents (unweighted) average group means. ANOVA settings, also guarantees lower order effects represent main effects (simple conditional effects, case using R's default stats::contr.treatment()).  Deviation coding (contr.deviation) type effects coding. deviation coding, coefficients factor variables interpreted difference factor level base level (interpretation treatment/dummy coding). example, factor group levels \"\", \"B\", \"C\", contr.devation, intercept represents overall mean (average group means 3 groups), coefficients groupB groupC represent differences group mean B C group means, respectively.  Sum coding (stats::contr.sum()) another type effects coding. sum coding, coefficients factor variables interpreted difference factor level grand (across-groups) mean. example, factor group levels \"\", \"B\", \"C\", contr.sum, intercept represents overall mean (average group means 3 groups), coefficients group1 group2 represent differences B group means overall mean, respectively.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deviation Contrast Matrix — contr.deviation","text":"","code":"if (FALSE) {   data(\"mtcars\")    mtcars <- data_modify(mtcars, cyl = factor(cyl))    c.treatment <- cbind(Intercept = 1, contrasts(mtcars$cyl))   solve(c.treatment)   #>            4 6 8   #> Intercept  1 0 0  # mean of the 1st level   #> 6         -1 1 0  # 2nd level - 1st level   #> 8         -1 0 1  # 3rd level - 1st level    contrasts(mtcars$cyl) <- contr.sum   c.sum <- cbind(Intercept = 1, contrasts(mtcars$cyl))   solve(c.sum)   #>                4      6      8   #> Intercept  0.333  0.333  0.333   # overall mean   #>            0.667 -0.333 -0.333   # deviation of 1st from overall mean   #>           -0.333  0.667 -0.333   # deviation of 2nd from overall mean     contrasts(mtcars$cyl) <- contr.deviation   c.deviation <- cbind(Intercept = 1, contrasts(mtcars$cyl))   solve(c.deviation)   #>                4     6     8   #> Intercept  0.333 0.333 0.333   # overall mean   #> 6         -1.000 1.000 0.000   # 2nd level - 1st level   #> 8         -1.000 0.000 1.000   # 3rd level - 1st level    ## With Interactions -----------------------------------------   mtcars <- data_modify(mtcars, am = C(am, contr = contr.deviation))   mtcars <- data_arrange(mtcars, select = c(\"cyl\", \"am\"))    mm <- unique(model.matrix(~ cyl * am, data = mtcars))   rownames(mm) <- c(     \"cyl4.am0\", \"cyl4.am1\", \"cyl6.am0\",     \"cyl6.am1\", \"cyl8.am0\", \"cyl8.am1\"   )    solve(mm)   #>             cyl4.am0 cyl4.am1 cyl6.am0 cyl6.am1 cyl8.am0 cyl8.am1   #> (Intercept)    0.167    0.167    0.167    0.167    0.167    0.167  # overall mean   #> cyl6          -0.500   -0.500    0.500    0.500    0.000    0.000  # cyl MAIN eff: 2nd - 1st   #> cyl8          -0.500   -0.500    0.000    0.000    0.500    0.500  # cyl MAIN eff: 2nd - 1st   #> am1           -0.333    0.333   -0.333    0.333   -0.333    0.333  # am MAIN eff   #> cyl6:am1       1.000   -1.000   -1.000    1.000    0.000    0.000   #> cyl8:am1       1.000   -1.000    0.000    0.000   -1.000    1.000 }"},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace missing values in a variable or a data frame. — convert_na_to","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"Replace missing values variable data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"","code":"convert_na_to(x, ...)  # S3 method for numeric convert_na_to(x, replacement = NULL, verbose = TRUE, ...)  # S3 method for character convert_na_to(x, replacement = NULL, verbose = TRUE, ...)  # S3 method for data.frame convert_na_to(   x,   select = NULL,   exclude = NULL,   replacement = NULL,   replace_num = replacement,   replace_char = replacement,   replace_fac = replacement,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"x numeric, factor, character vector, data frame. ... used. replacement Numeric character value used replace NA. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. replace_num Value replace NA variable type numeric. replace_char Value replace NA variable type character. replace_fac Value replace NA variable type factor. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"x, NA values replaced replacement.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"","code":"# Convert NA to 0 in a numeric vector convert_na_to(   c(9, 3, NA, 2, 3, 1, NA, 8),   replacement = 0 ) #> [1] 9 3 0 2 3 1 0 8  # Convert NA to \"missing\" in a character vector convert_na_to(   c(\"a\", NA, \"d\", \"z\", NA, \"t\"),   replacement = \"missing\" ) #> [1] \"a\"       \"missing\" \"d\"       \"z\"       \"missing\" \"t\"        ### For data frames  test_df <- data.frame(   x = c(1, 2, NA),   x2 = c(4, 5, NA),   y = c(\"a\", \"b\", NA) )  # Convert all NA to 0 in numeric variables, and all NA to \"missing\" in # character variables convert_na_to(   test_df,   replace_num = 0,   replace_char = \"missing\" ) #>   x x2       y #> 1 1  4       a #> 2 2  5       b #> 3 0  0 missing  # Convert a specific variable in the data frame convert_na_to(   test_df,   replace_num = 0,   replace_char = \"missing\",   select = \"x\" ) #>   x x2    y #> 1 1  4    a #> 2 2  5    b #> 3 0 NA <NA>  # Convert all variables starting with \"x\" convert_na_to(   test_df,   replace_num = 0,   replace_char = \"missing\",   select = starts_with(\"x\") ) #>   x x2    y #> 1 1  4    a #> 2 2  5    b #> 3 0  0 <NA>  # Convert NA to 1 in variable 'x2' and to 0 in all other numeric # variables convert_na_to(   test_df,   replace_num = 0,   select = list(x2 = 1) ) #>   x x2    y #> 1 1  4    a #> 2 2  5    b #> 3 0  1 <NA>"},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert non-missing values in a variable into missing values. — convert_to_na","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"Convert non-missing values variable missing values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"","code":"convert_to_na(x, ...)  # S3 method for numeric convert_to_na(x, na = NULL, verbose = TRUE, ...)  # S3 method for factor convert_to_na(x, na = NULL, drop_levels = FALSE, verbose = TRUE, ...)  # S3 method for data.frame convert_to_na(   x,   select = NULL,   exclude = NULL,   na = NULL,   drop_levels = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"x vector, factor data frame. ... used. na Numeric, character vector logical (list numeric, character vectors logicals) values converted NA. Numeric values applied numeric vectors, character values used factors, character vectors date variables, logical values logical vectors. verbose Toggle warnings. drop_levels Logical, factors, specific levels replaced NA, unused levels dropped? select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"x, values na converted NA.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"","code":"x <- sample(1:6, size = 30, replace = TRUE) x #>  [1] 5 5 6 3 1 4 6 1 6 1 3 6 4 1 6 6 3 6 5 3 6 2 5 5 3 2 2 2 4 2 # values 4 and 5 to NA convert_to_na(x, na = 4:5) #>  [1] NA NA  6  3  1 NA  6  1  6  1  3  6 NA  1  6  6  3  6 NA  3  6  2 NA NA  3 #> [26]  2  2  2 NA  2  # data frames set.seed(123) x <- data.frame(   a = sample(1:6, size = 20, replace = TRUE),   b = sample(letters[1:6], size = 20, replace = TRUE),   c = sample(c(30:33, 99), size = 20, replace = TRUE) ) # for all numerics, convert 5 to NA. Character/factor will be ignored. convert_to_na(x, na = 5) #> Could not convert values into `NA` for a factor or character variable. #>   To do this, `na` needs to be a character vector, or a list that contains #>   character vector elements. #>     a b  c #> 1   3 a 33 #> 2   6 e 99 #> 3   3 c 99 #> 4   2 b 32 #> 5   2 b 30 #> 6   6 a 31 #> 7   3 f 99 #> 8  NA c 99 #> 9   4 d 33 #> 10  6 f 99 #> 11  6 a 31 #> 12  1 c 30 #> 13  2 e 30 #> 14  3 d 32 #> 15 NA b 30 #> 16  3 e 99 #> 17  3 a 30 #> 18  1 a 31 #> 19  4 b 33 #> 20  1 c 33  # for numerics, 5 to NA, for character/factor, \"f\" to NA convert_to_na(x, na = list(6, \"f\")) #>     a    b  c #> 1   3    a 33 #> 2  NA    e 99 #> 3   3    c 99 #> 4   2    b 32 #> 5   2    b 30 #> 6  NA    a 31 #> 7   3 <NA> 99 #> 8   5    c 99 #> 9   4    d 33 #> 10 NA <NA> 99 #> 11 NA    a 31 #> 12  1    c 30 #> 13  2    e 30 #> 14  3    d 32 #> 15  5    b 30 #> 16  3    e 99 #> 17  3    a 30 #> 18  1    a 31 #> 19  4    b 33 #> 20  1    c 33  # select specific variables convert_to_na(x, select = c(\"a\", \"b\"), na = list(6, \"f\")) #>     a    b  c #> 1   3    a 33 #> 2  NA    e 99 #> 3   3    c 99 #> 4   2    b 32 #> 5   2    b 30 #> 6  NA    a 31 #> 7   3 <NA> 99 #> 8   5    c 99 #> 9   4    d 33 #> 10 NA <NA> 99 #> 11 NA    a 31 #> 12  1    c 30 #> 13  2    e 30 #> 14  3    d 32 #> 15  5    b 30 #> 16  3    e 99 #> 17  3    a 30 #> 18  1    a 31 #> 19  4    b 33 #> 20  1    c 33"},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange rows by column values — data_arrange","title":"Arrange rows by column values — data_arrange","text":"data_arrange() orders rows data frame values selected columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange rows by column values — data_arrange","text":"","code":"data_arrange(data, select = NULL, safe = TRUE)"},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange rows by column values — data_arrange","text":"data data frame, object can coerced data frame. select Character vector column names. Use dash just column name arrange decreasing order, example \"-x1\". safe throw error one variables specified exist.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange rows by column values — data_arrange","text":"data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange rows by column values — data_arrange","text":"","code":"# Arrange using several variables data_arrange(head(mtcars), c(\"gear\", \"carb\")) #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  # Arrange in decreasing order data_arrange(head(mtcars), \"-carb\") #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  # Throw an error if one of the variables specified doesn't exist try(data_arrange(head(mtcars), c(\"gear\", \"foo\"), safe = FALSE)) #> Error : The following column(s) don't exist in the dataset: foo. #>   Possibly misspelled?"},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a codebook of a data frame. — data_codebook","title":"Generate a codebook of a data frame. — data_codebook","text":"data_codebook() generates codebooks data frames, .e. overviews variables information variable (like labels, values value range, frequencies, amount missing values).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a codebook of a data frame. — data_codebook","text":"","code":"data_codebook(   data,   select = NULL,   exclude = NULL,   variable_label_width = NULL,   value_label_width = NULL,   max_values = 10,   range_at = 6,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  # S3 method for data_codebook print_html(   x,   font_size = \"100%\",   line_padding = 3,   row_color = \"#eeeeee\",   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a codebook of a data frame. — data_codebook","text":"data data frame, object can coerced data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. variable_label_width Length variable labels. Longer labels wrapped variable_label_width chars. NULL, longer labels split multiple lines. applies labelled data. value_label_width Length value labels. Longer labels shortened, remaining part truncated. applies labelled data factor levels. max_values Number maximum values displayed. Can used avoid many rows variables lots unique values. range_at Indicates many unique values numeric vector needed order print range variable instead frequency table numeric values. Can useful data contains numeric variables unique values full frequency tables instead value ranges displayed. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings messages . ... Arguments passed methods. x (grouped) data frame, vector statistical model (unstandardize() model). font_size HTML tables, font size. line_padding HTML tables, distance (pixel) lines. row_color HTML tables, fill color odd rows.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a codebook of a data frame. — data_codebook","text":"formatted data frame, summarizing content data frame. Returned columns include column index variables original data frame (ID), column name, variable label (data labelled), type variable, number missing values, unique values (value range), value labels (labelled data), frequency table (N value). columns formatted character vectors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate a codebook of a data frame. — data_codebook","text":"methods print() data frame nicer output, well methods printing markdown HTML format (print_md() print_html()).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a codebook of a data frame. — data_codebook","text":"","code":"data(iris) data_codebook(iris, select = starts_with(\"Sepal\")) #> iris (150 rows and 5 variables, 2 shown) #>  #> ID | Name         | Type    | Missings |     Values |   N #> ---+--------------+---------+----------+------------+---- #> 1  | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150 #> ---+--------------+---------+----------+------------+---- #> 2  | Sepal.Width  | numeric | 0 (0.0%) |   [2, 4.4] | 150 #> ---------------------------------------------------------  data(efc) data_codebook(efc) #> efc (100 rows and 5 variables, 5 shown) #>  #> ID | Name     | Label                                    | Type        |   Missings |   Values | Value Labels                    |          N #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 1  | c12hour  | average number of hours of care per week | numeric     |   2 (2.0%) | [5, 168] |                                 |         98 #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 2  | e16sex   | elder's gender                           | numeric     |   0 (0.0%) |        1 | male                            | 46 (46.0%) #>    |          |                                          |             |            |        2 | female                          | 54 (54.0%) #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 3  | e42dep   | elder's dependency                       | categorical |   3 (3.0%) |        1 | independent                     |  2 ( 2.1%) #>    |          |                                          |             |            |        2 | slightly dependent              |  4 ( 4.1%) #>    |          |                                          |             |            |        3 | moderately dependent            | 28 (28.9%) #>    |          |                                          |             |            |        4 | severely dependent              | 63 (64.9%) #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 4  | c172code | carer's level of education               | numeric     | 10 (10.0%) |        1 | low level of education          |  8 ( 8.9%) #>    |          |                                          |             |            |        2 | intermediate level of education | 66 (73.3%) #>    |          |                                          |             |            |        3 | high level of education         | 16 (17.8%) #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 5  | neg_c_7  | Negative impact with 7 items             | numeric     |   3 (3.0%) |  [7, 28] |                                 |         97 #> ---------------------------------------------------------------------------------------------------------------------------------------------  # shorten labels data_codebook(efc, variable_label_width = 20, value_label_width = 15) #> efc (100 rows and 5 variables, 5 shown) #>  #> ID | Name     | Label              | Type        |   Missings |   Values | Value Labels     |          N #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 1  | c12hour  | average number of  | numeric     |   2 (2.0%) | [5, 168] |                  |         98 #>    |          | hours of care per  |             |            |          |                  |            #>    |          | week               |             |            |          |                  |            #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 2  | e16sex   | elder's gender     | numeric     |   0 (0.0%) |        1 | male             | 46 (46.0%) #>    |          |                    |             |            |        2 | female           | 54 (54.0%) #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 3  | e42dep   | elder's dependency | categorical |   3 (3.0%) |        1 | independent      |  2 ( 2.1%) #>    |          |                    |             |            |        2 | slightly...      |  4 ( 4.1%) #>    |          |                    |             |            |        3 | moderately...    | 28 (28.9%) #>    |          |                    |             |            |        4 | severely...      | 63 (64.9%) #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 4  | c172code | carer's level of   | numeric     | 10 (10.0%) |        1 | low level of...  |  8 ( 8.9%) #>    |          | education          |             |            |        2 | intermediate...  | 66 (73.3%) #>    |          |                    |             |            |        3 | high level of... | 16 (17.8%) #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 5  | neg_c_7  | Negative impact    | numeric     |   3 (3.0%) |  [7, 28] |                  |         97 #>    |          | with 7 items       |             |            |          |                  |            #> --------------------------------------------------------------------------------------------------------  # automatic range for numerics at more than 5 unique values data(mtcars) data_codebook(mtcars, select = starts_with(\"c\")) #> mtcars (32 rows and 11 variables, 2 shown) #>  #> ID | Name | Type    | Missings | Values |          N #> ---+------+---------+----------+--------+----------- #> 2  | cyl  | numeric | 0 (0.0%) |      4 | 11 (34.4%) #>    |      |         |          |      6 |  7 (21.9%) #>    |      |         |          |      8 | 14 (43.8%) #> ---+------+---------+----------+--------+----------- #> 11 | carb | numeric | 0 (0.0%) | [1, 8] |         32 #> ----------------------------------------------------  # force all values to be displayed data_codebook(mtcars, select = starts_with(\"c\"), range_at = 100) #> mtcars (32 rows and 11 variables, 2 shown) #>  #> ID | Name | Type    | Missings | Values |          N #> ---+------+---------+----------+--------+----------- #> 2  | cyl  | numeric | 0 (0.0%) |      4 | 11 (34.4%) #>    |      |         |          |      6 |  7 (21.9%) #>    |      |         |          |      8 | 14 (43.8%) #> ---+------+---------+----------+--------+----------- #> 11 | carb | numeric | 0 (0.0%) |      1 |  7 (21.9%) #>    |      |         |          |      2 | 10 (31.2%) #>    |      |         |          |      3 |  3 ( 9.4%) #>    |      |         |          |      4 | 10 (31.2%) #>    |      |         |          |      6 |  1 ( 3.1%) #>    |      |         |          |      8 |  1 ( 3.1%) #> ----------------------------------------------------"},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract all duplicates — data_duplicated","title":"Extract all duplicates — data_duplicated","text":"Extract duplicates, visual inspection. Note also contains first occurrence future duplicates, unlike duplicated() dplyr::distinct()). Also contains additional column reporting number missing values row, help decision-making selecting duplicates keep.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract all duplicates — data_duplicated","text":"","code":"data_duplicated(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract all duplicates — data_duplicated","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract all duplicates — data_duplicated","text":"dataframe, containing duplicates.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract all duplicates — data_duplicated","text":"","code":"df1 <- data.frame(   id = c(1, 2, 3, 1, 3),   year = c(2022, 2022, 2022, 2022, 2000),   item1 = c(NA, 1, 1, 2, 3),   item2 = c(NA, 1, 1, 2, 3),   item3 = c(NA, 1, 1, 2, 3) )  data_duplicated(df1, select = \"id\") #>   Row id year item1 item2 item3 count_na #> 1   1  1 2022    NA    NA    NA        3 #> 4   4  1 2022     2     2     2        0 #> 3   3  3 2022     1     1     1        0 #> 5   5  3 2000     3     3     3        0  data_duplicated(df1, select = c(\"id\", \"year\")) #>   Row id year item1 item2 item3 count_na #> 1   1  1 2022    NA    NA    NA        3 #> 4   4  1 2022     2     2     2        0  # Filter to exclude duplicates df2 <- df1[-c(1, 5), ] df2 #>   id year item1 item2 item3 #> 2  2 2022     1     1     1 #> 3  3 2022     1     1     1 #> 4  1 2022     2     2     2"},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract one or more columns or elements from an object — data_extract","title":"Extract one or more columns or elements from an object — data_extract","text":"data_extract() (alias extract()) similar $. extracts either single column element object (e.g., data frame, list), multiple columns resp. elements.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract one or more columns or elements from an object — data_extract","text":"","code":"data_extract(data, select, ...)  # S3 method for data.frame data_extract(   data,   select,   name = NULL,   extract = \"all\",   as_data_frame = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract one or more columns or elements from an object — data_extract","text":"data object subset. Methods currently available data frames data frame extensions (e.g., tibbles). select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". ... use future methods. name optional argument specifies column used names vector elements extraction. Must specified either literal variable name (e.g., column_name) string (\"column_name\"). name ignored data frame returned. extract String, indicating element extracted select matches multiple variables. Can \"\" (default) return matched variables, \"first\" \"last\" return first last match, \"odd\" \"even\" return odd-numbered even-numbered matches. Note \"first\" \"last\" return vector (unless as_data_frame = TRUE), \"\" can return vector (one match found) data frame (one match). Type safe return values possible extract \"first\" \"last\" (always return vector) as_data_frame = TRUE (always returns data frame). as_data_frame Logical, TRUE, always return data frame, even one variable matched. FALSE, either returns vector data frame. See extract details. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract one or more columns or elements from an object — data_extract","text":"vector (data frame) containing extracted element, NULL matching variable found.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract one or more columns or elements from an object — data_extract","text":"data_extract() can used select multiple variables pull single variable data frame. Thus, return value default type safe - data_extract() either returns vector data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"extracting-single-variables-vectors-","dir":"Reference","previous_headings":"","what":"Extracting single variables (vectors)","title":"Extract one or more columns or elements from an object — data_extract","text":"select name single column, select matches one column, vector returned. single variable also returned extract either \"first \"last\". Setting as_data_frame TRUE overrides behaviour always returns data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"extracting-a-data-frame-of-variables","dir":"Reference","previous_headings":"","what":"Extracting a data frame of variables","title":"Extract one or more columns or elements from an object — data_extract","text":"select character vector containing one column name (numeric vector one valid column indices), select uses one supported select-helpers match multiple columns, data frame returned. Setting as_data_frame TRUE always returns data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract one or more columns or elements from an object — data_extract","text":"","code":"# single variable data_extract(mtcars, cyl, name = gear) #> 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4  #> 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4  data_extract(mtcars, \"cyl\", name = gear) #> 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4  #> 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4  data_extract(mtcars, -1, name = gear) #>                     cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4             6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag         6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710            4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive        6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Hornet Sportabout     8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Valiant               6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Duster 360            8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 240D             4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> Merc 230              4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> Merc 280              6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C             6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Merc 450SE            8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL            8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC           8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood    8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial     8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Fiat 128              4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic           4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla        4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Toyota Corona         4 120.1  97 3.70 2.465 20.01  1  0    3    1 #> Dodge Challenger      8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin           8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28            8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird      8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Fiat X1-9             4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2         4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa          4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L        8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino          6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora         8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E            4 121.0 109 4.11 2.780 18.60  1  1    4    2 data_extract(mtcars, cyl, name = 0) #>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive  #>                   6                   6                   4                   6  #>   Hornet Sportabout             Valiant          Duster 360           Merc 240D  #>                   8                   6                   8                   4  #>            Merc 230            Merc 280           Merc 280C          Merc 450SE  #>                   4                   6                   6                   8  #>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental  #>                   8                   8                   8                   8  #>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla  #>                   8                   4                   4                   4  #>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28  #>                   4                   8                   8                   8  #>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa  #>                   8                   4                   4                   4  #>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E  #>                   8                   6                   8                   4  data_extract(mtcars, cyl, name = \"row.names\") #>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive  #>                   6                   6                   4                   6  #>   Hornet Sportabout             Valiant          Duster 360           Merc 240D  #>                   8                   6                   8                   4  #>            Merc 230            Merc 280           Merc 280C          Merc 450SE  #>                   4                   6                   6                   8  #>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental  #>                   8                   8                   8                   8  #>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla  #>                   8                   4                   4                   4  #>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28  #>                   4                   8                   8                   8  #>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa  #>                   8                   4                   4                   4  #>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E  #>                   8                   6                   8                   4   # selecting multiple variables head(data_extract(iris, starts_with(\"Sepal\"))) #>   Sepal.Length Sepal.Width #> 1          5.1         3.5 #> 2          4.9         3.0 #> 3          4.7         3.2 #> 4          4.6         3.1 #> 5          5.0         3.6 #> 6          5.4         3.9 head(data_extract(iris, ends_with(\"Width\"))) #>   Sepal.Width Petal.Width #> 1         3.5         0.2 #> 2         3.0         0.2 #> 3         3.2         0.2 #> 4         3.1         0.2 #> 5         3.6         0.2 #> 6         3.9         0.4 head(data_extract(iris, 2:4)) #>   Sepal.Width Petal.Length Petal.Width #> 1         3.5          1.4         0.2 #> 2         3.0          1.4         0.2 #> 3         3.2          1.3         0.2 #> 4         3.1          1.5         0.2 #> 5         3.6          1.4         0.2 #> 6         3.9          1.7         0.4  # select first of multiple variables data_extract(iris, starts_with(\"Sepal\"), extract = \"first\") #>   [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1 #>  [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0 #>  [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5 #>  [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1 #>  [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5 #>  [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 #> [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2 #> [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 #> [145] 6.7 6.7 6.3 6.5 6.2 5.9  # select first of multiple variables, return as data frame head(data_extract(iris, starts_with(\"Sepal\"), extract = \"first\", as_data_frame = TRUE)) #>   Sepal.Length #> 1          5.1 #> 2          4.9 #> 3          4.7 #> 4          4.6 #> 5          5.0 #> 6          5.4"},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a grouped data frame — data_group","title":"Create a grouped data frame — data_group","text":"function comparable dplyr::group_by(), just following datawizard function design. data_ungroup() removes grouping information grouped data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a grouped data frame — data_group","text":"","code":"data_group(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_ungroup(data, verbose = TRUE, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a grouped data frame — data_group","text":"data data frame select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings. ... Arguments passed functions. Mostly used yet.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a grouped data frame — data_group","text":"grouped data frame, .e. data frame additional information grouping structure saved attributes.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a grouped data frame — data_group","text":"","code":"data(efc) suppressPackageStartupMessages(library(poorman, quietly = TRUE))  # total mean efc %>%   summarize(mean_hours = mean(c12hour, na.rm = TRUE)) #>   mean_hours #> 1   85.65306  # mean by educational level efc %>%   data_group(c172code) %>%   summarize(mean_hours = mean(c12hour, na.rm = TRUE)) #> # A tibble: 3 × 2 #> # Groups:   c172code [3] #>   c172code mean_hours #>      <dbl>      <dbl> #> 1        1       87.1 #> 2        2       94.0 #> 3        3       75"},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":null,"dir":"Reference","previous_headings":"","what":"Return filtered or sliced data frame, or row indices — data_match","title":"Return filtered or sliced data frame, or row indices — data_match","text":"Return filtered (sliced) data frame row indices data frame match specific condition. data_filter() works like data_match(), works logical expressions row indices data frame specify matching conditions.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return filtered or sliced data frame, or row indices — data_match","text":"","code":"data_match(x, to, match = \"and\", return_indices = FALSE, drop_na = TRUE, ...)  data_filter(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return filtered or sliced data frame, or row indices — data_match","text":"x data frame. data frame matching specified conditions. Note match value \"\", original row order might changed. See 'Details'. match String, indicating logical operation matching conditions combined. Can \"\" (\"&\"), \"\" (\"|\") \"\" (\"!\"). return_indices Logical, FALSE, return vector rows can used filter original data frame. FALSE (default), returns directly filtered data frame instead row indices. drop_na Logical, TRUE, missing values (NAs) removed filtering data. default behaviour, however, sometimes row indices requested (.e. return_indices=TRUE), might useful preserve NA values, returned row indices match row indices original data frame. ... sequence logical expressions indicating rows keep, numeric vector indicating row indices rows keep. Can also string representation logical expression (e.g. \"x > 4\"), character vector (e.g. c(\"x > 4\", \"y == 2\")) variable contains string representation logical expression. might useful used packages avoid defining undefined global variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return filtered or sliced data frame, or row indices — data_match","text":"filtered data frame, row indices match specified configuration.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Return filtered or sliced data frame, or row indices — data_match","text":"data_match(), match either \"\" \"\", original row order x might changed. preserving row order required, use data_filter() instead.   data_match() works data frames match conditions , data_filter() basically wrapper around subset(subset = <filter>). However, unlike subset(), preserves label attributes useful working labelled data.","code":"# mimics subset() behaviour, preserving original row order head(data_filter(mtcars[c(\"mpg\", \"vs\", \"am\")], vs == 0 | am == 1)) #>                    mpg vs am #> Mazda RX4         21.0  0  1 #> Mazda RX4 Wag     21.0  0  1 #> Datsun 710        22.8  1  1 #> Hornet Sportabout 18.7  0  0 #> Duster 360        14.3  0  0 #> Merc 450SE        16.4  0  0  # re-sorting rows head(data_match(mtcars[c(\"mpg\", \"vs\", \"am\")],                 data.frame(vs = 0, am = 1),                 match = \"or\")) #>                    mpg vs am #> Mazda RX4         21.0  0  1 #> Mazda RX4 Wag     21.0  0  1 #> Hornet Sportabout 18.7  0  0 #> Duster 360        14.3  0  0 #> Merc 450SE        16.4  0  0 #> Merc 450SL        17.3  0  0"},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return filtered or sliced data frame, or row indices — data_match","text":"","code":"data_match(mtcars, data.frame(vs = 0, am = 1)) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 data_match(mtcars, data.frame(vs = 0, am = c(0, 1))) #>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8  # observations where \"vs\" is NOT 0 AND \"am\" is NOT 1 data_match(mtcars, data.frame(vs = 0, am = 1), match = \"not\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1 # equivalent to data_filter(mtcars, vs != 0 & am != 1) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  # observations where EITHER \"vs\" is 0 OR \"am\" is 1 data_match(mtcars, data.frame(vs = 0, am = 1), match = \"or\") #>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2 # equivalent to data_filter(mtcars, vs == 0 | am == 1) #>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  # slice data frame by row indices data_filter(mtcars, 5:10) #>                    mpg cyl  disp  hp drat   wt  qsec vs am gear carb #> Hornet Sportabout 18.7   8 360.0 175 3.15 3.44 17.02  0  0    3    2 #> Valiant           18.1   6 225.0 105 2.76 3.46 20.22  1  0    3    1 #> Duster 360        14.3   8 360.0 245 3.21 3.57 15.84  0  0    3    4 #> Merc 240D         24.4   4 146.7  62 3.69 3.19 20.00  1  0    4    2 #> Merc 230          22.8   4 140.8  95 3.92 3.15 22.90  1  0    4    2 #> Merc 280          19.2   6 167.6 123 3.92 3.44 18.30  1  0    4    4  # Define a custom function containing data_filter() my_filter <- function(data, variable) {   data_filter(data, variable) } my_filter(mtcars, \"cyl == 6\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  # Pass complete filter-condition as string. my_filter <- function(data, condition) {   data_filter(data, condition) } my_filter(mtcars, \"am != 0\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  # string can also be used directly as argument data_filter(mtcars, \"am != 0\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  # or as variable fl <- \"am != 0\" data_filter(mtcars, fl) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2"},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge (join) two data frames, or a list of data frames — data_merge","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Merge (join) two data frames, list data frames. However, unlike base R's merge(), data_merge() offers methods join data frames, drop data frame column attributes.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"","code":"data_merge(x, ...)  data_join(x, ...)  # S3 method for data.frame data_merge(x, y, join = \"left\", by = NULL, id = NULL, verbose = TRUE, ...)  # S3 method for list data_merge(x, join = \"left\", by = NULL, id = NULL, verbose = TRUE, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"x, y data frame merge. x may also list data frames merged. Note list-method y argument. ... used. join Character vector, indicating method joining data frames. Can \"full\", \"left\" (default), \"right\", \"inner\", \"anti\", \"semi\" \"bind\". See details . Specifications columns used merging. id Optional name ID column created indicate source data frames appended rows. applies join = \"bind\". verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"merged data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"merging-data-frames","dir":"Reference","previous_headings":"","what":"Merging data frames","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Merging data frames performed adding rows (cases), columns (variables) source data frame (y) target data frame (x). usually requires one variables included data frames used merging, typically indicated argument. contains variable present data frames, cases matched filtered identical values x y.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"left-and-right-joins","dir":"Reference","previous_headings":"","what":"Left- and right-joins","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Left- right joins usually add new rows (cases), new columns (variables) existing cases x. join = \"left\" join = \"right\" work, must indicate one columns included data frames. join = \"left\", identifier variable, included x y, variables y copied x, cases y matching values identifier variable x (.e. cases x also found y get related values new columns y). match identifiers x y, copied variable y get NA value particular case. variables occur x y, used identifiers (), renamed avoid multiple identical variable names. Cases y values identifier match x's identifier removed. join = \"right\" works similar way join = \"left\", just cases x matching values identifier variable y chosen.  base R, equivalent merge(x, y, .x = TRUE) merge(x, y, .y = TRUE).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"full-joins","dir":"Reference","previous_headings":"","what":"Full joins","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Full joins copy cases y x. matching cases data frames, values new variables copied y x. cases y present x, added new rows x. Thus, full joins add new columns (variables), also might add new rows (cases).  base R, equivalent merge(x, y, = TRUE).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"inner-joins","dir":"Reference","previous_headings":"","what":"Inner joins","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Inner joins merge two data frames, however, rows (cases) kept present data frames. Thus, inner joins usually add new columns (variables), also remove rows (cases) occur one data frame.  base R, equivalent merge(x, y).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"binds","dir":"Reference","previous_headings":"","what":"Binds","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"join = \"bind\" row-binds complete second data frame y x. Unlike simple rbind(), requires columns data frames, join = \"bind\" bind shared columns y x, add new columns y x.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"","code":"x <- data.frame(a = 1:3, b = c(\"a\", \"b\", \"c\"), c = 5:7, id = 1:3) y <- data.frame(c = 6:8, d = c(\"f\", \"g\", \"h\"), e = 100:102, id = 2:4)  x #>   a b c id #> 1 1 a 5  1 #> 2 2 b 6  2 #> 3 3 c 7  3 y #>   c d   e id #> 1 6 f 100  2 #> 2 7 g 101  3 #> 3 8 h 102  4  # \"by\" will default to all shared columns, i.e. \"c\" and \"id\". new columns # \"d\" and \"e\" will be copied from \"y\" to \"x\", but there are only two cases # in \"x\" that have the same values for \"c\" and \"id\" in \"y\". only those cases # have values in the copied columns, the other case gets \"NA\". data_merge(x, y, join = \"left\") #>   a b c id    d   e #> 3 1 a 5  1 <NA>  NA #> 1 2 b 6  2    f 100 #> 2 3 c 7  3    g 101  # we change the id-value here x <- data.frame(a = 1:3, b = c(\"a\", \"b\", \"c\"), c = 5:7, id = 1:3) y <- data.frame(c = 6:8, d = c(\"f\", \"g\", \"h\"), e = 100:102, id = 3:5)  x #>   a b c id #> 1 1 a 5  1 #> 2 2 b 6  2 #> 3 3 c 7  3 y #>   c d   e id #> 1 6 f 100  3 #> 2 7 g 101  4 #> 3 8 h 102  5  # no cases in \"y\" have the same matching \"c\" and \"id\" as in \"x\", thus # copied variables from \"y\" to \"x\" copy no values, all get NA. data_merge(x, y, join = \"left\") #>   a b c id    d  e #> 1 1 a 5  1 <NA> NA #> 2 2 b 6  2 <NA> NA #> 3 3 c 7  3 <NA> NA  # one case in \"y\" has a match in \"id\" with \"x\", thus values for this # case from the remaining variables in \"y\" are copied to \"x\", all other # values (cases) in those remaining variables get NA data_merge(x, y, join = \"left\", by = \"id\") #>   a b id    d   e c.x c.y #> 2 1 a  1 <NA>  NA   5  NA #> 3 2 b  2 <NA>  NA   6  NA #> 1 3 c  3    f 100   7   6  data(mtcars) x <- mtcars[1:5, 1:3] y <- mtcars[28:32, 4:6]  # add ID common column x$id <- 1:5 y$id <- 3:7  # left-join, add new variables and copy values from y to x, # where \"id\" values match data_merge(x, y) #>    mpg cyl disp id  hp drat    wt #> 4 21.0   6  160  1  NA   NA    NA #> 5 21.0   6  160  2  NA   NA    NA #> 1 22.8   4  108  3 113 3.77 1.513 #> 2 21.4   6  258  4 264 4.22 3.170 #> 3 18.7   8  360  5 175 3.62 2.770  # right-join, add new variables and copy values from x to y, # where \"id\" values match data_merge(x, y, join = \"right\") #>    mpg cyl disp id  hp drat    wt #> 1 22.8   4  108  3 113 3.77 1.513 #> 2 21.4   6  258  4 264 4.22 3.170 #> 3 18.7   8  360  5 175 3.62 2.770 #> 4   NA  NA   NA  6 335 3.54 3.570 #> 5   NA  NA   NA  7 109 4.11 2.780  # full-join data_merge(x, y, join = \"full\") #>    mpg cyl disp id  hp drat    wt #> 4 21.0   6  160  1  NA   NA    NA #> 5 21.0   6  160  2  NA   NA    NA #> 1 22.8   4  108  3 113 3.77 1.513 #> 2 21.4   6  258  4 264 4.22 3.170 #> 3 18.7   8  360  5 175 3.62 2.770 #> 6   NA  NA   NA  6 335 3.54 3.570 #> 7   NA  NA   NA  7 109 4.11 2.780   data(mtcars) x <- mtcars[1:5, 1:3] y <- mtcars[28:32, c(1, 4:5)]  # add ID common column x$id <- 1:5 y$id <- 3:7  # left-join, no matching rows (because columns \"id\" and \"disp\" are used) # new variables get all NA values data_merge(x, y) #>    mpg cyl disp id hp drat #> 1 21.0   6  160  1 NA   NA #> 2 21.0   6  160  2 NA   NA #> 3 22.8   4  108  3 NA   NA #> 4 21.4   6  258  4 NA   NA #> 5 18.7   8  360  5 NA   NA  # one common value in \"mpg\", so one row from y is copied to x data_merge(x, y, by = \"mpg\") #>    mpg cyl disp  hp drat id.x id.y #> 2 21.0   6  160  NA   NA    1   NA #> 3 21.0   6  160  NA   NA    2   NA #> 4 22.8   4  108  NA   NA    3   NA #> 1 21.4   6  258 109 4.11    4    7 #> 5 18.7   8  360  NA   NA    5   NA  # only keep rows with matching values in by-column data_merge(x, y, join = \"semi\", by = \"mpg\") #>                 mpg cyl disp id #> Hornet 4 Drive 21.4   6  258  4  # only keep rows with non-matching values in by-column data_merge(x, y, join = \"anti\", by = \"mpg\") #>                    mpg cyl disp id #> Mazda RX4         21.0   6  160  1 #> Mazda RX4 Wag     21.0   6  160  2 #> Datsun 710        22.8   4  108  3 #> Hornet Sportabout 18.7   8  360  5  # merge list of data frames. can be of different rows x <- mtcars[1:5, 1:3] y <- mtcars[28:31, 3:5] z <- mtcars[11:18, c(1, 3:4, 6:8)] x$id <- 1:5 y$id <- 4:7 z$id <- 3:10 data_merge(list(x, y, z), join = \"bind\", by = \"id\", id = \"source\") #>     mpg cyl  disp id  hp drat    wt  qsec vs source #> 1  21.0   6 160.0  1  NA   NA    NA    NA NA      1 #> 2  21.0   6 160.0  2  NA   NA    NA    NA NA      1 #> 3  22.8   4 108.0  3  NA   NA    NA    NA NA      1 #> 4  21.4   6 258.0  4  NA   NA    NA    NA NA      1 #> 5  18.7   8 360.0  5  NA   NA    NA    NA NA      1 #> 6    NA  NA  95.1  4 113 3.77    NA    NA NA      2 #> 7    NA  NA 351.0  5 264 4.22    NA    NA NA      2 #> 8    NA  NA 145.0  6 175 3.62    NA    NA NA      2 #> 9    NA  NA 301.0  7 335 3.54    NA    NA NA      2 #> 10 17.8  NA 167.6  3 123   NA 3.440 18.90  1      3 #> 11 16.4  NA 275.8  4 180   NA 4.070 17.40  0      3 #> 12 17.3  NA 275.8  5 180   NA 3.730 17.60  0      3 #> 13 15.2  NA 275.8  6 180   NA 3.780 18.00  0      3 #> 14 10.4  NA 472.0  7 205   NA 5.250 17.98  0      3 #> 15 10.4  NA 460.0  8 215   NA 5.424 17.82  0      3 #> 16 14.7  NA 440.0  9 230   NA 5.345 17.42  0      3 #> 17 32.4  NA  78.7 10  66   NA 2.200 19.47  1      3"},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":null,"dir":"Reference","previous_headings":"","what":"Create new variables in a data frame — data_modify","title":"Create new variables in a data frame — data_modify","text":"Create new variables modify existing variables data frame. Unlike base::transform(), data_modify() can used grouped data frames, newly created variables can directly used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create new variables in a data frame — data_modify","text":"","code":"data_modify(data, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create new variables in a data frame — data_modify","text":"data data frame ... One expressions define new variable name values recoding new variables. expressions can one : sequence named, literal expressions, left-hand side refers name new variable, right-hand side represent values new variable. Example: Sepal.Width = center(Sepal.Width). sequence string values, representing expressions. variable contains string representation expression. Example:   character vector expressions. Example: c(\"SW_double = 2 * Sepal.Width\", \"SW_fraction = SW_double / 10\"). type expression mixed expressions, .e. character vector provided, may add elements .... Using NULL right-hand side removes variable data frame. Example: Petal.Width = NULL. Note newly created variables can used subsequent expressions. See also 'Examples'.","code":"a <- \"2 * Sepal.Width\" data_modify(iris, a)"},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create new variables in a data frame — data_modify","text":"data_modify() can also used inside functions. However, recommended pass recode-expression character vector list characters.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create new variables in a data frame — data_modify","text":"","code":"data(efc) new_efc <- data_modify(   efc,   c12hour_c = center(c12hour),   c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),   c12hour_z2 = standardize(c12hour) ) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z c12hour_z2 #> 1      16      2      3        2      12 -69.65306 -1.0466657 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657 -1.0466657  # using strings instead of literal expressions new_efc <- data_modify(   efc,   \"c12hour_c = center(c12hour)\",   \"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)\",   \"c12hour_z2 = standardize(c12hour)\" ) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z c12hour_z2 #> 1      16      2      3        2      12 -69.65306 -1.0466657 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657 -1.0466657  # using character strings, provided as variable stand <- \"c12hour_c / sd(c12hour, na.rm = TRUE)\" new_efc <- data_modify(   efc,   c12hour_c = center(c12hour),   c12hour_z = stand ) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z #> 1      16      2      3        2      12 -69.65306 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657  # providing expressions as character vector new_exp <- c(   \"c12hour_c = center(c12hour)\",   \"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)\" ) new_efc <- data_modify(efc, new_exp) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z #> 1      16      2      3        2      12 -69.65306 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657  # attributes - in this case, value and variable labels - are preserved str(new_efc) #> 'data.frame':\t100 obs. of  7 variables: #>  $ c12hour  : num  16 148 70 NA 168 16 161 110 28 40 ... #>   ..- attr(*, \"label\")= chr \"average number of hours of care per week\" #>  $ e16sex   : num  2 2 2 2 2 2 1 2 2 2 ... #>   ..- attr(*, \"label\")= chr \"elder's gender\" #>   ..- attr(*, \"labels\")= Named num [1:2] 1 2 #>   .. ..- attr(*, \"names\")= chr [1:2] \"male\" \"female\" #>  $ e42dep   : Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 3 3 3 NA 4 4 4 4 4 4 ... #>   ..- attr(*, \"labels\")= Named num [1:4] 1 2 3 4 #>   .. ..- attr(*, \"names\")= chr [1:4] \"independent\" \"slightly dependent\" \"moderately dependent\" \"severely dependent\" #>   ..- attr(*, \"label\")= chr \"elder's dependency\" #>  $ c172code : num  2 2 1 2 2 2 2 2 NA 2 ... #>   ..- attr(*, \"label\")= chr \"carer's level of education\" #>   ..- attr(*, \"labels\")= Named num [1:3] 1 2 3 #>   .. ..- attr(*, \"names\")= chr [1:3] \"low level of education\" \"intermediate level of education\" \"high level of education\" #>  $ neg_c_7  : num  12 20 11 10 12 19 15 11 15 10 ... #>   ..- attr(*, \"label\")= chr \"Negative impact with 7 items\" #>  $ c12hour_c: 'dw_transformer' num  -69.7 62.3 -15.7 NA 82.3 ... #>   ..- attr(*, \"center\")= num 85.7 #>   ..- attr(*, \"scale\")= num 1 #>   ..- attr(*, \"robust\")= logi FALSE #>   ..- attr(*, \"label\")= chr \"average number of hours of care per week\" #>  $ c12hour_z: 'dw_transformer' num  -1.047 0.937 -0.235 NA 1.237 ... #>   ..- attr(*, \"center\")= num 85.7 #>   ..- attr(*, \"scale\")= num 1 #>   ..- attr(*, \"robust\")= logi FALSE #>   ..- attr(*, \"label\")= chr \"average number of hours of care per week\"  # overwrite existing variable, remove old variable out <- data_modify(iris, Petal.Length = 1 / Sepal.Length, Sepal.Length = NULL) head(out) #>   Sepal.Width Petal.Length Petal.Width Species #> 1         3.5    0.1960784         0.2  setosa #> 2         3.0    0.2040816         0.2  setosa #> 3         3.2    0.2127660         0.2  setosa #> 4         3.1    0.2173913         0.2  setosa #> 5         3.6    0.2000000         0.2  setosa #> 6         3.9    0.1851852         0.4  setosa  # works on grouped data grouped_efc <- data_group(efc, \"c172code\") new_efc <- data_modify(   grouped_efc,   c12hour_c = center(c12hour),   c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),   c12hour_z2 = standardize(c12hour) ) head(new_efc) #> # A tibble: 6 × 8 #> # Groups:   c172code [2] #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c c12hour_z c12hour_z2 #>     <dbl>  <dbl> <fct>     <dbl>   <dbl>     <dbl>     <dbl>      <dbl> #> 1      16      2 3             2      12     -78.0    -1.16      -1.16  #> 2     148      2 3             2      20      54.0     0.804      0.804 #> 3      70      2 3             1      11     -17.1    -0.250     -0.250 #> 4      NA      2 NA            2      10      NA      NA         NA     #> 5     168      2 4             2      12      74.0     1.10       1.10  #> 6      16      2 4             2      19     -78.0    -1.16      -1.16   # works from inside functions foo <- function(data, z) {   head(data_modify(data, z)) } foo(iris, \"var_a = Sepal.Width / 10\") #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species var_a #> 1          5.1         3.5          1.4         0.2  setosa  0.35 #> 2          4.9         3.0          1.4         0.2  setosa  0.30 #> 3          4.7         3.2          1.3         0.2  setosa  0.32 #> 4          4.6         3.1          1.5         0.2  setosa  0.31 #> 5          5.0         3.6          1.4         0.2  setosa  0.36 #> 6          5.4         3.9          1.7         0.4  setosa  0.39  new_exp <- c(\"SW_double = 2 * Sepal.Width\", \"SW_fraction = SW_double / 10\") foo(iris, new_exp) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species SW_double #> 1          5.1         3.5          1.4         0.2  setosa       7.0 #> 2          4.9         3.0          1.4         0.2  setosa       6.0 #> 3          4.7         3.2          1.3         0.2  setosa       6.4 #> 4          4.6         3.1          1.5         0.2  setosa       6.2 #> 5          5.0         3.6          1.4         0.2  setosa       7.2 #> 6          5.4         3.9          1.7         0.4  setosa       7.8 #>   SW_fraction #> 1        0.70 #> 2        0.60 #> 3        0.64 #> 4        0.62 #> 5        0.72 #> 6        0.78"},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":null,"dir":"Reference","previous_headings":"","what":"Partition data — data_partition","title":"Partition data — data_partition","text":"Creates data partitions (instance, training test set) based data frame can also stratified (.e., evenly spread given factor) using group argument.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partition data — data_partition","text":"","code":"data_partition(   data,   proportion = 0.7,   group = NULL,   seed = NULL,   row_id = \".row_id\",   verbose = TRUE,   training_proportion = proportion,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partition data — data_partition","text":"data data frame, object can coerced data frame. proportion Scalar (0 1) numeric vector, indicating proportion(s) training set(s). sum proportion must greater 1. remaining part used test set. group character vector indicating name(s) column(s) used stratified partitioning. seed random number generator seed. Enter integer (e.g. 123) random sampling time run function. row_id Character string, indicating name column contains row-id's. verbose Toggle messages warnings. training_proportion Deprecated, please use proportion. ... arguments passed functions.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partition data — data_partition","text":"list data frames. list includes one training set per given proportion remaining data test set. List elements training sets named given proportions (e.g., $p_0.7), test set named $test.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partition data — data_partition","text":"","code":"data(iris) out <- data_partition(iris, proportion = 0.9) out$test #>    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species .row_id #> 1           4.8         3.4          1.6         0.2     setosa      12 #> 2           5.8         4.0          1.2         0.2     setosa      15 #> 3           4.8         3.1          1.6         0.2     setosa      31 #> 4           5.0         3.5          1.3         0.3     setosa      41 #> 5           6.0         2.2          4.0         1.0 versicolor      63 #> 6           5.6         2.9          3.6         1.3 versicolor      65 #> 7           6.7         3.1          4.4         1.4 versicolor      66 #> 8           6.3         2.5          4.9         1.5 versicolor      73 #> 9           5.5         2.4          3.8         1.1 versicolor      81 #> 10          5.7         2.9          4.2         1.3 versicolor      97 #> 11          6.3         3.3          6.0         2.5  virginica     101 #> 12          6.3         2.9          5.6         1.8  virginica     104 #> 13          6.5         3.0          5.8         2.2  virginica     105 #> 14          5.7         2.5          5.0         2.0  virginica     114 #> 15          6.9         3.1          5.1         2.3  virginica     142 nrow(out$p_0.9) #> [1] 135  # Stratify by group (equal proportions of each species) out <- data_partition(iris, proportion = 0.9, group = \"Species\") out$test #>    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species .row_id #> 1           5.8         4.0          1.2         0.2     setosa      15 #> 2           5.7         4.4          1.5         0.4     setosa      16 #> 3           5.7         3.8          1.7         0.3     setosa      19 #> 4           5.1         3.7          1.5         0.4     setosa      22 #> 5           4.4         3.0          1.3         0.2     setosa      39 #> 6           7.0         3.2          4.7         1.4 versicolor      51 #> 7           6.6         2.9          4.6         1.3 versicolor      59 #> 8           5.6         2.9          3.6         1.3 versicolor      65 #> 9           6.2         2.2          4.5         1.5 versicolor      69 #> 10          6.6         3.0          4.4         1.4 versicolor      76 #> 11          6.3         3.3          6.0         2.5  virginica     101 #> 12          6.5         3.0          5.8         2.2  virginica     105 #> 13          6.3         2.7          4.9         1.8  virginica     124 #> 14          7.2         3.2          6.0         1.8  virginica     126 #> 15          6.7         3.0          5.2         2.3  virginica     146  # Create multiple partitions out <- data_partition(iris, proportion = c(0.3, 0.3)) lapply(out, head) #> $p_0.3 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id #> 1          4.7         3.2          1.3         0.2  setosa       3 #> 2          5.0         3.4          1.5         0.2  setosa       8 #> 3          4.4         2.9          1.4         0.2  setosa       9 #> 4          4.9         3.1          1.5         0.1  setosa      10 #> 5          5.4         3.7          1.5         0.2  setosa      11 #> 6          4.8         3.4          1.6         0.2  setosa      12 #>  #> $p_0.3 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id #> 1          5.0         3.6          1.4         0.2  setosa       5 #> 2          5.4         3.9          1.7         0.4  setosa       6 #> 3          4.6         3.4          1.4         0.3  setosa       7 #> 4          4.3         3.0          1.1         0.1  setosa      14 #> 5          5.4         3.9          1.3         0.4  setosa      17 #> 6          5.7         3.8          1.7         0.3  setosa      19 #>  #> $test #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id #> 1          5.1         3.5          1.4         0.2  setosa       1 #> 2          4.9         3.0          1.4         0.2  setosa       2 #> 3          4.6         3.1          1.5         0.2  setosa       4 #> 4          4.8         3.0          1.4         0.1  setosa      13 #> 5          5.8         4.0          1.2         0.2  setosa      15 #> 6          5.1         3.8          1.5         0.3  setosa      20 #>   # Create multiple partitions, stratified by group - 30% equally sampled # from species in first training set, 50% in second training set and # remaining 20% equally sampled from each species in test set. out <- data_partition(iris, proportion = c(0.3, 0.5), group = \"Species\") lapply(out, function(i) table(i$Species)) #> $p_0.3 #>  #>     setosa versicolor  virginica  #>         15         15         15  #>  #> $p_0.5 #>  #>     setosa versicolor  virginica  #>         25         25         25  #>  #> $test #>  #>     setosa versicolor  virginica  #>         10         10         10  #>"},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":null,"dir":"Reference","previous_headings":"","what":"Peek at values and type of variables in a data frame — data_peek","title":"Peek at values and type of variables in a data frame — data_peek","text":"function creates table data frame, showing column names, variable types first values (many fit screen).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Peek at values and type of variables in a data frame — data_peek","text":"","code":"data_peek(x, ...)  # S3 method for data.frame data_peek(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   width = NULL,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Peek at values and type of variables in a data frame — data_peek","text":"x data frame. ... used. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. width Maximum width line length display. NULL, width determined using options()$width. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Peek at values and type of variables in a data frame — data_peek","text":"data frame three columns, containing information name, type first values input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Peek at values and type of variables in a data frame — data_peek","text":"show specific limited number variables, use select argument, e.g. select = 1:5 show first five variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Peek at values and type of variables in a data frame — data_peek","text":"","code":"data(efc) data_peek(efc) #> Data frame with 100 rows and 5 variables #>  #> Variable | Type    | Values                                            #> ---------------------------------------------------------------------- #> c12hour  | numeric | 16, 148, 70, NA, 168, 16, 161, 110, 28, 40, ...   #> e16sex   | numeric | 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, ...  #> e42dep   | factor  | 3, 3, 3, NA, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 3, ... #> c172code | numeric | 2, 2, 1, 2, 2, 2, 2, 2, NA, 2, 2, 2, 3, 1, 3, ... #> neg_c_7  | numeric | 12, 20, 11, 10, 12, 19, 15, 11, 15, 10, 28, ...   # show variables two to four data_peek(efc, select = 2:4) #> Data frame with 100 rows and 5 variables #>  #> Variable | Type    | Values                                            #> ---------------------------------------------------------------------- #> e16sex   | numeric | 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, ...  #> e42dep   | factor  | 3, 3, 3, NA, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 3, ... #> c172code | numeric | 2, 2, 1, 2, 2, 2, 2, 2, NA, 2, 2, 2, 3, 1, 3, ..."},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":null,"dir":"Reference","previous_headings":"","what":"Read (import) data files from various sources — data_read","title":"Read (import) data files from various sources — data_read","text":"functions imports data various file types. small wrapper around haven::read_spss(), haven::read_stata(), haven::read_sas(), readxl::read_excel() data.table::fread() resp. readr::read_delim() (latter package data.table installed). Thus, supported file types importing data data files SPSS, SAS Stata, Excel files text files (like '.csv' files). file types passed rio::import(). data_write() works similar way.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read (import) data files from various sources — data_read","text":"","code":"data_read(   path,   path_catalog = NULL,   encoding = NULL,   convert_factors = TRUE,   verbose = TRUE,   ... )  data_write(   data,   path,   delimiter = \",\",   convert_factors = FALSE,   save_labels = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read (import) data files from various sources — data_read","text":"path Character string, file path data file. path_catalog Character string, path catalog file. relevant SAS data files. encoding character encoding used file. Usually needed. convert_factors TRUE (default), numeric variables, values value label, assumed categorical converted factors. FALSE, variable types guessed conversion numeric variables factors performed. See also section 'Differences packages'. data_write(), argument applies text (e.g. .txt .csv) spreadsheet file formats (like .xlsx). Converting factors might useful formats labelled numeric variables converted factors exported character columns - else, value labels lost numeric values written file. verbose Toggle warnings messages. ... Arguments passed related read_*() write_*() functions. data data frame written file. delimiter CSV-files, specifies delimiter. Defaults \",\", particular European regions, \";\" might useful alternative, especially exported CSV-files opened Excel. save_labels applies CSV files. TRUE, value variable labels () saved additional CSV file. file file name exported CSV file, includes \"_labels\" suffix (.e. file name \"mydat.csv\", additional file value variable labels named \"mydat_labels.csv\").","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read (import) data files from various sources — data_read","text":"data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"supported-file-types","dir":"Reference","previous_headings":"","what":"Supported file types","title":"Read (import) data files from various sources — data_read","text":"data_read() wrapper around haven, data.table, readr readxl rio packages. Currently supported file types .txt, .csv, .xls, .xlsx, .sav, .por, .dta .sas (related files). file types passed rio::import(). data_write() wrapper around haven, readr rio packages, supports writing files formats supported packages.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"compressed-files-zip-and-urls","dir":"Reference","previous_headings":"","what":"Compressed files (zip) and URLs","title":"Read (import) data files from various sources — data_read","text":"data_read() can also read mentioned files URLs inside zip-compressed files. Thus, path can also URL file like \"http://www.url.com/file.csv\". path points zip-compressed file, multiple files inside zip-archive, first supported file extracted loaded.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"general-behaviour","dir":"Reference","previous_headings":"","what":"General behaviour","title":"Read (import) data files from various sources — data_read","text":"data_read() detects appropriate read_*() function based file-extension data file. Thus, cases enough specify path argument. However, control needed, arguments ... passed related read_*() function. applies data_write(), .e. based file extension provided path, appropriate write_*() function used automatically.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"spss-specific-behaviour","dir":"Reference","previous_headings":"","what":"SPSS specific behaviour","title":"Read (import) data files from various sources — data_read","text":"data_read() import user-defined (\"tagged\") NA values SPSS, .e. argument user_na always set FALSE importing SPSS data haven package. Use convert_to_na() define missing values imported data, necessary. Furthermore, data_write() compresses SPSS files default. causes problems (older) SPSS versions, use compress = \"none\", example data_write(data, \"myfile.sav\", compress = \"none\").","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"differences-to-other-packages-that-read-foreign-data-formats","dir":"Reference","previous_headings":"","what":"Differences to other packages that read foreign data formats","title":"Read (import) data files from various sources — data_read","text":"data_read() comparable rio::import(). data files SPSS, SAS Stata, support labelled data, variables converted appropriate type. major difference rio::import() data_read() automatically converts fully labelled numeric variables factors, imported value labels set factor levels. numeric variable value labels less value labels values, converted factor. case, value labels preserved \"labels\" attribute. Character vectors preserved.  Use convert_factors = FALSE remove automatic conversion numeric variables factors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":null,"dir":"Reference","previous_headings":"","what":"Relocate (reorder) columns of a data frame — data_relocate","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"data_relocate() reorder columns specific positions, indicated . data_reorder() instead move selected columns beginning data frame. Finally, data_remove() removes columns data frame. functions support select-helpers allow flexible specification search pattern find matching columns, reordered removed.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"","code":"data_relocate(   data,   select,   before = NULL,   after = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_reorder(   data,   select,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_remove(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". , Destination columns. Supplying neither move columns left-hand side; specifying error. Can character vector, indicating name destination column, numeric value, indicating index number destination column. -1, added last column. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings. ... Arguments passed functions. Mostly used yet. exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"data frame reordered columns.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"","code":"# Reorder columns head(data_relocate(iris, select = \"Species\", before = \"Sepal.Length\")) #>   Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1  setosa          5.1         3.5          1.4         0.2 #> 2  setosa          4.9         3.0          1.4         0.2 #> 3  setosa          4.7         3.2          1.3         0.2 #> 4  setosa          4.6         3.1          1.5         0.2 #> 5  setosa          5.0         3.6          1.4         0.2 #> 6  setosa          5.4         3.9          1.7         0.4 head(data_relocate(iris, select = \"Species\", before = \"Sepal.Width\")) #>   Sepal.Length Species Sepal.Width Petal.Length Petal.Width #> 1          5.1  setosa         3.5          1.4         0.2 #> 2          4.9  setosa         3.0          1.4         0.2 #> 3          4.7  setosa         3.2          1.3         0.2 #> 4          4.6  setosa         3.1          1.5         0.2 #> 5          5.0  setosa         3.6          1.4         0.2 #> 6          5.4  setosa         3.9          1.7         0.4 head(data_relocate(iris, select = \"Sepal.Width\", after = \"Species\")) #>   Sepal.Length Petal.Length Petal.Width Species Sepal.Width #> 1          5.1          1.4         0.2  setosa         3.5 #> 2          4.9          1.4         0.2  setosa         3.0 #> 3          4.7          1.3         0.2  setosa         3.2 #> 4          4.6          1.5         0.2  setosa         3.1 #> 5          5.0          1.4         0.2  setosa         3.6 #> 6          5.4          1.7         0.4  setosa         3.9 # which is same as head(data_relocate(iris, select = \"Sepal.Width\", after = -1)) #>   Sepal.Length Petal.Length Petal.Width Species Sepal.Width #> 1          5.1          1.4         0.2  setosa         3.5 #> 2          4.9          1.4         0.2  setosa         3.0 #> 3          4.7          1.3         0.2  setosa         3.2 #> 4          4.6          1.5         0.2  setosa         3.1 #> 5          5.0          1.4         0.2  setosa         3.6 #> 6          5.4          1.7         0.4  setosa         3.9  # Reorder multiple columns head(data_relocate(iris, select = c(\"Species\", \"Petal.Length\"), after = \"Sepal.Width\")) #>   Sepal.Length Sepal.Width Species Petal.Length Petal.Width #> 1          5.1         3.5  setosa          1.4         0.2 #> 2          4.9         3.0  setosa          1.4         0.2 #> 3          4.7         3.2  setosa          1.3         0.2 #> 4          4.6         3.1  setosa          1.5         0.2 #> 5          5.0         3.6  setosa          1.4         0.2 #> 6          5.4         3.9  setosa          1.7         0.4 # which is same as head(data_relocate(iris, select = c(\"Species\", \"Petal.Length\"), after = 2)) #>   Sepal.Length Sepal.Width Species Petal.Length Petal.Width #> 1          5.1         3.5  setosa          1.4         0.2 #> 2          4.9         3.0  setosa          1.4         0.2 #> 3          4.7         3.2  setosa          1.3         0.2 #> 4          4.6         3.1  setosa          1.5         0.2 #> 5          5.0         3.6  setosa          1.4         0.2 #> 6          5.4         3.9  setosa          1.7         0.4  # Reorder columns head(data_reorder(iris, c(\"Species\", \"Sepal.Length\"))) #>   Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1  setosa          5.1         3.5          1.4         0.2 #> 2  setosa          4.9         3.0          1.4         0.2 #> 3  setosa          4.7         3.2          1.3         0.2 #> 4  setosa          4.6         3.1          1.5         0.2 #> 5  setosa          5.0         3.6          1.4         0.2 #> 6  setosa          5.4         3.9          1.7         0.4  # Remove columns head(data_remove(iris, \"Sepal.Length\")) #>   Sepal.Width Petal.Length Petal.Width Species #> 1         3.5          1.4         0.2  setosa #> 2         3.0          1.4         0.2  setosa #> 3         3.2          1.3         0.2  setosa #> 4         3.1          1.5         0.2  setosa #> 5         3.6          1.4         0.2  setosa #> 6         3.9          1.7         0.4  setosa head(data_remove(iris, starts_with(\"Sepal\"))) #>   Petal.Length Petal.Width Species #> 1          1.4         0.2  setosa #> 2          1.4         0.2  setosa #> 3          1.3         0.2  setosa #> 4          1.5         0.2  setosa #> 5          1.4         0.2  setosa #> 6          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":null,"dir":"Reference","previous_headings":"","what":"Rename columns and variable names — data_addprefix","title":"Rename columns and variable names — data_addprefix","text":"Safe intuitive functions rename variables rows data frames. data_rename() rename column names, .e. facilitates renaming variables data_addprefix() data_addsuffix() add prefixes suffixes column names. data_rename_rows() convenient shortcut add rename row names data frame, unlike row.names(), input output data frame, thus, integrating smoothly possible pipe-workflow.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rename columns and variable names — data_addprefix","text":"","code":"data_addprefix(   data,   pattern,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_addsuffix(   data,   pattern,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_rename(   data,   pattern = NULL,   replacement = NULL,   safe = TRUE,   verbose = TRUE,   ... )  data_rename_rows(data, rows = NULL)"},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rename columns and variable names — data_addprefix","text":"data data frame, object can coerced data frame. pattern Character vector. data_rename(), indicates columns selected renaming. Can NULL (case columns selected). data_addprefix() data_addsuffix(), character string, added prefix suffix column names. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings messages. ... arguments passed functions. replacement Character vector. Indicates new name columns selected pattern. Can NULL (case column numbered sequential order). NULL, pattern replacement must length. safe throw error instance variable renamed/removed exist. rows Vector row names.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rename columns and variable names — data_addprefix","text":"modified data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rename columns and variable names — data_addprefix","text":"","code":"# Add prefix / suffix to all columns head(data_addprefix(iris, \"NEW_\")) #>   NEW_Sepal.Length NEW_Sepal.Width NEW_Petal.Length NEW_Petal.Width NEW_Species #> 1              5.1             3.5              1.4             0.2      setosa #> 2              4.9             3.0              1.4             0.2      setosa #> 3              4.7             3.2              1.3             0.2      setosa #> 4              4.6             3.1              1.5             0.2      setosa #> 5              5.0             3.6              1.4             0.2      setosa #> 6              5.4             3.9              1.7             0.4      setosa head(data_addsuffix(iris, \"_OLD\")) #>   Sepal.Length_OLD Sepal.Width_OLD Petal.Length_OLD Petal.Width_OLD Species_OLD #> 1              5.1             3.5              1.4             0.2      setosa #> 2              4.9             3.0              1.4             0.2      setosa #> 3              4.7             3.2              1.3             0.2      setosa #> 4              4.6             3.1              1.5             0.2      setosa #> 5              5.0             3.6              1.4             0.2      setosa #> 6              5.4             3.9              1.7             0.4      setosa  # Rename columns head(data_rename(iris, \"Sepal.Length\", \"length\")) #>   length Sepal.Width Petal.Length Petal.Width Species #> 1    5.1         3.5          1.4         0.2  setosa #> 2    4.9         3.0          1.4         0.2  setosa #> 3    4.7         3.2          1.3         0.2  setosa #> 4    4.6         3.1          1.5         0.2  setosa #> 5    5.0         3.6          1.4         0.2  setosa #> 6    5.4         3.9          1.7         0.4  setosa # data_rename(iris, \"FakeCol\", \"length\", safe=FALSE)  # This fails head(data_rename(iris, \"FakeCol\", \"length\")) # This doesn't #> Variable `FakeCol` is not in your data frame :/ #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa head(data_rename(iris, c(\"Sepal.Length\", \"Sepal.Width\"), c(\"length\", \"width\"))) #>   length width Petal.Length Petal.Width Species #> 1    5.1   3.5          1.4         0.2  setosa #> 2    4.9   3.0          1.4         0.2  setosa #> 3    4.7   3.2          1.3         0.2  setosa #> 4    4.6   3.1          1.5         0.2  setosa #> 5    5.0   3.6          1.4         0.2  setosa #> 6    5.4   3.9          1.7         0.4  setosa  # Reset names head(data_rename(iris, NULL)) #>     1   2   3   4      5 #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa  # Change all head(data_rename(iris, replacement = paste0(\"Var\", 1:5))) #>   Var1 Var2 Var3 Var4   Var5 #> 1  5.1  3.5  1.4  0.2 setosa #> 2  4.9  3.0  1.4  0.2 setosa #> 3  4.7  3.2  1.3  0.2 setosa #> 4  4.6  3.1  1.5  0.2 setosa #> 5  5.0  3.6  1.4  0.2 setosa #> 6  5.4  3.9  1.7  0.4 setosa"},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":null,"dir":"Reference","previous_headings":"","what":"Restore the type of columns according to a reference data frame — data_restoretype","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"Restore type columns according reference data frame","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"","code":"data_restoretype(data, reference = NULL, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"data data frame pivot. reference reference data frame find correct column types. NULL, column converted numeric generate NAs. example, c(\"1\", \"2\") can converted numeric c(\"Sepal.Length\"). ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"data frame columns whose types restored based reference data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"","code":"data <- data.frame(   Sepal.Length = c(\"1\", \"3\", \"2\"),   Species = c(\"setosa\", \"versicolor\", \"setosa\"),   New = c(\"1\", \"3\", \"4\") )  fixed <- data_restoretype(data, reference = iris) summary(fixed) #>   Sepal.Length       Species      New            #>  Min.   :1.0   setosa    :2   Length:3           #>  1st Qu.:1.5   versicolor:1   Class :character   #>  Median :2.0   virginica :0   Mode  :character   #>  Mean   :2.0                                     #>  3rd Qu.:2.5                                     #>  Max.   :3.0"},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":null,"dir":"Reference","previous_headings":"","what":"Rotate a data frame — data_rotate","title":"Rotate a data frame — data_rotate","text":"function rotates data frame, .e. columns become rows vice versa. equivalent using t() restores data.frame class, preserves attributes prints warning data type modified (see example).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rotate a data frame — data_rotate","text":"","code":"data_rotate(data, rownames = NULL, colnames = FALSE, verbose = TRUE)  data_transpose(data, rownames = NULL, colnames = FALSE, verbose = TRUE)"},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rotate a data frame — data_rotate","text":"data data frame. rownames Character vector (optional). NULL, data frame's rownames added (first) column output, rownames name column. colnames Logical character vector (optional). TRUE, values first column x used column names rotated data frame. character vector, values column used column names. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rotate a data frame — data_rotate","text":"(rotated) data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rotate a data frame — data_rotate","text":"","code":"x <- mtcars[1:3, 1:4]  x #>                mpg cyl disp  hp #> Mazda RX4     21.0   6  160 110 #> Mazda RX4 Wag 21.0   6  160 110 #> Datsun 710    22.8   4  108  93  data_rotate(x) #>      Mazda RX4 Mazda RX4 Wag Datsun 710 #> mpg         21            21       22.8 #> cyl          6             6        4.0 #> disp       160           160      108.0 #> hp         110           110       93.0 data_rotate(x, rownames = \"property\") #>   property Mazda RX4 Mazda RX4 Wag Datsun 710 #> 1      mpg        21            21       22.8 #> 2      cyl         6             6        4.0 #> 3     disp       160           160      108.0 #> 4       hp       110           110       93.0  # use values in 1. column as column name data_rotate(x, colnames = TRUE) #>       21  21 22.8 #> cyl    6   6    4 #> disp 160 160  108 #> hp   110 110   93 data_rotate(x, rownames = \"property\", colnames = TRUE) #>   property  21  21 22.8 #> 1      cyl   6   6    4 #> 2     disp 160 160  108 #> 3       hp 110 110   93  # use either first column or specific column for column names x <- data.frame(a = 1:5, b = 11:15, c = 21:25) data_rotate(x, colnames = TRUE) #>    1  2  3  4  5 #> b 11 12 13 14 15 #> c 21 22 23 24 25 data_rotate(x, colnames = \"c\") #>   21 22 23 24 25 #> a  1  2  3  4  5 #> b 11 12 13 14 15"},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":null,"dir":"Reference","previous_headings":"","what":"Find variables by their names, variable or value labels — data_seek","title":"Find variables by their names, variable or value labels — data_seek","text":"functions seeks variables data frame, based patterns either match variable name (column name), variable labels, value labels factor levels. Matching variable value labels works \"labelled\" data, .e. variables either label attribute labels attribute. data_seek() particular useful larger data frames labelled data - finding correct variable name can challenge. function helps find required variables, certain patterns variable names labels known.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find variables by their names, variable or value labels — data_seek","text":"","code":"data_seek(data, pattern, seek = c(\"names\", \"labels\"), fuzzy = FALSE)"},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find variables by their names, variable or value labels — data_seek","text":"data data frame. pattern Character string (regular expression) matched data. May also character vector length > 1. pattern searched column names, variable label value labels attributes, factor levels variables data. seek Character vector, indicating pattern sought. Use one following options: \"names\": Searches column names. \"column_names\" \"columns\" aliases \"names\". \"labels\": Searches variable labels. applies label attribute set variable. \"values\": Searches value labels factor levels. applies labels attribute set variable, variable factor. \"levels\" alias \"values\". \"\": Searches . fuzzy Logical. TRUE, \"fuzzy matching\" (partial close distance matching) used find pattern.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find variables by their names, variable or value labels — data_seek","text":"data frame three columns: column index, column name - available - variable label matched variables data.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find variables by their names, variable or value labels — data_seek","text":"","code":"# seek variables with \"Length\" in variable name or labels data_seek(iris, \"Length\") #> index |       column |       labels #> ----------------------------------- #>     1 | Sepal.Length | Sepal.Length #>     3 | Petal.Length | Petal.Length  # seek variables with \"dependency\" in names or labels # column \"e42dep\" has a label-attribute \"elder's dependency\" data(efc) data_seek(efc, \"dependency\") #> index | column |             labels #> ----------------------------------- #>     3 | e42dep | elder's dependency  # \"female\" only appears as value label attribute - default search is in # variable names and labels only, so no match data_seek(efc, \"female\") #> No matches found. # when we seek in all sources, we find the variable \"e16sex\" data_seek(efc, \"female\", seek = \"all\") #> index | column |         labels #> ------------------------------- #>     2 | e16sex | elder's gender  # typo, no match data_seek(iris, \"Lenght\") #> No matches found. # typo, fuzzy match data_seek(iris, \"Lenght\", fuzzy = TRUE) #> index |       column |       labels #> ----------------------------------- #>     1 | Sepal.Length | Sepal.Length #>     3 | Petal.Length | Petal.Length"},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":null,"dir":"Reference","previous_headings":"","what":"Separate single variable into multiple variables — data_separate","title":"Separate single variable into multiple variables — data_separate","text":"Separates single variable multiple new variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Separate single variable into multiple variables — data_separate","text":"","code":"data_separate(   data,   select = NULL,   new_columns = NULL,   separator = \"[^[:alnum:]]+\",   guess_columns = NULL,   merge_multiple = FALSE,   merge_separator = \"\",   fill = \"right\",   extra = \"drop_right\",   convert_na = TRUE,   exclude = NULL,   append = FALSE,   ignore_case = FALSE,   verbose = TRUE,   regex = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Separate single variable into multiple variables — data_separate","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". new_columns names new columns, character vector. one variable selected (select), new names prefixed name original column. new_columns can also list (named) character vectors multiple variables separated. See 'Examples'. separator Separator columns. Can character vector, treated regular expression, numeric vector indicates positions string values split. guess_columns new_columns given, required number new columns guessed based results value splitting. example, variable split three new columns, considered required number new columns, columns named \"split_1\", \"split_2\" \"split_3\". values variable split different amount new columns, guess_column can either \"mode\" (number new columns based common number splits), \"min\" \"max\" use minimum resp. maximum number possible splits required number columns. merge_multiple Logical, TRUE one variable selected separating, new columns can merged. Value pairs split variables merged. merge_separator Separator string merge_multiple = TRUE. Defines string used merge values together. fill deal values return fewer new columns splitting? Can \"left\" (fill missing columns left NA), \"right\" (fill missing columns right NA) \"value_left\" \"value_right\" fill missing columns left right left-right-values. extra deal values return many new columns splitting? Can \"drop_left\" \"drop_right\" drop left-right-values, \"merge_left\" \"merge_right\" merge left- right-value together, keeping remaining values . convert_na Logical, TRUE, character \"NA\" values converted real NA values. exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical, FALSE (default), removes original columns separated. TRUE, columns preserved new columns appended data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. verbose Toggle warnings. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Separate single variable into multiple variables — data_separate","text":"data frame newly created variable(s), - append = TRUE - data including new variables.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Separate single variable into multiple variables — data_separate","text":"","code":"# simple case d <- data.frame(   x = c(\"1.a.6\", \"2.b.7\", \"3.c.8\"),   stringsAsFactors = FALSE ) d #>       x #> 1 1.a.6 #> 2 2.b.7 #> 3 3.c.8 data_separate(d, new_columns = c(\"a\", \"b\", \"c\")) #>   a b c #> 1 1 a 6 #> 2 2 b 7 #> 3 3 c 8  # guess number of columns d <- data.frame(   x = c(\"1.a.6\", NA, \"2.b.6.7\", \"3.c\", \"x.y.z\"),   stringsAsFactors = FALSE ) d #>         x #> 1   1.a.6 #> 2    <NA> #> 3 2.b.6.7 #> 4     3.c #> 5   x.y.z data_separate(d, guess_columns = \"mode\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been dropped. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b    6 #> 4    3    c <NA> #> 5    x    y    z  data_separate(d, guess_columns = \"max\") #> Column `x` had different number of values after splitting. Variable was #>   split into 4 columns. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3  x_4 #> 1    1    a    6 <NA> #> 2 <NA> <NA> <NA> <NA> #> 3    2    b    6    7 #> 4    3    c <NA> <NA> #> 5    x    y    z <NA>  # drop left-most column data_separate(d, guess_columns = \"mode\", extra = \"drop_left\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Left-most #>   columns have been dropped. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    b    6    7 #> 4    3    c <NA> #> 5    x    y    z  # merge right-most column data_separate(d, guess_columns = \"mode\", extra = \"merge_right\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been merged together. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b  6 7 #> 4    3    c <NA> #> 5    x    y    z  # fill columns with fewer values with left-most values data_separate(d, guess_columns = \"mode\", fill = \"value_left\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been dropped. #> `x`returned fewer columns than expected after splitting. Left-most #>   columns were filled with first value. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b    6 #> 4    3    3    c #> 5    x    y    z  # fill and merge data_separate(   d,   guess_columns = \"mode\",   fill = \"value_left\",   extra = \"merge_right\" ) #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been merged together. #> `x`returned fewer columns than expected after splitting. Left-most #>   columns were filled with first value. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b  6 7 #> 4    3    3    c #> 5    x    y    z  # multiple columns to split d <- data.frame(   x = c(\"1.a.6\", \"2.b.7\", \"3.c.8\"),   y = c(\"x.y.z\", \"10.11.12\", \"m.n.o\"),   stringsAsFactors = FALSE ) d #>       x        y #> 1 1.a.6    x.y.z #> 2 2.b.7 10.11.12 #> 3 3.c.8    m.n.o # split two columns, default column names data_separate(d, guess_columns = \"mode\") #>   x_1 x_2 x_3 y_1 y_2 y_3 #> 1   1   a   6   x   y   z #> 2   2   b   7  10  11  12 #> 3   3   c   8   m   n   o  # split into new named columns, repeating column names data_separate(d, new_columns = c(\"a\", \"b\", \"c\")) #>   x_a x_b x_c y_a y_b y_c #> 1   1   a   6   x   y   z #> 2   2   b   7  10  11  12 #> 3   3   c   8   m   n   o  # split selected variable new columns data_separate(d, select = \"y\", new_columns = c(\"a\", \"b\", \"c\")) #>       x  a  b  c #> 1 1.a.6  x  y  z #> 2 2.b.7 10 11 12 #> 3 3.c.8  m  n  o  # merge multiple split columns data_separate(   d,   new_columns = c(\"a\", \"b\", \"c\"),   merge_multiple = TRUE ) #>     a   b   c #> 1  1x  ay  6z #> 2 210 b11 712 #> 3  3m  cn  8o  # merge multiple split columns data_separate(   d,   new_columns = c(\"a\", \"b\", \"c\"),   merge_multiple = TRUE,   merge_separator = \"-\" ) #>      a    b    c #> 1  1-x  a-y  6-z #> 2 2-10 b-11 7-12 #> 3  3-m  c-n  8-o  # separate multiple columns, give proper column names d_sep <- data.frame(   x = c(\"1.a.6\", \"2.b.7.d\", \"3.c.8\", \"5.j\"),   y = c(\"m.n.99.22\", \"77.f.g.34\", \"44.9\", NA),   stringsAsFactors = FALSE )  data_separate(   d_sep,   select = c(\"x\", \"y\"),   new_columns = list(     x = c(\"A\", \"B\", \"C\"), # separate \"x\" into three columns     y = c(\"EE\", \"FF\", \"GG\", \"HH\") # separate \"y\" into four columns   ),   verbose = FALSE ) #>   A B    C   EE   FF   GG   HH #> 1 1 a    6    m    n   99   22 #> 2 2 b    7   77    f    g   34 #> 3 3 c    8   44    9 <NA> <NA> #> 4 5 j <NA> <NA> <NA> <NA> <NA>"},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Create frequency tables of variables — data_tabulate","title":"Create frequency tables of variables — data_tabulate","text":"function creates frequency tables variables, including number levels/values well distribution raw, valid cumulative percentages.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create frequency tables of variables — data_tabulate","text":"","code":"data_tabulate(x, ...)  # S3 method for default data_tabulate(x, drop_levels = FALSE, name = NULL, verbose = TRUE, ...)  # S3 method for data.frame data_tabulate(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   collapse = FALSE,   drop_levels = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create frequency tables of variables — data_tabulate","text":"x (grouped) data frame, vector factor. ... used. drop_levels Logical, TRUE, factor levels occur data included table (frequency zero), else unused factor levels dropped frequency table. name Optional character string, includes name used printing. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. collapse Logical, TRUE collapses multiple tables one larger table printing. affects printing, returned object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create frequency tables of variables — data_tabulate","text":"data frame, list data frames, one frequency table data frame per variable.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create frequency tables of variables — data_tabulate","text":"","code":"data(efc)  # vector/factor data_tabulate(efc$c172code) #> carer's level of education (efc$c172code) <numeric> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  8 |  8.00 |    8.89 |         8.89 #> 2     | 66 | 66.00 |   73.33 |        82.22 #> 3     | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>  # data frame data_tabulate(efc, c(\"e42dep\", \"c172code\")) #> elder's dependency (e42dep) <categorical> #> # total N=100 valid N=97 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  2 |  2.00 |    2.06 |         2.06 #> 2     |  4 |  4.00 |    4.12 |         6.19 #> 3     | 28 | 28.00 |   28.87 |        35.05 #> 4     | 63 | 63.00 |   64.95 |       100.00 #> <NA>  |  3 |  3.00 |    <NA> |         <NA> #>  #> carer's level of education (c172code) <numeric> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  8 |  8.00 |    8.89 |         8.89 #> 2     | 66 | 66.00 |   73.33 |        82.22 #> 3     | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>  # grouped data frame suppressPackageStartupMessages(library(poorman, quietly = TRUE)) efc %>%   group_by(c172code) %>%   data_tabulate(\"e16sex\") #> elder's gender (e16sex) <numeric> #> Grouped by c172code (1) #> # total N=8 valid N=8 #>  #> Value | N | Raw % | Valid % | Cumulative % #> ------+---+-------+---------+------------- #> 1     | 5 | 62.50 |   62.50 |        62.50 #> 2     | 3 | 37.50 |   37.50 |       100.00 #> <NA>  | 0 |  0.00 |    <NA> |         <NA> #>  #> elder's gender (e16sex) <numeric> #> Grouped by c172code (2) #> # total N=66 valid N=66 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     | 32 | 48.48 |   48.48 |        48.48 #> 2     | 34 | 51.52 |   51.52 |       100.00 #> <NA>  |  0 |  0.00 |    <NA> |         <NA> #>  #> elder's gender (e16sex) <numeric> #> Grouped by c172code (3) #> # total N=16 valid N=16 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  4 | 25.00 |   25.00 |        25.00 #> 2     | 12 | 75.00 |   75.00 |       100.00 #> <NA>  |  0 |  0.00 |    <NA> |         <NA> #>  #> elder's gender (e16sex) <numeric> #> Grouped by c172code (NA) #> # total N=10 valid N=10 #>  #> Value | N | Raw % | Valid % | Cumulative % #> ------+---+-------+---------+------------- #> 1     | 5 | 50.00 |   50.00 |        50.00 #> 2     | 5 | 50.00 |   50.00 |       100.00 #> <NA>  | 0 |  0.00 |    <NA> |         <NA>  # collapse tables efc %>%   group_by(c172code) %>%   data_tabulate(\"e16sex\", collapse = TRUE) #> # Frequency Table #>  #> Variable |         Group | Value |  N | Raw % | Valid % | Cumulative % #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   |  c172code (1) |     1 |  5 | 62.50 |   62.50 |        62.50 #>          |               |     2 |  3 | 37.50 |   37.50 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   |  c172code (2) |     1 | 32 | 48.48 |   48.48 |        48.48 #>          |               |     2 | 34 | 51.52 |   51.52 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   |  c172code (3) |     1 |  4 | 25.00 |   25.00 |        25.00 #>          |               |     2 | 12 | 75.00 |   75.00 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   | c172code (NA) |     1 |  5 | 50.00 |   50.00 |        50.00 #>          |               |     2 |  5 | 50.00 |   50.00 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ----------------------------------------------------------------------  # for larger N's (> 100000), a big mark is automatically added set.seed(123) x <- sample(1:3, 1e6, TRUE) data_tabulate(x, name = \"Large Number\") #> Large Number (x) <integer> #> # total N=1,000,000 valid N=1,000,000 #>  #> Value |       N | Raw % | Valid % | Cumulative % #> ------+---------+-------+---------+------------- #> 1     | 333,852 | 33.39 |   33.39 |        33.39 #> 2     | 332,910 | 33.29 |   33.29 |        66.68 #> 3     | 333,238 | 33.32 |   33.32 |       100.00 #> <NA>  |       0 |  0.00 |    <NA> |         <NA>  # to remove the big mark, use \"print(..., big_mark = \"\")\" print(data_tabulate(x), big_mark = \"\") #> x <integer> #> # total N=1000000 valid N=1000000 #>  #> Value |      N | Raw % | Valid % | Cumulative % #> ------+--------+-------+---------+------------- #> 1     | 333852 | 33.39 |   33.39 |        33.39 #> 2     | 332910 | 33.29 |   33.29 |        66.68 #> 3     | 333238 | 33.32 |   33.32 |       100.00 #> <NA>  |      0 |  0.00 |    <NA> |         <NA>"},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape (pivot) data from wide to long — data_to_long","title":"Reshape (pivot) data from wide to long — data_to_long","text":"function \"lengthens\" data, increasing number rows decreasing number columns. dependency-free base-R equivalent tidyr::pivot_longer().","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape (pivot) data from wide to long — data_to_long","text":"","code":"data_to_long(   data,   select = \"all\",   names_to = \"name\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   values_to = \"value\",   values_drop_na = FALSE,   rows_to = NULL,   ignore_case = FALSE,   regex = FALSE,   ...,   cols,   colnames_to )  reshape_longer(   data,   select = \"all\",   names_to = \"name\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   values_to = \"value\",   values_drop_na = FALSE,   rows_to = NULL,   ignore_case = FALSE,   regex = FALSE,   ...,   cols,   colnames_to )"},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape (pivot) data from wide to long — data_to_long","text":"data data frame pivot. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". names_to name new column contain column names. names_prefix regular expression used remove matching text start variable name. names_sep, names_pattern names_to contains multiple values, argument controls column name broken . names_pattern takes regular expression containing matching groups, .e. \"()\". values_to name new column contain values pivoted variables. values_drop_na TRUE, drop rows contain NA values_to column. effectively converts explicit missing values implicit missing values, generally used missing values data created structure. rows_to name column contain row names row numbers original data. NULL, removed. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. ... Currently used. cols Identical select. argument ensure compatibility tidyr::pivot_longer(). select cols provided, cols used. colnames_to Deprecated. Use names_to instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reshape (pivot) data from wide to long — data_to_long","text":"tibble provided input, reshape_longer() also returns tibble. Otherwise, returns data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape (pivot) data from wide to long — data_to_long","text":"","code":"wide_data <- data.frame(replicate(5, rnorm(10)))  # Default behaviour (equivalent to tidyr::pivot_longer(wide_data, cols = 1:5)) data_to_long(wide_data) #>    name       value #> 1    X1  0.96438796 #> 2    X2 -0.05906520 #> 3    X3 -0.49945619 #> 4    X4 -1.18164198 #> 5    X5 -0.26924131 #> 6    X1  0.78540975 #> 7    X2 -0.66621750 #> 8    X3  1.11673935 #> 9    X4 -0.43727481 #> 10   X5 -0.28183866 #> 11   X1 -0.68673736 #> 12   X2 -0.83318830 #> 13   X3  0.17915927 #> 14   X4 -0.27775661 #> 15   X5  1.62361443 #> 16   X1 -0.35491405 #> 17   X2 -1.41166020 #> 18   X3 -1.14038587 #> 19   X4 -0.65407266 #> 20   X5  0.82933164 #> 21   X1 -0.25090623 #> 22   X2  0.22522310 #> 23   X3  0.80261795 #> 24   X4  0.18375250 #> 25   X5 -0.79579710 #> 26   X1  0.71122444 #> 27   X2  1.30430074 #> 28   X3 -0.01983587 #> 29   X4 -0.67286229 #> 30   X5 -0.52205496 #> 31   X1  1.69081622 #> 32   X2  0.28000624 #> 33   X3  0.73973690 #> 34   X4  1.98316979 #> 35   X5 -0.02966633 #> 36   X1  0.21996840 #> 37   X2  1.21381633 #> 38   X3 -1.76563045 #> 39   X4  0.98711564 #> 40   X5  0.72787721 #> 41   X1 -0.88773194 #> 42   X2 -0.53360955 #> 43   X3  0.74375283 #> 44   X4 -0.49170812 #> 45   X5 -2.91160978 #> 46   X1 -0.91386940 #> 47   X2  0.78988333 #> 48   X3 -0.44083106 #> 49   X4 -0.35785520 #> 50   X5 -1.48411421  # Customizing the names data_to_long(wide_data,   select = c(1, 2),   names_to = \"Column\",   values_to = \"Numbers\",   rows_to = \"Row\" ) #>             X3         X4          X5 Row Column    Numbers #> 1  -0.49945619 -1.1816420 -0.26924131   1     X1  0.9643880 #> 2  -0.49945619 -1.1816420 -0.26924131   1     X2 -0.0590652 #> 3   1.11673935 -0.4372748 -0.28183866   2     X1  0.7854097 #> 4   1.11673935 -0.4372748 -0.28183866   2     X2 -0.6662175 #> 5   0.17915927 -0.2777566  1.62361443   3     X1 -0.6867374 #> 6   0.17915927 -0.2777566  1.62361443   3     X2 -0.8331883 #> 7  -1.14038587 -0.6540727  0.82933164   4     X1 -0.3549140 #> 8  -1.14038587 -0.6540727  0.82933164   4     X2 -1.4116602 #> 9   0.80261795  0.1837525 -0.79579710   5     X1 -0.2509062 #> 10  0.80261795  0.1837525 -0.79579710   5     X2  0.2252231 #> 11 -0.01983587 -0.6728623 -0.52205496   6     X1  0.7112244 #> 12 -0.01983587 -0.6728623 -0.52205496   6     X2  1.3043007 #> 13  0.73973690  1.9831698 -0.02966633   7     X1  1.6908162 #> 14  0.73973690  1.9831698 -0.02966633   7     X2  0.2800062 #> 15 -1.76563045  0.9871156  0.72787721   8     X1  0.2199684 #> 16 -1.76563045  0.9871156  0.72787721   8     X2  1.2138163 #> 17  0.74375283 -0.4917081 -2.91160978   9     X1 -0.8877319 #> 18  0.74375283 -0.4917081 -2.91160978   9     X2 -0.5336095 #> 19 -0.44083106 -0.3578552 -1.48411421  10     X1 -0.9138694 #> 20 -0.44083106 -0.3578552 -1.48411421  10     X2  0.7898833  # Full example # ------------------ data <- psych::bfi # Wide format with one row per participant's personality test  # Pivot long format data_to_long(data,   select = regex(\"\\\\d\"), # Select all columns that contain a digit   names_to = \"Item\",   values_to = \"Score\",   rows_to = \"Participant\" ) #>       gender education age Participant Item Score #> 1          1        NA  16       61617   A1     2 #> 2          1        NA  16       61617   A2     4 #> 3          1        NA  16       61617   A3     3 #> 4          1        NA  16       61617   A4     4 #> 5          1        NA  16       61617   A5     4 #> 6          1        NA  16       61617   C1     2 #> 7          1        NA  16       61617   C2     3 #> 8          1        NA  16       61617   C3     3 #> 9          1        NA  16       61617   C4     4 #> 10         1        NA  16       61617   C5     4 #> 11         1        NA  16       61617   E1     3 #> 12         1        NA  16       61617   E2     3 #> 13         1        NA  16       61617   E3     3 #> 14         1        NA  16       61617   E4     4 #> 15         1        NA  16       61617   E5     4 #> 16         1        NA  16       61617   N1     3 #> 17         1        NA  16       61617   N2     4 #> 18         1        NA  16       61617   N3     2 #> 19         1        NA  16       61617   N4     2 #> 20         1        NA  16       61617   N5     3 #> 21         1        NA  16       61617   O1     3 #> 22         1        NA  16       61617   O2     6 #> 23         1        NA  16       61617   O3     3 #> 24         1        NA  16       61617   O4     4 #> 25         1        NA  16       61617   O5     3 #> 26         2        NA  18       61618   A1     2 #> 27         2        NA  18       61618   A2     4 #> 28         2        NA  18       61618   A3     5 #> 29         2        NA  18       61618   A4     2 #> 30         2        NA  18       61618   A5     5 #> 31         2        NA  18       61618   C1     5 #> 32         2        NA  18       61618   C2     4 #> 33         2        NA  18       61618   C3     4 #> 34         2        NA  18       61618   C4     3 #> 35         2        NA  18       61618   C5     4 #> 36         2        NA  18       61618   E1     1 #> 37         2        NA  18       61618   E2     1 #> 38         2        NA  18       61618   E3     6 #> 39         2        NA  18       61618   E4     4 #> 40         2        NA  18       61618   E5     3 #> 41         2        NA  18       61618   N1     3 #> 42         2        NA  18       61618   N2     3 #> 43         2        NA  18       61618   N3     3 #> 44         2        NA  18       61618   N4     5 #> 45         2        NA  18       61618   N5     5 #> 46         2        NA  18       61618   O1     4 #> 47         2        NA  18       61618   O2     2 #> 48         2        NA  18       61618   O3     4 #> 49         2        NA  18       61618   O4     3 #> 50         2        NA  18       61618   O5     3 #> 51         2        NA  17       61620   A1     5 #> 52         2        NA  17       61620   A2     4 #> 53         2        NA  17       61620   A3     5 #> 54         2        NA  17       61620   A4     4 #> 55         2        NA  17       61620   A5     4 #> 56         2        NA  17       61620   C1     4 #> 57         2        NA  17       61620   C2     5 #> 58         2        NA  17       61620   C3     4 #> 59         2        NA  17       61620   C4     2 #> 60         2        NA  17       61620   C5     5 #> 61         2        NA  17       61620   E1     2 #> 62         2        NA  17       61620   E2     4 #> 63         2        NA  17       61620   E3     4 #> 64         2        NA  17       61620   E4     4 #> 65         2        NA  17       61620   E5     5 #> 66         2        NA  17       61620   N1     4 #> 67         2        NA  17       61620   N2     5 #> 68         2        NA  17       61620   N3     4 #> 69         2        NA  17       61620   N4     2 #> 70         2        NA  17       61620   N5     3 #> 71         2        NA  17       61620   O1     4 #> 72         2        NA  17       61620   O2     2 #> 73         2        NA  17       61620   O3     5 #> 74         2        NA  17       61620   O4     5 #> 75         2        NA  17       61620   O5     2 #> 76         2        NA  17       61621   A1     4 #> 77         2        NA  17       61621   A2     4 #> 78         2        NA  17       61621   A3     6 #> 79         2        NA  17       61621   A4     5 #> 80         2        NA  17       61621   A5     5 #> 81         2        NA  17       61621   C1     4 #> 82         2        NA  17       61621   C2     4 #> 83         2        NA  17       61621   C3     3 #> 84         2        NA  17       61621   C4     5 #> 85         2        NA  17       61621   C5     5 #> 86         2        NA  17       61621   E1     5 #> 87         2        NA  17       61621   E2     3 #> 88         2        NA  17       61621   E3     4 #> 89         2        NA  17       61621   E4     4 #> 90         2        NA  17       61621   E5     4 #> 91         2        NA  17       61621   N1     2 #> 92         2        NA  17       61621   N2     5 #> 93         2        NA  17       61621   N3     2 #> 94         2        NA  17       61621   N4     4 #> 95         2        NA  17       61621   N5     1 #> 96         2        NA  17       61621   O1     3 #> 97         2        NA  17       61621   O2     3 #> 98         2        NA  17       61621   O3     4 #> 99         2        NA  17       61621   O4     3 #> 100        2        NA  17       61621   O5     5 #> 101        1        NA  17       61622   A1     2 #> 102        1        NA  17       61622   A2     3 #> 103        1        NA  17       61622   A3     3 #> 104        1        NA  17       61622   A4     4 #> 105        1        NA  17       61622   A5     5 #> 106        1        NA  17       61622   C1     4 #> 107        1        NA  17       61622   C2     4 #> 108        1        NA  17       61622   C3     5 #> 109        1        NA  17       61622   C4     3 #> 110        1        NA  17       61622   C5     2 #> 111        1        NA  17       61622   E1     2 #> 112        1        NA  17       61622   E2     2 #> 113        1        NA  17       61622   E3     5 #> 114        1        NA  17       61622   E4     4 #> 115        1        NA  17       61622   E5     5 #> 116        1        NA  17       61622   N1     2 #> 117        1        NA  17       61622   N2     3 #> 118        1        NA  17       61622   N3     4 #> 119        1        NA  17       61622   N4     4 #> 120        1        NA  17       61622   N5     3 #> 121        1        NA  17       61622   O1     3 #> 122        1        NA  17       61622   O2     3 #> 123        1        NA  17       61622   O3     4 #> 124        1        NA  17       61622   O4     3 #> 125        1        NA  17       61622   O5     3 #> 126        2         3  21       61623   A1     6 #> 127        2         3  21       61623   A2     6 #> 128        2         3  21       61623   A3     5 #> 129        2         3  21       61623   A4     6 #> 130        2         3  21       61623   A5     5 #> 131        2         3  21       61623   C1     6 #> 132        2         3  21       61623   C2     6 #> 133        2         3  21       61623   C3     6 #> 134        2         3  21       61623   C4     1 #> 135        2         3  21       61623   C5     3 #> 136        2         3  21       61623   E1     2 #> 137        2         3  21       61623   E2     1 #> 138        2         3  21       61623   E3     6 #> 139        2         3  21       61623   E4     5 #> 140        2         3  21       61623   E5     6 #> 141        2         3  21       61623   N1     3 #> 142        2         3  21       61623   N2     5 #> 143        2         3  21       61623   N3     2 #> 144        2         3  21       61623   N4     2 #> 145        2         3  21       61623   N5     3 #> 146        2         3  21       61623   O1     4 #> 147        2         3  21       61623   O2     3 #> 148        2         3  21       61623   O3     5 #> 149        2         3  21       61623   O4     6 #> 150        2         3  21       61623   O5     1 #> 151        1        NA  18       61624   A1     2 #> 152        1        NA  18       61624   A2     5 #> 153        1        NA  18       61624   A3     5 #> 154        1        NA  18       61624   A4     3 #> 155        1        NA  18       61624   A5     5 #> 156        1        NA  18       61624   C1     5 #> 157        1        NA  18       61624   C2     4 #> 158        1        NA  18       61624   C3     4 #> 159        1        NA  18       61624   C4     2 #> 160        1        NA  18       61624   C5     3 #> 161        1        NA  18       61624   E1     4 #> 162        1        NA  18       61624   E2     3 #> 163        1        NA  18       61624   E3     4 #> 164        1        NA  18       61624   E4     5 #> 165        1        NA  18       61624   E5     5 #> 166        1        NA  18       61624   N1     1 #> 167        1        NA  18       61624   N2     2 #> 168        1        NA  18       61624   N3     2 #> 169        1        NA  18       61624   N4     1 #> 170        1        NA  18       61624   N5     1 #> 171        1        NA  18       61624   O1     5 #> 172        1        NA  18       61624   O2     2 #> 173        1        NA  18       61624   O3     5 #> 174        1        NA  18       61624   O4     6 #> 175        1        NA  18       61624   O5     1 #> 176        1         2  19       61629   A1     4 #> 177        1         2  19       61629   A2     3 #> 178        1         2  19       61629   A3     1 #> 179        1         2  19       61629   A4     5 #> 180        1         2  19       61629   A5     1 #> 181        1         2  19       61629   C1     3 #> 182        1         2  19       61629   C2     2 #> 183        1         2  19       61629   C3     4 #> 184        1         2  19       61629   C4     2 #> 185        1         2  19       61629   C5     4 #> 186        1         2  19       61629   E1     3 #> 187        1         2  19       61629   E2     6 #> 188        1         2  19       61629   E3     4 #> 189        1         2  19       61629   E4     2 #> 190        1         2  19       61629   E5     1 #> 191        1         2  19       61629   N1     6 #> 192        1         2  19       61629   N2     3 #> 193        1         2  19       61629   N3     2 #> 194        1         2  19       61629   N4     6 #> 195        1         2  19       61629   N5     4 #> 196        1         2  19       61629   O1     3 #> 197        1         2  19       61629   O2     2 #> 198        1         2  19       61629   O3     4 #> 199        1         2  19       61629   O4     5 #> 200        1         2  19       61629   O5     3 #> 201        1         1  19       61630   A1     4 #> 202        1         1  19       61630   A2     3 #> 203        1         1  19       61630   A3     6 #> 204        1         1  19       61630   A4     3 #> 205        1         1  19       61630   A5     3 #> 206        1         1  19       61630   C1     6 #> 207        1         1  19       61630   C2     6 #> 208        1         1  19       61630   C3     3 #> 209        1         1  19       61630   C4     4 #> 210        1         1  19       61630   C5     5 #> 211        1         1  19       61630   E1     5 #> 212        1         1  19       61630   E2     3 #> 213        1         1  19       61630   E3    NA #> 214        1         1  19       61630   E4     4 #> 215        1         1  19       61630   E5     3 #> 216        1         1  19       61630   N1     5 #> 217        1         1  19       61630   N2     5 #> 218        1         1  19       61630   N3     2 #> 219        1         1  19       61630   N4     3 #> 220        1         1  19       61630   N5     3 #> 221        1         1  19       61630   O1     6 #> 222        1         1  19       61630   O2     6 #> 223        1         1  19       61630   O3     6 #> 224        1         1  19       61630   O4     6 #> 225        1         1  19       61630   O5     1 #> 226        2        NA  17       61633   A1     2 #> 227        2        NA  17       61633   A2     5 #> 228        2        NA  17       61633   A3     6 #> 229        2        NA  17       61633   A4     6 #> 230        2        NA  17       61633   A5     5 #> 231        2        NA  17       61633   C1     6 #> 232        2        NA  17       61633   C2     5 #> 233        2        NA  17       61633   C3     6 #> 234        2        NA  17       61633   C4     2 #> 235        2        NA  17       61633   C5     1 #> 236        2        NA  17       61633   E1     2 #> 237        2        NA  17       61633   E2     2 #> 238        2        NA  17       61633   E3     4 #> 239        2        NA  17       61633   E4     5 #> 240        2        NA  17       61633   E5     5 #> 241        2        NA  17       61633   N1     5 #> 242        2        NA  17       61633   N2     5 #> 243        2        NA  17       61633   N3     5 #> 244        2        NA  17       61633   N4     2 #> 245        2        NA  17       61633   N5     4 #> 246        2        NA  17       61633   O1     5 #> 247        2        NA  17       61633   O2     1 #> 248        2        NA  17       61633   O3     5 #> 249        2        NA  17       61633   O4     5 #> 250        2        NA  17       61633   O5     2 #> 251        1         1  21       61634   A1     4 #> 252        1         1  21       61634   A2     4 #> 253        1         1  21       61634   A3     5 #> 254        1         1  21       61634   A4     6 #> 255        1         1  21       61634   A5     5 #> 256        1         1  21       61634   C1     4 #> 257        1         1  21       61634   C2     3 #> 258        1         1  21       61634   C3     5 #> 259        1         1  21       61634   C4     3 #> 260        1         1  21       61634   C5     2 #> 261        1         1  21       61634   E1     1 #> 262        1         1  21       61634   E2     3 #> 263        1         1  21       61634   E3     2 #> 264        1         1  21       61634   E4     5 #> 265        1         1  21       61634   E5     4 #> 266        1         1  21       61634   N1     3 #> 267        1         1  21       61634   N2     3 #> 268        1         1  21       61634   N3     4 #> 269        1         1  21       61634   N4     2 #> 270        1         1  21       61634   N5     3 #> 271        1         1  21       61634   O1     5 #> 272        1         1  21       61634   O2     3 #> 273        1         1  21       61634   O3     5 #> 274        1         1  21       61634   O4     6 #> 275        1         1  21       61634   O5     3 #> 276        1        NA  16       61636   A1     2 #> 277        1        NA  16       61636   A2     5 #> 278        1        NA  16       61636   A3     5 #> 279        1        NA  16       61636   A4     5 #> 280        1        NA  16       61636   A5     5 #> 281        1        NA  16       61636   C1     5 #> 282        1        NA  16       61636   C2     4 #> 283        1        NA  16       61636   C3     5 #> 284        1        NA  16       61636   C4     4 #> 285        1        NA  16       61636   C5     5 #> 286        1        NA  16       61636   E1     3 #> 287        1        NA  16       61636   E2     3 #> 288        1        NA  16       61636   E3     4 #> 289        1        NA  16       61636   E4     5 #> 290        1        NA  16       61636   E5     4 #> 291        1        NA  16       61636   N1     4 #> 292        1        NA  16       61636   N2     5 #> 293        1        NA  16       61636   N3     3 #> 294        1        NA  16       61636   N4     2 #> 295        1        NA  16       61636   N5    NA #> 296        1        NA  16       61636   O1     4 #> 297        1        NA  16       61636   O2     6 #> 298        1        NA  16       61636   O3     4 #> 299        1        NA  16       61636   O4     5 #> 300        1        NA  16       61636   O5     4 #> 301        2        NA  16       61637   A1     5 #> 302        2        NA  16       61637   A2     5 #> 303        2        NA  16       61637   A3     5 #> 304        2        NA  16       61637   A4     6 #> 305        2        NA  16       61637   A5     4 #> 306        2        NA  16       61637   C1     5 #> 307        2        NA  16       61637   C2     4 #> 308        2        NA  16       61637   C3     3 #> 309        2        NA  16       61637   C4     2 #> 310        2        NA  16       61637   C5     2 #> 311        2        NA  16       61637   E1     3 #> 312        2        NA  16       61637   E2     3 #> 313        2        NA  16       61637   E3     3 #> 314        2        NA  16       61637   E4     2 #> 315        2        NA  16       61637   E5     4 #> 316        2        NA  16       61637   N1     1 #> 317        2        NA  16       61637   N2     2 #> 318        2        NA  16       61637   N3     2 #> 319        2        NA  16       61637   N4     2 #> 320        2        NA  16       61637   N5     2 #> 321        2        NA  16       61637   O1     4 #> 322        2        NA  16       61637   O2     2 #> 323        2        NA  16       61637   O3     4 #> 324        2        NA  16       61637   O4     5 #> 325        2        NA  16       61637   O5     2 #> 326        1        NA  16       61639   A1     5 #> 327        1        NA  16       61639   A2     5 #> 328        1        NA  16       61639   A3     5 #> 329        1        NA  16       61639   A4     6 #> 330        1        NA  16       61639   A5     6 #> 331        1        NA  16       61639   C1     4 #> 332        1        NA  16       61639   C2     4 #> 333        1        NA  16       61639   C3     4 #> 334        1        NA  16       61639   C4     2 #> 335        1        NA  16       61639   C5     1 #> 336        1        NA  16       61639   E1     2 #> 337        1        NA  16       61639   E2     2 #> 338        1        NA  16       61639   E3     4 #> 339        1        NA  16       61639   E4     6 #> 340        1        NA  16       61639   E5     5 #> 341        1        NA  16       61639   N1     1 #> 342        1        NA  16       61639   N2     1 #> 343        1        NA  16       61639   N3     1 #> 344        1        NA  16       61639   N4     2 #> 345        1        NA  16       61639   N5     1 #> 346        1        NA  16       61639   O1     5 #> 347        1        NA  16       61639   O2     3 #> 348        1        NA  16       61639   O3     4 #> 349        1        NA  16       61639   O4     4 #> 350        1        NA  16       61639   O5     4 #> 351        1         1  17       61640   A1     4 #> 352        1         1  17       61640   A2     5 #> 353        1         1  17       61640   A3     2 #> 354        1         1  17       61640   A4     2 #> 355        1         1  17       61640   A5     1 #> 356        1         1  17       61640   C1     5 #> 357        1         1  17       61640   C2     5 #> 358        1         1  17       61640   C3     5 #> 359        1         1  17       61640   C4     2 #> 360        1         1  17       61640   C5     2 #> 361        1         1  17       61640   E1     3 #> 362        1         1  17       61640   E2     4 #> 363        1         1  17       61640   E3     3 #> 364        1         1  17       61640   E4     6 #> 365        1         1  17       61640   E5     5 #> 366        1         1  17       61640   N1     2 #> 367        1         1  17       61640   N2     4 #> 368        1         1  17       61640   N3     2 #> 369        1         1  17       61640   N4     2 #> 370        1         1  17       61640   N5     3 #> 371        1         1  17       61640   O1     5 #> 372        1         1  17       61640   O2     2 #> 373        1         1  17       61640   O3     5 #> 374        1         1  17       61640   O4     5 #> 375        1         1  17       61640   O5     5 #> 376        1        NA  17       61643   A1     4 #> 377        1        NA  17       61643   A2     3 #> 378        1        NA  17       61643   A3     6 #> 379        1        NA  17       61643   A4     6 #> 380        1        NA  17       61643   A5     3 #> 381        1        NA  17       61643   C1     5 #> 382        1        NA  17       61643   C2     5 #> 383        1        NA  17       61643   C3     5 #> 384        1        NA  17       61643   C4     3 #> 385        1        NA  17       61643   C5     5 #> 386        1        NA  17       61643   E1     1 #> 387        1        NA  17       61643   E2     1 #> 388        1        NA  17       61643   E3     6 #> 389        1        NA  17       61643   E4     6 #> 390        1        NA  17       61643   E5     4 #> 391        1        NA  17       61643   N1     4 #> 392        1        NA  17       61643   N2     5 #> 393        1        NA  17       61643   N3     4 #> 394        1        NA  17       61643   N4     5 #> 395        1        NA  17       61643   N5     5 #> 396        1        NA  17       61643   O1     6 #> 397        1        NA  17       61643   O2     6 #> 398        1        NA  17       61643   O3     6 #> 399        1        NA  17       61643   O4     3 #> 400        1        NA  17       61643   O5     2 #> 401        2        NA  17       61650   A1     4 #> 402        2        NA  17       61650   A2     6 #> 403        2        NA  17       61650   A3     6 #> 404        2        NA  17       61650   A4     2 #> 405        2        NA  17       61650   A5     5 #> 406        2        NA  17       61650   C1     4 #> 407        2        NA  17       61650   C2     4 #> 408        2        NA  17       61650   C3     4 #> 409        2        NA  17       61650   C4     4 #> 410        2        NA  17       61650   C5     4 #> 411        2        NA  17       61650   E1     1 #> 412        2        NA  17       61650   E2     2 #> 413        2        NA  17       61650   E3     5 #> 414        2        NA  17       61650   E4     5 #> 415        2        NA  17       61650   E5     5 #> 416        2        NA  17       61650   N1     4 #> 417        2        NA  17       61650   N2     4 #> 418        2        NA  17       61650   N3     4 #> 419        2        NA  17       61650   N4     4 #> 420        2        NA  17       61650   N5     5 #> 421        2        NA  17       61650   O1     5 #> 422        2        NA  17       61650   O2     1 #> 423        2        NA  17       61650   O3     5 #> 424        2        NA  17       61650   O4     6 #> 425        2        NA  17       61650   O5     3 #> 426        1        NA  17       61651   A1     5 #> 427        1        NA  17       61651   A2     5 #> 428        1        NA  17       61651   A3     5 #> 429        1        NA  17       61651   A4     4 #> 430        1        NA  17       61651   A5     5 #> 431        1        NA  17       61651   C1     5 #> 432        1        NA  17       61651   C2     5 #> 433        1        NA  17       61651   C3     5 #> 434        1        NA  17       61651   C4     4 #> 435        1        NA  17       61651   C5     3 #> 436        1        NA  17       61651   E1     2 #> 437        1        NA  17       61651   E2     2 #> 438        1        NA  17       61651   E3     4 #> 439        1        NA  17       61651   E4     6 #> 440        1        NA  17       61651   E5     6 #> 441        1        NA  17       61651   N1     6 #> 442        1        NA  17       61651   N2     5 #> 443        1        NA  17       61651   N3     5 #> 444        1        NA  17       61651   N4     4 #> 445        1        NA  17       61651   N5     4 #> 446        1        NA  17       61651   O1     5 #> 447        1        NA  17       61651   O2     1 #> 448        1        NA  17       61651   O3     4 #> 449        1        NA  17       61651   O4     5 #> 450        1        NA  17       61651   O5     4 #> 451        2        NA  16       61653   A1     4 #> 452        2        NA  16       61653   A2     4 #> 453        2        NA  16       61653   A3     5 #> 454        2        NA  16       61653   A4     4 #> 455        2        NA  16       61653   A5     3 #> 456        2        NA  16       61653   C1     5 #> 457        2        NA  16       61653   C2     4 #> 458        2        NA  16       61653   C3     5 #> 459        2        NA  16       61653   C4     4 #> 460        2        NA  16       61653   C5     6 #> 461        2        NA  16       61653   E1     1 #> 462        2        NA  16       61653   E2     2 #> 463        2        NA  16       61653   E3     4 #> 464        2        NA  16       61653   E4     5 #> 465        2        NA  16       61653   E5     5 #> 466        2        NA  16       61653   N1     5 #> 467        2        NA  16       61653   N2     6 #> 468        2        NA  16       61653   N3     5 #> 469        2        NA  16       61653   N4     5 #> 470        2        NA  16       61653   N5     2 #> 471        2        NA  16       61653   O1     4 #> 472        2        NA  16       61653   O2     2 #> 473        2        NA  16       61653   O3     2 #> 474        2        NA  16       61653   O4     4 #> 475        2        NA  16       61653   O5     2 #> 476        2        NA  17       61654   A1     4 #> 477        2        NA  17       61654   A2     4 #> 478        2        NA  17       61654   A3     6 #> 479        2        NA  17       61654   A4     5 #> 480        2        NA  17       61654   A5     5 #> 481        2        NA  17       61654   C1     1 #> 482        2        NA  17       61654   C2     1 #> 483        2        NA  17       61654   C3     1 #> 484        2        NA  17       61654   C4     5 #> 485        2        NA  17       61654   C5     6 #> 486        2        NA  17       61654   E1     1 #> 487        2        NA  17       61654   E2     1 #> 488        2        NA  17       61654   E3     4 #> 489        2        NA  17       61654   E4     5 #> 490        2        NA  17       61654   E5     6 #> 491        2        NA  17       61654   N1     5 #> 492        2        NA  17       61654   N2     5 #> 493        2        NA  17       61654   N3     5 #> 494        2        NA  17       61654   N4     1 #> 495        2        NA  17       61654   N5     1 #> 496        2        NA  17       61654   O1     4 #> 497        2        NA  17       61654   O2     1 #> 498        2        NA  17       61654   O3     5 #> 499        2        NA  17       61654   O4     3 #> 500        2        NA  17       61654   O5     2 #> 501        1        NA  17       61656   A1     5 #> 502        1        NA  17       61656   A2     4 #> 503        1        NA  17       61656   A3     2 #> 504        1        NA  17       61656   A4     1 #> 505        1        NA  17       61656   A5     2 #> 506        1        NA  17       61656   C1     4 #> 507        1        NA  17       61656   C2     6 #> 508        1        NA  17       61656   C3     5 #> 509        1        NA  17       61656   C4     5 #> 510        1        NA  17       61656   C5     4 #> 511        1        NA  17       61656   E1     3 #> 512        1        NA  17       61656   E2     3 #> 513        1        NA  17       61656   E3     5 #> 514        1        NA  17       61656   E4     5 #> 515        1        NA  17       61656   E5     4 #> 516        1        NA  17       61656   N1     1 #> 517        1        NA  17       61656   N2     3 #> 518        1        NA  17       61656   N3     3 #> 519        1        NA  17       61656   N4     2 #> 520        1        NA  17       61656   N5     1 #> 521        1        NA  17       61656   O1     6 #> 522        1        NA  17       61656   O2     1 #> 523        1        NA  17       61656   O3     3 #> 524        1        NA  17       61656   O4     2 #> 525        1        NA  17       61656   O5     4 #> 526        2        NA  17       61659   A1     1 #> 527        2        NA  17       61659   A2     6 #> 528        2        NA  17       61659   A3     6 #> 529        2        NA  17       61659   A4     1 #> 530        2        NA  17       61659   A5     5 #> 531        2        NA  17       61659   C1     5 #> 532        2        NA  17       61659   C2     4 #> 533        2        NA  17       61659   C3     4 #> 534        2        NA  17       61659   C4     2 #> 535        2        NA  17       61659   C5     3 #> 536        2        NA  17       61659   E1     1 #> 537        2        NA  17       61659   E2     2 #> 538        2        NA  17       61659   E3     4 #> 539        2        NA  17       61659   E4     3 #> 540        2        NA  17       61659   E5     4 #> 541        2        NA  17       61659   N1     2 #> 542        2        NA  17       61659   N2     5 #> 543        2        NA  17       61659   N3     5 #> 544        2        NA  17       61659   N4     4 #> 545        2        NA  17       61659   N5     6 #> 546        2        NA  17       61659   O1     5 #> 547        2        NA  17       61659   O2     1 #> 548        2        NA  17       61659   O3     6 #> 549        2        NA  17       61659   O4     6 #> 550        2        NA  17       61659   O5     2 #> 551        1         5  68       61661   A1     1 #> 552        1         5  68       61661   A2     5 #> 553        1         5  68       61661   A3     6 #> 554        1         5  68       61661   A4     5 #> 555        1         5  68       61661   A5     6 #> 556        1         5  68       61661   C1     4 #> 557        1         5  68       61661   C2     3 #> 558        1         5  68       61661   C3     2 #> 559        1         5  68       61661   C4     4 #> 560        1         5  68       61661   C5     5 #> 561        1         5  68       61661   E1     2 #> 562        1         5  68       61661   E2     1 #> 563        1         5  68       61661   E3     2 #> 564        1         5  68       61661   E4     5 #> 565        1         5  68       61661   E5     2 #> 566        1         5  68       61661   N1     2 #> 567        1         5  68       61661   N2     2 #> 568        1         5  68       61661   N3     2 #> 569        1         5  68       61661   N4     2 #> 570        1         5  68       61661   N5     2 #> 571        1         5  68       61661   O1     6 #> 572        1         5  68       61661   O2     1 #> 573        1         5  68       61661   O3     5 #> 574        1         5  68       61661   O4     5 #> 575        1         5  68       61661   O5     2 #> 576        2         2  27       61664   A1     2 #> 577        2         2  27       61664   A2     6 #> 578        2         2  27       61664   A3     5 #> 579        2         2  27       61664   A4     6 #> 580        2         2  27       61664   A5     5 #> 581        2         2  27       61664   C1     3 #> 582        2         2  27       61664   C2     5 #> 583        2         2  27       61664   C3     6 #> 584        2         2  27       61664   C4     3 #> 585        2         2  27       61664   C5     6 #> 586        2         2  27       61664   E1     2 #> 587        2         2  27       61664   E2     2 #> 588        2         2  27       61664   E3     4 #> 589        2         2  27       61664   E4     6 #> 590        2         2  27       61664   E5     6 #> 591        2         2  27       61664   N1     4 #> 592        2         2  27       61664   N2     4 #> 593        2         2  27       61664   N3     4 #> 594        2         2  27       61664   N4     6 #> 595        2         2  27       61664   N5     6 #> 596        2         2  27       61664   O1     6 #> 597        2         2  27       61664   O2     1 #> 598        2         2  27       61664   O3     5 #> 599        2         2  27       61664   O4     6 #> 600        2         2  27       61664   O5     1 #> 601        1         1  18       61667   A1     4 #> 602        1         1  18       61667   A2     5 #> 603        1         1  18       61667   A3     5 #> 604        1         1  18       61667   A4     6 #> 605        1         1  18       61667   A5     5 #> 606        1         1  18       61667   C1     5 #> 607        1         1  18       61667   C2     5 #> 608        1         1  18       61667   C3     4 #> 609        1         1  18       61667   C4     1 #> 610        1         1  18       61667   C5     1 #> 611        1         1  18       61667   E1     3 #> 612        1         1  18       61667   E2     2 #> 613        1         1  18       61667   E3     5 #> 614        1         1  18       61667   E4     5 #> 615        1         1  18       61667   E5     6 #> 616        1         1  18       61667   N1     2 #> 617        1         1  18       61667   N2     3 #> 618        1         1  18       61667   N3     3 #> 619        1         1  18       61667   N4     1 #> 620        1         1  18       61667   N5     1 #> 621        1         1  18       61667   O1     6 #> 622        1         1  18       61667   O2     2 #> 623        1         1  18       61667   O3     5 #> 624        1         1  18       61667   O4     6 #> 625        1         1  18       61667   O5     2 #> 626        2         3  20       61668   A1     1 #> 627        2         3  20       61668   A2     6 #> 628        2         3  20       61668   A3     6 #> 629        2         3  20       61668   A4     1 #> 630        2         3  20       61668   A5     6 #> 631        2         3  20       61668   C1     5 #> 632        2         3  20       61668   C2     2 #> 633        2         3  20       61668   C3     5 #> 634        2         3  20       61668   C4     1 #> 635        2         3  20       61668   C5     1 #> 636        2         3  20       61668   E1     1 #> 637        2         3  20       61668   E2     1 #> 638        2         3  20       61668   E3     6 #> 639        2         3  20       61668   E4     6 #> 640        2         3  20       61668   E5     6 #> 641        2         3  20       61668   N1     2 #> 642        2         3  20       61668   N2     3 #> 643        2         3  20       61668   N3     1 #> 644        2         3  20       61668   N4     2 #> 645        2         3  20       61668   N5     1 #> 646        2         3  20       61668   O1     6 #> 647        2         3  20       61668   O2     4 #> 648        2         3  20       61668   O3     5 #> 649        2         3  20       61668   O4     5 #> 650        2         3  20       61668   O5     3 #> 651        2         5  51       61669   A1     2 #> 652        2         5  51       61669   A2     4 #> 653        2         5  51       61669   A3     4 #> 654        2         5  51       61669   A4     4 #> 655        2         5  51       61669   A5     3 #> 656        2         5  51       61669   C1     6 #> 657        2         5  51       61669   C2     5 #> 658        2         5  51       61669   C3     6 #> 659        2         5  51       61669   C4     1 #> 660        2         5  51       61669   C5     1 #> 661        2         5  51       61669   E1     2 #> 662        2         5  51       61669   E2     4 #> 663        2         5  51       61669   E3     4 #> 664        2         5  51       61669   E4     2 #> 665        2         5  51       61669   E5     6 #> 666        2         5  51       61669   N1     3 #> 667        2         5  51       61669   N2     3 #> 668        2         5  51       61669   N3     5 #> 669        2         5  51       61669   N4     3 #> 670        2         5  51       61669   N5     2 #> 671        2         5  51       61669   O1     5 #> 672        2         5  51       61669   O2     2 #> 673        2         5  51       61669   O3     6 #> 674        2         5  51       61669   O4     6 #> 675        2         5  51       61669   O5     1 #> 676        2        NA  14       61670   A1     2 #> 677        2        NA  14       61670   A2     5 #> 678        2        NA  14       61670   A3     6 #> 679        2        NA  14       61670   A4     6 #> 680        2        NA  14       61670   A5     6 #> 681        2        NA  14       61670   C1     4 #> 682        2        NA  14       61670   C2     5 #> 683        2        NA  14       61670   C3     4 #> 684        2        NA  14       61670   C4     3 #> 685        2        NA  14       61670   C5     4 #> 686        2        NA  14       61670   E1     1 #> 687        2        NA  14       61670   E2     2 #> 688        2        NA  14       61670   E3     6 #> 689        2        NA  14       61670   E4     6 #> 690        2        NA  14       61670   E5     6 #> 691        2        NA  14       61670   N1     4 #> 692        2        NA  14       61670   N2     4 #> 693        2        NA  14       61670   N3     5 #> 694        2        NA  14       61670   N4     2 #> 695        2        NA  14       61670   N5     3 #> 696        2        NA  14       61670   O1     6 #> 697        2        NA  14       61670   O2     1 #> 698        2        NA  14       61670   O3     6 #> 699        2        NA  14       61670   O4     4 #> 700        2        NA  14       61670   O5     3 #> 701        2         3  33       61672   A1     2 #> 702        2         3  33       61672   A2     5 #> 703        2         3  33       61672   A3     1 #> 704        2         3  33       61672   A4     3 #> 705        2         3  33       61672   A5     5 #> 706        2         3  33       61672   C1     5 #> 707        2         3  33       61672   C2     4 #> 708        2         3  33       61672   C3     5 #> 709        2         3  33       61672   C4     2 #> 710        2         3  33       61672   C5     5 #> 711        2         3  33       61672   E1     1 #> 712        2         3  33       61672   E2     2 #> 713        2         3  33       61672   E3     6 #> 714        2         3  33       61672   E4     5 #> 715        2         3  33       61672   E5     4 #> 716        2         3  33       61672   N1     1 #> 717        2         3  33       61672   N2     4 #> 718        2         3  33       61672   N3     2 #> 719        2         3  33       61672   N4     2 #> 720        2         3  33       61672   N5     5 #> 721        2         3  33       61672   O1     2 #> 722        2         3  33       61672   O2     4 #> 723        2         3  33       61672   O3     5 #> 724        2         3  33       61672   O4     4 #> 725        2         3  33       61672   O5     1 #> 726        2         3  18       61673   A1     4 #> 727        2         3  18       61673   A2     5 #> 728        2         3  18       61673   A3     6 #> 729        2         3  18       61673   A4     5 #> 730        2         3  18       61673   A5     5 #> 731        2         3  18       61673   C1     5 #> 732        2         3  18       61673   C2     5 #> 733        2         3  18       61673   C3     3 #> 734        2         3  18       61673   C4     5 #> 735        2         3  18       61673   C5     4 #> 736        2         3  18       61673   E1     1 #> 737        2         3  18       61673   E2     2 #> 738        2         3  18       61673   E3     6 #> 739        2         3  18       61673   E4     5 #> 740        2         3  18       61673   E5     5 #> 741        2         3  18       61673   N1     5 #> 742        2         3  18       61673   N2     4 #> 743        2         3  18       61673   N3     4 #> 744        2         3  18       61673   N4     3 #> 745        2         3  18       61673   N5     1 #> 746        2         3  18       61673   O1     4 #> 747        2         3  18       61673   O2     4 #> 748        2         3  18       61673   O3     6 #> 749        2         3  18       61673   O4     5 #> 750        2         3  18       61673   O5     1 #> 751        2        NA  17       61678   A1     1 #> 752        2        NA  17       61678   A2     6 #> 753        2        NA  17       61678   A3     5 #> 754        2        NA  17       61678   A4     6 #> 755        2        NA  17       61678   A5     3 #> 756        2        NA  17       61678   C1     5 #> 757        2        NA  17       61678   C2     5 #> 758        2        NA  17       61678   C3     5 #> 759        2        NA  17       61678   C4     4 #> 760        2        NA  17       61678   C5     3 #> 761        2        NA  17       61678   E1     2 #> 762        2        NA  17       61678   E2     5 #> 763        2        NA  17       61678   E3     1 #> 764        2        NA  17       61678   E4     5 #> 765        2        NA  17       61678   E5     3 #> 766        2        NA  17       61678   N1     5 #> 767        2        NA  17       61678   N2     5 #> 768        2        NA  17       61678   N3     5 #> 769        2        NA  17       61678   N4     6 #> 770        2        NA  17       61678   N5     6 #> 771        2        NA  17       61678   O1     4 #> 772        2        NA  17       61678   O2     3 #> 773        2        NA  17       61678   O3     3 #> 774        2        NA  17       61678   O4     6 #> 775        2        NA  17       61678   O5     5 #> 776        2         3  41       61679   A1     2 #> 777        2         3  41       61679   A2     5 #> 778        2         3  41       61679   A3     6 #> 779        2         3  41       61679   A4     6 #> 780        2         3  41       61679   A5     6 #> 781        2         3  41       61679   C1     5 #> 782        2         3  41       61679   C2     5 #> 783        2         3  41       61679   C3     5 #> 784        2         3  41       61679   C4     2 #> 785        2         3  41       61679   C5     4 #> 786        2         3  41       61679   E1     1 #> 787        2         3  41       61679   E2     2 #> 788        2         3  41       61679   E3     4 #> 789        2         3  41       61679   E4     5 #> 790        2         3  41       61679   E5     5 #> 791        2         3  41       61679   N1     3 #> 792        2         3  41       61679   N2     2 #> 793        2         3  41       61679   N3     4 #> 794        2         3  41       61679   N4     1 #> 795        2         3  41       61679   N5     2 #> 796        2         3  41       61679   O1     5 #> 797        2         3  41       61679   O2     2 #> 798        2         3  41       61679   O3     5 #> 799        2         3  41       61679   O4     5 #> 800        2         3  41       61679   O5     2 #> 801        1         5  23       61682   A1     1 #> 802        1         5  23       61682   A2     5 #> 803        1         5  23       61682   A3     6 #> 804        1         5  23       61682   A4     5 #> 805        1         5  23       61682   A5     4 #> 806        1         5  23       61682   C1     1 #> 807        1         5  23       61682   C2     5 #> 808        1         5  23       61682   C3     6 #> 809        1         5  23       61682   C4     4 #> 810        1         5  23       61682   C5     6 #> 811        1         5  23       61682   E1     6 #> 812        1         5  23       61682   E2     6 #> 813        1         5  23       61682   E3     2 #> 814        1         5  23       61682   E4     1 #> 815        1         5  23       61682   E5     1 #> 816        1         5  23       61682   N1     1 #> 817        1         5  23       61682   N2     2 #> 818        1         5  23       61682   N3     1 #> 819        1         5  23       61682   N4     3 #> 820        1         5  23       61682   N5     6 #> 821        1         5  23       61682   O1     6 #> 822        1         5  23       61682   O2     6 #> 823        1         5  23       61682   O3     5 #> 824        1         5  23       61682   O4     6 #> 825        1         5  23       61682   O5     1 #> 826        2        NA  17       61683   A1     2 #> 827        2        NA  17       61683   A2     4 #> 828        2        NA  17       61683   A3     5 #> 829        2        NA  17       61683   A4     6 #> 830        2        NA  17       61683   A5     5 #> 831        2        NA  17       61683   C1     4 #> 832        2        NA  17       61683   C2     6 #> 833        2        NA  17       61683   C3     4 #> 834        2        NA  17       61683   C4     2 #> 835        2        NA  17       61683   C5     4 #> 836        2        NA  17       61683   E1     2 #> 837        2        NA  17       61683   E2     2 #> 838        2        NA  17       61683   E3     3 #> 839        2        NA  17       61683   E4     5 #> 840        2        NA  17       61683   E5     3 #> 841        2        NA  17       61683   N1     2 #> 842        2        NA  17       61683   N2     2 #> 843        2        NA  17       61683   N3     4 #> 844        2        NA  17       61683   N4     1 #> 845        2        NA  17       61683   N5     3 #> 846        2        NA  17       61683   O1     5 #> 847        2        NA  17       61683   O2     5 #> 848        2        NA  17       61683   O3     5 #> 849        2        NA  17       61683   O4     4 #> 850        2        NA  17       61683   O5     2 #> 851        1         3  20       61684   A1     4 #> 852        1         3  20       61684   A2     4 #> 853        1         3  20       61684   A3     4 #> 854        1         3  20       61684   A4     4 #> 855        1         3  20       61684   A5     4 #> 856        1         3  20       61684   C1     4 #> 857        1         3  20       61684   C2     3 #> 858        1         3  20       61684   C3     3 #> 859        1         3  20       61684   C4     3 #> 860        1         3  20       61684   C5     4 #> 861        1         3  20       61684   E1     2 #> 862        1         3  20       61684   E2     3 #> 863        1         3  20       61684   E3     4 #> 864        1         3  20       61684   E4     2 #> 865        1         3  20       61684   E5     3 #> 866        1         3  20       61684   N1    NA #> 867        1         3  20       61684   N2     2 #> 868        1         3  20       61684   N3     1 #> 869        1         3  20       61684   N4     2 #> 870        1         3  20       61684   N5     2 #> 871        1         3  20       61684   O1     4 #> 872        1         3  20       61684   O2     3 #> 873        1         3  20       61684   O3     5 #> 874        1         3  20       61684   O4     5 #> 875        1         3  20       61684   O5     3 #> 876        1         3  23       61685   A1     5 #> 877        1         3  23       61685   A2     3 #> 878        1         3  23       61685   A3     5 #> 879        1         3  23       61685   A4     4 #> 880        1         3  23       61685   A5     2 #> 881        1         3  23       61685   C1     2 #> 882        1         3  23       61685   C2     2 #> 883        1         3  23       61685   C3     4 #> 884        1         3  23       61685   C4     3 #> 885        1         3  23       61685   C5     4 #> 886        1         3  23       61685   E1     3 #> 887        1         3  23       61685   E2     4 #> 888        1         3  23       61685   E3     3 #> 889        1         3  23       61685   E4     2 #> 890        1         3  23       61685   E5     3 #> 891        1         3  23       61685   N1     5 #> 892        1         3  23       61685   N2     3 #> 893        1         3  23       61685   N3     4 #> 894        1         3  23       61685   N4     4 #> 895        1         3  23       61685   N5     3 #> 896        1         3  23       61685   O1     4 #> 897        1         3  23       61685   O2     5 #> 898        1         3  23       61685   O3     4 #> 899        1         3  23       61685   O4     4 #> 900        1         3  23       61685   O5     3 #> 901        1         3  20       61686   A1     1 #> 902        1         3  20       61686   A2     6 #> 903        1         3  20       61686   A3     4 #> 904        1         3  20       61686   A4     6 #> 905        1         3  20       61686   A5     4 #> 906        1         3  20       61686   C1     5 #> 907        1         3  20       61686   C2     6 #> 908        1         3  20       61686   C3     3 #> 909        1         3  20       61686   C4     1 #> 910        1         3  20       61686   C5     5 #> 911        1         3  20       61686   E1     6 #> 912        1         3  20       61686   E2     6 #> 913        1         3  20       61686   E3     3 #> 914        1         3  20       61686   E4     2 #> 915        1         3  20       61686   E5     2 #> 916        1         3  20       61686   N1     2 #> 917        1         3  20       61686   N2     2 #> 918        1         3  20       61686   N3     2 #> 919        1         3  20       61686   N4     4 #> 920        1         3  20       61686   N5     1 #> 921        1         3  20       61686   O1     5 #> 922        1         3  20       61686   O2     5 #> 923        1         3  20       61686   O3     4 #> 924        1         3  20       61686   O4     5 #> 925        1         3  20       61686   O5     3 #> 926        1         3  21       61687   A1     1 #> 927        1         3  21       61687   A2     4 #> 928        1         3  21       61687   A3     4 #> 929        1         3  21       61687   A4     2 #> 930        1         3  21       61687   A5     3 #> 931        1         3  21       61687   C1     6 #> 932        1         3  21       61687   C2     5 #> 933        1         3  21       61687   C3     6 #> 934        1         3  21       61687   C4     3 #> 935        1         3  21       61687   C5     4 #> 936        1         3  21       61687   E1     3 #> 937        1         3  21       61687   E2     4 #> 938        1         3  21       61687   E3     3 #> 939        1         3  21       61687   E4     3 #> 940        1         3  21       61687   E5     5 #> 941        1         3  21       61687   N1     5 #> 942        1         3  21       61687   N2     6 #> 943        1         3  21       61687   N3     5 #> 944        1         3  21       61687   N4     5 #> 945        1         3  21       61687   N5     4 #> 946        1         3  21       61687   O1     5 #> 947        1         3  21       61687   O2     5 #> 948        1         3  21       61687   O3     4 #> 949        1         3  21       61687   O4     5 #> 950        1         3  21       61687   O5     2 #> 951        1        NA  30       61688   A1     1 #> 952        1        NA  30       61688   A2     6 #> 953        1        NA  30       61688   A3     6 #> 954        1        NA  30       61688   A4     6 #> 955        1        NA  30       61688   A5     6 #> 956        1        NA  30       61688   C1     6 #> 957        1        NA  30       61688   C2     6 #> 958        1        NA  30       61688   C3     6 #> 959        1        NA  30       61688   C4     1 #> 960        1        NA  30       61688   C5     1 #> 961        1        NA  30       61688   E1     1 #> 962        1        NA  30       61688   E2     1 #> 963        1        NA  30       61688   E3     1 #> 964        1        NA  30       61688   E4     6 #> 965        1        NA  30       61688   E5     6 #> 966        1        NA  30       61688   N1     1 #> 967        1        NA  30       61688   N2     1 #> 968        1        NA  30       61688   N3     1 #> 969        1        NA  30       61688   N4     1 #> 970        1        NA  30       61688   N5     1 #> 971        1        NA  30       61688   O1     6 #> 972        1        NA  30       61688   O2     1 #> 973        1        NA  30       61688   O3     6 #> 974        1        NA  30       61688   O4     6 #> 975        1        NA  30       61688   O5     1 #> 976        2         5  48       61691   A1     1 #> 977        2         5  48       61691   A2     5 #> 978        2         5  48       61691   A3     4 #> 979        2         5  48       61691   A4     3 #> 980        2         5  48       61691   A5     5 #> 981        2         5  48       61691   C1     6 #> 982        2         5  48       61691   C2     5 #> 983        2         5  48       61691   C3     5 #> 984        2         5  48       61691   C4     2 #> 985        2         5  48       61691   C5     2 #> 986        2         5  48       61691   E1     3 #> 987        2         5  48       61691   E2     2 #> 988        2         5  48       61691   E3     3 #> 989        2         5  48       61691   E4     6 #> 990        2         5  48       61691   E5     5 #> 991        2         5  48       61691   N1     1 #> 992        2         5  48       61691   N2     2 #> 993        2         5  48       61691   N3     1 #> 994        2         5  48       61691   N4     2 #> 995        2         5  48       61691   N5     1 #> 996        2         5  48       61691   O1     5 #> 997        2         5  48       61691   O2     1 #> 998        2         5  48       61691   O3     6 #> 999        2         5  48       61691   O4     6 #> 1000       2         5  48       61691   O5     1 #> 1001       2         3  40       61692   A1     1 #> 1002       2         3  40       61692   A2     5 #> 1003       2         3  40       61692   A3     5 #> 1004       2         3  40       61692   A4     6 #> 1005       2         3  40       61692   A5     5 #> 1006       2         3  40       61692   C1     4 #> 1007       2         3  40       61692   C2     4 #> 1008       2         3  40       61692   C3     4 #> 1009       2         3  40       61692   C4     3 #> 1010       2         3  40       61692   C5     4 #> 1011       2         3  40       61692   E1     4 #> 1012       2         3  40       61692   E2     3 #> 1013       2         3  40       61692   E3     4 #> 1014       2         3  40       61692   E4     4 #> 1015       2         3  40       61692   E5     4 #> 1016       2         3  40       61692   N1     2 #> 1017       2         3  40       61692   N2     2 #> 1018       2         3  40       61692   N3     3 #> 1019       2         3  40       61692   N4     3 #> 1020       2         3  40       61692   N5     3 #> 1021       2         3  40       61692   O1     4 #> 1022       2         3  40       61692   O2     3 #> 1023       2         3  40       61692   O3     2 #> 1024       2         3  40       61692   O4     5 #> 1025       2         3  40       61692   O5     2 #> 1026       2         4  27       61693   A1     5 #> 1027       2         4  27       61693   A2     4 #> 1028       2         4  27       61693   A3     3 #> 1029       2         4  27       61693   A4     6 #> 1030       2         4  27       61693   A5     4 #> 1031       2         4  27       61693   C1     5 #> 1032       2         4  27       61693   C2     2 #> 1033       2         4  27       61693   C3     5 #> 1034       2         4  27       61693   C4     2 #> 1035       2         4  27       61693   C5     4 #> 1036       2         4  27       61693   E1     6 #> 1037       2         4  27       61693   E2     4 #> 1038       2         4  27       61693   E3     2 #> 1039       2         4  27       61693   E4     4 #> 1040       2         4  27       61693   E5     4 #> 1041       2         4  27       61693   N1     1 #> 1042       2         4  27       61693   N2     2 #> 1043       2         4  27       61693   N3     1 #> 1044       2         4  27       61693   N4     2 #> 1045       2         4  27       61693   N5    NA #> 1046       2         4  27       61693   O1     3 #> 1047       2         4  27       61693   O2     3 #> 1048       2         4  27       61693   O3     2 #> 1049       2         4  27       61693   O4     2 #> 1050       2         4  27       61693   O5     5 #> 1051       1         1  18       61696   A1     1 #> 1052       1         1  18       61696   A2     5 #> 1053       1         1  18       61696   A3     4 #> 1054       1         1  18       61696   A4     4 #> 1055       1         1  18       61696   A5     5 #> 1056       1         1  18       61696   C1     4 #> 1057       1         1  18       61696   C2     5 #> 1058       1         1  18       61696   C3     4 #> 1059       1         1  18       61696   C4     3 #> 1060       1         1  18       61696   C5     3 #> 1061       1         1  18       61696   E1     3 #> 1062       1         1  18       61696   E2     3 #> 1063       1         1  18       61696   E3     2 #> 1064       1         1  18       61696   E4     5 #> 1065       1         1  18       61696   E5     4 #> 1066       1         1  18       61696   N1     2 #> 1067       1         1  18       61696   N2     3 #> 1068       1         1  18       61696   N3     1 #> 1069       1         1  18       61696   N4     3 #> 1070       1         1  18       61696   N5     2 #> 1071       1         1  18       61696   O1     4 #> 1072       1         1  18       61696   O2     3 #> 1073       1         1  18       61696   O3     5 #> 1074       1         1  18       61696   O4     4 #> 1075       1         1  18       61696   O5     3 #> 1076       1         4  20       61698   A1     5 #> 1077       1         4  20       61698   A2     6 #> 1078       1         4  20       61698   A3     4 #> 1079       1         4  20       61698   A4     3 #> 1080       1         4  20       61698   A5     5 #> 1081       1         4  20       61698   C1     5 #> 1082       1         4  20       61698   C2     3 #> 1083       1         4  20       61698   C3     3 #> 1084       1         4  20       61698   C4     4 #> 1085       1         4  20       61698   C5     5 #> 1086       1         4  20       61698   E1     6 #> 1087       1         4  20       61698   E2     4 #> 1088       1         4  20       61698   E3     4 #> 1089       1         4  20       61698   E4     4 #> 1090       1         4  20       61698   E5     3 #> 1091       1         4  20       61698   N1     2 #> 1092       1         4  20       61698   N2     2 #> 1093       1         4  20       61698   N3     3 #> 1094       1         4  20       61698   N4     4 #> 1095       1         4  20       61698   N5     5 #> 1096       1         4  20       61698   O1     3 #> 1097       1         4  20       61698   O2     5 #> 1098       1         4  20       61698   O3     4 #> 1099       1         4  20       61698   O4     4 #> 1100       1         4  20       61698   O5     4 #> 1101       2         5  24       61700   A1     2 #> 1102       2         5  24       61700   A2     6 #> 1103       2         5  24       61700   A3     6 #> 1104       2         5  24       61700   A4     6 #> 1105       2         5  24       61700   A5     6 #> 1106       2         5  24       61700   C1     5 #> 1107       2         5  24       61700   C2     4 #> 1108       2         5  24       61700   C3     5 #> 1109       2         5  24       61700   C4     3 #> 1110       2         5  24       61700   C5     4 #> 1111       2         5  24       61700   E1     2 #> 1112       2         5  24       61700   E2     2 #> 1113       2         5  24       61700   E3     4 #> 1114       2         5  24       61700   E4     5 #> 1115       2         5  24       61700   E5     5 #> 1116       2         5  24       61700   N1     2 #> 1117       2         5  24       61700   N2     2 #> 1118       2         5  24       61700   N3     2 #> 1119       2         5  24       61700   N4     2 #> 1120       2         5  24       61700   N5     3 #> 1121       2         5  24       61700   O1     5 #> 1122       2         5  24       61700   O2     2 #> 1123       2         5  24       61700   O3     5 #> 1124       2         5  24       61700   O4     5 #> 1125       2         5  24       61700   O5     1 #> 1126       1         3  25       61701   A1     1 #> 1127       1         3  25       61701   A2     6 #> 1128       1         3  25       61701   A3     6 #> 1129       1         3  25       61701   A4     6 #> 1130       1         3  25       61701   A5     6 #> 1131       1         3  25       61701   C1     5 #> 1132       1         3  25       61701   C2     2 #> 1133       1         3  25       61701   C3     1 #> 1134       1         3  25       61701   C4     2 #> 1135       1         3  25       61701   C5     1 #> 1136       1         3  25       61701   E1     6 #> 1137       1         3  25       61701   E2     5 #> 1138       1         3  25       61701   E3     6 #> 1139       1         3  25       61701   E4     6 #> 1140       1         3  25       61701   E5     5 #> 1141       1         3  25       61701   N1     2 #> 1142       1         3  25       61701   N2     1 #> 1143       1         3  25       61701   N3     4 #> 1144       1         3  25       61701   N4     6 #> 1145       1         3  25       61701   N5     5 #> 1146       1         3  25       61701   O1     6 #> 1147       1         3  25       61701   O2     5 #> 1148       1         3  25       61701   O3     6 #> 1149       1         3  25       61701   O4     6 #> 1150       1         3  25       61701   O5     1 #> 1151       1         2  22       61702   A1     5 #> 1152       1         2  22       61702   A2     5 #> 1153       1         2  22       61702   A3     3 #> 1154       1         2  22       61702   A4     4 #> 1155       1         2  22       61702   A5     3 #> 1156       1         2  22       61702   C1     4 #> 1157       1         2  22       61702   C2     4 #> 1158       1         2  22       61702   C3     3 #> 1159       1         2  22       61702   C4     4 #> 1160       1         2  22       61702   C5     5 #> 1161       1         2  22       61702   E1     4 #> 1162       1         2  22       61702   E2     4 #> 1163       1         2  22       61702   E3     5 #> 1164       1         2  22       61702   E4     2 #> 1165       1         2  22       61702   E5     4 #> 1166       1         2  22       61702   N1     4 #> 1167       1         2  22       61702   N2     5 #> 1168       1         2  22       61702   N3     3 #> 1169       1         2  22       61702   N4     5 #> 1170       1         2  22       61702   N5     2 #> 1171       1         2  22       61702   O1     3 #> 1172       1         2  22       61702   O2     5 #> 1173       1         2  22       61702   O3     4 #> 1174       1         2  22       61702   O4     4 #> 1175       1         2  22       61702   O5     2 #> 1176       2         1  18       61703   A1     2 #> 1177       2         1  18       61703   A2     6 #> 1178       2         1  18       61703   A3     4 #> 1179       2         1  18       61703   A4     5 #> 1180       2         1  18       61703   A5     5 #> 1181       2         1  18       61703   C1     3 #> 1182       2         1  18       61703   C2     2 #> 1183       2         1  18       61703   C3     3 #> 1184       2         1  18       61703   C4     4 #> 1185       2         1  18       61703   C5     6 #> 1186       2         1  18       61703   E1     2 #> 1187       2         1  18       61703   E2     4 #> 1188       2         1  18       61703   E3     2 #> 1189       2         1  18       61703   E4     4 #> 1190       2         1  18       61703   E5     4 #> 1191       2         1  18       61703   N1     3 #> 1192       2         1  18       61703   N2     4 #> 1193       2         1  18       61703   N3     2 #> 1194       2         1  18       61703   N4     2 #> 1195       2         1  18       61703   N5     4 #> 1196       2         1  18       61703   O1     5 #> 1197       2         1  18       61703   O2     4 #> 1198       2         1  18       61703   O3     5 #> 1199       2         1  18       61703   O4     3 #> 1200       2         1  18       61703   O5     2 #> 1201       2         1  43       61713   A1     1 #> 1202       2         1  43       61713   A2     5 #> 1203       2         1  43       61713   A3     3 #> 1204       2         1  43       61713   A4     2 #> 1205       2         1  43       61713   A5     3 #> 1206       2         1  43       61713   C1     3 #> 1207       2         1  43       61713   C2     6 #> 1208       2         1  43       61713   C3     3 #> 1209       2         1  43       61713   C4     1 #> 1210       2         1  43       61713   C5     3 #> 1211       2         1  43       61713   E1     5 #> 1212       2         1  43       61713   E2     5 #> 1213       2         1  43       61713   E3     5 #> 1214       2         1  43       61713   E4     5 #> 1215       2         1  43       61713   E5     3 #> 1216       2         1  43       61713   N1     5 #> 1217       2         1  43       61713   N2     5 #> 1218       2         1  43       61713   N3     5 #> 1219       2         1  43       61713   N4     5 #> 1220       2         1  43       61713   N5     3 #> 1221       2         1  43       61713   O1     3 #> 1222       2         1  43       61713   O2     3 #> 1223       2         1  43       61713   O3     2 #> 1224       2         1  43       61713   O4     5 #> 1225       2         1  43       61713   O5     1 #> 1226       1         3  20       61715   A1     1 #> 1227       1         3  20       61715   A2     6 #> 1228       1         3  20       61715   A3     6 #> 1229       1         3  20       61715   A4     6 #> 1230       1         3  20       61715   A5     6 #> 1231       1         3  20       61715   C1     5 #> 1232       1         3  20       61715   C2     5 #> 1233       1         3  20       61715   C3     4 #> 1234       1         3  20       61715   C4     1 #> 1235       1         3  20       61715   C5     2 #> 1236       1         3  20       61715   E1     1 #> 1237       1         3  20       61715   E2     1 #> 1238       1         3  20       61715   E3     6 #> 1239       1         3  20       61715   E4     6 #> 1240       1         3  20       61715   E5     6 #> 1241       1         3  20       61715   N1     4 #> 1242       1         3  20       61715   N2     4 #> 1243       1         3  20       61715   N3     1 #> 1244       1         3  20       61715   N4     1 #> 1245       1         3  20       61715   N5     1 #> 1246       1         3  20       61715   O1     6 #> 1247       1         3  20       61715   O2     3 #> 1248       1         3  20       61715   O3     6 #> 1249       1         3  20       61715   O4     6 #> 1250       1         3  20       61715   O5     1 #> 1251       2         3  24       61716   A1     1 #> 1252       2         3  24       61716   A2     6 #> 1253       2         3  24       61716   A3     6 #> 1254       2         3  24       61716   A4     6 #> 1255       2         3  24       61716   A5     4 #> 1256       2         3  24       61716   C1     4 #> 1257       2         3  24       61716   C2     3 #> 1258       2         3  24       61716   C3     1 #> 1259       2         3  24       61716   C4     4 #> 1260       2         3  24       61716   C5     2 #> 1261       2         3  24       61716   E1     2 #> 1262       2         3  24       61716   E2     2 #> 1263       2         3  24       61716   E3     5 #> 1264       2         3  24       61716   E4     4 #> 1265       2         3  24       61716   E5     4 #> 1266       2         3  24       61716   N1     6 #> 1267       2         3  24       61716   N2     6 #> 1268       2         3  24       61716   N3     6 #> 1269       2         3  24       61716   N4     3 #> 1270       2         3  24       61716   N5     3 #> 1271       2         3  24       61716   O1     3 #> 1272       2         3  24       61716   O2     1 #> 1273       2         3  24       61716   O3     4 #> 1274       2         3  24       61716   O4     6 #> 1275       2         3  24       61716   O5     2 #> 1276       2         4  26       61723   A1     1 #> 1277       2         4  26       61723   A2     5 #> 1278       2         4  26       61723   A3     6 #> 1279       2         4  26       61723   A4     5 #> 1280       2         4  26       61723   A5     4 #> 1281       2         4  26       61723   C1     6 #> 1282       2         4  26       61723   C2     6 #> 1283       2         4  26       61723   C3     6 #> 1284       2         4  26       61723   C4     6 #> 1285       2         4  26       61723   C5     2 #> 1286       2         4  26       61723   E1     4 #> 1287       2         4  26       61723   E2     4 #> 1288       2         4  26       61723   E3     4 #> 1289       2         4  26       61723   E4     3 #> 1290       2         4  26       61723   E5     6 #> 1291       2         4  26       61723   N1     1 #> 1292       2         4  26       61723   N2     1 #> 1293       2         4  26       61723   N3     1 #> 1294       2         4  26       61723   N4     6 #> 1295       2         4  26       61723   N5     5 #> 1296       2         4  26       61723   O1     5 #> 1297       2         4  26       61723   O2     6 #> 1298       2         4  26       61723   O3     3 #> 1299       2         4  26       61723   O4     6 #> 1300       2         4  26       61723   O5     3 #> 1301       1         4  26       61724   A1     3 #> 1302       1         4  26       61724   A2     6 #> 1303       1         4  26       61724   A3     4 #> 1304       1         4  26       61724   A4     4 #> 1305       1         4  26       61724   A5     4 #> 1306       1         4  26       61724   C1     5 #> 1307       1         4  26       61724   C2     5 #> 1308       1         4  26       61724   C3     3 #> 1309       1         4  26       61724   C4     2 #> 1310       1         4  26       61724   C5     5 #> 1311       1         4  26       61724   E1     1 #> 1312       1         4  26       61724   E2     1 #> 1313       1         4  26       61724   E3     4 #> 1314       1         4  26       61724   E4     6 #> 1315       1         4  26       61724   E5     5 #> 1316       1         4  26       61724   N1     2 #> 1317       1         4  26       61724   N2     2 #> 1318       1         4  26       61724   N3     1 #> 1319       1         4  26       61724   N4     1 #> 1320       1         4  26       61724   N5     1 #> 1321       1         4  26       61724   O1     5 #> 1322       1         4  26       61724   O2     1 #> 1323       1         4  26       61724   O3     5 #> 1324       1         4  26       61724   O4     5 #> 1325       1         4  26       61724   O5     6 #> 1326       2         3  25       61725   A1     4 #> 1327       2         3  25       61725   A2     3 #> 1328       2         3  25       61725   A3     5 #> 1329       2         3  25       61725   A4     6 #> 1330       2         3  25       61725   A5     3 #> 1331       2         3  25       61725   C1     5 #> 1332       2         3  25       61725   C2     6 #> 1333       2         3  25       61725   C3     2 #> 1334       2         3  25       61725   C4     5 #> 1335       2         3  25       61725   C5     2 #> 1336       2         3  25       61725   E1     3 #> 1337       2         3  25       61725   E2     5 #> 1338       2         3  25       61725   E3     2 #> 1339       2         3  25       61725   E4     6 #> 1340       2         3  25       61725   E5     2 #> 1341       2         3  25       61725   N1     6 #> 1342       2         3  25       61725   N2     5 #> 1343       2         3  25       61725   N3     5 #> 1344       2         3  25       61725   N4     5 #> 1345       2         3  25       61725   N5     6 #> 1346       2         3  25       61725   O1     2 #> 1347       2         3  25       61725   O2     5 #> 1348       2         3  25       61725   O3     2 #> 1349       2         3  25       61725   O4     6 #> 1350       2         3  25       61725   O5     4 #> 1351       1         4  25       61728   A1     1 #> 1352       1         4  25       61728   A2     6 #> 1353       1         4  25       61728   A3     6 #> 1354       1         4  25       61728   A4     6 #> 1355       1         4  25       61728   A5     6 #> 1356       1         4  25       61728   C1     6 #> 1357       1         4  25       61728   C2     5 #> 1358       1         4  25       61728   C3     5 #> 1359       1         4  25       61728   C4     2 #> 1360       1         4  25       61728   C5     2 #> 1361       1         4  25       61728   E1     1 #> 1362       1         4  25       61728   E2     2 #> 1363       1         4  25       61728   E3     5 #> 1364       1         4  25       61728   E4     6 #> 1365       1         4  25       61728   E5     5 #> 1366       1         4  25       61728   N1     2 #> 1367       1         4  25       61728   N2     3 #> 1368       1         4  25       61728   N3     2 #> 1369       1         4  25       61728   N4     3 #> 1370       1         4  25       61728   N5     2 #> 1371       1         4  25       61728   O1     5 #> 1372       1         4  25       61728   O2     3 #> 1373       1         4  25       61728   O3     5 #> 1374       1         4  25       61728   O4     5 #> 1375       1         4  25       61728   O5     2 #> 1376       1         5  26       61730   A1     1 #> 1377       1         5  26       61730   A2     4 #> 1378       1         5  26       61730   A3     3 #> 1379       1         5  26       61730   A4     5 #> 1380       1         5  26       61730   A5     5 #> 1381       1         5  26       61730   C1     5 #> 1382       1         5  26       61730   C2     5 #> 1383       1         5  26       61730   C3     4 #> 1384       1         5  26       61730   C4     4 #> 1385       1         5  26       61730   C5     5 #> 1386       1         5  26       61730   E1     2 #> 1387       1         5  26       61730   E2     5 #> 1388       1         5  26       61730   E3     4 #> 1389       1         5  26       61730   E4     5 #> 1390       1         5  26       61730   E5     5 #> 1391       1         5  26       61730   N1     2 #> 1392       1         5  26       61730   N2     4 #> 1393       1         5  26       61730   N3     4 #> 1394       1         5  26       61730   N4     5 #> 1395       1         5  26       61730   N5     3 #> 1396       1         5  26       61730   O1     5 #> 1397       1         5  26       61730   O2     1 #> 1398       1         5  26       61730   O3     6 #> 1399       1         5  26       61730   O4     6 #> 1400       1         5  26       61730   O5     1 #> 1401       2         3  21       61731   A1     1 #> 1402       2         3  21       61731   A2     4 #> 1403       2         3  21       61731   A3     2 #> 1404       2         3  21       61731   A4     2 #> 1405       2         3  21       61731   A5     2 #> 1406       2         3  21       61731   C1     5 #> 1407       2         3  21       61731   C2     5 #> 1408       2         3  21       61731   C3     5 #> 1409       2         3  21       61731   C4     5 #> 1410       2         3  21       61731   C5     1 #> 1411       2         3  21       61731   E1     4 #> 1412       2         3  21       61731   E2     5 #> 1413       2         3  21       61731   E3     4 #> 1414       2         3  21       61731   E4     3 #> 1415       2         3  21       61731   E5     4 #> 1416       2         3  21       61731   N1     3 #> 1417       2         3  21       61731   N2     4 #> 1418       2         3  21       61731   N3     5 #> 1419       2         3  21       61731   N4     5 #> 1420       2         3  21       61731   N5     5 #> 1421       2         3  21       61731   O1     4 #> 1422       2         3  21       61731   O2     4 #> 1423       2         3  21       61731   O3     6 #> 1424       2         3  21       61731   O4     6 #> 1425       2         3  21       61731   O5     3 #> 1426       1         5  24       61732   A1     3 #> 1427       1         5  24       61732   A2     4 #> 1428       1         5  24       61732   A3     5 #> 1429       1         5  24       61732   A4     2 #> 1430       1         5  24       61732   A5     4 #> 1431       1         5  24       61732   C1     5 #> 1432       1         5  24       61732   C2     4 #> 1433       1         5  24       61732   C3     5 #> 1434       1         5  24       61732   C4     2 #> 1435       1         5  24       61732   C5     4 #> 1436       1         5  24       61732   E1     5 #> 1437       1         5  24       61732   E2     5 #> 1438       1         5  24       61732   E3     5 #> 1439       1         5  24       61732   E4     4 #> 1440       1         5  24       61732   E5     5 #> 1441       1         5  24       61732   N1     5 #> 1442       1         5  24       61732   N2     5 #> 1443       1         5  24       61732   N3     5 #> 1444       1         5  24       61732   N4     3 #> 1445       1         5  24       61732   N5     2 #> 1446       1         5  24       61732   O1     5 #> 1447       1         5  24       61732   O2     2 #> 1448       1         5  24       61732   O3     5 #> 1449       1         5  24       61732   O4     5 #> 1450       1         5  24       61732   O5     5 #> 1451       2         2  50       61740   A1     1 #> 1452       2         2  50       61740   A2     6 #> 1453       2         2  50       61740   A3     5 #> 1454       2         2  50       61740   A4     4 #> 1455       2         2  50       61740   A5     4 #> 1456       2         2  50       61740   C1     6 #> 1457       2         2  50       61740   C2     6 #> 1458       2         2  50       61740   C3     6 #> 1459       2         2  50       61740   C4     1 #> 1460       2         2  50       61740   C5     4 #> 1461       2         2  50       61740   E1     4 #> 1462       2         2  50       61740   E2     4 #> 1463       2         2  50       61740   E3     1 #> 1464       2         2  50       61740   E4     2 #> 1465       2         2  50       61740   E5     5 #> 1466       2         2  50       61740   N1     3 #> 1467       2         2  50       61740   N2     4 #> 1468       2         2  50       61740   N3     4 #> 1469       2         2  50       61740   N4     4 #> 1470       2         2  50       61740   N5     4 #> 1471       2         2  50       61740   O1     4 #> 1472       2         2  50       61740   O2     4 #> 1473       2         2  50       61740   O3     4 #> 1474       2         2  50       61740   O4     2 #> 1475       2         2  50       61740   O5     1 #> 1476       1         5  29       61742   A1     3 #> 1477       1         5  29       61742   A2     3 #> 1478       1         5  29       61742   A3     5 #> 1479       1         5  29       61742   A4     4 #> 1480       1         5  29       61742   A5     5 #> 1481       1         5  29       61742   C1     6 #> 1482       1         5  29       61742   C2     4 #> 1483       1         5  29       61742   C3     4 #> 1484       1         5  29       61742   C4     2 #> 1485       1         5  29       61742   C5     2 #> 1486       1         5  29       61742   E1     2 #> 1487       1         5  29       61742   E2     1 #> 1488       1         5  29       61742   E3     4 #> 1489       1         5  29       61742   E4     6 #> 1490       1         5  29       61742   E5     4 #> 1491       1         5  29       61742   N1     1 #> 1492       1         5  29       61742   N2     2 #> 1493       1         5  29       61742   N3     1 #> 1494       1         5  29       61742   N4     4 #> 1495       1         5  29       61742   N5     1 #> 1496       1         5  29       61742   O1     4 #> 1497       1         5  29       61742   O2     3 #> 1498       1         5  29       61742   O3     5 #> 1499       1         5  29       61742   O4     5 #> 1500       1         5  29       61742   O5     2 #> 1501       1         1  32       61748   A1     2 #> 1502       1         1  32       61748   A2     3 #> 1503       1         1  32       61748   A3     4 #> 1504       1         1  32       61748   A4     4 #> 1505       1         1  32       61748   A5     5 #> 1506       1         1  32       61748   C1     5 #> 1507       1         1  32       61748   C2     3 #> 1508       1         1  32       61748   C3     2 #> 1509       1         1  32       61748   C4     4 #> 1510       1         1  32       61748   C5     6 #> 1511       1         1  32       61748   E1     4 #> 1512       1         1  32       61748   E2     4 #> 1513       1         1  32       61748   E3     3 #> 1514       1         1  32       61748   E4     5 #> 1515       1         1  32       61748   E5     2 #> 1516       1         1  32       61748   N1     4 #> 1517       1         1  32       61748   N2     4 #> 1518       1         1  32       61748   N3     6 #> 1519       1         1  32       61748   N4     5 #> 1520       1         1  32       61748   N5     2 #> 1521       1         1  32       61748   O1     2 #> 1522       1         1  32       61748   O2     4 #> 1523       1         1  32       61748   O3     3 #> 1524       1         1  32       61748   O4     5 #> 1525       1         1  32       61748   O5     5 #> 1526       1         1  18       61749   A1     2 #> 1527       1         1  18       61749   A2     4 #> 1528       1         1  18       61749   A3     4 #> 1529       1         1  18       61749   A4     5 #> 1530       1         1  18       61749   A5     3 #> 1531       1         1  18       61749   C1     5 #> 1532       1         1  18       61749   C2     5 #> 1533       1         1  18       61749   C3     4 #> 1534       1         1  18       61749   C4     3 #> 1535       1         1  18       61749   C5     4 #> 1536       1         1  18       61749   E1     6 #> 1537       1         1  18       61749   E2     5 #> 1538       1         1  18       61749   E3     4 #> 1539       1         1  18       61749   E4     3 #> 1540       1         1  18       61749   E5     4 #> 1541       1         1  18       61749   N1     4 #> 1542       1         1  18       61749   N2     5 #> 1543       1         1  18       61749   N3     4 #> 1544       1         1  18       61749   N4     5 #> 1545       1         1  18       61749   N5     5 #> 1546       1         1  18       61749   O1     6 #> 1547       1         1  18       61749   O2     6 #> 1548       1         1  18       61749   O3     4 #> 1549       1         1  18       61749   O4     6 #> 1550       1         1  18       61749   O5     2 #> 1551       2         4  32       61754   A1     1 #> 1552       2         4  32       61754   A2     4 #> 1553       2         4  32       61754   A3     6 #> 1554       2         4  32       61754   A4     6 #> 1555       2         4  32       61754   A5     6 #> 1556       2         4  32       61754   C1    NA #> 1557       2         4  32       61754   C2     6 #> 1558       2         4  32       61754   C3     6 #> 1559       2         4  32       61754   C4     2 #> 1560       2         4  32       61754   C5     3 #> 1561       2         4  32       61754   E1     1 #> 1562       2         4  32       61754   E2     1 #> 1563       2         4  32       61754   E3     5 #> 1564       2         4  32       61754   E4     6 #> 1565       2         4  32       61754   E5     6 #> 1566       2         4  32       61754   N1     4 #> 1567       2         4  32       61754   N2     4 #> 1568       2         4  32       61754   N3     3 #> 1569       2         4  32       61754   N4     1 #> 1570       2         4  32       61754   N5     3 #> 1571       2         4  32       61754   O1     5 #> 1572       2         4  32       61754   O2     3 #> 1573       2         4  32       61754   O3     3 #> 1574       2         4  32       61754   O4     6 #> 1575       2         4  32       61754   O5     3 #> 1576       2         3  26       61756   A1     4 #> 1577       2         3  26       61756   A2     5 #> 1578       2         3  26       61756   A3     3 #> 1579       2         3  26       61756   A4     5 #> 1580       2         3  26       61756   A5     4 #> 1581       2         3  26       61756   C1     6 #> 1582       2         3  26       61756   C2     5 #> 1583       2         3  26       61756   C3     5 #> 1584       2         3  26       61756   C4     2 #> 1585       2         3  26       61756   C5     2 #> 1586       2         3  26       61756   E1     1 #> 1587       2         3  26       61756   E2     1 #> 1588       2         3  26       61756   E3     5 #> 1589       2         3  26       61756   E4     6 #> 1590       2         3  26       61756   E5     6 #> 1591       2         3  26       61756   N1     3 #> 1592       2         3  26       61756   N2     5 #> 1593       2         3  26       61756   N3     3 #> 1594       2         3  26       61756   N4     1 #> 1595       2         3  26       61756   N5     3 #> 1596       2         3  26       61756   O1     4 #> 1597       2         3  26       61756   O2     1 #> 1598       2         3  26       61756   O3     4 #> 1599       2         3  26       61756   O4     6 #> 1600       2         3  26       61756   O5     1 #> 1601       2         5  27       61757   A1     2 #> 1602       2         5  27       61757   A2     6 #> 1603       2         5  27       61757   A3     6 #> 1604       2         5  27       61757   A4     6 #> 1605       2         5  27       61757   A5     6 #> 1606       2         5  27       61757   C1     4 #> 1607       2         5  27       61757   C2     6 #> 1608       2         5  27       61757   C3     6 #> 1609       2         5  27       61757   C4     1 #> 1610       2         5  27       61757   C5     4 #> 1611       2         5  27       61757   E1     3 #> 1612       2         5  27       61757   E2     6 #> 1613       2         5  27       61757   E3     5 #> 1614       2         5  27       61757   E4     4 #> 1615       2         5  27       61757   E5     4 #> 1616       2         5  27       61757   N1     4 #> 1617       2         5  27       61757   N2     3 #> 1618       2         5  27       61757   N3     6 #> 1619       2         5  27       61757   N4     6 #> 1620       2         5  27       61757   N5     4 #> 1621       2         5  27       61757   O1     4 #> 1622       2         5  27       61757   O2     1 #> 1623       2         5  27       61757   O3     6 #> 1624       2         5  27       61757   O4     6 #> 1625       2         5  27       61757   O5     3 #> 1626       2         3  19       61759   A1     2 #> 1627       2         3  19       61759   A2    NA #> 1628       2         3  19       61759   A3     4 #> 1629       2         3  19       61759   A4     6 #> 1630       2         3  19       61759   A5     4 #> 1631       2         3  19       61759   C1     5 #> 1632       2         3  19       61759   C2     4 #> 1633       2         3  19       61759   C3     5 #> 1634       2         3  19       61759   C4     2 #> 1635       2         3  19       61759   C5     1 #> 1636       2         3  19       61759   E1     5 #> 1637       2         3  19       61759   E2     5 #> 1638       2         3  19       61759   E3     3 #> 1639       2         3  19       61759   E4     3 #> 1640       2         3  19       61759   E5     3 #> 1641       2         3  19       61759   N1     1 #> 1642       2         3  19       61759   N2     1 #> 1643       2         3  19       61759   N3     1 #> 1644       2         3  19       61759   N4    NA #> 1645       2         3  19       61759   N5     1 #> 1646       2         3  19       61759   O1     2 #> 1647       2         3  19       61759   O2     1 #> 1648       2         3  19       61759   O3     4 #> 1649       2         3  19       61759   O4     6 #> 1650       2         3  19       61759   O5     1 #> 1651       1         4  21       61761   A1     1 #> 1652       1         4  21       61761   A2     5 #> 1653       1         4  21       61761   A3     4 #> 1654       1         4  21       61761   A4     2 #> 1655       1         4  21       61761   A5     5 #> 1656       1         4  21       61761   C1     1 #> 1657       1         4  21       61761   C2     2 #> 1658       1         4  21       61761   C3     2 #> 1659       1         4  21       61761   C4     2 #> 1660       1         4  21       61761   C5     6 #> 1661       1         4  21       61761   E1     2 #> 1662       1         4  21       61761   E2     5 #> 1663       1         4  21       61761   E3     2 #> 1664       1         4  21       61761   E4     2 #> 1665       1         4  21       61761   E5     1 #> 1666       1         4  21       61761   N1     2 #> 1667       1         4  21       61761   N2     5 #> 1668       1         4  21       61761   N3     5 #> 1669       1         4  21       61761   N4     4 #> 1670       1         4  21       61761   N5     2 #> 1671       1         4  21       61761   O1     5 #> 1672       1         4  21       61761   O2     4 #> 1673       1         4  21       61761   O3     4 #> 1674       1         4  21       61761   O4     6 #> 1675       1         4  21       61761   O5     1 #> 1676       1         3  21       61762   A1     4 #> 1677       1         3  21       61762   A2     3 #> 1678       1         3  21       61762   A3     2 #> 1679       1         3  21       61762   A4     2 #> 1680       1         3  21       61762   A5     2 #> 1681       1         3  21       61762   C1     4 #> 1682       1         3  21       61762   C2     2 #> 1683       1         3  21       61762   C3     2 #> 1684       1         3  21       61762   C4     4 #> 1685       1         3  21       61762   C5     5 #> 1686       1         3  21       61762   E1     4 #> 1687       1         3  21       61762   E2     3 #> 1688       1         3  21       61762   E3     4 #> 1689       1         3  21       61762   E4     2 #> 1690       1         3  21       61762   E5     4 #> 1691       1         3  21       61762   N1     1 #> 1692       1         3  21       61762   N2     2 #> 1693       1         3  21       61762   N3     1 #> 1694       1         3  21       61762   N4     5 #> 1695       1         3  21       61762   N5     2 #> 1696       1         3  21       61762   O1     6 #> 1697       1         3  21       61762   O2     1 #> 1698       1         3  21       61762   O3     6 #> 1699       1         3  21       61762   O4     6 #> 1700       1         3  21       61762   O5     1 #> 1701       2         5  36       61763   A1     2 #> 1702       2         5  36       61763   A2     3 #> 1703       2         5  36       61763   A3     4 #> 1704       2         5  36       61763   A4     5 #> 1705       2         5  36       61763   A5     6 #> 1706       2         5  36       61763   C1     5 #> 1707       2         5  36       61763   C2     5 #> 1708       2         5  36       61763   C3     4 #> 1709       2         5  36       61763   C4     2 #> 1710       2         5  36       61763   C5     2 #> 1711       2         5  36       61763   E1     1 #> 1712       2         5  36       61763   E2     2 #> 1713       2         5  36       61763   E3     4 #> 1714       2         5  36       61763   E4     4 #> 1715       2         5  36       61763   E5     4 #> 1716       2         5  36       61763   N1     1 #> 1717       2         5  36       61763   N2     2 #> 1718       2         5  36       61763   N3     2 #> 1719       2         5  36       61763   N4     4 #> 1720       2         5  36       61763   N5     2 #> 1721       2         5  36       61763   O1     3 #> 1722       2         5  36       61763   O2     2 #> 1723       2         5  36       61763   O3     5 #> 1724       2         5  36       61763   O4     5 #> 1725       2         5  36       61763   O5     2 #> 1726       2         2  48       61764   A1     1 #> 1727       2         2  48       61764   A2     6 #> 1728       2         2  48       61764   A3     6 #> 1729       2         2  48       61764   A4     3 #> 1730       2         2  48       61764   A5     6 #> 1731       2         2  48       61764   C1     6 #> 1732       2         2  48       61764   C2     5 #> 1733       2         2  48       61764   C3     6 #> 1734       2         2  48       61764   C4     1 #> 1735       2         2  48       61764   C5     4 #> 1736       2         2  48       61764   E1     4 #> 1737       2         2  48       61764   E2     4 #> 1738       2         2  48       61764   E3     2 #> 1739       2         2  48       61764   E4     3 #> 1740       2         2  48       61764   E5     3 #> 1741       2         2  48       61764   N1     1 #> 1742       2         2  48       61764   N2     2 #> 1743       2         2  48       61764   N3     2 #> 1744       2         2  48       61764   N4     5 #> 1745       2         2  48       61764   N5     2 #> 1746       2         2  48       61764   O1     6 #> 1747       2         2  48       61764   O2     2 #> 1748       2         2  48       61764   O3     3 #> 1749       2         2  48       61764   O4     5 #> 1750       2         2  48       61764   O5     2 #> 1751       2         3  22       61771   A1     4 #> 1752       2         3  22       61771   A2     5 #> 1753       2         3  22       61771   A3     6 #> 1754       2         3  22       61771   A4     6 #> 1755       2         3  22       61771   A5     4 #> 1756       2         3  22       61771   C1     4 #> 1757       2         3  22       61771   C2     6 #> 1758       2         3  22       61771   C3     6 #> 1759       2         3  22       61771   C4     1 #> 1760       2         3  22       61771   C5     2 #> 1761       2         3  22       61771   E1     4 #> 1762       2         3  22       61771   E2     3 #> 1763       2         3  22       61771   E3     5 #> 1764       2         3  22       61771   E4     5 #> 1765       2         3  22       61771   E5     5 #> 1766       2         3  22       61771   N1     2 #> 1767       2         3  22       61771   N2     3 #> 1768       2         3  22       61771   N3     3 #> 1769       2         3  22       61771   N4     1 #> 1770       2         3  22       61771   N5     3 #> 1771       2         3  22       61771   O1     4 #> 1772       2         3  22       61771   O2     1 #> 1773       2         3  22       61771   O3     4 #> 1774       2         3  22       61771   O4     6 #> 1775       2         3  22       61771   O5     2 #> 1776       2         2  23       61772   A1     2 #> 1777       2         2  23       61772   A2     4 #> 1778       2         2  23       61772   A3     6 #> 1779       2         2  23       61772   A4     2 #> 1780       2         2  23       61772   A5     5 #> 1781       2         2  23       61772   C1     2 #> 1782       2         2  23       61772   C2     4 #> 1783       2         2  23       61772   C3     4 #> 1784       2         2  23       61772   C4     1 #> 1785       2         2  23       61772   C5     1 #> 1786       2         2  23       61772   E1     1 #> 1787       2         2  23       61772   E2     1 #> 1788       2         2  23       61772   E3     4 #> 1789       2         2  23       61772   E4     4 #> 1790       2         2  23       61772   E5    NA #> 1791       2         2  23       61772   N1     1 #> 1792       2         2  23       61772   N2     3 #> 1793       2         2  23       61772   N3     2 #> 1794       2         2  23       61772   N4     2 #> 1795       2         2  23       61772   N5     1 #> 1796       2         2  23       61772   O1     6 #> 1797       2         2  23       61772   O2     2 #> 1798       2         2  23       61772   O3     4 #> 1799       2         2  23       61772   O4     5 #> 1800       2         2  23       61772   O5     3 #> 1801       1         3  21       61773   A1     4 #> 1802       1         3  21       61773   A2     4 #> 1803       1         3  21       61773   A3     4 #> 1804       1         3  21       61773   A4     5 #> 1805       1         3  21       61773   A5     3 #> 1806       1         3  21       61773   C1     5 #> 1807       1         3  21       61773   C2     4 #> 1808       1         3  21       61773   C3     6 #> 1809       1         3  21       61773   C4     2 #> 1810       1         3  21       61773   C5     4 #> 1811       1         3  21       61773   E1     2 #> 1812       1         3  21       61773   E2     2 #> 1813       1         3  21       61773   E3     4 #> 1814       1         3  21       61773   E4     6 #> 1815       1         3  21       61773   E5     4 #> 1816       1         3  21       61773   N1     5 #> 1817       1         3  21       61773   N2     5 #> 1818       1         3  21       61773   N3     4 #> 1819       1         3  21       61773   N4     3 #> 1820       1         3  21       61773   N5     5 #> 1821       1         3  21       61773   O1     4 #> 1822       1         3  21       61773   O2     1 #> 1823       1         3  21       61773   O3     4 #> 1824       1         3  21       61773   O4     2 #> 1825       1         3  21       61773   O5     3 #> 1826       2         3  20       61775   A1     1 #> 1827       2         3  20       61775   A2     5 #> 1828       2         3  20       61775   A3     5 #> 1829       2         3  20       61775   A4     5 #> 1830       2         3  20       61775   A5     4 #> 1831       2         3  20       61775   C1     5 #> 1832       2         3  20       61775   C2     5 #> 1833       2         3  20       61775   C3     5 #> 1834       2         3  20       61775   C4     1 #> 1835       2         3  20       61775   C5     2 #> 1836       2         3  20       61775   E1     4 #> 1837       2         3  20       61775   E2     4 #> 1838       2         3  20       61775   E3     3 #> 1839       2         3  20       61775   E4     2 #> 1840       2         3  20       61775   E5     5 #> 1841       2         3  20       61775   N1     1 #> 1842       2         3  20       61775   N2     2 #> 1843       2         3  20       61775   N3     4 #> 1844       2         3  20       61775   N4     4 #> 1845       2         3  20       61775   N5     4 #> 1846       2         3  20       61775   O1     5 #> 1847       2         3  20       61775   O2     1 #> 1848       2         3  20       61775   O3     5 #> 1849       2         3  20       61775   O4     4 #> 1850       2         3  20       61775   O5     2 #> 1851       2         3  23       61776   A1     3 #> 1852       2         3  23       61776   A2     5 #> 1853       2         3  23       61776   A3     5 #> 1854       2         3  23       61776   A4     5 #> 1855       2         3  23       61776   A5     4 #> 1856       2         3  23       61776   C1     5 #> 1857       2         3  23       61776   C2     5 #> 1858       2         3  23       61776   C3     5 #> 1859       2         3  23       61776   C4     1 #> 1860       2         3  23       61776   C5     1 #> 1861       2         3  23       61776   E1     2 #> 1862       2         3  23       61776   E2     3 #> 1863       2         3  23       61776   E3     5 #> 1864       2         3  23       61776   E4     5 #> 1865       2         3  23       61776   E5     4 #> 1866       2         3  23       61776   N1     2 #> 1867       2         3  23       61776   N2     1 #> 1868       2         3  23       61776   N3     2 #> 1869       2         3  23       61776   N4     2 #> 1870       2         3  23       61776   N5     3 #> 1871       2         3  23       61776   O1     5 #> 1872       2         3  23       61776   O2     1 #> 1873       2         3  23       61776   O3     5 #> 1874       2         3  23       61776   O4     4 #> 1875       2         3  23       61776   O5     2 #> 1876       2         4  43       61777   A1     2 #> 1877       2         4  43       61777   A2     5 #> 1878       2         4  43       61777   A3     5 #> 1879       2         4  43       61777   A4     6 #> 1880       2         4  43       61777   A5     5 #> 1881       2         4  43       61777   C1     5 #> 1882       2         4  43       61777   C2     4 #> 1883       2         4  43       61777   C3     6 #> 1884       2         4  43       61777   C4     3 #> 1885       2         4  43       61777   C5     2 #> 1886       2         4  43       61777   E1     1 #> 1887       2         4  43       61777   E2     1 #> 1888       2         4  43       61777   E3     4 #> 1889       2         4  43       61777   E4     6 #> 1890       2         4  43       61777   E5     5 #> 1891       2         4  43       61777   N1     2 #> 1892       2         4  43       61777   N2     2 #> 1893       2         4  43       61777   N3     3 #> 1894       2         4  43       61777   N4     3 #> 1895       2         4  43       61777   N5     2 #> 1896       2         4  43       61777   O1     5 #> 1897       2         4  43       61777   O2     1 #> 1898       2         4  43       61777   O3     4 #> 1899       2         4  43       61777   O4     3 #> 1900       2         4  43       61777   O5     4 #> 1901       2        NA  16       61778   A1     2 #> 1902       2        NA  16       61778   A2     6 #> 1903       2        NA  16       61778   A3     6 #> 1904       2        NA  16       61778   A4     6 #> 1905       2        NA  16       61778   A5     6 #> 1906       2        NA  16       61778   C1     5 #> 1907       2        NA  16       61778   C2     4 #> 1908       2        NA  16       61778   C3     5 #> 1909       2        NA  16       61778   C4     1 #> 1910       2        NA  16       61778   C5     2 #> 1911       2        NA  16       61778   E1     1 #> 1912       2        NA  16       61778   E2     1 #> 1913       2        NA  16       61778   E3     6 #> 1914       2        NA  16       61778   E4     6 #> 1915       2        NA  16       61778   E5     5 #> 1916       2        NA  16       61778   N1     2 #> 1917       2        NA  16       61778   N2     4 #> 1918       2        NA  16       61778   N3     2 #> 1919       2        NA  16       61778   N4     1 #> 1920       2        NA  16       61778   N5     1 #> 1921       2        NA  16       61778   O1     6 #> 1922       2        NA  16       61778   O2     3 #> 1923       2        NA  16       61778   O3     5 #> 1924       2        NA  16       61778   O4     4 #> 1925       2        NA  16       61778   O5     1 #> 1926       2        NA  14       61780   A1     5 #> 1927       2        NA  14       61780   A2     6 #> 1928       2        NA  14       61780   A3     6 #> 1929       2        NA  14       61780   A4     6 #> 1930       2        NA  14       61780   A5     5 #> 1931       2        NA  14       61780   C1     5 #> 1932       2        NA  14       61780   C2     5 #> 1933       2        NA  14       61780   C3     6 #> 1934       2        NA  14       61780   C4     3 #> 1935       2        NA  14       61780   C5     4 #> 1936       2        NA  14       61780   E1     1 #> 1937       2        NA  14       61780   E2     2 #> 1938       2        NA  14       61780   E3     5 #> 1939       2        NA  14       61780   E4     6 #> 1940       2        NA  14       61780   E5     6 #> 1941       2        NA  14       61780   N1     4 #> 1942       2        NA  14       61780   N2     3 #> 1943       2        NA  14       61780   N3     4 #> 1944       2        NA  14       61780   N4     4 #> 1945       2        NA  14       61780   N5     6 #> 1946       2        NA  14       61780   O1     5 #> 1947       2        NA  14       61780   O2     4 #> 1948       2        NA  14       61780   O3     5 #> 1949       2        NA  14       61780   O4     6 #> 1950       2        NA  14       61780   O5     3 #> 1951       2         3  54       61782   A1     1 #> 1952       2         3  54       61782   A2     2 #> 1953       2         3  54       61782   A3     2 #> 1954       2         3  54       61782   A4     4 #> 1955       2         3  54       61782   A5     2 #> 1956       2         3  54       61782   C1     2 #> 1957       2         3  54       61782   C2     4 #> 1958       2         3  54       61782   C3     2 #> 1959       2         3  54       61782   C4     5 #> 1960       2         3  54       61782   C5     1 #> 1961       2         3  54       61782   E1     2 #> 1962       2         3  54       61782   E2     2 #> 1963       2         3  54       61782   E3     4 #> 1964       2         3  54       61782   E4     2 #> 1965       2         3  54       61782   E5     2 #> 1966       2         3  54       61782   N1     4 #> 1967       2         3  54       61782   N2     2 #> 1968       2         3  54       61782   N3     2 #> 1969       2         3  54       61782   N4     2 #> 1970       2         3  54       61782   N5     4 #> 1971       2         3  54       61782   O1     2 #> 1972       2         3  54       61782   O2     3 #> 1973       2         3  54       61782   O3     4 #> 1974       2         3  54       61782   O4     2 #> 1975       2         3  54       61782   O5     4 #> 1976       1         2  20       61783   A1     2 #> 1977       1         2  20       61783   A2     5 #> 1978       1         2  20       61783   A3     5 #> 1979       1         2  20       61783   A4     5 #> 1980       1         2  20       61783   A5     5 #> 1981       1         2  20       61783   C1     4 #> 1982       1         2  20       61783   C2     2 #> 1983       1         2  20       61783   C3     3 #> 1984       1         2  20       61783   C4     5 #> 1985       1         2  20       61783   C5     4 #> 1986       1         2  20       61783   E1     4 #> 1987       1         2  20       61783   E2     4 #> 1988       1         2  20       61783   E3     4 #> 1989       1         2  20       61783   E4     5 #> 1990       1         2  20       61783   E5     4 #> 1991       1         2  20       61783   N1     2 #> 1992       1         2  20       61783   N2     3 #> 1993       1         2  20       61783   N3     4 #> 1994       1         2  20       61783   N4     5 #> 1995       1         2  20       61783   N5     2 #> 1996       1         2  20       61783   O1     4 #> 1997       1         2  20       61783   O2     4 #> 1998       1         2  20       61783   O3     3 #> 1999       1         2  20       61783   O4     5 #> 2000       1         2  20       61783   O5     1 #> 2001       1         4  28       61784   A1     1 #> 2002       1         4  28       61784   A2     5 #> 2003       1         4  28       61784   A3     6 #> 2004       1         4  28       61784   A4     5 #> 2005       1         4  28       61784   A5     3 #> 2006       1         4  28       61784   C1     6 #> 2007       1         4  28       61784   C2     5 #> 2008       1         4  28       61784   C3     4 #> 2009       1         4  28       61784   C4     4 #> 2010       1         4  28       61784   C5     3 #> 2011       1         4  28       61784   E1     4 #> 2012       1         4  28       61784   E2     5 #> 2013       1         4  28       61784   E3     4 #> 2014       1         4  28       61784   E4     4 #> 2015       1         4  28       61784   E5     5 #> 2016       1         4  28       61784   N1     3 #> 2017       1         4  28       61784   N2     3 #> 2018       1         4  28       61784   N3     3 #> 2019       1         4  28       61784   N4     4 #> 2020       1         4  28       61784   N5     3 #> 2021       1         4  28       61784   O1     6 #> 2022       1         4  28       61784   O2     2 #> 2023       1         4  28       61784   O3     5 #> 2024       1         4  28       61784   O4     6 #> 2025       1         4  28       61784   O5     2 #> 2026       2         4  38       61788   A1     1 #> 2027       2         4  38       61788   A2     6 #> 2028       2         4  38       61788   A3     3 #> 2029       2         4  38       61788   A4     3 #> 2030       2         4  38       61788   A5     1 #> 2031       2         4  38       61788   C1     6 #> 2032       2         4  38       61788   C2     6 #> 2033       2         4  38       61788   C3     5 #> 2034       2         4  38       61788   C4     1 #> 2035       2         4  38       61788   C5     6 #> 2036       2         4  38       61788   E1     4 #> 2037       2         4  38       61788   E2     5 #> 2038       2         4  38       61788   E3     3 #> 2039       2         4  38       61788   E4     5 #> 2040       2         4  38       61788   E5     1 #> 2041       2         4  38       61788   N1     4 #> 2042       2         4  38       61788   N2     4 #> 2043       2         4  38       61788   N3     6 #> 2044       2         4  38       61788   N4     6 #> 2045       2         4  38       61788   N5     5 #> 2046       2         4  38       61788   O1     6 #> 2047       2         4  38       61788   O2     1 #> 2048       2         4  38       61788   O3     6 #> 2049       2         4  38       61788   O4     6 #> 2050       2         4  38       61788   O5     1 #> 2051       1        NA  38       61789   A1     1 #> 2052       1        NA  38       61789   A2     4 #> 2053       1        NA  38       61789   A3     6 #> 2054       1        NA  38       61789   A4     2 #> 2055       1        NA  38       61789   A5     6 #> 2056       1        NA  38       61789   C1     3 #> 2057       1        NA  38       61789   C2     3 #> 2058       1        NA  38       61789   C3     4 #> 2059       1        NA  38       61789   C4     4 #> 2060       1        NA  38       61789   C5     5 #> 2061       1        NA  38       61789   E1     6 #> 2062       1        NA  38       61789   E2     4 #> 2063       1        NA  38       61789   E3     5 #> 2064       1        NA  38       61789   E4     6 #> 2065       1        NA  38       61789   E5     2 #> 2066       1        NA  38       61789   N1     3 #> 2067       1        NA  38       61789   N2     5 #> 2068       1        NA  38       61789   N3     4 #> 2069       1        NA  38       61789   N4     6 #> 2070       1        NA  38       61789   N5     4 #> 2071       1        NA  38       61789   O1     6 #> 2072       1        NA  38       61789   O2     2 #> 2073       1        NA  38       61789   O3     5 #> 2074       1        NA  38       61789   O4     6 #> 2075       1        NA  38       61789   O5     1 #> 2076       1         3  27       61793   A1     1 #> 2077       1         3  27       61793   A2     6 #> 2078       1         3  27       61793   A3     6 #> 2079       1         3  27       61793   A4     5 #> 2080       1         3  27       61793   A5     6 #> 2081       1         3  27       61793   C1     5 #> 2082       1         3  27       61793   C2     6 #> 2083       1         3  27       61793   C3     5 #> 2084       1         3  27       61793   C4     1 #> 2085       1         3  27       61793   C5     1 #> 2086       1         3  27       61793   E1     3 #> 2087       1         3  27       61793   E2     2 #> 2088       1         3  27       61793   E3     5 #> 2089       1         3  27       61793   E4     5 #> 2090       1         3  27       61793   E5     5 #> 2091       1         3  27       61793   N1     3 #> 2092       1         3  27       61793   N2     3 #> 2093       1         3  27       61793   N3     2 #> 2094       1         3  27       61793   N4     2 #> 2095       1         3  27       61793   N5     2 #> 2096       1         3  27       61793   O1     4 #> 2097       1         3  27       61793   O2     5 #> 2098       1         3  27       61793   O3     4 #> 2099       1         3  27       61793   O4     5 #> 2100       1         3  27       61793   O5     2 #> 2101       2         1  18       61794   A1     1 #> 2102       2         1  18       61794   A2     6 #> 2103       2         1  18       61794   A3     5 #> 2104       2         1  18       61794   A4     2 #> 2105       2         1  18       61794   A5     6 #> 2106       2         1  18       61794   C1     4 #> 2107       2         1  18       61794   C2     5 #> 2108       2         1  18       61794   C3     5 #> 2109       2         1  18       61794   C4     2 #> 2110       2         1  18       61794   C5     3 #> 2111       2         1  18       61794   E1     3 #> 2112       2         1  18       61794   E2     4 #> 2113       2         1  18       61794   E3     4 #> 2114       2         1  18       61794   E4     5 #> 2115       2         1  18       61794   E5     3 #> 2116       2         1  18       61794   N1     2 #> 2117       2         1  18       61794   N2     4 #> 2118       2         1  18       61794   N3     5 #> 2119       2         1  18       61794   N4     6 #> 2120       2         1  18       61794   N5     6 #> 2121       2         1  18       61794   O1     6 #> 2122       2         1  18       61794   O2     5 #> 2123       2         1  18       61794   O3     4 #> 2124       2         1  18       61794   O4     6 #> 2125       2         1  18       61794   O5     1 #> 2126       1         3  29       61797   A1     4 #> 2127       1         3  29       61797   A2     4 #> 2128       1         3  29       61797   A3     4 #> 2129       1         3  29       61797   A4     6 #> 2130       1         3  29       61797   A5     5 #> 2131       1         3  29       61797   C1     2 #> 2132       1         3  29       61797   C2     5 #> 2133       1         3  29       61797   C3     4 #> 2134       1         3  29       61797   C4     4 #> 2135       1         3  29       61797   C5     3 #> 2136       1         3  29       61797   E1     5 #> 2137       1         3  29       61797   E2     3 #> 2138       1         3  29       61797   E3     3 #> 2139       1         3  29       61797   E4     3 #> 2140       1         3  29       61797   E5     5 #> 2141       1         3  29       61797   N1     2 #> 2142       1         3  29       61797   N2     2 #> 2143       1         3  29       61797   N3     4 #> 2144       1         3  29       61797   N4     2 #> 2145       1         3  29       61797   N5     4 #> 2146       1         3  29       61797   O1     3 #> 2147       1         3  29       61797   O2     4 #> 2148       1         3  29       61797   O3     3 #> 2149       1         3  29       61797   O4     4 #> 2150       1         3  29       61797   O5     4 #> 2151       2         4  50       61798   A1     1 #> 2152       2         4  50       61798   A2     5 #> 2153       2         4  50       61798   A3     5 #> 2154       2         4  50       61798   A4     5 #> 2155       2         4  50       61798   A5     5 #> 2156       2         4  50       61798   C1     4 #> 2157       2         4  50       61798   C2     4 #> 2158       2         4  50       61798   C3     5 #> 2159       2         4  50       61798   C4     2 #> 2160       2         4  50       61798   C5     2 #> 2161       2         4  50       61798   E1     5 #> 2162       2         4  50       61798   E2     2 #> 2163       2         4  50       61798   E3     2 #> 2164       2         4  50       61798   E4     5 #> 2165       2         4  50       61798   E5     5 #> 2166       2         4  50       61798   N1     3 #> 2167       2         4  50       61798   N2     3 #> 2168       2         4  50       61798   N3     2 #> 2169       2         4  50       61798   N4     2 #> 2170       2         4  50       61798   N5     2 #> 2171       2         4  50       61798   O1     4 #> 2172       2         4  50       61798   O2     5 #> 2173       2         4  50       61798   O3     5 #> 2174       2         4  50       61798   O4     4 #> 2175       2         4  50       61798   O5     4 #> 2176       2         3  50       61801   A1     1 #> 2177       2         3  50       61801   A2     6 #> 2178       2         3  50       61801   A3     5 #> 2179       2         3  50       61801   A4     4 #> 2180       2         3  50       61801   A5     6 #> 2181       2         3  50       61801   C1     4 #> 2182       2         3  50       61801   C2     5 #> 2183       2         3  50       61801   C3     4 #> 2184       2         3  50       61801   C4     4 #> 2185       2         3  50       61801   C5     5 #> 2186       2         3  50       61801   E1     2 #> 2187       2         3  50       61801   E2     2 #> 2188       2         3  50       61801   E3     4 #> 2189       2         3  50       61801   E4     5 #> 2190       2         3  50       61801   E5     5 #> 2191       2         3  50       61801   N1     1 #> 2192       2         3  50       61801   N2     3 #> 2193       2         3  50       61801   N3     2 #> 2194       2         3  50       61801   N4     2 #> 2195       2         3  50       61801   N5     2 #> 2196       2         3  50       61801   O1     5 #> 2197       2         3  50       61801   O2     1 #> 2198       2         3  50       61801   O3     4 #> 2199       2         3  50       61801   O4     5 #> 2200       2         3  50       61801   O5     2 #> 2201       2         3  20       61808   A1     1 #> 2202       2         3  20       61808   A2     6 #> 2203       2         3  20       61808   A3     4 #> 2204       2         3  20       61808   A4     6 #> 2205       2         3  20       61808   A5     4 #> 2206       2         3  20       61808   C1     4 #> 2207       2         3  20       61808   C2     4 #> 2208       2         3  20       61808   C3     5 #> 2209       2         3  20       61808   C4     2 #> 2210       2         3  20       61808   C5     4 #> 2211       2         3  20       61808   E1     1 #> 2212       2         3  20       61808   E2     2 #> 2213       2         3  20       61808   E3     4 #> 2214       2         3  20       61808   E4     5 #> 2215       2         3  20       61808   E5     5 #> 2216       2         3  20       61808   N1     2 #> 2217       2         3  20       61808   N2     3 #> 2218       2         3  20       61808   N3     2 #> 2219       2         3  20       61808   N4     2 #> 2220       2         3  20       61808   N5     2 #> 2221       2         3  20       61808   O1     4 #> 2222       2         3  20       61808   O2     2 #> 2223       2         3  20       61808   O3     3 #> 2224       2         3  20       61808   O4     4 #> 2225       2         3  20       61808   O5     2 #> 2226       2         3  19       61812   A1     3 #> 2227       2         3  19       61812   A2     4 #> 2228       2         3  19       61812   A3     5 #> 2229       2         3  19       61812   A4     5 #> 2230       2         3  19       61812   A5     4 #> 2231       2         3  19       61812   C1     3 #> 2232       2         3  19       61812   C2     3 #> 2233       2         3  19       61812   C3    NA #> 2234       2         3  19       61812   C4     3 #> 2235       2         3  19       61812   C5     3 #> 2236       2         3  19       61812   E1     4 #> 2237       2         3  19       61812   E2     4 #> 2238       2         3  19       61812   E3     4 #> 2239       2         3  19       61812   E4     3 #> 2240       2         3  19       61812   E5     4 #> 2241       2         3  19       61812   N1     3 #> 2242       2         3  19       61812   N2     3 #> 2243       2         3  19       61812   N3     4 #> 2244       2         3  19       61812   N4     4 #> 2245       2         3  19       61812   N5     3 #> 2246       2         3  19       61812   O1     4 #> 2247       2         3  19       61812   O2     2 #> 2248       2         3  19       61812   O3     4 #> 2249       2         3  19       61812   O4     4 #> 2250       2         3  19       61812   O5     3 #> 2251       2         4  56       61813   A1     1 #> 2252       2         4  56       61813   A2     5 #> 2253       2         4  56       61813   A3     5 #> 2254       2         4  56       61813   A4     5 #> 2255       2         4  56       61813   A5     5 #> 2256       2         4  56       61813   C1     5 #> 2257       2         4  56       61813   C2     2 #> 2258       2         4  56       61813   C3     4 #> 2259       2         4  56       61813   C4     1 #> 2260       2         4  56       61813   C5     2 #> 2261       2         4  56       61813   E1     4 #> 2262       2         4  56       61813   E2     1 #> 2263       2         4  56       61813   E3     5 #> 2264       2         4  56       61813   E4     5 #> 2265       2         4  56       61813   E5     5 #> 2266       2         4  56       61813   N1     1 #> 2267       2         4  56       61813   N2     1 #> 2268       2         4  56       61813   N3     1 #> 2269       2         4  56       61813   N4     4 #> 2270       2         4  56       61813   N5     1 #> 2271       2         4  56       61813   O1     6 #> 2272       2         4  56       61813   O2     1 #> 2273       2         4  56       61813   O3     5 #> 2274       2         4  56       61813   O4     5 #> 2275       2         4  56       61813   O5     2 #> 2276       2         4  29       61816   A1     1 #> 2277       2         4  29       61816   A2     5 #> 2278       2         4  29       61816   A3     6 #> 2279       2         4  29       61816   A4     2 #> 2280       2         4  29       61816   A5     5 #> 2281       2         4  29       61816   C1     5 #> 2282       2         4  29       61816   C2     4 #> 2283       2         4  29       61816   C3     5 #> 2284       2         4  29       61816   C4     3 #> 2285       2         4  29       61816   C5     4 #> 2286       2         4  29       61816   E1     3 #> 2287       2         4  29       61816   E2     5 #> 2288       2         4  29       61816   E3     4 #> 2289       2         4  29       61816   E4     4 #> 2290       2         4  29       61816   E5     4 #> 2291       2         4  29       61816   N1     3 #> 2292       2         4  29       61816   N2     3 #> 2293       2         4  29       61816   N3     4 #> 2294       2         4  29       61816   N4     3 #> 2295       2         4  29       61816   N5     4 #> 2296       2         4  29       61816   O1     6 #> 2297       2         4  29       61816   O2     1 #> 2298       2         4  29       61816   O3     5 #> 2299       2         4  29       61816   O4     6 #> 2300       2         4  29       61816   O5     2 #> 2301       2         5  29       61818   A1     2 #> 2302       2         5  29       61818   A2     4 #> 2303       2         5  29       61818   A3     5 #> 2304       2         5  29       61818   A4     4 #> 2305       2         5  29       61818   A5     5 #> 2306       2         5  29       61818   C1     2 #> 2307       2         5  29       61818   C2     2 #> 2308       2         5  29       61818   C3     3 #> 2309       2         5  29       61818   C4     4 #> 2310       2         5  29       61818   C5     4 #> 2311       2         5  29       61818   E1     1 #> 2312       2         5  29       61818   E2     5 #> 2313       2         5  29       61818   E3     2 #> 2314       2         5  29       61818   E4     6 #> 2315       2         5  29       61818   E5     2 #> 2316       2         5  29       61818   N1     2 #> 2317       2         5  29       61818   N2     3 #> 2318       2         5  29       61818   N3     6 #> 2319       2         5  29       61818   N4     4 #> 2320       2         5  29       61818   N5     6 #> 2321       2         5  29       61818   O1     3 #> 2322       2         5  29       61818   O2     2 #> 2323       2         5  29       61818   O3     2 #> 2324       2         5  29       61818   O4     2 #> 2325       2         5  29       61818   O5     5 #> 2326       2         5  48       61819   A1     1 #> 2327       2         5  48       61819   A2     6 #> 2328       2         5  48       61819   A3     6 #> 2329       2         5  48       61819   A4     3 #> 2330       2         5  48       61819   A5     6 #> 2331       2         5  48       61819   C1     6 #> 2332       2         5  48       61819   C2     6 #> 2333       2         5  48       61819   C3     5 #> 2334       2         5  48       61819   C4     1 #> 2335       2         5  48       61819   C5     1 #> 2336       2         5  48       61819   E1     1 #> 2337       2         5  48       61819   E2     3 #> 2338       2         5  48       61819   E3     5 #> 2339       2         5  48       61819   E4     6 #> 2340       2         5  48       61819   E5     6 #> 2341       2         5  48       61819   N1     2 #> 2342       2         5  48       61819   N2     5 #> 2343       2         5  48       61819   N3     4 #> 2344       2         5  48       61819   N4     6 #> 2345       2         5  48       61819   N5     1 #> 2346       2         5  48       61819   O1     6 #> 2347       2         5  48       61819   O2     2 #> 2348       2         5  48       61819   O3     5 #> 2349       2         5  48       61819   O4     6 #> 2350       2         5  48       61819   O5     1 #> 2351       2         1  30       61821   A1     2 #> 2352       2         1  30       61821   A2     6 #> 2353       2         1  30       61821   A3     4 #> 2354       2         1  30       61821   A4     6 #> 2355       2         1  30       61821   A5     4 #> 2356       2         1  30       61821   C1     4 #> 2357       2         1  30       61821   C2     5 #> 2358       2         1  30       61821   C3     6 #> 2359       2         1  30       61821   C4     3 #> 2360       2         1  30       61821   C5     1 #> 2361       2         1  30       61821   E1     2 #> 2362       2         1  30       61821   E2     4 #> 2363       2         1  30       61821   E3     2 #> 2364       2         1  30       61821   E4     5 #> 2365       2         1  30       61821   E5     4 #> 2366       2         1  30       61821   N1     4 #> 2367       2         1  30       61821   N2     5 #> 2368       2         1  30       61821   N3     2 #> 2369       2         1  30       61821   N4     3 #> 2370       2         1  30       61821   N5     6 #> 2371       2         1  30       61821   O1     3 #> 2372       2         1  30       61821   O2     4 #> 2373       2         1  30       61821   O3     3 #> 2374       2         1  30       61821   O4     5 #> 2375       2         1  30       61821   O5     6 #> 2376       2         2  21       61822   A1     2 #> 2377       2         2  21       61822   A2     3 #> 2378       2         2  21       61822   A3     3 #> 2379       2         2  21       61822   A4     4 #> 2380       2         2  21       61822   A5     3 #> 2381       2         2  21       61822   C1     5 #> 2382       2         2  21       61822   C2     6 #> 2383       2         2  21       61822   C3     4 #> 2384       2         2  21       61822   C4     1 #> 2385       2         2  21       61822   C5     4 #> 2386       2         2  21       61822   E1     5 #> 2387       2         2  21       61822   E2     6 #> 2388       2         2  21       61822   E3     2 #> 2389       2         2  21       61822   E4     2 #> 2390       2         2  21       61822   E5     4 #> 2391       2         2  21       61822   N1     3 #> 2392       2         2  21       61822   N2     4 #> 2393       2         2  21       61822   N3     6 #> 2394       2         2  21       61822   N4     6 #> 2395       2         2  21       61822   N5     6 #> 2396       2         2  21       61822   O1     5 #> 2397       2         2  21       61822   O2     2 #> 2398       2         2  21       61822   O3     3 #> 2399       2         2  21       61822   O4     5 #> 2400       2         2  21       61822   O5     4 #> 2401       1         3  19       61825   A1     3 #> 2402       1         3  19       61825   A2     1 #> 2403       1         3  19       61825   A3     2 #> 2404       1         3  19       61825   A4     2 #> 2405       1         3  19       61825   A5     2 #> 2406       1         3  19       61825   C1     2 #> 2407       1         3  19       61825   C2     1 #> 2408       1         3  19       61825   C3     2 #> 2409       1         3  19       61825   C4     6 #> 2410       1         3  19       61825   C5     6 #> 2411       1         3  19       61825   E1     6 #> 2412       1         3  19       61825   E2     6 #> 2413       1         3  19       61825   E3     1 #> 2414       1         3  19       61825   E4     1 #> 2415       1         3  19       61825   E5     1 #> 2416       1         3  19       61825   N1     3 #> 2417       1         3  19       61825   N2     5 #> 2418       1         3  19       61825   N3     4 #> 2419       1         3  19       61825   N4     4 #> 2420       1         3  19       61825   N5     4 #> 2421       1         3  19       61825   O1     5 #> 2422       1         3  19       61825   O2     1 #> 2423       1         3  19       61825   O3     3 #> 2424       1         3  19       61825   O4     6 #> 2425       1         3  19       61825   O5     1 #> 2426       1         5  42       61826   A1     1 #> 2427       1         5  42       61826   A2     4 #> 2428       1         5  42       61826   A3     5 #> 2429       1         5  42       61826   A4     5 #> 2430       1         5  42       61826   A5     6 #> 2431       1         5  42       61826   C1     6 #> 2432       1         5  42       61826   C2     4 #> 2433       1         5  42       61826   C3     4 #> 2434       1         5  42       61826   C4     1 #> 2435       1         5  42       61826   C5     1 #> 2436       1         5  42       61826   E1     5 #> 2437       1         5  42       61826   E2     1 #> 2438       1         5  42       61826   E3     2 #> 2439       1         5  42       61826   E4     3 #> 2440       1         5  42       61826   E5     5 #> 2441       1         5  42       61826   N1     1 #> 2442       1         5  42       61826   N2     2 #> 2443       1         5  42       61826   N3     1 #> 2444       1         5  42       61826   N4     1 #> 2445       1         5  42       61826   N5     1 #> 2446       1         5  42       61826   O1     5 #> 2447       1         5  42       61826   O2     1 #> 2448       1         5  42       61826   O3     5 #> 2449       1         5  42       61826   O4     6 #> 2450       1         5  42       61826   O5     1 #> 2451       2         3  18       61829   A1     4 #> 2452       2         3  18       61829   A2     3 #> 2453       2         3  18       61829   A3     2 #> 2454       2         3  18       61829   A4     6 #> 2455       2         3  18       61829   A5     5 #> 2456       2         3  18       61829   C1     4 #> 2457       2         3  18       61829   C2     5 #> 2458       2         3  18       61829   C3     6 #> 2459       2         3  18       61829   C4     2 #> 2460       2         3  18       61829   C5     6 #> 2461       2         3  18       61829   E1     5 #> 2462       2         3  18       61829   E2     5 #> 2463       2         3  18       61829   E3     1 #> 2464       2         3  18       61829   E4     5 #> 2465       2         3  18       61829   E5     5 #> 2466       2         3  18       61829   N1     4 #> 2467       2         3  18       61829   N2     5 #> 2468       2         3  18       61829   N3     6 #> 2469       2         3  18       61829   N4     6 #> 2470       2         3  18       61829   N5     6 #> 2471       2         3  18       61829   O1     5 #> 2472       2         3  18       61829   O2     1 #> 2473       2         3  18       61829   O3     2 #> 2474       2         3  18       61829   O4     6 #> 2475       2         3  18       61829   O5     4 #> 2476       2         4  50       61831   A1     2 #> 2477       2         4  50       61831   A2     6 #> 2478       2         4  50       61831   A3     2 #> 2479       2         4  50       61831   A4     3 #> 2480       2         4  50       61831   A5     4 #> 2481       2         4  50       61831   C1     4 #> 2482       2         4  50       61831   C2     2 #> 2483       2         4  50       61831   C3     5 #> 2484       2         4  50       61831   C4     3 #> 2485       2         4  50       61831   C5     5 #> 2486       2         4  50       61831   E1     5 #> 2487       2         4  50       61831   E2     3 #> 2488       2         4  50       61831   E3     2 #> 2489       2         4  50       61831   E4     3 #> 2490       2         4  50       61831   E5     5 #> 2491       2         4  50       61831   N1     1 #> 2492       2         4  50       61831   N2     1 #> 2493       2         4  50       61831   N3     1 #> 2494       2         4  50       61831   N4     2 #> 2495       2         4  50       61831   N5     2 #> 2496       2         4  50       61831   O1     2 #> 2497       2         4  50       61831   O2     1 #> 2498       2         4  50       61831   O3     5 #> 2499       2         4  50       61831   O4     3 #> 2500       2         4  50       61831   O5     3 #> 2501       2         3  24       61834   A1     2 #> 2502       2         3  24       61834   A2     5 #> 2503       2         3  24       61834   A3     5 #> 2504       2         3  24       61834   A4     3 #> 2505       2         3  24       61834   A5     5 #> 2506       2         3  24       61834   C1     4 #> 2507       2         3  24       61834   C2     3 #> 2508       2         3  24       61834   C3     4 #> 2509       2         3  24       61834   C4     3 #> 2510       2         3  24       61834   C5     5 #> 2511       2         3  24       61834   E1     3 #> 2512       2         3  24       61834   E2     5 #> 2513       2         3  24       61834   E3     4 #> 2514       2         3  24       61834   E4     5 #> 2515       2         3  24       61834   E5     5 #> 2516       2         3  24       61834   N1    NA #> 2517       2         3  24       61834   N2     5 #> 2518       2         3  24       61834   N3     6 #> 2519       2         3  24       61834   N4     4 #> 2520       2         3  24       61834   N5     4 #> 2521       2         3  24       61834   O1     6 #> 2522       2         3  24       61834   O2     2 #> 2523       2         3  24       61834   O3     5 #> 2524       2         3  24       61834   O4     6 #> 2525       2         3  24       61834   O5     1 #> 2526       1         5  25       61835   A1     3 #> 2527       1         5  25       61835   A2     4 #> 2528       1         5  25       61835   A3     4 #> 2529       1         5  25       61835   A4     2 #> 2530       1         5  25       61835   A5     4 #> 2531       1         5  25       61835   C1     4 #> 2532       1         5  25       61835   C2     3 #> 2533       1         5  25       61835   C3     4 #> 2534       1         5  25       61835   C4     4 #> 2535       1         5  25       61835   C5     2 #> 2536       1         5  25       61835   E1     2 #> 2537       1         5  25       61835   E2     3 #> 2538       1         5  25       61835   E3     4 #> 2539       1         5  25       61835   E4     3 #> 2540       1         5  25       61835   E5     5 #> 2541       1         5  25       61835   N1     4 #> 2542       1         5  25       61835   N2     4 #> 2543       1         5  25       61835   N3     2 #> 2544       1         5  25       61835   N4     2 #> 2545       1         5  25       61835   N5     4 #> 2546       1         5  25       61835   O1     4 #> 2547       1         5  25       61835   O2     2 #> 2548       1         5  25       61835   O3     5 #> 2549       1         5  25       61835   O4     4 #> 2550       1         5  25       61835   O5     2 #> 2551       1         5  47       61838   A1     2 #> 2552       1         5  47       61838   A2     5 #> 2553       1         5  47       61838   A3     5 #> 2554       1         5  47       61838   A4     5 #> 2555       1         5  47       61838   A5     2 #> 2556       1         5  47       61838   C1     6 #> 2557       1         5  47       61838   C2     6 #> 2558       1         5  47       61838   C3     4 #> 2559       1         5  47       61838   C4     1 #> 2560       1         5  47       61838   C5     5 #> 2561       1         5  47       61838   E1     3 #> 2562       1         5  47       61838   E2     2 #> 2563       1         5  47       61838   E3     2 #> 2564       1         5  47       61838   E4     2 #> 2565       1         5  47       61838   E5     5 #> 2566       1         5  47       61838   N1     4 #> 2567       1         5  47       61838   N2     5 #> 2568       1         5  47       61838   N3     5 #> 2569       1         5  47       61838   N4     5 #> 2570       1         5  47       61838   N5     4 #> 2571       1         5  47       61838   O1     6 #> 2572       1         5  47       61838   O2     2 #> 2573       1         5  47       61838   O3     5 #> 2574       1         5  47       61838   O4     6 #> 2575       1         5  47       61838   O5     1 #> 2576       2         1  18       61839   A1     1 #> 2577       2         1  18       61839   A2     4 #> 2578       2         1  18       61839   A3     5 #> 2579       2         1  18       61839   A4     2 #> 2580       2         1  18       61839   A5     6 #> 2581       2         1  18       61839   C1     2 #> 2582       2         1  18       61839   C2     1 #> 2583       2         1  18       61839   C3     2 #> 2584       2         1  18       61839   C4     4 #> 2585       2         1  18       61839   C5     6 #> 2586       2         1  18       61839   E1     6 #> 2587       2         1  18       61839   E2     5 #> 2588       2         1  18       61839   E3     3 #> 2589       2         1  18       61839   E4     3 #> 2590       2         1  18       61839   E5     3 #> 2591       2         1  18       61839   N1     2 #> 2592       2         1  18       61839   N2     1 #> 2593       2         1  18       61839   N3     3 #> 2594       2         1  18       61839   N4     3 #> 2595       2         1  18       61839   N5     1 #> 2596       2         1  18       61839   O1     4 #> 2597       2         1  18       61839   O2     3 #> 2598       2         1  18       61839   O3     4 #> 2599       2         1  18       61839   O4     5 #> 2600       2         1  18       61839   O5     6 #> 2601       2         4  37       61840   A1     1 #> 2602       2         4  37       61840   A2     6 #> 2603       2         4  37       61840   A3     4 #> 2604       2         4  37       61840   A4     1 #> 2605       2         4  37       61840   A5     1 #> 2606       2         4  37       61840   C1     6 #> 2607       2         4  37       61840   C2     4 #> 2608       2         4  37       61840   C3     5 #> 2609       2         4  37       61840   C4     2 #> 2610       2         4  37       61840   C5     1 #> 2611       2         4  37       61840   E1     6 #> 2612       2         4  37       61840   E2     6 #> 2613       2         4  37       61840   E3     3 #> 2614       2         4  37       61840   E4     1 #> 2615       2         4  37       61840   E5     1 #> 2616       2         4  37       61840   N1     4 #> 2617       2         4  37       61840   N2     3 #> 2618       2         4  37       61840   N3     6 #> 2619       2         4  37       61840   N4     6 #> 2620       2         4  37       61840   N5     5 #> 2621       2         4  37       61840   O1     2 #> 2622       2         4  37       61840   O2     3 #> 2623       2         4  37       61840   O3     3 #> 2624       2         4  37       61840   O4     6 #> 2625       2         4  37       61840   O5     3 #> 2626       2         4  33       61841   A1     1 #> 2627       2         4  33       61841   A2     6 #> 2628       2         4  33       61841   A3     5 #> 2629       2         4  33       61841   A4     6 #> 2630       2         4  33       61841   A5     6 #> 2631       2         4  33       61841   C1     5 #> 2632       2         4  33       61841   C2     3 #> 2633       2         4  33       61841   C3     4 #> 2634       2         4  33       61841   C4     6 #> 2635       2         4  33       61841   C5     5 #> 2636       2         4  33       61841   E1     2 #> 2637       2         4  33       61841   E2     4 #> 2638       2         4  33       61841   E3     3 #> 2639       2         4  33       61841   E4     4 #> 2640       2         4  33       61841   E5     5 #> 2641       2         4  33       61841   N1     4 #> 2642       2         4  33       61841   N2     5 #> 2643       2         4  33       61841   N3     4 #> 2644       2         4  33       61841   N4     3 #> 2645       2         4  33       61841   N5     4 #> 2646       2         4  33       61841   O1     5 #> 2647       2         4  33       61841   O2     4 #> 2648       2         4  33       61841   O3     4 #> 2649       2         4  33       61841   O4     6 #> 2650       2         4  33       61841   O5     2 #> 2651       2         5  40       61847   A1     1 #> 2652       2         5  40       61847   A2     5 #> 2653       2         5  40       61847   A3     5 #> 2654       2         5  40       61847   A4     5 #> 2655       2         5  40       61847   A5     2 #> 2656       2         5  40       61847   C1     6 #> 2657       2         5  40       61847   C2    NA #> 2658       2         5  40       61847   C3     6 #> 2659       2         5  40       61847   C4     1 #> 2660       2         5  40       61847   C5     1 #> 2661       2         5  40       61847   E1     3 #> 2662       2         5  40       61847   E2     2 #> 2663       2         5  40       61847   E3     1 #> 2664       2         5  40       61847   E4     2 #> 2665       2         5  40       61847   E5     6 #> 2666       2         5  40       61847   N1     5 #> 2667       2         5  40       61847   N2    NA #> 2668       2         5  40       61847   N3     5 #> 2669       2         5  40       61847   N4     4 #> 2670       2         5  40       61847   N5     3 #> 2671       2         5  40       61847   O1     4 #> 2672       2         5  40       61847   O2     1 #> 2673       2         5  40       61847   O3     6 #> 2674       2         5  40       61847   O4     6 #> 2675       2         5  40       61847   O5     1 #> 2676       1        NA  18       61848   A1     6 #> 2677       1        NA  18       61848   A2     1 #> 2678       1        NA  18       61848   A3     1 #> 2679       1        NA  18       61848   A4     1 #> 2680       1        NA  18       61848   A5     2 #> 2681       1        NA  18       61848   C1     3 #> 2682       1        NA  18       61848   C2     6 #> 2683       1        NA  18       61848   C3     3 #> 2684       1        NA  18       61848   C4     3 #> 2685       1        NA  18       61848   C5     4 #> 2686       1        NA  18       61848   E1     1 #> 2687       1        NA  18       61848   E2     4 #> 2688       1        NA  18       61848   E3     5 #> 2689       1        NA  18       61848   E4     2 #> 2690       1        NA  18       61848   E5     6 #> 2691       1        NA  18       61848   N1     6 #> 2692       1        NA  18       61848   N2     6 #> 2693       1        NA  18       61848   N3     3 #> 2694       1        NA  18       61848   N4     2 #> 2695       1        NA  18       61848   N5     5 #> 2696       1        NA  18       61848   O1     6 #> 2697       1        NA  18       61848   O2     1 #> 2698       1        NA  18       61848   O3     6 #> 2699       1        NA  18       61848   O4     5 #> 2700       1        NA  18       61848   O5     2 #> 2701       2         3  21       61851   A1     2 #> 2702       2         3  21       61851   A2     6 #> 2703       2         3  21       61851   A3     5 #> 2704       2         3  21       61851   A4     6 #> 2705       2         3  21       61851   A5     5 #> 2706       2         3  21       61851   C1     5 #> 2707       2         3  21       61851   C2     4 #> 2708       2         3  21       61851   C3     4 #> 2709       2         3  21       61851   C4     4 #> 2710       2         3  21       61851   C5     6 #> 2711       2         3  21       61851   E1     1 #> 2712       2         3  21       61851   E2     3 #> 2713       2         3  21       61851   E3     4 #> 2714       2         3  21       61851   E4     6 #> 2715       2         3  21       61851   E5     4 #> 2716       2         3  21       61851   N1     5 #> 2717       2         3  21       61851   N2     6 #> 2718       2         3  21       61851   N3     2 #> 2719       2         3  21       61851   N4     4 #> 2720       2         3  21       61851   N5     6 #> 2721       2         3  21       61851   O1     6 #> 2722       2         3  21       61851   O2     5 #> 2723       2         3  21       61851   O3     6 #> 2724       2         3  21       61851   O4     6 #> 2725       2         3  21       61851   O5     2 #> 2726       2         3  27       61852   A1     3 #> 2727       2         3  27       61852   A2     6 #> 2728       2         3  27       61852   A3     5 #> 2729       2         3  27       61852   A4     6 #> 2730       2         3  27       61852   A5     4 #> 2731       2         3  27       61852   C1     3 #> 2732       2         3  27       61852   C2     5 #> 2733       2         3  27       61852   C3     2 #> 2734       2         3  27       61852   C4     3 #> 2735       2         3  27       61852   C5     4 #> 2736       2         3  27       61852   E1     2 #> 2737       2         3  27       61852   E2     5 #> 2738       2         3  27       61852   E3     6 #> 2739       2         3  27       61852   E4     6 #> 2740       2         3  27       61852   E5     5 #> 2741       2         3  27       61852   N1     6 #> 2742       2         3  27       61852   N2     6 #> 2743       2         3  27       61852   N3     6 #> 2744       2         3  27       61852   N4     4 #> 2745       2         3  27       61852   N5     6 #> 2746       2         3  27       61852   O1     5 #> 2747       2         3  27       61852   O2     4 #> 2748       2         3  27       61852   O3     4 #> 2749       2         3  27       61852   O4     6 #> 2750       2         3  27       61852   O5     4 #> 2751       2         3  19       61854   A1     1 #> 2752       2         3  19       61854   A2     6 #> 2753       2         3  19       61854   A3     6 #> 2754       2         3  19       61854   A4     6 #> 2755       2         3  19       61854   A5     6 #> 2756       2         3  19       61854   C1     4 #> 2757       2         3  19       61854   C2     4 #> 2758       2         3  19       61854   C3     5 #> 2759       2         3  19       61854   C4     3 #> 2760       2         3  19       61854   C5     4 #> 2761       2         3  19       61854   E1     3 #> 2762       2         3  19       61854   E2     2 #> 2763       2         3  19       61854   E3     4 #> 2764       2         3  19       61854   E4     5 #> 2765       2         3  19       61854   E5     4 #> 2766       2         3  19       61854   N1     2 #> 2767       2         3  19       61854   N2     3 #> 2768       2         3  19       61854   N3     2 #> 2769       2         3  19       61854   N4     3 #> 2770       2         3  19       61854   N5     4 #> 2771       2         3  19       61854   O1     4 #> 2772       2         3  19       61854   O2     1 #> 2773       2         3  19       61854   O3     5 #> 2774       2         3  19       61854   O4     6 #> 2775       2         3  19       61854   O5     1 #> 2776       2         4  36       61856   A1    NA #> 2777       2         4  36       61856   A2     4 #> 2778       2         4  36       61856   A3     5 #> 2779       2         4  36       61856   A4     6 #> 2780       2         4  36       61856   A5     4 #> 2781       2         4  36       61856   C1     5 #> 2782       2         4  36       61856   C2     5 #> 2783       2         4  36       61856   C3     5 #> 2784       2         4  36       61856   C4     1 #> 2785       2         4  36       61856   C5     5 #> 2786       2         4  36       61856   E1     3 #> 2787       2         4  36       61856   E2     4 #> 2788       2         4  36       61856   E3     3 #> 2789       2         4  36       61856   E4     2 #> 2790       2         4  36       61856   E5     3 #> 2791       2         4  36       61856   N1     1 #> 2792       2         4  36       61856   N2     2 #> 2793       2         4  36       61856   N3     1 #> 2794       2         4  36       61856   N4     4 #> 2795       2         4  36       61856   N5     2 #> 2796       2         4  36       61856   O1     3 #> 2797       2         4  36       61856   O2     6 #> 2798       2         4  36       61856   O3     1 #> 2799       2         4  36       61856   O4     3 #> 2800       2         4  36       61856   O5     3 #> 2801       2         3  19       61857   A1     1 #> 2802       2         3  19       61857   A2     6 #> 2803       2         3  19       61857   A3     6 #> 2804       2         3  19       61857   A4     5 #> 2805       2         3  19       61857   A5     4 #> 2806       2         3  19       61857   C1     5 #> 2807       2         3  19       61857   C2     3 #> 2808       2         3  19       61857   C3     5 #> 2809       2         3  19       61857   C4     1 #> 2810       2         3  19       61857   C5     2 #> 2811       2         3  19       61857   E1     2 #> 2812       2         3  19       61857   E2     4 #> 2813       2         3  19       61857   E3     4 #> 2814       2         3  19       61857   E4     5 #> 2815       2         3  19       61857   E5     4 #> 2816       2         3  19       61857   N1     1 #> 2817       2         3  19       61857   N2     1 #> 2818       2         3  19       61857   N3     1 #> 2819       2         3  19       61857   N4     2 #> 2820       2         3  19       61857   N5     2 #> 2821       2         3  19       61857   O1     4 #> 2822       2         3  19       61857   O2     2 #> 2823       2         3  19       61857   O3     4 #> 2824       2         3  19       61857   O4     6 #> 2825       2         3  19       61857   O5     2 #> 2826       2         5  29       61861   A1     1 #> 2827       2         5  29       61861   A2     6 #> 2828       2         5  29       61861   A3     6 #> 2829       2         5  29       61861   A4     6 #> 2830       2         5  29       61861   A5     6 #> 2831       2         5  29       61861   C1     6 #> 2832       2         5  29       61861   C2     6 #> 2833       2         5  29       61861   C3     5 #> 2834       2         5  29       61861   C4     1 #> 2835       2         5  29       61861   C5     6 #> 2836       2         5  29       61861   E1     5 #> 2837       2         5  29       61861   E2     5 #> 2838       2         5  29       61861   E3     2 #> 2839       2         5  29       61861   E4     5 #> 2840       2         5  29       61861   E5     5 #> 2841       2         5  29       61861   N1     1 #> 2842       2         5  29       61861   N2     5 #> 2843       2         5  29       61861   N3     5 #> 2844       2         5  29       61861   N4     5 #> 2845       2         5  29       61861   N5     6 #> 2846       2         5  29       61861   O1     6 #> 2847       2         5  29       61861   O2     1 #> 2848       2         5  29       61861   O3     6 #> 2849       2         5  29       61861   O4     6 #> 2850       2         5  29       61861   O5     2 #> 2851       2         5  23       61862   A1     1 #> 2852       2         5  23       61862   A2     6 #> 2853       2         5  23       61862   A3     5 #> 2854       2         5  23       61862   A4     4 #> 2855       2         5  23       61862   A5     5 #> 2856       2         5  23       61862   C1     5 #> 2857       2         5  23       61862   C2     4 #> 2858       2         5  23       61862   C3     5 #> 2859       2         5  23       61862   C4     2 #> 2860       2         5  23       61862   C5     2 #> 2861       2         5  23       61862   E1     1 #> 2862       2         5  23       61862   E2     1 #> 2863       2         5  23       61862   E3     6 #> 2864       2         5  23       61862   E4     6 #> 2865       2         5  23       61862   E5     5 #> 2866       2         5  23       61862   N1     2 #> 2867       2         5  23       61862   N2     2 #> 2868       2         5  23       61862   N3     2 #> 2869       2         5  23       61862   N4     2 #> 2870       2         5  23       61862   N5     2 #> 2871       2         5  23       61862   O1     6 #> 2872       2         5  23       61862   O2     1 #> 2873       2         5  23       61862   O3     5 #> 2874       2         5  23       61862   O4     4 #> 2875       2         5  23       61862   O5     1 #> 2876       1         5  23       61865   A1     4 #> 2877       1         5  23       61865   A2     2 #> 2878       1         5  23       61865   A3     1 #> 2879       1         5  23       61865   A4     2 #> 2880       1         5  23       61865   A5     2 #> 2881       1         5  23       61865   C1     4 #> 2882       1         5  23       61865   C2     1 #> 2883       1         5  23       61865   C3     2 #> 2884       1         5  23       61865   C4     6 #> 2885       1         5  23       61865   C5     6 #> 2886       1         5  23       61865   E1     5 #> 2887       1         5  23       61865   E2     5 #> 2888       1         5  23       61865   E3     2 #> 2889       1         5  23       61865   E4     2 #> 2890       1         5  23       61865   E5     1 #> 2891       1         5  23       61865   N1     2 #> 2892       1         5  23       61865   N2     4 #> 2893       1         5  23       61865   N3     2 #> 2894       1         5  23       61865   N4     4 #> 2895       1         5  23       61865   N5     2 #> 2896       1         5  23       61865   O1     2 #> 2897       1         5  23       61865   O2     6 #> 2898       1         5  23       61865   O3     3 #> 2899       1         5  23       61865   O4     5 #> 2900       1         5  23       61865   O5     5 #> 2901       1         3  20       61868   A1     1 #> 2902       1         3  20       61868   A2     5 #> 2903       1         3  20       61868   A3     4 #> 2904       1         3  20       61868   A4     3 #> 2905       1         3  20       61868   A5     4 #> 2906       1         3  20       61868   C1     5 #> 2907       1         3  20       61868   C2     6 #> 2908       1         3  20       61868   C3     4 #> 2909       1         3  20       61868   C4     1 #> 2910       1         3  20       61868   C5     4 #> 2911       1         3  20       61868   E1     3 #> 2912       1         3  20       61868   E2     2 #> 2913       1         3  20       61868   E3     4 #> 2914       1         3  20       61868   E4     5 #> 2915       1         3  20       61868   E5     6 #> 2916       1         3  20       61868   N1     1 #> 2917       1         3  20       61868   N2     2 #> 2918       1         3  20       61868   N3     2 #> 2919       1         3  20       61868   N4     4 #> 2920       1         3  20       61868   N5     1 #> 2921       1         3  20       61868   O1     4 #> 2922       1         3  20       61868   O2     1 #> 2923       1         3  20       61868   O3     6 #> 2924       1         3  20       61868   O4     5 #> 2925       1         3  20       61868   O5     1 #> 2926       1         4  24       61873   A1     5 #> 2927       1         4  24       61873   A2     2 #> 2928       1         4  24       61873   A3     2 #> 2929       1         4  24       61873   A4     2 #> 2930       1         4  24       61873   A5     1 #> 2931       1         4  24       61873   C1     2 #> 2932       1         4  24       61873   C2     2 #> 2933       1         4  24       61873   C3     2 #> 2934       1         4  24       61873   C4     3 #> 2935       1         4  24       61873   C5     5 #> 2936       1         4  24       61873   E1     5 #> 2937       1         4  24       61873   E2     6 #> 2938       1         4  24       61873   E3     2 #> 2939       1         4  24       61873   E4     1 #> 2940       1         4  24       61873   E5     2 #> 2941       1         4  24       61873   N1     4 #> 2942       1         4  24       61873   N2     4 #> 2943       1         4  24       61873   N3     2 #> 2944       1         4  24       61873   N4     2 #> 2945       1         4  24       61873   N5     2 #> 2946       1         4  24       61873   O1     5 #> 2947       1         4  24       61873   O2     1 #> 2948       1         4  24       61873   O3     5 #> 2949       1         4  24       61873   O4     5 #> 2950       1         4  24       61873   O5     2 #> 2951       1         5  24       61874   A1     2 #> 2952       1         5  24       61874   A2     5 #> 2953       1         5  24       61874   A3     4 #> 2954       1         5  24       61874   A4     3 #> 2955       1         5  24       61874   A5     4 #> 2956       1         5  24       61874   C1     5 #> 2957       1         5  24       61874   C2     4 #> 2958       1         5  24       61874   C3     5 #> 2959       1         5  24       61874   C4     2 #> 2960       1         5  24       61874   C5     3 #> 2961       1         5  24       61874   E1     1 #> 2962       1         5  24       61874   E2     2 #> 2963       1         5  24       61874   E3     4 #> 2964       1         5  24       61874   E4     5 #> 2965       1         5  24       61874   E5     5 #> 2966       1         5  24       61874   N1     4 #> 2967       1         5  24       61874   N2     4 #> 2968       1         5  24       61874   N3     3 #> 2969       1         5  24       61874   N4     2 #> 2970       1         5  24       61874   N5     3 #> 2971       1         5  24       61874   O1     6 #> 2972       1         5  24       61874   O2     1 #> 2973       1         5  24       61874   O3     5 #> 2974       1         5  24       61874   O4     5 #> 2975       1         5  24       61874   O5     2 #> 2976       1         5  26       61880   A1     1 #> 2977       1         5  26       61880   A2     5 #> 2978       1         5  26       61880   A3     6 #> 2979       1         5  26       61880   A4     6 #> 2980       1         5  26       61880   A5     6 #> 2981       1         5  26       61880   C1     6 #> 2982       1         5  26       61880   C2     5 #> 2983       1         5  26       61880   C3     4 #> 2984       1         5  26       61880   C4     2 #> 2985       1         5  26       61880   C5     6 #> 2986       1         5  26       61880   E1     1 #> 2987       1         5  26       61880   E2     2 #> 2988       1         5  26       61880   E3     5 #> 2989       1         5  26       61880   E4     6 #> 2990       1         5  26       61880   E5     6 #> 2991       1         5  26       61880   N1     1 #> 2992       1         5  26       61880   N2     2 #> 2993       1         5  26       61880   N3     2 #> 2994       1         5  26       61880   N4     2 #> 2995       1         5  26       61880   N5     4 #> 2996       1         5  26       61880   O1     6 #> 2997       1         5  26       61880   O2     1 #> 2998       1         5  26       61880   O3     6 #> 2999       1         5  26       61880   O4     6 #> 3000       1         5  26       61880   O5     1 #> 3001       2         3  25       61886   A1     4 #> 3002       2         3  25       61886   A2     6 #> 3003       2         3  25       61886   A3     5 #> 3004       2         3  25       61886   A4     5 #> 3005       2         3  25       61886   A5     6 #> 3006       2         3  25       61886   C1     4 #> 3007       2         3  25       61886   C2     6 #> 3008       2         3  25       61886   C3     5 #> 3009       2         3  25       61886   C4     5 #> 3010       2         3  25       61886   C5     2 #> 3011       2         3  25       61886   E1     1 #> 3012       2         3  25       61886   E2     3 #> 3013       2         3  25       61886   E3     6 #> 3014       2         3  25       61886   E4     6 #> 3015       2         3  25       61886   E5     6 #> 3016       2         3  25       61886   N1     4 #> 3017       2         3  25       61886   N2     3 #> 3018       2         3  25       61886   N3     1 #> 3019       2         3  25       61886   N4     3 #> 3020       2         3  25       61886   N5     4 #> 3021       2         3  25       61886   O1     6 #> 3022       2         3  25       61886   O2     3 #> 3023       2         3  25       61886   O3     6 #> 3024       2         3  25       61886   O4     5 #> 3025       2         3  25       61886   O5     3 #> 3026       1         4  24       61888   A1     2 #> 3027       1         4  24       61888   A2     5 #> 3028       1         4  24       61888   A3     4 #> 3029       1         4  24       61888   A4     4 #> 3030       1         4  24       61888   A5     4 #> 3031       1         4  24       61888   C1     4 #> 3032       1         4  24       61888   C2     5 #> 3033       1         4  24       61888   C3     5 #> 3034       1         4  24       61888   C4     5 #> 3035       1         4  24       61888   C5     5 #> 3036       1         4  24       61888   E1     5 #> 3037       1         4  24       61888   E2     6 #> 3038       1         4  24       61888   E3     3 #> 3039       1         4  24       61888   E4     2 #> 3040       1         4  24       61888   E5     2 #> 3041       1         4  24       61888   N1     3 #> 3042       1         4  24       61888   N2     4 #> 3043       1         4  24       61888   N3     2 #> 3044       1         4  24       61888   N4     5 #> 3045       1         4  24       61888   N5     6 #> 3046       1         4  24       61888   O1     2 #> 3047       1         4  24       61888   O2     4 #> 3048       1         4  24       61888   O3     4 #> 3049       1         4  24       61888   O4     6 #> 3050       1         4  24       61888   O5     4 #> 3051       1         5  24       61889   A1     6 #> 3052       1         5  24       61889   A2     5 #> 3053       1         5  24       61889   A3     5 #> 3054       1         5  24       61889   A4     2 #> 3055       1         5  24       61889   A5     5 #> 3056       1         5  24       61889   C1     5 #> 3057       1         5  24       61889   C2     5 #> 3058       1         5  24       61889   C3     6 #> 3059       1         5  24       61889   C4     1 #> 3060       1         5  24       61889   C5     1 #> 3061       1         5  24       61889   E1     1 #> 3062       1         5  24       61889   E2     2 #> 3063       1         5  24       61889   E3     5 #> 3064       1         5  24       61889   E4     5 #> 3065       1         5  24       61889   E5     6 #> 3066       1         5  24       61889   N1     5 #> 3067       1         5  24       61889   N2     4 #> 3068       1         5  24       61889   N3     4 #> 3069       1         5  24       61889   N4     3 #> 3070       1         5  24       61889   N5     1 #> 3071       1         5  24       61889   O1     6 #> 3072       1         5  24       61889   O2     2 #> 3073       1         5  24       61889   O3     6 #> 3074       1         5  24       61889   O4     5 #> 3075       1         5  24       61889   O5     1 #> 3076       1         5  28       61890   A1     2 #> 3077       1         5  28       61890   A2     5 #> 3078       1         5  28       61890   A3     5 #> 3079       1         5  28       61890   A4     1 #> 3080       1         5  28       61890   A5     6 #> 3081       1         5  28       61890   C1     5 #> 3082       1         5  28       61890   C2     5 #> 3083       1         5  28       61890   C3     5 #> 3084       1         5  28       61890   C4     3 #> 3085       1         5  28       61890   C5     6 #> 3086       1         5  28       61890   E1     2 #> 3087       1         5  28       61890   E2     4 #> 3088       1         5  28       61890   E3     6 #> 3089       1         5  28       61890   E4     6 #> 3090       1         5  28       61890   E5     5 #> 3091       1         5  28       61890   N1     4 #> 3092       1         5  28       61890   N2     4 #> 3093       1         5  28       61890   N3     3 #> 3094       1         5  28       61890   N4     3 #> 3095       1         5  28       61890   N5     1 #> 3096       1         5  28       61890   O1     5 #> 3097       1         5  28       61890   O2     3 #> 3098       1         5  28       61890   O3     6 #> 3099       1         5  28       61890   O4     4 #> 3100       1         5  28       61890   O5     2 #> 3101       1         4  22       61891   A1     2 #> 3102       1         4  22       61891   A2     4 #> 3103       1         4  22       61891   A3     5 #> 3104       1         4  22       61891   A4     5 #> 3105       1         4  22       61891   A5     5 #> 3106       1         4  22       61891   C1     5 #> 3107       1         4  22       61891   C2     5 #> 3108       1         4  22       61891   C3     4 #> 3109       1         4  22       61891   C4     6 #> 3110       1         4  22       61891   C5     6 #> 3111       1         4  22       61891   E1     5 #> 3112       1         4  22       61891   E2     5 #> 3113       1         4  22       61891   E3     5 #> 3114       1         4  22       61891   E4     5 #> 3115       1         4  22       61891   E5     3 #> 3116       1         4  22       61891   N1     1 #> 3117       1         4  22       61891   N2     2 #> 3118       1         4  22       61891   N3     2 #> 3119       1         4  22       61891   N4     4 #> 3120       1         4  22       61891   N5     2 #> 3121       1         4  22       61891   O1     5 #> 3122       1         4  22       61891   O2     2 #> 3123       1         4  22       61891   O3     5 #> 3124       1         4  22       61891   O4     6 #> 3125       1         4  22       61891   O5     1 #> 3126       1         5  26       61895   A1     2 #> 3127       1         5  26       61895   A2     5 #> 3128       1         5  26       61895   A3     5 #> 3129       1         5  26       61895   A4     5 #> 3130       1         5  26       61895   A5     2 #> 3131       1         5  26       61895   C1     5 #> 3132       1         5  26       61895   C2     5 #> 3133       1         5  26       61895   C3     5 #> 3134       1         5  26       61895   C4     2 #> 3135       1         5  26       61895   C5     6 #> 3136       1         5  26       61895   E1     5 #> 3137       1         5  26       61895   E2     5 #> 3138       1         5  26       61895   E3     3 #> 3139       1         5  26       61895   E4     2 #> 3140       1         5  26       61895   E5     2 #> 3141       1         5  26       61895   N1     2 #> 3142       1         5  26       61895   N2     3 #> 3143       1         5  26       61895   N3     2 #> 3144       1         5  26       61895   N4     5 #> 3145       1         5  26       61895   N5     5 #> 3146       1         5  26       61895   O1     5 #> 3147       1         5  26       61895   O2     1 #> 3148       1         5  26       61895   O3     6 #> 3149       1         5  26       61895   O4     6 #> 3150       1         5  26       61895   O5     1 #> 3151       1         5  25       61896   A1     2 #> 3152       1         5  25       61896   A2     5 #> 3153       1         5  25       61896   A3     6 #> 3154       1         5  25       61896   A4     5 #> 3155       1         5  25       61896   A5     6 #> 3156       1         5  25       61896   C1     5 #> 3157       1         5  25       61896   C2     4 #> 3158       1         5  25       61896   C3     5 #> 3159       1         5  25       61896   C4     3 #> 3160       1         5  25       61896   C5     2 #> 3161       1         5  25       61896   E1     2 #> 3162       1         5  25       61896   E2     2 #> 3163       1         5  25       61896   E3     4 #> 3164       1         5  25       61896   E4     5 #> 3165       1         5  25       61896   E5     5 #> 3166       1         5  25       61896   N1     4 #> 3167       1         5  25       61896   N2     4 #> 3168       1         5  25       61896   N3     2 #> 3169       1         5  25       61896   N4     2 #> 3170       1         5  25       61896   N5     2 #> 3171       1         5  25       61896   O1     4 #> 3172       1         5  25       61896   O2     3 #> 3173       1         5  25       61896   O3     5 #> 3174       1         5  25       61896   O4     4 #> 3175       1         5  25       61896   O5     2 #> 3176       1         4  23       61900   A1     2 #> 3177       1         4  23       61900   A2     5 #> 3178       1         4  23       61900   A3     4 #> 3179       1         4  23       61900   A4     4 #> 3180       1         4  23       61900   A5     5 #> 3181       1         4  23       61900   C1     5 #> 3182       1         4  23       61900   C2     4 #> 3183       1         4  23       61900   C3     5 #> 3184       1         4  23       61900   C4     3 #> 3185       1         4  23       61900   C5     5 #> 3186       1         4  23       61900   E1     2 #> 3187       1         4  23       61900   E2     3 #> 3188       1         4  23       61900   E3     5 #> 3189       1         4  23       61900   E4     4 #> 3190       1         4  23       61900   E5     5 #> 3191       1         4  23       61900   N1     2 #> 3192       1         4  23       61900   N2     3 #> 3193       1         4  23       61900   N3     3 #> 3194       1         4  23       61900   N4     4 #> 3195       1         4  23       61900   N5     2 #> 3196       1         4  23       61900   O1     5 #> 3197       1         4  23       61900   O2     2 #> 3198       1         4  23       61900   O3     6 #> 3199       1         4  23       61900   O4     6 #> 3200       1         4  23       61900   O5     2 #> 3201       1         3  22       61901   A1     2 #> 3202       1         3  22       61901   A2     5 #> 3203       1         3  22       61901   A3     5 #> 3204       1         3  22       61901   A4     3 #> 3205       1         3  22       61901   A5     2 #> 3206       1         3  22       61901   C1     2 #> 3207       1         3  22       61901   C2     3 #> 3208       1         3  22       61901   C3     4 #> 3209       1         3  22       61901   C4     5 #> 3210       1         3  22       61901   C5     6 #> 3211       1         3  22       61901   E1     2 #> 3212       1         3  22       61901   E2     5 #> 3213       1         3  22       61901   E3     2 #> 3214       1         3  22       61901   E4     2 #> 3215       1         3  22       61901   E5     2 #> 3216       1         3  22       61901   N1     2 #> 3217       1         3  22       61901   N2     4 #> 3218       1         3  22       61901   N3     4 #> 3219       1         3  22       61901   N4     4 #> 3220       1         3  22       61901   N5     2 #> 3221       1         3  22       61901   O1     5 #> 3222       1         3  22       61901   O2     2 #> 3223       1         3  22       61901   O3     4 #> 3224       1         3  22       61901   O4     6 #> 3225       1         3  22       61901   O5     2 #> 3226       2         3  19       61907   A1     4 #> 3227       2         3  19       61907   A2     5 #> 3228       2         3  19       61907   A3     4 #> 3229       2         3  19       61907   A4    NA #> 3230       2         3  19       61907   A5     3 #> 3231       2         3  19       61907   C1     5 #> 3232       2         3  19       61907   C2     3 #> 3233       2         3  19       61907   C3     4 #> 3234       2         3  19       61907   C4     2 #> 3235       2         3  19       61907   C5     5 #> 3236       2         3  19       61907   E1     1 #> 3237       2         3  19       61907   E2     4 #> 3238       2         3  19       61907   E3     3 #> 3239       2         3  19       61907   E4     5 #> 3240       2         3  19       61907   E5     4 #> 3241       2         3  19       61907   N1     4 #> 3242       2         3  19       61907   N2     5 #> 3243       2         3  19       61907   N3     5 #> 3244       2         3  19       61907   N4     4 #> 3245       2         3  19       61907   N5     5 #> 3246       2         3  19       61907   O1     4 #> 3247       2         3  19       61907   O2     4 #> 3248       2         3  19       61907   O3     3 #> 3249       2         3  19       61907   O4     6 #> 3250       2         3  19       61907   O5     3 #> 3251       2         4  25       61908   A1     2 #> 3252       2         4  25       61908   A2     4 #> 3253       2         4  25       61908   A3     4 #> 3254       2         4  25       61908   A4     4 #> 3255       2         4  25       61908   A5     4 #> 3256       2         4  25       61908   C1     4 #> 3257       2         4  25       61908   C2     4 #> 3258       2         4  25       61908   C3     4 #> 3259       2         4  25       61908   C4     4 #> 3260       2         4  25       61908   C5     5 #> 3261       2         4  25       61908   E1     4 #> 3262       2         4  25       61908   E2     3 #> 3263       2         4  25       61908   E3     4 #> 3264       2         4  25       61908   E4     6 #> 3265       2         4  25       61908   E5     5 #> 3266       2         4  25       61908   N1     4 #> 3267       2         4  25       61908   N2     5 #> 3268       2         4  25       61908   N3     3 #> 3269       2         4  25       61908   N4     5 #> 3270       2         4  25       61908   N5     4 #> 3271       2         4  25       61908   O1     4 #> 3272       2         4  25       61908   O2     2 #> 3273       2         4  25       61908   O3     4 #> 3274       2         4  25       61908   O4     6 #> 3275       2         4  25       61908   O5     3 #> 3276       1         3  25       61909   A1     3 #> 3277       1         3  25       61909   A2     5 #> 3278       1         3  25       61909   A3     4 #> 3279       1         3  25       61909   A4     5 #> 3280       1         3  25       61909   A5     4 #> 3281       1         3  25       61909   C1     4 #> 3282       1         3  25       61909   C2     4 #> 3283       1         3  25       61909   C3     5 #> 3284       1         3  25       61909   C4     3 #> 3285       1         3  25       61909   C5     5 #> 3286       1         3  25       61909   E1     2 #> 3287       1         3  25       61909   E2     4 #> 3288       1         3  25       61909   E3     5 #> 3289       1         3  25       61909   E4     5 #> 3290       1         3  25       61909   E5     4 #> 3291       1         3  25       61909   N1     5 #> 3292       1         3  25       61909   N2     5 #> 3293       1         3  25       61909   N3     5 #> 3294       1         3  25       61909   N4     5 #> 3295       1         3  25       61909   N5     5 #> 3296       1         3  25       61909   O1     6 #> 3297       1         3  25       61909   O2     3 #> 3298       1         3  25       61909   O3     4 #> 3299       1         3  25       61909   O4     5 #> 3300       1         3  25       61909   O5     2 #> 3301       1         5  27       61911   A1     4 #> 3302       1         5  27       61911   A2     3 #> 3303       1         5  27       61911   A3     3 #> 3304       1         5  27       61911   A4     3 #> 3305       1         5  27       61911   A5     3 #> 3306       1         5  27       61911   C1     6 #> 3307       1         5  27       61911   C2     5 #> 3308       1         5  27       61911   C3     5 #> 3309       1         5  27       61911   C4     2 #> 3310       1         5  27       61911   C5     3 #> 3311       1         5  27       61911   E1     5 #> 3312       1         5  27       61911   E2    NA #> 3313       1         5  27       61911   E3     3 #> 3314       1         5  27       61911   E4     2 #> 3315       1         5  27       61911   E5     3 #> 3316       1         5  27       61911   N1     3 #> 3317       1         5  27       61911   N2     3 #> 3318       1         5  27       61911   N3     3 #> 3319       1         5  27       61911   N4     4 #> 3320       1         5  27       61911   N5     4 #> 3321       1         5  27       61911   O1     5 #> 3322       1         5  27       61911   O2     1 #> 3323       1         5  27       61911   O3     5 #> 3324       1         5  27       61911   O4     6 #> 3325       1         5  27       61911   O5     6 #> 3326       2         3  20       61913   A1     2 #> 3327       2         3  20       61913   A2     4 #> 3328       2         3  20       61913   A3     3 #> 3329       2         3  20       61913   A4     3 #> 3330       2         3  20       61913   A5     3 #> 3331       2         3  20       61913   C1     4 #> 3332       2         3  20       61913   C2     4 #> 3333       2         3  20       61913   C3     4 #> 3334       2         3  20       61913   C4     3 #> 3335       2         3  20       61913   C5     6 #> 3336       2         3  20       61913   E1     2 #> 3337       2         3  20       61913   E2     5 #> 3338       2         3  20       61913   E3     3 #> 3339       2         3  20       61913   E4     2 #> 3340       2         3  20       61913   E5     5 #> 3341       2         3  20       61913   N1     4 #> 3342       2         3  20       61913   N2     6 #> 3343       2         3  20       61913   N3     4 #> 3344       2         3  20       61913   N4     4 #> 3345       2         3  20       61913   N5     5 #> 3346       2         3  20       61913   O1     3 #> 3347       2         3  20       61913   O2     5 #> 3348       2         3  20       61913   O3     3 #> 3349       2         3  20       61913   O4     5 #> 3350       2         3  20       61913   O5     3 #> 3351       2         3  25       61915   A1     1 #> 3352       2         3  25       61915   A2     6 #> 3353       2         3  25       61915   A3     6 #> 3354       2         3  25       61915   A4     6 #> 3355       2         3  25       61915   A5     6 #> 3356       2         3  25       61915   C1     6 #> 3357       2         3  25       61915   C2     6 #> 3358       2         3  25       61915   C3     3 #> 3359       2         3  25       61915   C4     6 #> 3360       2         3  25       61915   C5     5 #> 3361       2         3  25       61915   E1     6 #> 3362       2         3  25       61915   E2     5 #> 3363       2         3  25       61915   E3     5 #> 3364       2         3  25       61915   E4     6 #> 3365       2         3  25       61915   E5     6 #> 3366       2         3  25       61915   N1     6 #> 3367       2         3  25       61915   N2     6 #> 3368       2         3  25       61915   N3     4 #> 3369       2         3  25       61915   N4     5 #> 3370       2         3  25       61915   N5     6 #> 3371       2         3  25       61915   O1     5 #> 3372       2         3  25       61915   O2     6 #> 3373       2         3  25       61915   O3     6 #> 3374       2         3  25       61915   O4     6 #> 3375       2         3  25       61915   O5     2 #> 3376       2         3  49       61918   A1     1 #> 3377       2         3  49       61918   A2     6 #> 3378       2         3  49       61918   A3     6 #> 3379       2         3  49       61918   A4     5 #> 3380       2         3  49       61918   A5     6 #> 3381       2         3  49       61918   C1     6 #> 3382       2         3  49       61918   C2     1 #> 3383       2         3  49       61918   C3     3 #> 3384       2         3  49       61918   C4     4 #> 3385       2         3  49       61918   C5     5 #> 3386       2         3  49       61918   E1     3 #> 3387       2         3  49       61918   E2     4 #> 3388       2         3  49       61918   E3     4 #> 3389       2         3  49       61918   E4     6 #> 3390       2         3  49       61918   E5     3 #> 3391       2         3  49       61918   N1     5 #> 3392       2         3  49       61918   N2     5 #> 3393       2         3  49       61918   N3     2 #> 3394       2         3  49       61918   N4     5 #> 3395       2         3  49       61918   N5     2 #> 3396       2         3  49       61918   O1     4 #> 3397       2         3  49       61918   O2     4 #> 3398       2         3  49       61918   O3     6 #> 3399       2         3  49       61918   O4     6 #> 3400       2         3  49       61918   O5     1 #> 3401       2         3  26       61921   A1     1 #> 3402       2         3  26       61921   A2     6 #> 3403       2         3  26       61921   A3     6 #> 3404       2         3  26       61921   A4     4 #> 3405       2         3  26       61921   A5     6 #> 3406       2         3  26       61921   C1     1 #> 3407       2         3  26       61921   C2     1 #> 3408       2         3  26       61921   C3     3 #> 3409       2         3  26       61921   C4     1 #> 3410       2         3  26       61921   C5     1 #> 3411       2         3  26       61921   E1     1 #> 3412       2         3  26       61921   E2     1 #> 3413       2         3  26       61921   E3     6 #> 3414       2         3  26       61921   E4     6 #> 3415       2         3  26       61921   E5     6 #> 3416       2         3  26       61921   N1     4 #> 3417       2         3  26       61921   N2     4 #> 3418       2         3  26       61921   N3     1 #> 3419       2         3  26       61921   N4    NA #> 3420       2         3  26       61921   N5     2 #> 3421       2         3  26       61921   O1     4 #> 3422       2         3  26       61921   O2     1 #> 3423       2         3  26       61921   O3     6 #> 3424       2         3  26       61921   O4     2 #> 3425       2         3  26       61921   O5     2 #> 3426       2         3  25       61922   A1     1 #> 3427       2         3  25       61922   A2     6 #> 3428       2         3  25       61922   A3     5 #> 3429       2         3  25       61922   A4     6 #> 3430       2         3  25       61922   A5     5 #> 3431       2         3  25       61922   C1     6 #> 3432       2         3  25       61922   C2     6 #> 3433       2         3  25       61922   C3     4 #> 3434       2         3  25       61922   C4     1 #> 3435       2         3  25       61922   C5     1 #> 3436       2         3  25       61922   E1     1 #> 3437       2         3  25       61922   E2     2 #> 3438       2         3  25       61922   E3     5 #> 3439       2         3  25       61922   E4     4 #> 3440       2         3  25       61922   E5     5 #> 3441       2         3  25       61922   N1     4 #> 3442       2         3  25       61922   N2     6 #> 3443       2         3  25       61922   N3     2 #> 3444       2         3  25       61922   N4     1 #> 3445       2         3  25       61922   N5     2 #> 3446       2         3  25       61922   O1     6 #> 3447       2         3  25       61922   O2     1 #> 3448       2         3  25       61922   O3     6 #> 3449       2         3  25       61922   O4     5 #> 3450       2         3  25       61922   O5     3 #> 3451       2         3  25       61923   A1     1 #> 3452       2         3  25       61923   A2     6 #> 3453       2         3  25       61923   A3     5 #> 3454       2         3  25       61923   A4     6 #> 3455       2         3  25       61923   A5     5 #> 3456       2         3  25       61923   C1     5 #> 3457       2         3  25       61923   C2     5 #> 3458       2         3  25       61923   C3     4 #> 3459       2         3  25       61923   C4     1 #> 3460       2         3  25       61923   C5     4 #> 3461       2         3  25       61923   E1     2 #> 3462       2         3  25       61923   E2     3 #> 3463       2         3  25       61923   E3     6 #> 3464       2         3  25       61923   E4     6 #> 3465       2         3  25       61923   E5     5 #> 3466       2         3  25       61923   N1     4 #> 3467       2         3  25       61923   N2     4 #> 3468       2         3  25       61923   N3     5 #> 3469       2         3  25       61923   N4     4 #> 3470       2         3  25       61923   N5     5 #> 3471       2         3  25       61923   O1     5 #> 3472       2         3  25       61923   O2     2 #> 3473       2         3  25       61923   O3     5 #> 3474       2         3  25       61923   O4     6 #> 3475       2         3  25       61923   O5     4 #> 3476       1        NA  18       61925   A1     3 #> 3477       1        NA  18       61925   A2     6 #> 3478       1        NA  18       61925   A3     6 #> 3479       1        NA  18       61925   A4     4 #> 3480       1        NA  18       61925   A5     6 #> 3481       1        NA  18       61925   C1     2 #> 3482       1        NA  18       61925   C2     3 #> 3483       1        NA  18       61925   C3     2 #> 3484       1        NA  18       61925   C4     5 #> 3485       1        NA  18       61925   C5     5 #> 3486       1        NA  18       61925   E1     1 #> 3487       1        NA  18       61925   E2     1 #> 3488       1        NA  18       61925   E3     6 #> 3489       1        NA  18       61925   E4     6 #> 3490       1        NA  18       61925   E5     6 #> 3491       1        NA  18       61925   N1     4 #> 3492       1        NA  18       61925   N2     4 #> 3493       1        NA  18       61925   N3     4 #> 3494       1        NA  18       61925   N4     3 #> 3495       1        NA  18       61925   N5     3 #> 3496       1        NA  18       61925   O1     6 #> 3497       1        NA  18       61925   O2     3 #> 3498       1        NA  18       61925   O3     4 #> 3499       1        NA  18       61925   O4     5 #> 3500       1        NA  18       61925   O5     2 #> 3501       1         3  21       61926   A1     2 #> 3502       1         3  21       61926   A2     3 #> 3503       1         3  21       61926   A3     1 #> 3504       1         3  21       61926   A4     3 #> 3505       1         3  21       61926   A5     1 #> 3506       1         3  21       61926   C1     3 #> 3507       1         3  21       61926   C2     3 #> 3508       1         3  21       61926   C3     3 #> 3509       1         3  21       61926   C4     2 #> 3510       1         3  21       61926   C5     6 #> 3511       1         3  21       61926   E1     6 #> 3512       1         3  21       61926   E2     6 #> 3513       1         3  21       61926   E3     1 #> 3514       1         3  21       61926   E4     1 #> 3515       1         3  21       61926   E5     3 #> 3516       1         3  21       61926   N1     5 #> 3517       1         3  21       61926   N2     4 #> 3518       1         3  21       61926   N3     3 #> 3519       1         3  21       61926   N4     6 #> 3520       1         3  21       61926   N5     4 #> 3521       1         3  21       61926   O1     2 #> 3522       1         3  21       61926   O2     2 #> 3523       1         3  21       61926   O3     1 #> 3524       1         3  21       61926   O4     5 #> 3525       1         3  21       61926   O5     3 #> 3526       2         3  22       61928   A1     2 #> 3527       2         3  22       61928   A2     4 #> 3528       2         3  22       61928   A3     5 #> 3529       2         3  22       61928   A4     5 #> 3530       2         3  22       61928   A5     5 #> 3531       2         3  22       61928   C1     4 #> 3532       2         3  22       61928   C2     4 #> 3533       2         3  22       61928   C3     6 #> 3534       2         3  22       61928   C4     4 #> 3535       2         3  22       61928   C5     2 #> 3536       2         3  22       61928   E1     1 #> 3537       2         3  22       61928   E2     2 #> 3538       2         3  22       61928   E3     4 #> 3539       2         3  22       61928   E4     6 #> 3540       2         3  22       61928   E5     5 #> 3541       2         3  22       61928   N1     6 #> 3542       2         3  22       61928   N2     6 #> 3543       2         3  22       61928   N3     4 #> 3544       2         3  22       61928   N4     4 #> 3545       2         3  22       61928   N5     6 #> 3546       2         3  22       61928   O1     4 #> 3547       2         3  22       61928   O2     4 #> 3548       2         3  22       61928   O3     3 #> 3549       2         3  22       61928   O4     4 #> 3550       2         3  22       61928   O5     5 #> 3551       2         3  37       61932   A1     2 #> 3552       2         3  37       61932   A2     5 #> 3553       2         3  37       61932   A3     3 #> 3554       2         3  37       61932   A4     5 #> 3555       2         3  37       61932   A5     1 #> 3556       2         3  37       61932   C1     6 #> 3557       2         3  37       61932   C2     5 #> 3558       2         3  37       61932   C3     4 #> 3559       2         3  37       61932   C4     2 #> 3560       2         3  37       61932   C5     2 #> 3561       2         3  37       61932   E1     1 #> 3562       2         3  37       61932   E2     1 #> 3563       2         3  37       61932   E3     5 #> 3564       2         3  37       61932   E4     6 #> 3565       2         3  37       61932   E5     2 #> 3566       2         3  37       61932   N1     3 #> 3567       2         3  37       61932   N2     5 #> 3568       2         3  37       61932   N3     4 #> 3569       2         3  37       61932   N4     4 #> 3570       2         3  37       61932   N5     5 #> 3571       2         3  37       61932   O1     4 #> 3572       2         3  37       61932   O2     2 #> 3573       2         3  37       61932   O3     5 #> 3574       2         3  37       61932   O4     4 #> 3575       2         3  37       61932   O5     3 #> 3576       2         3  20       61935   A1     1 #> 3577       2         3  20       61935   A2     6 #> 3578       2         3  20       61935   A3     5 #> 3579       2         3  20       61935   A4     6 #> 3580       2         3  20       61935   A5     6 #> 3581       2         3  20       61935   C1     3 #> 3582       2         3  20       61935   C2     2 #> 3583       2         3  20       61935   C3     4 #> 3584       2         3  20       61935   C4     2 #> 3585       2         3  20       61935   C5     3 #> 3586       2         3  20       61935   E1     3 #> 3587       2         3  20       61935   E2     2 #> 3588       2         3  20       61935   E3     5 #> 3589       2         3  20       61935   E4     5 #> 3590       2         3  20       61935   E5     3 #> 3591       2         3  20       61935   N1     2 #> 3592       2         3  20       61935   N2     2 #> 3593       2         3  20       61935   N3     2 #> 3594       2         3  20       61935   N4     2 #> 3595       2         3  20       61935   N5     1 #> 3596       2         3  20       61935   O1     4 #> 3597       2         3  20       61935   O2     4 #> 3598       2         3  20       61935   O3     4 #> 3599       2         3  20       61935   O4     5 #> 3600       2         3  20       61935   O5     3 #> 3601       2         1  22       61936   A1     5 #> 3602       2         1  22       61936   A2     4 #> 3603       2         1  22       61936   A3     4 #> 3604       2         1  22       61936   A4     5 #> 3605       2         1  22       61936   A5     6 #> 3606       2         1  22       61936   C1     3 #> 3607       2         1  22       61936   C2     5 #> 3608       2         1  22       61936   C3     3 #> 3609       2         1  22       61936   C4     3 #> 3610       2         1  22       61936   C5     6 #> 3611       2         1  22       61936   E1     4 #> 3612       2         1  22       61936   E2     4 #> 3613       2         1  22       61936   E3     6 #> 3614       2         1  22       61936   E4     5 #> 3615       2         1  22       61936   E5     5 #> 3616       2         1  22       61936   N1     2 #> 3617       2         1  22       61936   N2     5 #> 3618       2         1  22       61936   N3     2 #> 3619       2         1  22       61936   N4     1 #> 3620       2         1  22       61936   N5     2 #> 3621       2         1  22       61936   O1     6 #> 3622       2         1  22       61936   O2     2 #> 3623       2         1  22       61936   O3     5 #> 3624       2         1  22       61936   O4     5 #> 3625       2         1  22       61936   O5     2 #> 3626       2         5  41       61939   A1     2 #> 3627       2         5  41       61939   A2     5 #> 3628       2         5  41       61939   A3     4 #> 3629       2         5  41       61939   A4     3 #> 3630       2         5  41       61939   A5     3 #> 3631       2         5  41       61939   C1     6 #> 3632       2         5  41       61939   C2     4 #> 3633       2         5  41       61939   C3     4 #> 3634       2         5  41       61939   C4     2 #> 3635       2         5  41       61939   C5     4 #> 3636       2         5  41       61939   E1     6 #> 3637       2         5  41       61939   E2     4 #> 3638       2         5  41       61939   E3     3 #> 3639       2         5  41       61939   E4     3 #> 3640       2         5  41       61939   E5     4 #> 3641       2         5  41       61939   N1     1 #> 3642       2         5  41       61939   N2     1 #> 3643       2         5  41       61939   N3     1 #> 3644       2         5  41       61939   N4     4 #> 3645       2         5  41       61939   N5     3 #> 3646       2         5  41       61939   O1     6 #> 3647       2         5  41       61939   O2     1 #> 3648       2         5  41       61939   O3     4 #> 3649       2         5  41       61939   O4     6 #> 3650       2         5  41       61939   O5     1 #> 3651       2         5  22       61944   A1     1 #> 3652       2         5  22       61944   A2     6 #> 3653       2         5  22       61944   A3     6 #> 3654       2         5  22       61944   A4     3 #> 3655       2         5  22       61944   A5     6 #> 3656       2         5  22       61944   C1     6 #> 3657       2         5  22       61944   C2     6 #> 3658       2         5  22       61944   C3     6 #> 3659       2         5  22       61944   C4     1 #> 3660       2         5  22       61944   C5     5 #> 3661       2         5  22       61944   E1     1 #> 3662       2         5  22       61944   E2     4 #> 3663       2         5  22       61944   E3     5 #> 3664       2         5  22       61944   E4     5 #> 3665       2         5  22       61944   E5     6 #> 3666       2         5  22       61944   N1     3 #> 3667       2         5  22       61944   N2     6 #> 3668       2         5  22       61944   N3     4 #> 3669       2         5  22       61944   N4     4 #> 3670       2         5  22       61944   N5     6 #> 3671       2         5  22       61944   O1     5 #> 3672       2         5  22       61944   O2     1 #> 3673       2         5  22       61944   O3     4 #> 3674       2         5  22       61944   O4     6 #> 3675       2         5  22       61944   O5     1 #> 3676       1         5  24       61945   A1     2 #> 3677       1         5  24       61945   A2     6 #> 3678       1         5  24       61945   A3     4 #> 3679       1         5  24       61945   A4     5 #> 3680       1         5  24       61945   A5     4 #> 3681       1         5  24       61945   C1     4 #> 3682       1         5  24       61945   C2     4 #> 3683       1         5  24       61945   C3     5 #> 3684       1         5  24       61945   C4     3 #> 3685       1         5  24       61945   C5     5 #> 3686       1         5  24       61945   E1     3 #> 3687       1         5  24       61945   E2     3 #> 3688       1         5  24       61945   E3     4 #> 3689       1         5  24       61945   E4     4 #> 3690       1         5  24       61945   E5     4 #> 3691       1         5  24       61945   N1     3 #> 3692       1         5  24       61945   N2     3 #> 3693       1         5  24       61945   N3     3 #> 3694       1         5  24       61945   N4     4 #> 3695       1         5  24       61945   N5     3 #> 3696       1         5  24       61945   O1     4 #> 3697       1         5  24       61945   O2     4 #> 3698       1         5  24       61945   O3     5 #> 3699       1         5  24       61945   O4     5 #> 3700       1         5  24       61945   O5     2 #> 3701       2         4  23       61949   A1     2 #> 3702       2         4  23       61949   A2     6 #> 3703       2         4  23       61949   A3     3 #> 3704       2         4  23       61949   A4     5 #> 3705       2         4  23       61949   A5     2 #> 3706       2         4  23       61949   C1     4 #> 3707       2         4  23       61949   C2     4 #> 3708       2         4  23       61949   C3     6 #> 3709       2         4  23       61949   C4     4 #> 3710       2         4  23       61949   C5     5 #> 3711       2         4  23       61949   E1     2 #> 3712       2         4  23       61949   E2     1 #> 3713       2         4  23       61949   E3     5 #> 3714       2         4  23       61949   E4     4 #> 3715       2         4  23       61949   E5     6 #> 3716       2         4  23       61949   N1     1 #> 3717       2         4  23       61949   N2     1 #> 3718       2         4  23       61949   N3     1 #> 3719       2         4  23       61949   N4     1 #> 3720       2         4  23       61949   N5     1 #> 3721       2         4  23       61949   O1     6 #> 3722       2         4  23       61949   O2     2 #> 3723       2         4  23       61949   O3     5 #> 3724       2         4  23       61949   O4     2 #> 3725       2         4  23       61949   O5     1 #> 3726       1         4  32       61952   A1     1 #> 3727       1         4  32       61952   A2     5 #> 3728       1         4  32       61952   A3     5 #> 3729       1         4  32       61952   A4     6 #> 3730       1         4  32       61952   A5     5 #> 3731       1         4  32       61952   C1     5 #> 3732       1         4  32       61952   C2     5 #> 3733       1         4  32       61952   C3     5 #> 3734       1         4  32       61952   C4     2 #> 3735       1         4  32       61952   C5     2 #> 3736       1         4  32       61952   E1     2 #> 3737       1         4  32       61952   E2     2 #> 3738       1         4  32       61952   E3     1 #> 3739       1         4  32       61952   E4     5 #> 3740       1         4  32       61952   E5     3 #> 3741       1         4  32       61952   N1     5 #> 3742       1         4  32       61952   N2     2 #> 3743       1         4  32       61952   N3     4 #> 3744       1         4  32       61952   N4     4 #> 3745       1         4  32       61952   N5     2 #> 3746       1         4  32       61952   O1     5 #> 3747       1         4  32       61952   O2     2 #> 3748       1         4  32       61952   O3     5 #> 3749       1         4  32       61952   O4     5 #> 3750       1         4  32       61952   O5     1 #> 3751       1         5  43       61953   A1     1 #> 3752       1         5  43       61953   A2     5 #> 3753       1         5  43       61953   A3     5 #> 3754       1         5  43       61953   A4     3 #> 3755       1         5  43       61953   A5     3 #> 3756       1         5  43       61953   C1     5 #> 3757       1         5  43       61953   C2     2 #> 3758       1         5  43       61953   C3     2 #> 3759       1         5  43       61953   C4     5 #> 3760       1         5  43       61953   C5     6 #> 3761       1         5  43       61953   E1     2 #> 3762       1         5  43       61953   E2     3 #> 3763       1         5  43       61953   E3     2 #> 3764       1         5  43       61953   E4     3 #> 3765       1         5  43       61953   E5     4 #> 3766       1         5  43       61953   N1     5 #> 3767       1         5  43       61953   N2     5 #> 3768       1         5  43       61953   N3     5 #> 3769       1         5  43       61953   N4     3 #> 3770       1         5  43       61953   N5     2 #> 3771       1         5  43       61953   O1     5 #> 3772       1         5  43       61953   O2     2 #> 3773       1         5  43       61953   O3     5 #> 3774       1         5  43       61953   O4     6 #> 3775       1         5  43       61953   O5     2 #> 3776       2         4  30       61954   A1     1 #> 3777       2         4  30       61954   A2     6 #> 3778       2         4  30       61954   A3     6 #> 3779       2         4  30       61954   A4     5 #> 3780       2         4  30       61954   A5     6 #> 3781       2         4  30       61954   C1     6 #> 3782       2         4  30       61954   C2     5 #> 3783       2         4  30       61954   C3     3 #> 3784       2         4  30       61954   C4     1 #> 3785       2         4  30       61954   C5     1 #> 3786       2         4  30       61954   E1     1 #> 3787       2         4  30       61954   E2     2 #> 3788       2         4  30       61954   E3     5 #> 3789       2         4  30       61954   E4     5 #> 3790       2         4  30       61954   E5     5 #> 3791       2         4  30       61954   N1     1 #> 3792       2         4  30       61954   N2     1 #> 3793       2         4  30       61954   N3     1 #> 3794       2         4  30       61954   N4     2 #> 3795       2         4  30       61954   N5     4 #> 3796       2         4  30       61954   O1     6 #> 3797       2         4  30       61954   O2     1 #> 3798       2         4  30       61954   O3     6 #> 3799       2         4  30       61954   O4     6 #> 3800       2         4  30       61954   O5     1 #> 3801       2         2  50       61957   A1     2 #> 3802       2         2  50       61957   A2     5 #> 3803       2         2  50       61957   A3     5 #> 3804       2         2  50       61957   A4     6 #> 3805       2         2  50       61957   A5     5 #> 3806       2         2  50       61957   C1     4 #> 3807       2         2  50       61957   C2     4 #> 3808       2         2  50       61957   C3     2 #> 3809       2         2  50       61957   C4     5 #> 3810       2         2  50       61957   C5     4 #> 3811       2         2  50       61957   E1     4 #> 3812       2         2  50       61957   E2     3 #> 3813       2         2  50       61957   E3     5 #> 3814       2         2  50       61957   E4     5 #> 3815       2         2  50       61957   E5     4 #> 3816       2         2  50       61957   N1     3 #> 3817       2         2  50       61957   N2     4 #> 3818       2         2  50       61957   N3     2 #> 3819       2         2  50       61957   N4     1 #> 3820       2         2  50       61957   N5     1 #> 3821       2         2  50       61957   O1     6 #> 3822       2         2  50       61957   O2     5 #> 3823       2         2  50       61957   O3     4 #> 3824       2         2  50       61957   O4     6 #> 3825       2         2  50       61957   O5     2 #> 3826       1         1  18       61958   A1     4 #> 3827       1         1  18       61958   A2     4 #> 3828       1         1  18       61958   A3     3 #> 3829       1         1  18       61958   A4     1 #> 3830       1         1  18       61958   A5     4 #> 3831       1         1  18       61958   C1     5 #> 3832       1         1  18       61958   C2     4 #> 3833       1         1  18       61958   C3     4 #> 3834       1         1  18       61958   C4     1 #> 3835       1         1  18       61958   C5     1 #> 3836       1         1  18       61958   E1     1 #> 3837       1         1  18       61958   E2     1 #> 3838       1         1  18       61958   E3     4 #> 3839       1         1  18       61958   E4     6 #> 3840       1         1  18       61958   E5     6 #> 3841       1         1  18       61958   N1     3 #> 3842       1         1  18       61958   N2     4 #> 3843       1         1  18       61958   N3     2 #> 3844       1         1  18       61958   N4     2 #> 3845       1         1  18       61958   N5     1 #> 3846       1         1  18       61958   O1     4 #> 3847       1         1  18       61958   O2     1 #> 3848       1         1  18       61958   O3     6 #> 3849       1         1  18       61958   O4     4 #> 3850       1         1  18       61958   O5     2 #> 3851       2         3  16       61965   A1     2 #> 3852       2         3  16       61965   A2     6 #> 3853       2         3  16       61965   A3     5 #> 3854       2         3  16       61965   A4     5 #> 3855       2         3  16       61965   A5     3 #> 3856       2         3  16       61965   C1     5 #> 3857       2         3  16       61965   C2     5 #> 3858       2         3  16       61965   C3     6 #> 3859       2         3  16       61965   C4     2 #> 3860       2         3  16       61965   C5     1 #> 3861       2         3  16       61965   E1     2 #> 3862       2         3  16       61965   E2     5 #> 3863       2         3  16       61965   E3     3 #> 3864       2         3  16       61965   E4     4 #> 3865       2         3  16       61965   E5     5 #> 3866       2         3  16       61965   N1     1 #> 3867       2         3  16       61965   N2     3 #> 3868       2         3  16       61965   N3     2 #> 3869       2         3  16       61965   N4     1 #> 3870       2         3  16       61965   N5     3 #> 3871       2         3  16       61965   O1     6 #> 3872       2         3  16       61965   O2     2 #> 3873       2         3  16       61965   O3     4 #> 3874       2         3  16       61965   O4     6 #> 3875       2         3  16       61965   O5     5 #> 3876       2         5  34       61967   A1     3 #> 3877       2         5  34       61967   A2     5 #> 3878       2         5  34       61967   A3     6 #> 3879       2         5  34       61967   A4     5 #> 3880       2         5  34       61967   A5     5 #> 3881       2         5  34       61967   C1     4 #> 3882       2         5  34       61967   C2     5 #> 3883       2         5  34       61967   C3     4 #> 3884       2         5  34       61967   C4     2 #> 3885       2         5  34       61967   C5     4 #> 3886       2         5  34       61967   E1     2 #> 3887       2         5  34       61967   E2     2 #> 3888       2         5  34       61967   E3     5 #> 3889       2         5  34       61967   E4     5 #> 3890       2         5  34       61967   E5     5 #> 3891       2         5  34       61967   N1     3 #> 3892       2         5  34       61967   N2     3 #> 3893       2         5  34       61967   N3     5 #> 3894       2         5  34       61967   N4     4 #> 3895       2         5  34       61967   N5     3 #> 3896       2         5  34       61967   O1     5 #> 3897       2         5  34       61967   O2     2 #> 3898       2         5  34       61967   O3     6 #> 3899       2         5  34       61967   O4     5 #> 3900       2         5  34       61967   O5     3 #> 3901       2         2  18       61968   A1     1 #> 3902       2         2  18       61968   A2     6 #> 3903       2         2  18       61968   A3     6 #> 3904       2         2  18       61968   A4     6 #> 3905       2         2  18       61968   A5     5 #> 3906       2         2  18       61968   C1     5 #> 3907       2         2  18       61968   C2     3 #> 3908       2         2  18       61968   C3     4 #> 3909       2         2  18       61968   C4     4 #> 3910       2         2  18       61968   C5     4 #> 3911       2         2  18       61968   E1     1 #> 3912       2         2  18       61968   E2     2 #> 3913       2         2  18       61968   E3     5 #> 3914       2         2  18       61968   E4     6 #> 3915       2         2  18       61968   E5     5 #> 3916       2         2  18       61968   N1     2 #> 3917       2         2  18       61968   N2     2 #> 3918       2         2  18       61968   N3     2 #> 3919       2         2  18       61968   N4     2 #> 3920       2         2  18       61968   N5     3 #> 3921       2         2  18       61968   O1     5 #> 3922       2         2  18       61968   O2     4 #> 3923       2         2  18       61968   O3     5 #> 3924       2         2  18       61968   O4     5 #> 3925       2         2  18       61968   O5     2 #> 3926       2         5  24       61969   A1     2 #> 3927       2         5  24       61969   A2     5 #> 3928       2         5  24       61969   A3     5 #> 3929       2         5  24       61969   A4     5 #> 3930       2         5  24       61969   A5     4 #> 3931       2         5  24       61969   C1     3 #> 3932       2         5  24       61969   C2     1 #> 3933       2         5  24       61969   C3     4 #> 3934       2         5  24       61969   C4     4 #> 3935       2         5  24       61969   C5     5 #> 3936       2         5  24       61969   E1     5 #> 3937       2         5  24       61969   E2     5 #> 3938       2         5  24       61969   E3     5 #> 3939       2         5  24       61969   E4     3 #> 3940       2         5  24       61969   E5     5 #> 3941       2         5  24       61969   N1     2 #> 3942       2         5  24       61969   N2     4 #> 3943       2         5  24       61969   N3     5 #> 3944       2         5  24       61969   N4     4 #> 3945       2         5  24       61969   N5     5 #> 3946       2         5  24       61969   O1     5 #> 3947       2         5  24       61969   O2     4 #> 3948       2         5  24       61969   O3     5 #> 3949       2         5  24       61969   O4     5 #> 3950       2         5  24       61969   O5     1 #> 3951       1         2  18       61971   A1     2 #> 3952       1         2  18       61971   A2     4 #> 3953       1         2  18       61971   A3     4 #> 3954       1         2  18       61971   A4     1 #> 3955       1         2  18       61971   A5     3 #> 3956       1         2  18       61971   C1     3 #> 3957       1         2  18       61971   C2     5 #> 3958       1         2  18       61971   C3     4 #> 3959       1         2  18       61971   C4     4 #> 3960       1         2  18       61971   C5     5 #> 3961       1         2  18       61971   E1     4 #> 3962       1         2  18       61971   E2     4 #> 3963       1         2  18       61971   E3     4 #> 3964       1         2  18       61971   E4     5 #> 3965       1         2  18       61971   E5     2 #> 3966       1         2  18       61971   N1     1 #> 3967       1         2  18       61971   N2     2 #> 3968       1         2  18       61971   N3     1 #> 3969       1         2  18       61971   N4     4 #> 3970       1         2  18       61971   N5     4 #> 3971       1         2  18       61971   O1     5 #> 3972       1         2  18       61971   O2     1 #> 3973       1         2  18       61971   O3     6 #> 3974       1         2  18       61971   O4     6 #> 3975       1         2  18       61971   O5     1 #> 3976       2         3  22       61972   A1     2 #> 3977       2         3  22       61972   A2     4 #> 3978       2         3  22       61972   A3     5 #> 3979       2         3  22       61972   A4     5 #> 3980       2         3  22       61972   A5     4 #> 3981       2         3  22       61972   C1     5 #> 3982       2         3  22       61972   C2     6 #> 3983       2         3  22       61972   C3     5 #> 3984       2         3  22       61972   C4     2 #> 3985       2         3  22       61972   C5     1 #> 3986       2         3  22       61972   E1     3 #> 3987       2         3  22       61972   E2     4 #> 3988       2         3  22       61972   E3     4 #> 3989       2         3  22       61972   E4     2 #> 3990       2         3  22       61972   E5     6 #> 3991       2         3  22       61972   N1     6 #> 3992       2         3  22       61972   N2     6 #> 3993       2         3  22       61972   N3     2 #> 3994       2         3  22       61972   N4     3 #> 3995       2         3  22       61972   N5     4 #> 3996       2         3  22       61972   O1     5 #> 3997       2         3  22       61972   O2     5 #> 3998       2         3  22       61972   O3     4 #> 3999       2         3  22       61972   O4     5 #> 4000       2         3  22       61972   O5     5 #> 4001       2         2  36       61973   A1     1 #> 4002       2         2  36       61973   A2     5 #> 4003       2         2  36       61973   A3     5 #> 4004       2         2  36       61973   A4     6 #> 4005       2         2  36       61973   A5     5 #> 4006       2         2  36       61973   C1     5 #> 4007       2         2  36       61973   C2     4 #> 4008       2         2  36       61973   C3     5 #> 4009       2         2  36       61973   C4     1 #> 4010       2         2  36       61973   C5     1 #> 4011       2         2  36       61973   E1     4 #> 4012       2         2  36       61973   E2     2 #> 4013       2         2  36       61973   E3     4 #> 4014       2         2  36       61973   E4     4 #> 4015       2         2  36       61973   E5     4 #> 4016       2         2  36       61973   N1     3 #> 4017       2         2  36       61973   N2     4 #> 4018       2         2  36       61973   N3     2 #> 4019       2         2  36       61973   N4    NA #> 4020       2         2  36       61973   N5     1 #> 4021       2         2  36       61973   O1     5 #> 4022       2         2  36       61973   O2     1 #> 4023       2         2  36       61973   O3     4 #> 4024       2         2  36       61973   O4     4 #> 4025       2         2  36       61973   O5     4 #> 4026       2         3  19       61974   A1     1 #> 4027       2         3  19       61974   A2     6 #> 4028       2         3  19       61974   A3     6 #> 4029       2         3  19       61974   A4     2 #> 4030       2         3  19       61974   A5     5 #> 4031       2         3  19       61974   C1     5 #> 4032       2         3  19       61974   C2     3 #> 4033       2         3  19       61974   C3     2 #> 4034       2         3  19       61974   C4     3 #> 4035       2         3  19       61974   C5     4 #> 4036       2         3  19       61974   E1     2 #> 4037       2         3  19       61974   E2     2 #> 4038       2         3  19       61974   E3     3 #> 4039       2         3  19       61974   E4     5 #> 4040       2         3  19       61974   E5     5 #> 4041       2         3  19       61974   N1     2 #> 4042       2         3  19       61974   N2     4 #> 4043       2         3  19       61974   N3     2 #> 4044       2         3  19       61974   N4     3 #> 4045       2         3  19       61974   N5     5 #> 4046       2         3  19       61974   O1     4 #> 4047       2         3  19       61974   O2     2 #> 4048       2         3  19       61974   O3     4 #> 4049       2         3  19       61974   O4     6 #> 4050       2         3  19       61974   O5     2 #> 4051       1         3  20       61975   A1     2 #> 4052       1         3  20       61975   A2     6 #> 4053       1         3  20       61975   A3     4 #> 4054       1         3  20       61975   A4     5 #> 4055       1         3  20       61975   A5     4 #> 4056       1         3  20       61975   C1     2 #> 4057       1         3  20       61975   C2     3 #> 4058       1         3  20       61975   C3     2 #> 4059       1         3  20       61975   C4     4 #> 4060       1         3  20       61975   C5     5 #> 4061       1         3  20       61975   E1     2 #> 4062       1         3  20       61975   E2     3 #> 4063       1         3  20       61975   E3     4 #> 4064       1         3  20       61975   E4     5 #> 4065       1         3  20       61975   E5     4 #> 4066       1         3  20       61975   N1     4 #> 4067       1         3  20       61975   N2     3 #> 4068       1         3  20       61975   N3     2 #> 4069       1         3  20       61975   N4     4 #> 4070       1         3  20       61975   N5     2 #> 4071       1         3  20       61975   O1     5 #> 4072       1         3  20       61975   O2     5 #> 4073       1         3  20       61975   O3     4 #> 4074       1         3  20       61975   O4     4 #> 4075       1         3  20       61975   O5     2 #> 4076       1         3  24       61976   A1     1 #> 4077       1         3  24       61976   A2     5 #> 4078       1         3  24       61976   A3     2 #> 4079       1         3  24       61976   A4     3 #> 4080       1         3  24       61976   A5     4 #> 4081       1         3  24       61976   C1     2 #> 4082       1         3  24       61976   C2     5 #> 4083       1         3  24       61976   C3     6 #> 4084       1         3  24       61976   C4     5 #> 4085       1         3  24       61976   C5     2 #> 4086       1         3  24       61976   E1     5 #> 4087       1         3  24       61976   E2     2 #> 4088       1         3  24       61976   E3     1 #> 4089       1         3  24       61976   E4     3 #> 4090       1         3  24       61976   E5     4 #> 4091       1         3  24       61976   N1     1 #> 4092       1         3  24       61976   N2     2 #> 4093       1         3  24       61976   N3     4 #> 4094       1         3  24       61976   N4     1 #> 4095       1         3  24       61976   N5     1 #> 4096       1         3  24       61976   O1     5 #> 4097       1         3  24       61976   O2     6 #> 4098       1         3  24       61976   O3     2 #> 4099       1         3  24       61976   O4     2 #> 4100       1         3  24       61976   O5     6 #> 4101       2         3  20       61978   A1     1 #> 4102       2         3  20       61978   A2     6 #> 4103       2         3  20       61978   A3     6 #> 4104       2         3  20       61978   A4     6 #> 4105       2         3  20       61978   A5     6 #> 4106       2         3  20       61978   C1     5 #> 4107       2         3  20       61978   C2     5 #> 4108       2         3  20       61978   C3     5 #> 4109       2         3  20       61978   C4     2 #> 4110       2         3  20       61978   C5     2 #> 4111       2         3  20       61978   E1     2 #> 4112       2         3  20       61978   E2     2 #> 4113       2         3  20       61978   E3     5 #> 4114       2         3  20       61978   E4     6 #> 4115       2         3  20       61978   E5     4 #> 4116       2         3  20       61978   N1     2 #> 4117       2         3  20       61978   N2     2 #> 4118       2         3  20       61978   N3     4 #> 4119       2         3  20       61978   N4     3 #> 4120       2         3  20       61978   N5     4 #> 4121       2         3  20       61978   O1     5 #> 4122       2         3  20       61978   O2     6 #> 4123       2         3  20       61978   O3     5 #> 4124       2         3  20       61978   O4     6 #> 4125       2         3  20       61978   O5     4 #> 4126       1         5  34       61979   A1     2 #> 4127       1         5  34       61979   A2     4 #> 4128       1         5  34       61979   A3     5 #> 4129       1         5  34       61979   A4     5 #> 4130       1         5  34       61979   A5     5 #> 4131       1         5  34       61979   C1     1 #> 4132       1         5  34       61979   C2     1 #> 4133       1         5  34       61979   C3     3 #> 4134       1         5  34       61979   C4     4 #> 4135       1         5  34       61979   C5     5 #> 4136       1         5  34       61979   E1     1 #> 4137       1         5  34       61979   E2     2 #> 4138       1         5  34       61979   E3     5 #> 4139       1         5  34       61979   E4     5 #> 4140       1         5  34       61979   E5     2 #> 4141       1         5  34       61979   N1     1 #> 4142       1         5  34       61979   N2     2 #> 4143       1         5  34       61979   N3     1 #> 4144       1         5  34       61979   N4     2 #> 4145       1         5  34       61979   N5     1 #> 4146       1         5  34       61979   O1     6 #> 4147       1         5  34       61979   O2     1 #> 4148       1         5  34       61979   O3     5 #> 4149       1         5  34       61979   O4     5 #> 4150       1         5  34       61979   O5     6 #> 4151       1         3  21       61983   A1     2 #> 4152       1         3  21       61983   A2     4 #> 4153       1         3  21       61983   A3     5 #> 4154       1         3  21       61983   A4     6 #> 4155       1         3  21       61983   A5     5 #> 4156       1         3  21       61983   C1     4 #> 4157       1         3  21       61983   C2     6 #> 4158       1         3  21       61983   C3     4 #> 4159       1         3  21       61983   C4     1 #> 4160       1         3  21       61983   C5     2 #> 4161       1         3  21       61983   E1     1 #> 4162       1         3  21       61983   E2     1 #> 4163       1         3  21       61983   E3     6 #> 4164       1         3  21       61983   E4     6 #> 4165       1         3  21       61983   E5     5 #> 4166       1         3  21       61983   N1     1 #> 4167       1         3  21       61983   N2     1 #> 4168       1         3  21       61983   N3     2 #> 4169       1         3  21       61983   N4     2 #> 4170       1         3  21       61983   N5     1 #> 4171       1         3  21       61983   O1     5 #> 4172       1         3  21       61983   O2     1 #> 4173       1         3  21       61983   O3     5 #> 4174       1         3  21       61983   O4     5 #> 4175       1         3  21       61983   O5     2 #> 4176       1         3  19       61986   A1     2 #> 4177       1         3  19       61986   A2     6 #> 4178       1         3  19       61986   A3     5 #> 4179       1         3  19       61986   A4     4 #> 4180       1         3  19       61986   A5     4 #> 4181       1         3  19       61986   C1     4 #> 4182       1         3  19       61986   C2     2 #> 4183       1         3  19       61986   C3     4 #> 4184       1         3  19       61986   C4     5 #> 4185       1         3  19       61986   C5     5 #> 4186       1         3  19       61986   E1     2 #> 4187       1         3  19       61986   E2    NA #> 4188       1         3  19       61986   E3     4 #> 4189       1         3  19       61986   E4     3 #> 4190       1         3  19       61986   E5     5 #> 4191       1         3  19       61986   N1     4 #> 4192       1         3  19       61986   N2     4 #> 4193       1         3  19       61986   N3     2 #> 4194       1         3  19       61986   N4     4 #> 4195       1         3  19       61986   N5     1 #> 4196       1         3  19       61986   O1     6 #> 4197       1         3  19       61986   O2     4 #> 4198       1         3  19       61986   O3     5 #> 4199       1         3  19       61986   O4     5 #> 4200       1         3  19       61986   O5     2 #> 4201       2         2  18       61987   A1     1 #> 4202       2         2  18       61987   A2     5 #> 4203       2         2  18       61987   A3     5 #> 4204       2         2  18       61987   A4     5 #> 4205       2         2  18       61987   A5     5 #> 4206       2         2  18       61987   C1     5 #> 4207       2         2  18       61987   C2     6 #> 4208       2         2  18       61987   C3     4 #> 4209       2         2  18       61987   C4     2 #> 4210       2         2  18       61987   C5     2 #> 4211       2         2  18       61987   E1     4 #> 4212       2         2  18       61987   E2     1 #> 4213       2         2  18       61987   E3     5 #> 4214       2         2  18       61987   E4     4 #> 4215       2         2  18       61987   E5     6 #> 4216       2         2  18       61987   N1     4 #> 4217       2         2  18       61987   N2     5 #> 4218       2         2  18       61987   N3     5 #> 4219       2         2  18       61987   N4     4 #> 4220       2         2  18       61987   N5     2 #> 4221       2         2  18       61987   O1     5 #> 4222       2         2  18       61987   O2     1 #> 4223       2         2  18       61987   O3     5 #> 4224       2         2  18       61987   O4     6 #> 4225       2         2  18       61987   O5     1 #> 4226       2        NA  25       61989   A1     1 #> 4227       2        NA  25       61989   A2     6 #> 4228       2        NA  25       61989   A3     6 #> 4229       2        NA  25       61989   A4     1 #> 4230       2        NA  25       61989   A5     3 #> 4231       2        NA  25       61989   C1     6 #> 4232       2        NA  25       61989   C2     6 #> 4233       2        NA  25       61989   C3     5 #> 4234       2        NA  25       61989   C4     1 #> 4235       2        NA  25       61989   C5     4 #> 4236       2        NA  25       61989   E1     5 #> 4237       2        NA  25       61989   E2     6 #> 4238       2        NA  25       61989   E3     1 #> 4239       2        NA  25       61989   E4     4 #> 4240       2        NA  25       61989   E5     1 #> 4241       2        NA  25       61989   N1     6 #> 4242       2        NA  25       61989   N2     6 #> 4243       2        NA  25       61989   N3     6 #> 4244       2        NA  25       61989   N4     5 #> 4245       2        NA  25       61989   N5     6 #> 4246       2        NA  25       61989   O1     5 #> 4247       2        NA  25       61989   O2     1 #> 4248       2        NA  25       61989   O3     2 #> 4249       2        NA  25       61989   O4     5 #> 4250       2        NA  25       61989   O5     1 #> 4251       2         5  27       61990   A1     3 #> 4252       2         5  27       61990   A2     4 #> 4253       2         5  27       61990   A3     4 #> 4254       2         5  27       61990   A4     3 #> 4255       2         5  27       61990   A5     4 #> 4256       2         5  27       61990   C1     3 #> 4257       2         5  27       61990   C2     3 #> 4258       2         5  27       61990   C3     5 #> 4259       2         5  27       61990   C4     3 #> 4260       2         5  27       61990   C5     6 #> 4261       2         5  27       61990   E1     2 #> 4262       2         5  27       61990   E2     5 #> 4263       2         5  27       61990   E3     5 #> 4264       2         5  27       61990   E4     3 #> 4265       2         5  27       61990   E5     4 #> 4266       2         5  27       61990   N1     5 #> 4267       2         5  27       61990   N2     4 #> 4268       2         5  27       61990   N3     2 #> 4269       2         5  27       61990   N4     2 #> 4270       2         5  27       61990   N5     4 #> 4271       2         5  27       61990   O1     5 #> 4272       2         5  27       61990   O2     4 #> 4273       2         5  27       61990   O3     4 #> 4274       2         5  27       61990   O4     4 #> 4275       2         5  27       61990   O5     5 #> 4276       1         5  33       61992   A1     4 #> 4277       1         5  33       61992   A2     4 #> 4278       1         5  33       61992   A3     5 #> 4279       1         5  33       61992   A4     6 #> 4280       1         5  33       61992   A5     4 #> 4281       1         5  33       61992   C1     5 #> 4282       1         5  33       61992   C2     6 #> 4283       1         5  33       61992   C3     6 #> 4284       1         5  33       61992   C4     4 #> 4285       1         5  33       61992   C5     2 #> 4286       1         5  33       61992   E1     2 #> 4287       1         5  33       61992   E2     2 #> 4288       1         5  33       61992   E3     5 #> 4289       1         5  33       61992   E4     5 #> 4290       1         5  33       61992   E5     5 #> 4291       1         5  33       61992   N1     4 #> 4292       1         5  33       61992   N2     2 #> 4293       1         5  33       61992   N3     4 #> 4294       1         5  33       61992   N4     5 #> 4295       1         5  33       61992   N5     4 #> 4296       1         5  33       61992   O1     4 #> 4297       1         5  33       61992   O2     2 #> 4298       1         5  33       61992   O3     5 #> 4299       1         5  33       61992   O4     3 #> 4300       1         5  33       61992   O5     3 #> 4301       1         3  20       61993   A1     1 #> 4302       1         3  20       61993   A2     5 #> 4303       1         3  20       61993   A3     5 #> 4304       1         3  20       61993   A4     5 #> 4305       1         3  20       61993   A5     5 #> 4306       1         3  20       61993   C1     4 #> 4307       1         3  20       61993   C2     4 #> 4308       1         3  20       61993   C3     4 #> 4309       1         3  20       61993   C4     4 #> 4310       1         3  20       61993   C5     5 #> 4311       1         3  20       61993   E1     2 #> 4312       1         3  20       61993   E2     2 #> 4313       1         3  20       61993   E3     5 #> 4314       1         3  20       61993   E4     5 #> 4315       1         3  20       61993   E5     5 #> 4316       1         3  20       61993   N1     3 #> 4317       1         3  20       61993   N2     3 #> 4318       1         3  20       61993   N3     3 #> 4319       1         3  20       61993   N4     3 #> 4320       1         3  20       61993   N5     4 #> 4321       1         3  20       61993   O1     6 #> 4322       1         3  20       61993   O2     2 #> 4323       1         3  20       61993   O3     5 #> 4324       1         3  20       61993   O4     4 #> 4325       1         3  20       61993   O5     5 #> 4326       2         3  19       61994   A1     2 #> 4327       2         3  19       61994   A2     6 #> 4328       2         3  19       61994   A3     5 #> 4329       2         3  19       61994   A4     5 #> 4330       2         3  19       61994   A5     4 #> 4331       2         3  19       61994   C1     5 #> 4332       2         3  19       61994   C2     4 #> 4333       2         3  19       61994   C3     5 #> 4334       2         3  19       61994   C4     3 #> 4335       2         3  19       61994   C5     4 #> 4336       2         3  19       61994   E1     4 #> 4337       2         3  19       61994   E2     6 #> 4338       2         3  19       61994   E3     5 #> 4339       2         3  19       61994   E4     6 #> 4340       2         3  19       61994   E5     6 #> 4341       2         3  19       61994   N1     3 #> 4342       2         3  19       61994   N2     3 #> 4343       2         3  19       61994   N3     2 #> 4344       2         3  19       61994   N4     2 #> 4345       2         3  19       61994   N5     2 #> 4346       2         3  19       61994   O1     5 #> 4347       2         3  19       61994   O2     6 #> 4348       2         3  19       61994   O3     6 #> 4349       2         3  19       61994   O4     5 #> 4350       2         3  19       61994   O5     1 #> 4351       2        NA  16       61999   A1     2 #> 4352       2        NA  16       61999   A2     6 #> 4353       2        NA  16       61999   A3     6 #> 4354       2        NA  16       61999   A4     6 #> 4355       2        NA  16       61999   A5     5 #> 4356       2        NA  16       61999   C1     5 #> 4357       2        NA  16       61999   C2     5 #> 4358       2        NA  16       61999   C3     5 #> 4359       2        NA  16       61999   C4     2 #> 4360       2        NA  16       61999   C5     1 #> 4361       2        NA  16       61999   E1     2 #> 4362       2        NA  16       61999   E2     1 #> 4363       2        NA  16       61999   E3     5 #> 4364       2        NA  16       61999   E4     5 #> 4365       2        NA  16       61999   E5     6 #> 4366       2        NA  16       61999   N1     1 #> 4367       2        NA  16       61999   N2     2 #> 4368       2        NA  16       61999   N3     2 #> 4369       2        NA  16       61999   N4     2 #> 4370       2        NA  16       61999   N5     3 #> 4371       2        NA  16       61999   O1     5 #> 4372       2        NA  16       61999   O2     1 #> 4373       2        NA  16       61999   O3     6 #> 4374       2        NA  16       61999   O4     6 #> 4375       2        NA  16       61999   O5     3 #> 4376       2         3  18       62001   A1     1 #> 4377       2         3  18       62001   A2     2 #> 4378       2         3  18       62001   A3     2 #> 4379       2         3  18       62001   A4     4 #> 4380       2         3  18       62001   A5     3 #> 4381       2         3  18       62001   C1     6 #> 4382       2         3  18       62001   C2     4 #> 4383       2         3  18       62001   C3     6 #> 4384       2         3  18       62001   C4     5 #> 4385       2         3  18       62001   C5     6 #> 4386       2         3  18       62001   E1     4 #> 4387       2         3  18       62001   E2     5 #> 4388       2         3  18       62001   E3     1 #> 4389       2         3  18       62001   E4     6 #> 4390       2         3  18       62001   E5     3 #> 4391       2         3  18       62001   N1     4 #> 4392       2         3  18       62001   N2     6 #> 4393       2         3  18       62001   N3     6 #> 4394       2         3  18       62001   N4     4 #> 4395       2         3  18       62001   N5     5 #> 4396       2         3  18       62001   O1     3 #> 4397       2         3  18       62001   O2     4 #> 4398       2         3  18       62001   O3     3 #> 4399       2         3  18       62001   O4     6 #> 4400       2         3  18       62001   O5     4 #> 4401       2         4  35       62003   A1     1 #> 4402       2         4  35       62003   A2     6 #> 4403       2         4  35       62003   A3     5 #> 4404       2         4  35       62003   A4     6 #> 4405       2         4  35       62003   A5     6 #> 4406       2         4  35       62003   C1     4 #> 4407       2         4  35       62003   C2     5 #> 4408       2         4  35       62003   C3     3 #> 4409       2         4  35       62003   C4     3 #> 4410       2         4  35       62003   C5     4 #> 4411       2         4  35       62003   E1     6 #> 4412       2         4  35       62003   E2     3 #> 4413       2         4  35       62003   E3     4 #> 4414       2         4  35       62003   E4     5 #> 4415       2         4  35       62003   E5     5 #> 4416       2         4  35       62003   N1     1 #> 4417       2         4  35       62003   N2     2 #> 4418       2         4  35       62003   N3     2 #> 4419       2         4  35       62003   N4     2 #> 4420       2         4  35       62003   N5     3 #> 4421       2         4  35       62003   O1     6 #> 4422       2         4  35       62003   O2     1 #> 4423       2         4  35       62003   O3     5 #> 4424       2         4  35       62003   O4     6 #> 4425       2         4  35       62003   O5     1 #> 4426       2         3  23       62004   A1     6 #> 4427       2         3  23       62004   A2     5 #> 4428       2         3  23       62004   A3     6 #> 4429       2         3  23       62004   A4     6 #> 4430       2         3  23       62004   A5     5 #> 4431       2         3  23       62004   C1     6 #> 4432       2         3  23       62004   C2     6 #> 4433       2         3  23       62004   C3     6 #> 4434       2         3  23       62004   C4     1 #> 4435       2         3  23       62004   C5     3 #> 4436       2         3  23       62004   E1     6 #> 4437       2         3  23       62004   E2     6 #> 4438       2         3  23       62004   E3     4 #> 4439       2         3  23       62004   E4     4 #> 4440       2         3  23       62004   E5     5 #> 4441       2         3  23       62004   N1     4 #> 4442       2         3  23       62004   N2     5 #> 4443       2         3  23       62004   N3     4 #> 4444       2         3  23       62004   N4     4 #> 4445       2         3  23       62004   N5     4 #> 4446       2         3  23       62004   O1     6 #> 4447       2         3  23       62004   O2     1 #> 4448       2         3  23       62004   O3     3 #> 4449       2         3  23       62004   O4     6 #> 4450       2         3  23       62004   O5     3 #> 4451       2         5  27       62005   A1     4 #> 4452       2         5  27       62005   A2     3 #> 4453       2         5  27       62005   A3     4 #> 4454       2         5  27       62005   A4     3 #> 4455       2         5  27       62005   A5     4 #> 4456       2         5  27       62005   C1     4 #> 4457       2         5  27       62005   C2     3 #> 4458       2         5  27       62005   C3     5 #> 4459       2         5  27       62005   C4     4 #> 4460       2         5  27       62005   C5     4 #> 4461       2         5  27       62005   E1     2 #> 4462       2         5  27       62005   E2     4 #> 4463       2         5  27       62005   E3     4 #> 4464       2         5  27       62005   E4     3 #> 4465       2         5  27       62005   E5     4 #> 4466       2         5  27       62005   N1     5 #> 4467       2         5  27       62005   N2     5 #> 4468       2         5  27       62005   N3     3 #> 4469       2         5  27       62005   N4     2 #> 4470       2         5  27       62005   N5     3 #> 4471       2         5  27       62005   O1     5 #> 4472       2         5  27       62005   O2     4 #> 4473       2         5  27       62005   O3     4 #> 4474       2         5  27       62005   O4     4 #> 4475       2         5  27       62005   O5     4 #> 4476       1         3  18       62007   A1     3 #> 4477       1         3  18       62007   A2     4 #> 4478       1         3  18       62007   A3     2 #> 4479       1         3  18       62007   A4     2 #> 4480       1         3  18       62007   A5     3 #> 4481       1         3  18       62007   C1     3 #> 4482       1         3  18       62007   C2     2 #> 4483       1         3  18       62007   C3     2 #> 4484       1         3  18       62007   C4     4 #> 4485       1         3  18       62007   C5     1 #> 4486       1         3  18       62007   E1     2 #> 4487       1         3  18       62007   E2     2 #> 4488       1         3  18       62007   E3     5 #> 4489       1         3  18       62007   E4     4 #> 4490       1         3  18       62007   E5     5 #> 4491       1         3  18       62007   N1     2 #> 4492       1         3  18       62007   N2     3 #> 4493       1         3  18       62007   N3     1 #> 4494       1         3  18       62007   N4     1 #> 4495       1         3  18       62007   N5     1 #> 4496       1         3  18       62007   O1     6 #> 4497       1         3  18       62007   O2     3 #> 4498       1         3  18       62007   O3     6 #> 4499       1         3  18       62007   O4     5 #> 4500       1         3  18       62007   O5     1 #> 4501       1         2  31       62009   A1     4 #> 4502       1         2  31       62009   A2     3 #> 4503       1         2  31       62009   A3     4 #> 4504       1         2  31       62009   A4     3 #> 4505       1         2  31       62009   A5     3 #> 4506       1         2  31       62009   C1     5 #> 4507       1         2  31       62009   C2     2 #> 4508       1         2  31       62009   C3     4 #> 4509       1         2  31       62009   C4     4 #> 4510       1         2  31       62009   C5     5 #> 4511       1         2  31       62009   E1     3 #> 4512       1         2  31       62009   E2     5 #> 4513       1         2  31       62009   E3     4 #> 4514       1         2  31       62009   E4     1 #> 4515       1         2  31       62009   E5     4 #> 4516       1         2  31       62009   N1     5 #> 4517       1         2  31       62009   N2     5 #> 4518       1         2  31       62009   N3     5 #> 4519       1         2  31       62009   N4     4 #> 4520       1         2  31       62009   N5     4 #> 4521       1         2  31       62009   O1     6 #> 4522       1         2  31       62009   O2     1 #> 4523       1         2  31       62009   O3     6 #> 4524       1         2  31       62009   O4     6 #> 4525       1         2  31       62009   O5     1 #> 4526       2        NA  17       62011   A1     1 #> 4527       2        NA  17       62011   A2     6 #> 4528       2        NA  17       62011   A3     5 #> 4529       2        NA  17       62011   A4     6 #> 4530       2        NA  17       62011   A5     6 #> 4531       2        NA  17       62011   C1     5 #> 4532       2        NA  17       62011   C2     4 #> 4533       2        NA  17       62011   C3     5 #> 4534       2        NA  17       62011   C4     2 #> 4535       2        NA  17       62011   C5     4 #> 4536       2        NA  17       62011   E1     3 #> 4537       2        NA  17       62011   E2     4 #> 4538       2        NA  17       62011   E3     5 #> 4539       2        NA  17       62011   E4     6 #> 4540       2        NA  17       62011   E5     4 #> 4541       2        NA  17       62011   N1     2 #> 4542       2        NA  17       62011   N2     3 #> 4543       2        NA  17       62011   N3     2 #> 4544       2        NA  17       62011   N4     1 #> 4545       2        NA  17       62011   N5     1 #> 4546       2        NA  17       62011   O1     5 #> 4547       2        NA  17       62011   O2     5 #> 4548       2        NA  17       62011   O3     3 #> 4549       2        NA  17       62011   O4     5 #> 4550       2        NA  17       62011   O5     3 #> 4551       1         1  53       62013   A1     3 #> 4552       1         1  53       62013   A2     5 #> 4553       1         1  53       62013   A3     4 #> 4554       1         1  53       62013   A4     4 #> 4555       1         1  53       62013   A5     4 #> 4556       1         1  53       62013   C1     3 #> 4557       1         1  53       62013   C2     3 #> 4558       1         1  53       62013   C3     4 #> 4559       1         1  53       62013   C4     4 #> 4560       1         1  53       62013   C5     4 #> 4561       1         1  53       62013   E1     5 #> 4562       1         1  53       62013   E2     4 #> 4563       1         1  53       62013   E3     3 #> 4564       1         1  53       62013   E4     2 #> 4565       1         1  53       62013   E5     4 #> 4566       1         1  53       62013   N1     2 #> 4567       1         1  53       62013   N2     3 #> 4568       1         1  53       62013   N3     2 #> 4569       1         1  53       62013   N4     3 #> 4570       1         1  53       62013   N5     2 #> 4571       1         1  53       62013   O1     5 #> 4572       1         1  53       62013   O2     4 #> 4573       1         1  53       62013   O3     4 #> 4574       1         1  53       62013   O4     3 #> 4575       1         1  53       62013   O5     4 #> 4576       1         5  29       62014   A1     4 #> 4577       1         5  29       62014   A2     2 #> 4578       1         5  29       62014   A3     2 #> 4579       1         5  29       62014   A4     3 #> 4580       1         5  29       62014   A5     3 #> 4581       1         5  29       62014   C1     4 #> 4582       1         5  29       62014   C2     3 #> 4583       1         5  29       62014   C3     3 #> 4584       1         5  29       62014   C4     4 #> 4585       1         5  29       62014   C5     5 #> 4586       1         5  29       62014   E1     4 #> 4587       1         5  29       62014   E2     5 #> 4588       1         5  29       62014   E3     2 #> 4589       1         5  29       62014   E4     2 #> 4590       1         5  29       62014   E5     5 #> 4591       1         5  29       62014   N1     5 #> 4592       1         5  29       62014   N2     4 #> 4593       1         5  29       62014   N3     4 #> 4594       1         5  29       62014   N4     5 #> 4595       1         5  29       62014   N5     4 #> 4596       1         5  29       62014   O1     5 #> 4597       1         5  29       62014   O2     2 #> 4598       1         5  29       62014   O3     5 #> 4599       1         5  29       62014   O4     6 #> 4600       1         5  29       62014   O5     2 #> 4601       1         2  19       62015   A1     3 #> 4602       1         2  19       62015   A2     4 #> 4603       1         2  19       62015   A3     4 #> 4604       1         2  19       62015   A4     3 #> 4605       1         2  19       62015   A5     3 #> 4606       1         2  19       62015   C1     5 #> 4607       1         2  19       62015   C2     5 #> 4608       1         2  19       62015   C3     4 #> 4609       1         2  19       62015   C4     3 #> 4610       1         2  19       62015   C5     2 #> 4611       1         2  19       62015   E1     3 #> 4612       1         2  19       62015   E2     4 #> 4613       1         2  19       62015   E3     4 #> 4614       1         2  19       62015   E4     2 #> 4615       1         2  19       62015   E5     3 #> 4616       1         2  19       62015   N1     4 #> 4617       1         2  19       62015   N2     4 #> 4618       1         2  19       62015   N3     5 #> 4619       1         2  19       62015   N4     4 #> 4620       1         2  19       62015   N5     4 #> 4621       1         2  19       62015   O1     6 #> 4622       1         2  19       62015   O2     1 #> 4623       1         2  19       62015   O3     6 #> 4624       1         2  19       62015   O4     6 #> 4625       1         2  19       62015   O5     1 #> 4626       2         1  29       62022   A1     1 #> 4627       2         1  29       62022   A2     6 #> 4628       2         1  29       62022   A3     5 #> 4629       2         1  29       62022   A4     6 #> 4630       2         1  29       62022   A5     3 #> 4631       2         1  29       62022   C1     3 #> 4632       2         1  29       62022   C2     5 #> 4633       2         1  29       62022   C3     1 #> 4634       2         1  29       62022   C4     1 #> 4635       2         1  29       62022   C5     6 #> 4636       2         1  29       62022   E1     1 #> 4637       2         1  29       62022   E2     3 #> 4638       2         1  29       62022   E3     3 #> 4639       2         1  29       62022   E4     3 #> 4640       2         1  29       62022   E5     6 #> 4641       2         1  29       62022   N1     6 #> 4642       2         1  29       62022   N2     6 #> 4643       2         1  29       62022   N3     6 #> 4644       2         1  29       62022   N4     2 #> 4645       2         1  29       62022   N5     6 #> 4646       2         1  29       62022   O1     3 #> 4647       2         1  29       62022   O2     6 #> 4648       2         1  29       62022   O3     1 #> 4649       2         1  29       62022   O4     1 #> 4650       2         1  29       62022   O5     6 #> 4651       1         5  41       62023   A1     1 #> 4652       1         5  41       62023   A2     5 #> 4653       1         5  41       62023   A3     5 #> 4654       1         5  41       62023   A4     4 #> 4655       1         5  41       62023   A5     5 #> 4656       1         5  41       62023   C1     4 #> 4657       1         5  41       62023   C2     4 #> 4658       1         5  41       62023   C3     4 #> 4659       1         5  41       62023   C4     4 #> 4660       1         5  41       62023   C5     4 #> 4661       1         5  41       62023   E1     4 #> 4662       1         5  41       62023   E2     1 #> 4663       1         5  41       62023   E3     5 #> 4664       1         5  41       62023   E4     5 #> 4665       1         5  41       62023   E5     4 #> 4666       1         5  41       62023   N1     1 #> 4667       1         5  41       62023   N2     2 #> 4668       1         5  41       62023   N3     1 #> 4669       1         5  41       62023   N4     1 #> 4670       1         5  41       62023   N5     2 #> 4671       1         5  41       62023   O1     6 #> 4672       1         5  41       62023   O2     4 #> 4673       1         5  41       62023   O3     6 #> 4674       1         5  41       62023   O4     6 #> 4675       1         5  41       62023   O5     1 #> 4676       2         5  31       62024   A1     2 #> 4677       2         5  31       62024   A2     2 #> 4678       2         5  31       62024   A3     1 #> 4679       2         5  31       62024   A4     6 #> 4680       2         5  31       62024   A5     3 #> 4681       2         5  31       62024   C1     6 #> 4682       2         5  31       62024   C2     6 #> 4683       2         5  31       62024   C3     6 #> 4684       2         5  31       62024   C4     1 #> 4685       2         5  31       62024   C5     1 #> 4686       2         5  31       62024   E1     2 #> 4687       2         5  31       62024   E2     2 #> 4688       2         5  31       62024   E3     5 #> 4689       2         5  31       62024   E4     6 #> 4690       2         5  31       62024   E5     6 #> 4691       2         5  31       62024   N1     1 #> 4692       2         5  31       62024   N2     3 #> 4693       2         5  31       62024   N3     1 #> 4694       2         5  31       62024   N4     1 #> 4695       2         5  31       62024   N5     1 #> 4696       2         5  31       62024   O1     6 #> 4697       2         5  31       62024   O2     1 #> 4698       2         5  31       62024   O3     6 #> 4699       2         5  31       62024   O4     6 #> 4700       2         5  31       62024   O5     2 #> 4701       2         3  45       62025   A1     2 #> 4702       2         3  45       62025   A2     4 #> 4703       2         3  45       62025   A3     4 #> 4704       2         3  45       62025   A4     5 #> 4705       2         3  45       62025   A5     5 #> 4706       2         3  45       62025   C1     5 #> 4707       2         3  45       62025   C2     5 #> 4708       2         3  45       62025   C3     5 #> 4709       2         3  45       62025   C4     1 #> 4710       2         3  45       62025   C5     2 #> 4711       2         3  45       62025   E1     6 #> 4712       2         3  45       62025   E2     5 #> 4713       2         3  45       62025   E3     2 #> 4714       2         3  45       62025   E4     5 #> 4715       2         3  45       62025   E5     5 #> 4716       2         3  45       62025   N1     5 #> 4717       2         3  45       62025   N2     5 #> 4718       2         3  45       62025   N3     5 #> 4719       2         3  45       62025   N4     6 #> 4720       2         3  45       62025   N5     3 #> 4721       2         3  45       62025   O1     2 #> 4722       2         3  45       62025   O2     2 #> 4723       2         3  45       62025   O3     3 #> 4724       2         3  45       62025   O4     5 #> 4725       2         3  45       62025   O5     2 #> 4726       2         2  47       62026   A1     1 #> 4727       2         2  47       62026   A2     6 #> 4728       2         2  47       62026   A3     6 #> 4729       2         2  47       62026   A4     2 #> 4730       2         2  47       62026   A5     5 #> 4731       2         2  47       62026   C1     5 #> 4732       2         2  47       62026   C2     5 #> 4733       2         2  47       62026   C3     5 #> 4734       2         2  47       62026   C4     1 #> 4735       2         2  47       62026   C5     2 #> 4736       2         2  47       62026   E1     2 #> 4737       2         2  47       62026   E2     2 #> 4738       2         2  47       62026   E3     6 #> 4739       2         2  47       62026   E4     6 #> 4740       2         2  47       62026   E5     6 #> 4741       2         2  47       62026   N1     6 #> 4742       2         2  47       62026   N2     6 #> 4743       2         2  47       62026   N3     4 #> 4744       2         2  47       62026   N4     2 #> 4745       2         2  47       62026   N5     4 #> 4746       2         2  47       62026   O1     6 #> 4747       2         2  47       62026   O2     1 #> 4748       2         2  47       62026   O3     5 #> 4749       2         2  47       62026   O4     5 #> 4750       2         2  47       62026   O5     1 #> 4751       1         4  27       62029   A1     2 #> 4752       1         4  27       62029   A2     3 #> 4753       1         4  27       62029   A3     4 #> 4754       1         4  27       62029   A4     4 #> 4755       1         4  27       62029   A5     4 #> 4756       1         4  27       62029   C1     3 #> 4757       1         4  27       62029   C2     1 #> 4758       1         4  27       62029   C3     1 #> 4759       1         4  27       62029   C4     3 #> 4760       1         4  27       62029   C5     2 #> 4761       1         4  27       62029   E1     3 #> 4762       1         4  27       62029   E2     3 #> 4763       1         4  27       62029   E3     3 #> 4764       1         4  27       62029   E4     3 #> 4765       1         4  27       62029   E5     4 #> 4766       1         4  27       62029   N1     2 #> 4767       1         4  27       62029   N2     3 #> 4768       1         4  27       62029   N3     2 #> 4769       1         4  27       62029   N4     3 #> 4770       1         4  27       62029   N5     2 #> 4771       1         4  27       62029   O1     6 #> 4772       1         4  27       62029   O2     2 #> 4773       1         4  27       62029   O3     4 #> 4774       1         4  27       62029   O4     5 #> 4775       1         4  27       62029   O5     2 #> 4776       2         2  24       62031   A1     1 #> 4777       2         2  24       62031   A2     6 #> 4778       2         2  24       62031   A3     4 #> 4779       2         2  24       62031   A4     3 #> 4780       2         2  24       62031   A5     6 #> 4781       2         2  24       62031   C1     4 #> 4782       2         2  24       62031   C2     4 #> 4783       2         2  24       62031   C3     5 #> 4784       2         2  24       62031   C4     2 #> 4785       2         2  24       62031   C5     5 #> 4786       2         2  24       62031   E1     2 #> 4787       2         2  24       62031   E2     5 #> 4788       2         2  24       62031   E3     4 #> 4789       2         2  24       62031   E4     5 #> 4790       2         2  24       62031   E5     6 #> 4791       2         2  24       62031   N1     4 #> 4792       2         2  24       62031   N2     5 #> 4793       2         2  24       62031   N3     5 #> 4794       2         2  24       62031   N4     2 #> 4795       2         2  24       62031   N5     6 #> 4796       2         2  24       62031   O1     5 #> 4797       2         2  24       62031   O2     2 #> 4798       2         2  24       62031   O3     6 #> 4799       2         2  24       62031   O4     6 #> 4800       2         2  24       62031   O5     1 #> 4801       1         4  20       62032   A1     2 #> 4802       1         4  20       62032   A2     3 #> 4803       1         4  20       62032   A3     4 #> 4804       1         4  20       62032   A4     5 #> 4805       1         4  20       62032   A5     3 #> 4806       1         4  20       62032   C1     4 #> 4807       1         4  20       62032   C2     5 #> 4808       1         4  20       62032   C3     4 #> 4809       1         4  20       62032   C4     2 #> 4810       1         4  20       62032   C5     5 #> 4811       1         4  20       62032   E1     5 #> 4812       1         4  20       62032   E2     3 #> 4813       1         4  20       62032   E3     3 #> 4814       1         4  20       62032   E4     4 #> 4815       1         4  20       62032   E5     4 #> 4816       1         4  20       62032   N1     2 #> 4817       1         4  20       62032   N2     2 #> 4818       1         4  20       62032   N3     4 #> 4819       1         4  20       62032   N4     4 #> 4820       1         4  20       62032   N5     3 #> 4821       1         4  20       62032   O1     4 #> 4822       1         4  20       62032   O2     3 #> 4823       1         4  20       62032   O3     4 #> 4824       1         4  20       62032   O4     5 #> 4825       1         4  20       62032   O5     3 #> 4826       1         2  23       62033   A1     5 #> 4827       1         2  23       62033   A2     4 #> 4828       1         2  23       62033   A3     3 #> 4829       1         2  23       62033   A4     5 #> 4830       1         2  23       62033   A5     4 #> 4831       1         2  23       62033   C1     4 #> 4832       1         2  23       62033   C2     4 #> 4833       1         2  23       62033   C3     4 #> 4834       1         2  23       62033   C4     4 #> 4835       1         2  23       62033   C5     5 #> 4836       1         2  23       62033   E1     1 #> 4837       1         2  23       62033   E2     5 #> 4838       1         2  23       62033   E3     5 #> 4839       1         2  23       62033   E4     6 #> 4840       1         2  23       62033   E5     4 #> 4841       1         2  23       62033   N1     1 #> 4842       1         2  23       62033   N2     1 #> 4843       1         2  23       62033   N3     1 #> 4844       1         2  23       62033   N4     4 #> 4845       1         2  23       62033   N5     5 #> 4846       1         2  23       62033   O1     5 #> 4847       1         2  23       62033   O2     4 #> 4848       1         2  23       62033   O3     4 #> 4849       1         2  23       62033   O4     4 #> 4850       1         2  23       62033   O5     5 #> 4851       1         5  28       62034   A1     1 #> 4852       1         5  28       62034   A2     5 #> 4853       1         5  28       62034   A3     4 #> 4854       1         5  28       62034   A4     4 #> 4855       1         5  28       62034   A5     4 #> 4856       1         5  28       62034   C1     5 #> 4857       1         5  28       62034   C2     5 #> 4858       1         5  28       62034   C3     5 #> 4859       1         5  28       62034   C4     2 #> 4860       1         5  28       62034   C5     4 #> 4861       1         5  28       62034   E1     3 #> 4862       1         5  28       62034   E2     4 #> 4863       1         5  28       62034   E3     2 #> 4864       1         5  28       62034   E4     4 #> 4865       1         5  28       62034   E5     4 #> 4866       1         5  28       62034   N1     4 #> 4867       1         5  28       62034   N2     4 #> 4868       1         5  28       62034   N3     4 #> 4869       1         5  28       62034   N4     3 #> 4870       1         5  28       62034   N5     4 #> 4871       1         5  28       62034   O1     4 #> 4872       1         5  28       62034   O2     2 #> 4873       1         5  28       62034   O3     3 #> 4874       1         5  28       62034   O4     5 #> 4875       1         5  28       62034   O5     2 #> 4876       2        NA  16       62038   A1     1 #> 4877       2        NA  16       62038   A2     6 #> 4878       2        NA  16       62038   A3     6 #> 4879       2        NA  16       62038   A4     5 #> 4880       2        NA  16       62038   A5     3 #> 4881       2        NA  16       62038   C1     1 #> 4882       2        NA  16       62038   C2     3 #> 4883       2        NA  16       62038   C3     2 #> 4884       2        NA  16       62038   C4     2 #> 4885       2        NA  16       62038   C5     5 #> 4886       2        NA  16       62038   E1     6 #> 4887       2        NA  16       62038   E2     6 #> 4888       2        NA  16       62038   E3     5 #> 4889       2        NA  16       62038   E4     1 #> 4890       2        NA  16       62038   E5     2 #> 4891       2        NA  16       62038   N1     6 #> 4892       2        NA  16       62038   N2     4 #> 4893       2        NA  16       62038   N3     6 #> 4894       2        NA  16       62038   N4     6 #> 4895       2        NA  16       62038   N5     3 #> 4896       2        NA  16       62038   O1     3 #> 4897       2        NA  16       62038   O2     1 #> 4898       2        NA  16       62038   O3     3 #> 4899       2        NA  16       62038   O4     3 #> 4900       2        NA  16       62038   O5     4 #> 4901       1         1  18       62039   A1     2 #> 4902       1         1  18       62039   A2     5 #> 4903       1         1  18       62039   A3     6 #> 4904       1         1  18       62039   A4     3 #> 4905       1         1  18       62039   A5     5 #> 4906       1         1  18       62039   C1     5 #> 4907       1         1  18       62039   C2     3 #> 4908       1         1  18       62039   C3     3 #> 4909       1         1  18       62039   C4     3 #> 4910       1         1  18       62039   C5     4 #> 4911       1         1  18       62039   E1     1 #> 4912       1         1  18       62039   E2     2 #> 4913       1         1  18       62039   E3     6 #> 4914       1         1  18       62039   E4     5 #> 4915       1         1  18       62039   E5     6 #> 4916       1         1  18       62039   N1     5 #> 4917       1         1  18       62039   N2     4 #> 4918       1         1  18       62039   N3     1 #> 4919       1         1  18       62039   N4     2 #> 4920       1         1  18       62039   N5     2 #> 4921       1         1  18       62039   O1     4 #> 4922       1         1  18       62039   O2     4 #> 4923       1         1  18       62039   O3     5 #> 4924       1         1  18       62039   O4     4 #> 4925       1         1  18       62039   O5     2 #> 4926       2        NA  16       62041   A1     2 #> 4927       2        NA  16       62041   A2     4 #> 4928       2        NA  16       62041   A3     5 #> 4929       2        NA  16       62041   A4     6 #> 4930       2        NA  16       62041   A5     5 #> 4931       2        NA  16       62041   C1     2 #> 4932       2        NA  16       62041   C2     4 #> 4933       2        NA  16       62041   C3     4 #> 4934       2        NA  16       62041   C4     3 #> 4935       2        NA  16       62041   C5     2 #> 4936       2        NA  16       62041   E1     4 #> 4937       2        NA  16       62041   E2     4 #> 4938       2        NA  16       62041   E3     3 #> 4939       2        NA  16       62041   E4     5 #> 4940       2        NA  16       62041   E5     2 #> 4941       2        NA  16       62041   N1     2 #> 4942       2        NA  16       62041   N2     4 #> 4943       2        NA  16       62041   N3     3 #> 4944       2        NA  16       62041   N4     2 #> 4945       2        NA  16       62041   N5     3 #> 4946       2        NA  16       62041   O1     3 #> 4947       2        NA  16       62041   O2     1 #> 4948       2        NA  16       62041   O3     3 #> 4949       2        NA  16       62041   O4     5 #> 4950       2        NA  16       62041   O5     3 #> 4951       1        NA  17       62042   A1     2 #> 4952       1        NA  17       62042   A2     6 #> 4953       1        NA  17       62042   A3     5 #> 4954       1        NA  17       62042   A4     5 #> 4955       1        NA  17       62042   A5     5 #> 4956       1        NA  17       62042   C1     2 #> 4957       1        NA  17       62042   C2     4 #> 4958       1        NA  17       62042   C3     3 #> 4959       1        NA  17       62042   C4     4 #> 4960       1        NA  17       62042   C5     6 #> 4961       1        NA  17       62042   E1     1 #> 4962       1        NA  17       62042   E2     3 #> 4963       1        NA  17       62042   E3     5 #> 4964       1        NA  17       62042   E4     6 #> 4965       1        NA  17       62042   E5     6 #> 4966       1        NA  17       62042   N1     1 #> 4967       1        NA  17       62042   N2     1 #> 4968       1        NA  17       62042   N3     2 #> 4969       1        NA  17       62042   N4     1 #> 4970       1        NA  17       62042   N5     1 #> 4971       1        NA  17       62042   O1     6 #> 4972       1        NA  17       62042   O2     3 #> 4973       1        NA  17       62042   O3     4 #> 4974       1        NA  17       62042   O4     5 #> 4975       1        NA  17       62042   O5     3 #> 4976       2         1  18       62043   A1     1 #> 4977       2         1  18       62043   A2     5 #> 4978       2         1  18       62043   A3     6 #> 4979       2         1  18       62043   A4     4 #> 4980       2         1  18       62043   A5     5 #> 4981       2         1  18       62043   C1     3 #> 4982       2         1  18       62043   C2     2 #> 4983       2         1  18       62043   C3     5 #> 4984       2         1  18       62043   C4     2 #> 4985       2         1  18       62043   C5     1 #> 4986       2         1  18       62043   E1     1 #> 4987       2         1  18       62043   E2     1 #> 4988       2         1  18       62043   E3     2 #> 4989       2         1  18       62043   E4     5 #> 4990       2         1  18       62043   E5     4 #> 4991       2         1  18       62043   N1     1 #> 4992       2         1  18       62043   N2     2 #> 4993       2         1  18       62043   N3     2 #> 4994       2         1  18       62043   N4     2 #> 4995       2         1  18       62043   N5     4 #> 4996       2         1  18       62043   O1     5 #> 4997       2         1  18       62043   O2     1 #> 4998       2         1  18       62043   O3     5 #> 4999       2         1  18       62043   O4     5 #> 5000       2         1  18       62043   O5     1 #> 5001       1        NA  16       62044   A1     2 #> 5002       1        NA  16       62044   A2     5 #> 5003       1        NA  16       62044   A3     5 #> 5004       1        NA  16       62044   A4     5 #> 5005       1        NA  16       62044   A5     5 #> 5006       1        NA  16       62044   C1     5 #> 5007       1        NA  16       62044   C2     6 #> 5008       1        NA  16       62044   C3     5 #> 5009       1        NA  16       62044   C4     1 #> 5010       1        NA  16       62044   C5     2 #> 5011       1        NA  16       62044   E1     2 #> 5012       1        NA  16       62044   E2     3 #> 5013       1        NA  16       62044   E3     3 #> 5014       1        NA  16       62044   E4     5 #> 5015       1        NA  16       62044   E5     4 #> 5016       1        NA  16       62044   N1     1 #> 5017       1        NA  16       62044   N2     3 #> 5018       1        NA  16       62044   N3     1 #> 5019       1        NA  16       62044   N4     2 #> 5020       1        NA  16       62044   N5     5 #> 5021       1        NA  16       62044   O1     4 #> 5022       1        NA  16       62044   O2     3 #> 5023       1        NA  16       62044   O3     4 #> 5024       1        NA  16       62044   O4     5 #> 5025       1        NA  16       62044   O5     5 #> 5026       2        NA  16       62047   A1     1 #> 5027       2        NA  16       62047   A2     5 #> 5028       2        NA  16       62047   A3     2 #> 5029       2        NA  16       62047   A4     2 #> 5030       2        NA  16       62047   A5     2 #> 5031       2        NA  16       62047   C1     2 #> 5032       2        NA  16       62047   C2     2 #> 5033       2        NA  16       62047   C3     6 #> 5034       2        NA  16       62047   C4     2 #> 5035       2        NA  16       62047   C5     3 #> 5036       2        NA  16       62047   E1     5 #> 5037       2        NA  16       62047   E2     6 #> 5038       2        NA  16       62047   E3     4 #> 5039       2        NA  16       62047   E4     1 #> 5040       2        NA  16       62047   E5     5 #> 5041       2        NA  16       62047   N1     3 #> 5042       2        NA  16       62047   N2     5 #> 5043       2        NA  16       62047   N3     5 #> 5044       2        NA  16       62047   N4     5 #> 5045       2        NA  16       62047   N5     4 #> 5046       2        NA  16       62047   O1     2 #> 5047       2        NA  16       62047   O2     5 #> 5048       2        NA  16       62047   O3     4 #> 5049       2        NA  16       62047   O4     6 #> 5050       2        NA  16       62047   O5     1 #> 5051       2         1  18       62048   A1     3 #> 5052       2         1  18       62048   A2     3 #> 5053       2         1  18       62048   A3     5 #> 5054       2         1  18       62048   A4     5 #> 5055       2         1  18       62048   A5     5 #> 5056       2         1  18       62048   C1     4 #> 5057       2         1  18       62048   C2     4 #> 5058       2         1  18       62048   C3     4 #> 5059       2         1  18       62048   C4     3 #> 5060       2         1  18       62048   C5     4 #> 5061       2         1  18       62048   E1     3 #> 5062       2         1  18       62048   E2     2 #> 5063       2         1  18       62048   E3     3 #> 5064       2         1  18       62048   E4     5 #> 5065       2         1  18       62048   E5     4 #> 5066       2         1  18       62048   N1     3 #> 5067       2         1  18       62048   N2     5 #> 5068       2         1  18       62048   N3     3 #> 5069       2         1  18       62048   N4     3 #> 5070       2         1  18       62048   N5     4 #> 5071       2         1  18       62048   O1     4 #> 5072       2         1  18       62048   O2     4 #> 5073       2         1  18       62048   O3     3 #> 5074       2         1  18       62048   O4     2 #> 5075       2         1  18       62048   O5     3 #> 5076       1        NA  17       62051   A1     2 #> 5077       1        NA  17       62051   A2     4 #> 5078       1        NA  17       62051   A3     5 #> 5079       1        NA  17       62051   A4     6 #> 5080       1        NA  17       62051   A5     5 #> 5081       1        NA  17       62051   C1     5 #> 5082       1        NA  17       62051   C2     6 #> 5083       1        NA  17       62051   C3     4 #> 5084       1        NA  17       62051   C4     1 #> 5085       1        NA  17       62051   C5     2 #> 5086       1        NA  17       62051   E1     2 #> 5087       1        NA  17       62051   E2     1 #> 5088       1        NA  17       62051   E3     4 #> 5089       1        NA  17       62051   E4     6 #> 5090       1        NA  17       62051   E5     5 #> 5091       1        NA  17       62051   N1     1 #> 5092       1        NA  17       62051   N2     2 #> 5093       1        NA  17       62051   N3     1 #> 5094       1        NA  17       62051   N4     1 #> 5095       1        NA  17       62051   N5     1 #> 5096       1        NA  17       62051   O1     5 #> 5097       1        NA  17       62051   O2     3 #> 5098       1        NA  17       62051   O3     4 #> 5099       1        NA  17       62051   O4     3 #> 5100       1        NA  17       62051   O5     2 #> 5101       1        NA  17       62052   A1     1 #> 5102       1        NA  17       62052   A2     6 #> 5103       1        NA  17       62052   A3     5 #> 5104       1        NA  17       62052   A4     5 #> 5105       1        NA  17       62052   A5     5 #> 5106       1        NA  17       62052   C1     5 #> 5107       1        NA  17       62052   C2     4 #> 5108       1        NA  17       62052   C3     4 #> 5109       1        NA  17       62052   C4     3 #> 5110       1        NA  17       62052   C5     4 #> 5111       1        NA  17       62052   E1     3 #> 5112       1        NA  17       62052   E2     2 #> 5113       1        NA  17       62052   E3     4 #> 5114       1        NA  17       62052   E4     5 #> 5115       1        NA  17       62052   E5     4 #> 5116       1        NA  17       62052   N1     1 #> 5117       1        NA  17       62052   N2     2 #> 5118       1        NA  17       62052   N3     4 #> 5119       1        NA  17       62052   N4     2 #> 5120       1        NA  17       62052   N5     1 #> 5121       1        NA  17       62052   O1     6 #> 5122       1        NA  17       62052   O2     2 #> 5123       1        NA  17       62052   O3     4 #> 5124       1        NA  17       62052   O4     5 #> 5125       1        NA  17       62052   O5     3 #> 5126       1        NA  16       62054   A1     2 #> 5127       1        NA  16       62054   A2     6 #> 5128       1        NA  16       62054   A3     4 #> 5129       1        NA  16       62054   A4     6 #> 5130       1        NA  16       62054   A5     2 #> 5131       1        NA  16       62054   C1     4 #> 5132       1        NA  16       62054   C2     5 #> 5133       1        NA  16       62054   C3     5 #> 5134       1        NA  16       62054   C4     1 #> 5135       1        NA  16       62054   C5     1 #> 5136       1        NA  16       62054   E1     6 #> 5137       1        NA  16       62054   E2     4 #> 5138       1        NA  16       62054   E3     5 #> 5139       1        NA  16       62054   E4     4 #> 5140       1        NA  16       62054   E5     6 #> 5141       1        NA  16       62054   N1     1 #> 5142       1        NA  16       62054   N2     2 #> 5143       1        NA  16       62054   N3     1 #> 5144       1        NA  16       62054   N4     5 #> 5145       1        NA  16       62054   N5     4 #> 5146       1        NA  16       62054   O1     5 #> 5147       1        NA  16       62054   O2     2 #> 5148       1        NA  16       62054   O3     1 #> 5149       1        NA  16       62054   O4     5 #> 5150       1        NA  16       62054   O5     1 #> 5151       2         1  18       62055   A1     2 #> 5152       2         1  18       62055   A2     5 #> 5153       2         1  18       62055   A3     5 #> 5154       2         1  18       62055   A4     6 #> 5155       2         1  18       62055   A5     4 #> 5156       2         1  18       62055   C1     2 #> 5157       2         1  18       62055   C2     4 #> 5158       2         1  18       62055   C3     2 #> 5159       2         1  18       62055   C4     2 #> 5160       2         1  18       62055   C5     4 #> 5161       2         1  18       62055   E1     5 #> 5162       2         1  18       62055   E2     4 #> 5163       2         1  18       62055   E3     3 #> 5164       2         1  18       62055   E4     3 #> 5165       2         1  18       62055   E5     5 #> 5166       2         1  18       62055   N1     4 #> 5167       2         1  18       62055   N2     4 #> 5168       2         1  18       62055   N3     4 #> 5169       2         1  18       62055   N4     3 #> 5170       2         1  18       62055   N5     2 #> 5171       2         1  18       62055   O1     3 #> 5172       2         1  18       62055   O2     2 #> 5173       2         1  18       62055   O3     3 #> 5174       2         1  18       62055   O4     4 #> 5175       2         1  18       62055   O5     3 #> 5176       2         5  50       62056   A1     1 #> 5177       2         5  50       62056   A2     4 #> 5178       2         5  50       62056   A3    NA #> 5179       2         5  50       62056   A4     6 #> 5180       2         5  50       62056   A5     6 #> 5181       2         5  50       62056   C1     6 #> 5182       2         5  50       62056   C2     4 #> 5183       2         5  50       62056   C3     4 #> 5184       2         5  50       62056   C4     1 #> 5185       2         5  50       62056   C5     1 #> 5186       2         5  50       62056   E1     6 #> 5187       2         5  50       62056   E2     6 #> 5188       2         5  50       62056   E3     6 #> 5189       2         5  50       62056   E4     1 #> 5190       2         5  50       62056   E5     6 #> 5191       2         5  50       62056   N1     1 #> 5192       2         5  50       62056   N2     1 #> 5193       2         5  50       62056   N3     1 #> 5194       2         5  50       62056   N4     1 #> 5195       2         5  50       62056   N5     1 #> 5196       2         5  50       62056   O1     6 #> 5197       2         5  50       62056   O2     1 #> 5198       2         5  50       62056   O3     4 #> 5199       2         5  50       62056   O4     6 #> 5200       2         5  50       62056   O5     1 #> 5201       2         4  25       62059   A1     1 #> 5202       2         4  25       62059   A2     5 #> 5203       2         4  25       62059   A3     6 #> 5204       2         4  25       62059   A4     5 #> 5205       2         4  25       62059   A5     5 #> 5206       2         4  25       62059   C1     3 #> 5207       2         4  25       62059   C2     3 #> 5208       2         4  25       62059   C3     3 #> 5209       2         4  25       62059   C4     4 #> 5210       2         4  25       62059   C5     4 #> 5211       2         4  25       62059   E1     1 #> 5212       2         4  25       62059   E2     1 #> 5213       2         4  25       62059   E3     5 #> 5214       2         4  25       62059   E4     5 #> 5215       2         4  25       62059   E5     5 #> 5216       2         4  25       62059   N1     3 #> 5217       2         4  25       62059   N2     4 #> 5218       2         4  25       62059   N3     4 #> 5219       2         4  25       62059   N4     2 #> 5220       2         4  25       62059   N5     3 #> 5221       2         4  25       62059   O1     4 #> 5222       2         4  25       62059   O2     3 #> 5223       2         4  25       62059   O3     5 #> 5224       2         4  25       62059   O4     4 #> 5225       2         4  25       62059   O5     3 #> 5226       1         5  48       62060   A1     2 #> 5227       1         5  48       62060   A2     3 #> 5228       1         5  48       62060   A3     2 #> 5229       1         5  48       62060   A4     1 #> 5230       1         5  48       62060   A5     4 #> 5231       1         5  48       62060   C1     1 #> 5232       1         5  48       62060   C2     2 #> 5233       1         5  48       62060   C3     2 #> 5234       1         5  48       62060   C4     1 #> 5235       1         5  48       62060   C5     5 #> 5236       1         5  48       62060   E1     1 #> 5237       1         5  48       62060   E2     5 #> 5238       1         5  48       62060   E3     2 #> 5239       1         5  48       62060   E4     4 #> 5240       1         5  48       62060   E5     5 #> 5241       1         5  48       62060   N1     1 #> 5242       1         5  48       62060   N2     5 #> 5243       1         5  48       62060   N3     1 #> 5244       1         5  48       62060   N4     2 #> 5245       1         5  48       62060   N5     1 #> 5246       1         5  48       62060   O1     4 #> 5247       1         5  48       62060   O2     1 #> 5248       1         5  48       62060   O3     2 #> 5249       1         5  48       62060   O4     5 #> 5250       1         5  48       62060   O5     1 #> 5251       2         4  27       62063   A1     2 #> 5252       2         4  27       62063   A2     5 #> 5253       2         4  27       62063   A3     5 #> 5254       2         4  27       62063   A4     5 #> 5255       2         4  27       62063   A5     5 #> 5256       2         4  27       62063   C1     6 #> 5257       2         4  27       62063   C2     2 #> 5258       2         4  27       62063   C3     5 #> 5259       2         4  27       62063   C4     2 #> 5260       2         4  27       62063   C5     2 #> 5261       2         4  27       62063   E1     2 #> 5262       2         4  27       62063   E2     2 #> 5263       2         4  27       62063   E3     4 #> 5264       2         4  27       62063   E4     6 #> 5265       2         4  27       62063   E5     5 #> 5266       2         4  27       62063   N1     2 #> 5267       2         4  27       62063   N2     2 #> 5268       2         4  27       62063   N3     2 #> 5269       2         4  27       62063   N4     1 #> 5270       2         4  27       62063   N5     2 #> 5271       2         4  27       62063   O1     5 #> 5272       2         4  27       62063   O2     1 #> 5273       2         4  27       62063   O3     5 #> 5274       2         4  27       62063   O4     6 #> 5275       2         4  27       62063   O5     1 #> 5276       2         3  22       62064   A1     1 #> 5277       2         3  22       62064   A2     6 #> 5278       2         3  22       62064   A3     5 #> 5279       2         3  22       62064   A4     6 #> 5280       2         3  22       62064   A5     6 #> 5281       2         3  22       62064   C1     4 #> 5282       2         3  22       62064   C2     5 #> 5283       2         3  22       62064   C3     4 #> 5284       2         3  22       62064   C4     5 #> 5285       2         3  22       62064   C5     6 #> 5286       2         3  22       62064   E1     3 #> 5287       2         3  22       62064   E2     4 #> 5288       2         3  22       62064   E3     6 #> 5289       2         3  22       62064   E4     5 #> 5290       2         3  22       62064   E5     5 #> 5291       2         3  22       62064   N1     5 #> 5292       2         3  22       62064   N2     5 #> 5293       2         3  22       62064   N3     6 #> 5294       2         3  22       62064   N4     2 #> 5295       2         3  22       62064   N5    NA #> 5296       2         3  22       62064   O1     6 #> 5297       2         3  22       62064   O2     1 #> 5298       2         3  22       62064   O3     1 #> 5299       2         3  22       62064   O4     5 #> 5300       2         3  22       62064   O5     2 #> 5301       1         3  20       62067   A1     5 #> 5302       1         3  20       62067   A2     4 #> 5303       1         3  20       62067   A3     4 #> 5304       1         3  20       62067   A4     4 #> 5305       1         3  20       62067   A5     5 #> 5306       1         3  20       62067   C1     5 #> 5307       1         3  20       62067   C2     4 #> 5308       1         3  20       62067   C3    NA #> 5309       1         3  20       62067   C4     4 #> 5310       1         3  20       62067   C5     4 #> 5311       1         3  20       62067   E1     4 #> 5312       1         3  20       62067   E2     4 #> 5313       1         3  20       62067   E3     4 #> 5314       1         3  20       62067   E4     5 #> 5315       1         3  20       62067   E5     3 #> 5316       1         3  20       62067   N1     2 #> 5317       1         3  20       62067   N2     2 #> 5318       1         3  20       62067   N3     2 #> 5319       1         3  20       62067   N4     2 #> 5320       1         3  20       62067   N5     1 #> 5321       1         3  20       62067   O1     5 #> 5322       1         3  20       62067   O2     3 #> 5323       1         3  20       62067   O3     4 #> 5324       1         3  20       62067   O4     5 #> 5325       1         3  20       62067   O5     4 #> 5326       2        NA  17       62070   A1     3 #> 5327       2        NA  17       62070   A2     4 #> 5328       2        NA  17       62070   A3     3 #> 5329       2        NA  17       62070   A4     3 #> 5330       2        NA  17       62070   A5     3 #> 5331       2        NA  17       62070   C1     3 #> 5332       2        NA  17       62070   C2     3 #> 5333       2        NA  17       62070   C3     3 #> 5334       2        NA  17       62070   C4     4 #> 5335       2        NA  17       62070   C5     6 #> 5336       2        NA  17       62070   E1     5 #> 5337       2        NA  17       62070   E2     5 #> 5338       2        NA  17       62070   E3     2 #> 5339       2        NA  17       62070   E4     2 #> 5340       2        NA  17       62070   E5     4 #> 5341       2        NA  17       62070   N1     6 #> 5342       2        NA  17       62070   N2     6 #> 5343       2        NA  17       62070   N3     6 #> 5344       2        NA  17       62070   N4     6 #> 5345       2        NA  17       62070   N5     3 #> 5346       2        NA  17       62070   O1     3 #> 5347       2        NA  17       62070   O2     4 #> 5348       2        NA  17       62070   O3     2 #> 5349       2        NA  17       62070   O4     4 #> 5350       2        NA  17       62070   O5     4 #> 5351       2         5  59       62073   A1     1 #> 5352       2         5  59       62073   A2     6 #> 5353       2         5  59       62073   A3     6 #> 5354       2         5  59       62073   A4     4 #> 5355       2         5  59       62073   A5     6 #> 5356       2         5  59       62073   C1     5 #> 5357       2         5  59       62073   C2     5 #> 5358       2         5  59       62073   C3     5 #> 5359       2         5  59       62073   C4     1 #> 5360       2         5  59       62073   C5     3 #> 5361       2         5  59       62073   E1     3 #> 5362       2         5  59       62073   E2     1 #> 5363       2         5  59       62073   E3     6 #> 5364       2         5  59       62073   E4     2 #> 5365       2         5  59       62073   E5    NA #> 5366       2         5  59       62073   N1     2 #> 5367       2         5  59       62073   N2     2 #> 5368       2         5  59       62073   N3     1 #> 5369       2         5  59       62073   N4     1 #> 5370       2         5  59       62073   N5     2 #> 5371       2         5  59       62073   O1     6 #> 5372       2         5  59       62073   O2     1 #> 5373       2         5  59       62073   O3     6 #> 5374       2         5  59       62073   O4     6 #> 5375       2         5  59       62073   O5     1 #> 5376       2         1  17       62075   A1     2 #> 5377       2         1  17       62075   A2     4 #> 5378       2         1  17       62075   A3     4 #> 5379       2         1  17       62075   A4     1 #> 5380       2         1  17       62075   A5     4 #> 5381       2         1  17       62075   C1     5 #> 5382       2         1  17       62075   C2     4 #> 5383       2         1  17       62075   C3     4 #> 5384       2         1  17       62075   C4     2 #> 5385       2         1  17       62075   C5     2 #> 5386       2         1  17       62075   E1     4 #> 5387       2         1  17       62075   E2     5 #> 5388       2         1  17       62075   E3     3 #> 5389       2         1  17       62075   E4     3 #> 5390       2         1  17       62075   E5     5 #> 5391       2         1  17       62075   N1     4 #> 5392       2         1  17       62075   N2     5 #> 5393       2         1  17       62075   N3     1 #> 5394       2         1  17       62075   N4     2 #> 5395       2         1  17       62075   N5     3 #> 5396       2         1  17       62075   O1     5 #> 5397       2         1  17       62075   O2     1 #> 5398       2         1  17       62075   O3     5 #> 5399       2         1  17       62075   O4     6 #> 5400       2         1  17       62075   O5     2 #> 5401       2         3  32       62077   A1     1 #> 5402       2         3  32       62077   A2     6 #> 5403       2         3  32       62077   A3     6 #> 5404       2         3  32       62077   A4     6 #> 5405       2         3  32       62077   A5     6 #> 5406       2         3  32       62077   C1     6 #> 5407       2         3  32       62077   C2     5 #> 5408       2         3  32       62077   C3     4 #> 5409       2         3  32       62077   C4     1 #> 5410       2         3  32       62077   C5     2 #> 5411       2         3  32       62077   E1     1 #> 5412       2         3  32       62077   E2     1 #> 5413       2         3  32       62077   E3     5 #> 5414       2         3  32       62077   E4     6 #> 5415       2         3  32       62077   E5     6 #> 5416       2         3  32       62077   N1     3 #> 5417       2         3  32       62077   N2     4 #> 5418       2         3  32       62077   N3     3 #> 5419       2         3  32       62077   N4     2 #> 5420       2         3  32       62077   N5     2 #> 5421       2         3  32       62077   O1     6 #> 5422       2         3  32       62077   O2     6 #> 5423       2         3  32       62077   O3     5 #> 5424       2         3  32       62077   O4     5 #> 5425       2         3  32       62077   O5     1 #> 5426       1         3  22       62079   A1     2 #> 5427       1         3  22       62079   A2     4 #> 5428       1         3  22       62079   A3     4 #> 5429       1         3  22       62079   A4     4 #> 5430       1         3  22       62079   A5     3 #> 5431       1         3  22       62079   C1     5 #> 5432       1         3  22       62079   C2     1 #> 5433       1         3  22       62079   C3     3 #> 5434       1         3  22       62079   C4     2 #> 5435       1         3  22       62079   C5     1 #> 5436       1         3  22       62079   E1     1 #> 5437       1         3  22       62079   E2     1 #> 5438       1         3  22       62079   E3     3 #> 5439       1         3  22       62079   E4     6 #> 5440       1         3  22       62079   E5     5 #> 5441       1         3  22       62079   N1     5 #> 5442       1         3  22       62079   N2     6 #> 5443       1         3  22       62079   N3     6 #> 5444       1         3  22       62079   N4     1 #> 5445       1         3  22       62079   N5     1 #> 5446       1         3  22       62079   O1     4 #> 5447       1         3  22       62079   O2     3 #> 5448       1         3  22       62079   O3     5 #> 5449       1         3  22       62079   O4     2 #> 5450       1         3  22       62079   O5     3 #> 5451       1         3  21       62082   A1     5 #> 5452       1         3  21       62082   A2     5 #> 5453       1         3  21       62082   A3     4 #> 5454       1         3  21       62082   A4     5 #> 5455       1         3  21       62082   A5     4 #> 5456       1         3  21       62082   C1     6 #> 5457       1         3  21       62082   C2     6 #> 5458       1         3  21       62082   C3     6 #> 5459       1         3  21       62082   C4     1 #> 5460       1         3  21       62082   C5     6 #> 5461       1         3  21       62082   E1     2 #> 5462       1         3  21       62082   E2     2 #> 5463       1         3  21       62082   E3     4 #> 5464       1         3  21       62082   E4     5 #> 5465       1         3  21       62082   E5     5 #> 5466       1         3  21       62082   N1     1 #> 5467       1         3  21       62082   N2     1 #> 5468       1         3  21       62082   N3     4 #> 5469       1         3  21       62082   N4     3 #> 5470       1         3  21       62082   N5     4 #> 5471       1         3  21       62082   O1     6 #> 5472       1         3  21       62082   O2     4 #> 5473       1         3  21       62082   O3     4 #> 5474       1         3  21       62082   O4     5 #> 5475       1         3  21       62082   O5     2 #> 5476       1         5  29       62084   A1     5 #> 5477       1         5  29       62084   A2     3 #> 5478       1         5  29       62084   A3     1 #> 5479       1         5  29       62084   A4     2 #> 5480       1         5  29       62084   A5     3 #> 5481       1         5  29       62084   C1     6 #> 5482       1         5  29       62084   C2     2 #> 5483       1         5  29       62084   C3     2 #> 5484       1         5  29       62084   C4     4 #> 5485       1         5  29       62084   C5     5 #> 5486       1         5  29       62084   E1     5 #> 5487       1         5  29       62084   E2     4 #> 5488       1         5  29       62084   E3     3 #> 5489       1         5  29       62084   E4     4 #> 5490       1         5  29       62084   E5     2 #> 5491       1         5  29       62084   N1     1 #> 5492       1         5  29       62084   N2     4 #> 5493       1         5  29       62084   N3     4 #> 5494       1         5  29       62084   N4     4 #> 5495       1         5  29       62084   N5     2 #> 5496       1         5  29       62084   O1     6 #> 5497       1         5  29       62084   O2     2 #> 5498       1         5  29       62084   O3     6 #> 5499       1         5  29       62084   O4     5 #> 5500       1         5  29       62084   O5     2 #> 5501       1         1  18       62090   A1     2 #> 5502       1         1  18       62090   A2     5 #> 5503       1         1  18       62090   A3     5 #> 5504       1         1  18       62090   A4     5 #> 5505       1         1  18       62090   A5     4 #> 5506       1         1  18       62090   C1     5 #> 5507       1         1  18       62090   C2     5 #> 5508       1         1  18       62090   C3     5 #> 5509       1         1  18       62090   C4     4 #> 5510       1         1  18       62090   C5     3 #> 5511       1         1  18       62090   E1     2 #> 5512       1         1  18       62090   E2     4 #> 5513       1         1  18       62090   E3     5 #> 5514       1         1  18       62090   E4     5 #> 5515       1         1  18       62090   E5     4 #> 5516       1         1  18       62090   N1     3 #> 5517       1         1  18       62090   N2     4 #> 5518       1         1  18       62090   N3     4 #> 5519       1         1  18       62090   N4     3 #> 5520       1         1  18       62090   N5     4 #> 5521       1         1  18       62090   O1    NA #> 5522       1         1  18       62090   O2     5 #> 5523       1         1  18       62090   O3     3 #> 5524       1         1  18       62090   O4     4 #> 5525       1         1  18       62090   O5     3 #> 5526       2         3  40       62092   A1     1 #> 5527       2         3  40       62092   A2     5 #> 5528       2         3  40       62092   A3    NA #> 5529       2         3  40       62092   A4     5 #> 5530       2         3  40       62092   A5     6 #> 5531       2         3  40       62092   C1     6 #> 5532       2         3  40       62092   C2     6 #> 5533       2         3  40       62092   C3     1 #> 5534       2         3  40       62092   C4     1 #> 5535       2         3  40       62092   C5     1 #> 5536       2         3  40       62092   E1     6 #> 5537       2         3  40       62092   E2     1 #> 5538       2         3  40       62092   E3     1 #> 5539       2         3  40       62092   E4     5 #> 5540       2         3  40       62092   E5     1 #> 5541       2         3  40       62092   N1     1 #> 5542       2         3  40       62092   N2     1 #> 5543       2         3  40       62092   N3     1 #> 5544       2         3  40       62092   N4     6 #> 5545       2         3  40       62092   N5     1 #> 5546       2         3  40       62092   O1     6 #> 5547       2         3  40       62092   O2     6 #> 5548       2         3  40       62092   O3     6 #> 5549       2         3  40       62092   O4     1 #> 5550       2         3  40       62092   O5     1 #> 5551       2         5  48       62094   A1     1 #> 5552       2         5  48       62094   A2     5 #> 5553       2         5  48       62094   A3     5 #> 5554       2         5  48       62094   A4     6 #> 5555       2         5  48       62094   A5     6 #> 5556       2         5  48       62094   C1     5 #> 5557       2         5  48       62094   C2     5 #> 5558       2         5  48       62094   C3     5 #> 5559       2         5  48       62094   C4     3 #> 5560       2         5  48       62094   C5     3 #> 5561       2         5  48       62094   E1     1 #> 5562       2         5  48       62094   E2     1 #> 5563       2         5  48       62094   E3     5 #> 5564       2         5  48       62094   E4     5 #> 5565       2         5  48       62094   E5     5 #> 5566       2         5  48       62094   N1     1 #> 5567       2         5  48       62094   N2     1 #> 5568       2         5  48       62094   N3     1 #> 5569       2         5  48       62094   N4     1 #> 5570       2         5  48       62094   N5     2 #> 5571       2         5  48       62094   O1     5 #> 5572       2         5  48       62094   O2     2 #> 5573       2         5  48       62094   O3     5 #> 5574       2         5  48       62094   O4     5 #> 5575       2         5  48       62094   O5     5 #> 5576       2         4  39       62099   A1     1 #> 5577       2         4  39       62099   A2     5 #> 5578       2         4  39       62099   A3     6 #> 5579       2         4  39       62099   A4     6 #> 5580       2         4  39       62099   A5     4 #> 5581       2         4  39       62099   C1     5 #> 5582       2         4  39       62099   C2     3 #> 5583       2         4  39       62099   C3     4 #> 5584       2         4  39       62099   C4     3 #> 5585       2         4  39       62099   C5     5 #> 5586       2         4  39       62099   E1     2 #> 5587       2         4  39       62099   E2     2 #> 5588       2         4  39       62099   E3     3 #> 5589       2         4  39       62099   E4     6 #> 5590       2         4  39       62099   E5     4 #> 5591       2         4  39       62099   N1     2 #> 5592       2         4  39       62099   N2     2 #> 5593       2         4  39       62099   N3     2 #> 5594       2         4  39       62099   N4     2 #> 5595       2         4  39       62099   N5     4 #> 5596       2         4  39       62099   O1     5 #> 5597       2         4  39       62099   O2     1 #> 5598       2         4  39       62099   O3     5 #> 5599       2         4  39       62099   O4     5 #> 5600       2         4  39       62099   O5     3 #> 5601       2         4  50       62101   A1     1 #> 5602       2         4  50       62101   A2     5 #> 5603       2         4  50       62101   A3     6 #> 5604       2         4  50       62101   A4     5 #> 5605       2         4  50       62101   A5     6 #> 5606       2         4  50       62101   C1     2 #> 5607       2         4  50       62101   C2     5 #> 5608       2         4  50       62101   C3     5 #> 5609       2         4  50       62101   C4     2 #> 5610       2         4  50       62101   C5     2 #> 5611       2         4  50       62101   E1     1 #> 5612       2         4  50       62101   E2     3 #> 5613       2         4  50       62101   E3     4 #> 5614       2         4  50       62101   E4     6 #> 5615       2         4  50       62101   E5     6 #> 5616       2         4  50       62101   N1     3 #> 5617       2         4  50       62101   N2     5 #> 5618       2         4  50       62101   N3     2 #> 5619       2         4  50       62101   N4     2 #> 5620       2         4  50       62101   N5     3 #> 5621       2         4  50       62101   O1     5 #> 5622       2         4  50       62101   O2     1 #> 5623       2         4  50       62101   O3     5 #> 5624       2         4  50       62101   O4     6 #> 5625       2         4  50       62101   O5     1 #> 5626       2         5  26       62102   A1     1 #> 5627       2         5  26       62102   A2     6 #> 5628       2         5  26       62102   A3     6 #> 5629       2         5  26       62102   A4     6 #> 5630       2         5  26       62102   A5     4 #> 5631       2         5  26       62102   C1     1 #> 5632       2         5  26       62102   C2     5 #> 5633       2         5  26       62102   C3     6 #> 5634       2         5  26       62102   C4     3 #> 5635       2         5  26       62102   C5     2 #> 5636       2         5  26       62102   E1     4 #> 5637       2         5  26       62102   E2     2 #> 5638       2         5  26       62102   E3     6 #> 5639       2         5  26       62102   E4     4 #> 5640       2         5  26       62102   E5     2 #> 5641       2         5  26       62102   N1     2 #> 5642       2         5  26       62102   N2     5 #> 5643       2         5  26       62102   N3     5 #> 5644       2         5  26       62102   N4     4 #> 5645       2         5  26       62102   N5     4 #> 5646       2         5  26       62102   O1     5 #> 5647       2         5  26       62102   O2     5 #> 5648       2         5  26       62102   O3     5 #> 5649       2         5  26       62102   O4     6 #> 5650       2         5  26       62102   O5     1 #> 5651       2         2  21       62103   A1     3 #> 5652       2         2  21       62103   A2     5 #> 5653       2         2  21       62103   A3     5 #> 5654       2         2  21       62103   A4     3 #> 5655       2         2  21       62103   A5     4 #> 5656       2         2  21       62103   C1     4 #> 5657       2         2  21       62103   C2     3 #> 5658       2         2  21       62103   C3     4 #> 5659       2         2  21       62103   C4     3 #> 5660       2         2  21       62103   C5     3 #> 5661       2         2  21       62103   E1     3 #> 5662       2         2  21       62103   E2     4 #> 5663       2         2  21       62103   E3     3 #> 5664       2         2  21       62103   E4     4 #> 5665       2         2  21       62103   E5     3 #> 5666       2         2  21       62103   N1     3 #> 5667       2         2  21       62103   N2     4 #> 5668       2         2  21       62103   N3     3 #> 5669       2         2  21       62103   N4     3 #> 5670       2         2  21       62103   N5     3 #> 5671       2         2  21       62103   O1     3 #> 5672       2         2  21       62103   O2     2 #> 5673       2         2  21       62103   O3     4 #> 5674       2         2  21       62103   O4     4 #> 5675       2         2  21       62103   O5     3 #> 5676       1         4  55       62105   A1     2 #> 5677       1         4  55       62105   A2     5 #> 5678       1         4  55       62105   A3     4 #> 5679       1         4  55       62105   A4     3 #> 5680       1         4  55       62105   A5     2 #> 5681       1         4  55       62105   C1     6 #> 5682       1         4  55       62105   C2     5 #> 5683       1         4  55       62105   C3     6 #> 5684       1         4  55       62105   C4     2 #> 5685       1         4  55       62105   C5     4 #> 5686       1         4  55       62105   E1     2 #> 5687       1         4  55       62105   E2     4 #> 5688       1         4  55       62105   E3     2 #> 5689       1         4  55       62105   E4     2 #> 5690       1         4  55       62105   E5     5 #> 5691       1         4  55       62105   N1     4 #> 5692       1         4  55       62105   N2     5 #> 5693       1         4  55       62105   N3     2 #> 5694       1         4  55       62105   N4     2 #> 5695       1         4  55       62105   N5     1 #> 5696       1         4  55       62105   O1     5 #> 5697       1         4  55       62105   O2     1 #> 5698       1         4  55       62105   O3    NA #> 5699       1         4  55       62105   O4     5 #> 5700       1         4  55       62105   O5     1 #> 5701       2         5  37       62106   A1     1 #> 5702       2         5  37       62106   A2     5 #> 5703       2         5  37       62106   A3     6 #> 5704       2         5  37       62106   A4     5 #> 5705       2         5  37       62106   A5     5 #> 5706       2         5  37       62106   C1     5 #> 5707       2         5  37       62106   C2     3 #> 5708       2         5  37       62106   C3     4 #> 5709       2         5  37       62106   C4     1 #> 5710       2         5  37       62106   C5     5 #> 5711       2         5  37       62106   E1     2 #> 5712       2         5  37       62106   E2     2 #> 5713       2         5  37       62106   E3     4 #> 5714       2         5  37       62106   E4     5 #> 5715       2         5  37       62106   E5     5 #> 5716       2         5  37       62106   N1     2 #> 5717       2         5  37       62106   N2     2 #> 5718       2         5  37       62106   N3     2 #> 5719       2         5  37       62106   N4     2 #> 5720       2         5  37       62106   N5     1 #> 5721       2         5  37       62106   O1     5 #> 5722       2         5  37       62106   O2     3 #> 5723       2         5  37       62106   O3     4 #> 5724       2         5  37       62106   O4     5 #> 5725       2         5  37       62106   O5     2 #> 5726       2         5  38       62107   A1     2 #> 5727       2         5  38       62107   A2     6 #> 5728       2         5  38       62107   A3     6 #> 5729       2         5  38       62107   A4     5 #> 5730       2         5  38       62107   A5     5 #> 5731       2         5  38       62107   C1     5 #> 5732       2         5  38       62107   C2     5 #> 5733       2         5  38       62107   C3     4 #> 5734       2         5  38       62107   C4     2 #> 5735       2         5  38       62107   C5     1 #> 5736       2         5  38       62107   E1     2 #> 5737       2         5  38       62107   E2     3 #> 5738       2         5  38       62107   E3     5 #> 5739       2         5  38       62107   E4     4 #> 5740       2         5  38       62107   E5     6 #> 5741       2         5  38       62107   N1     2 #> 5742       2         5  38       62107   N2     3 #> 5743       2         5  38       62107   N3     2 #> 5744       2         5  38       62107   N4     2 #> 5745       2         5  38       62107   N5     1 #> 5746       2         5  38       62107   O1     6 #> 5747       2         5  38       62107   O2     1 #> 5748       2         5  38       62107   O3     5 #> 5749       2         5  38       62107   O4     6 #> 5750       2         5  38       62107   O5     2 #> 5751       1         3  19       62111   A1     5 #> 5752       1         3  19       62111   A2     4 #> 5753       1         3  19       62111   A3     5 #> 5754       1         3  19       62111   A4     6 #> 5755       1         3  19       62111   A5     4 #> 5756       1         3  19       62111   C1     1 #> 5757       1         3  19       62111   C2     4 #> 5758       1         3  19       62111   C3     6 #> 5759       1         3  19       62111   C4     2 #> 5760       1         3  19       62111   C5     2 #> 5761       1         3  19       62111   E1     3 #> 5762       1         3  19       62111   E2     3 #> 5763       1         3  19       62111   E3     5 #> 5764       1         3  19       62111   E4     4 #> 5765       1         3  19       62111   E5     5 #> 5766       1         3  19       62111   N1     2 #> 5767       1         3  19       62111   N2     2 #> 5768       1         3  19       62111   N3     1 #> 5769       1         3  19       62111   N4     3 #> 5770       1         3  19       62111   N5     2 #> 5771       1         3  19       62111   O1     5 #> 5772       1         3  19       62111   O2     6 #> 5773       1         3  19       62111   O3     4 #> 5774       1         3  19       62111   O4     6 #> 5775       1         3  19       62111   O5     3 #> 5776       2        NA  17       62115   A1     2 #> 5777       2        NA  17       62115   A2     6 #> 5778       2        NA  17       62115   A3     4 #> 5779       2        NA  17       62115   A4     3 #> 5780       2        NA  17       62115   A5     5 #> 5781       2        NA  17       62115   C1     5 #> 5782       2        NA  17       62115   C2     3 #> 5783       2        NA  17       62115   C3     3 #> 5784       2        NA  17       62115   C4     2 #> 5785       2        NA  17       62115   C5     4 #> 5786       2        NA  17       62115   E1     4 #> 5787       2        NA  17       62115   E2     4 #> 5788       2        NA  17       62115   E3     3 #> 5789       2        NA  17       62115   E4     3 #> 5790       2        NA  17       62115   E5     3 #> 5791       2        NA  17       62115   N1     2 #> 5792       2        NA  17       62115   N2     3 #> 5793       2        NA  17       62115   N3     2 #> 5794       2        NA  17       62115   N4     2 #> 5795       2        NA  17       62115   N5     4 #> 5796       2        NA  17       62115   O1     6 #> 5797       2        NA  17       62115   O2     4 #> 5798       2        NA  17       62115   O3     4 #> 5799       2        NA  17       62115   O4     6 #> 5800       2        NA  17       62115   O5     3 #> 5801       2         3  20       62118   A1     5 #> 5802       2         3  20       62118   A2     4 #> 5803       2         3  20       62118   A3     5 #> 5804       2         3  20       62118   A4     6 #> 5805       2         3  20       62118   A5     2 #> 5806       2         3  20       62118   C1     5 #> 5807       2         3  20       62118   C2     6 #> 5808       2         3  20       62118   C3     4 #> 5809       2         3  20       62118   C4     1 #> 5810       2         3  20       62118   C5     1 #> 5811       2         3  20       62118   E1     1 #> 5812       2         3  20       62118   E2     1 #> 5813       2         3  20       62118   E3     3 #> 5814       2         3  20       62118   E4     6 #> 5815       2         3  20       62118   E5     3 #> 5816       2         3  20       62118   N1     2 #> 5817       2         3  20       62118   N2     4 #> 5818       2         3  20       62118   N3     2 #> 5819       2         3  20       62118   N4     3 #> 5820       2         3  20       62118   N5     1 #> 5821       2         3  20       62118   O1     6 #> 5822       2         3  20       62118   O2     2 #> 5823       2         3  20       62118   O3     6 #> 5824       2         3  20       62118   O4     3 #> 5825       2         3  20       62118   O5     2 #> 5826       2         5  34       62119   A1     3 #> 5827       2         5  34       62119   A2     5 #> 5828       2         5  34       62119   A3     2 #> 5829       2         5  34       62119   A4     4 #> 5830       2         5  34       62119   A5     5 #> 5831       2         5  34       62119   C1     5 #> 5832       2         5  34       62119   C2     4 #> 5833       2         5  34       62119   C3     6 #> 5834       2         5  34       62119   C4     3 #> 5835       2         5  34       62119   C5     3 #> 5836       2         5  34       62119   E1     3 #> 5837       2         5  34       62119   E2     5 #> 5838       2         5  34       62119   E3     2 #> 5839       2         5  34       62119   E4     3 #> 5840       2         5  34       62119   E5     2 #> 5841       2         5  34       62119   N1     1 #> 5842       2         5  34       62119   N2     3 #> 5843       2         5  34       62119   N3     3 #> 5844       2         5  34       62119   N4     2 #> 5845       2         5  34       62119   N5     4 #> 5846       2         5  34       62119   O1     5 #> 5847       2         5  34       62119   O2     2 #> 5848       2         5  34       62119   O3     5 #> 5849       2         5  34       62119   O4     5 #> 5850       2         5  34       62119   O5     2 #> 5851       2         3  38       62120   A1     3 #> 5852       2         3  38       62120   A2     6 #> 5853       2         3  38       62120   A3     5 #> 5854       2         3  38       62120   A4     6 #> 5855       2         3  38       62120   A5     6 #> 5856       2         3  38       62120   C1     6 #> 5857       2         3  38       62120   C2     5 #> 5858       2         3  38       62120   C3     5 #> 5859       2         3  38       62120   C4     1 #> 5860       2         3  38       62120   C5     1 #> 5861       2         3  38       62120   E1     1 #> 5862       2         3  38       62120   E2     2 #> 5863       2         3  38       62120   E3     4 #> 5864       2         3  38       62120   E4     6 #> 5865       2         3  38       62120   E5     6 #> 5866       2         3  38       62120   N1     2 #> 5867       2         3  38       62120   N2     2 #> 5868       2         3  38       62120   N3     1 #> 5869       2         3  38       62120   N4     2 #> 5870       2         3  38       62120   N5     3 #> 5871       2         3  38       62120   O1     5 #> 5872       2         3  38       62120   O2     2 #> 5873       2         3  38       62120   O3     4 #> 5874       2         3  38       62120   O4     6 #> 5875       2         3  38       62120   O5     1 #> 5876       2         3  18       62121   A1     2 #> 5877       2         3  18       62121   A2     5 #> 5878       2         3  18       62121   A3     4 #> 5879       2         3  18       62121   A4     4 #> 5880       2         3  18       62121   A5     4 #> 5881       2         3  18       62121   C1     3 #> 5882       2         3  18       62121   C2     3 #> 5883       2         3  18       62121   C3     4 #> 5884       2         3  18       62121   C4     5 #> 5885       2         3  18       62121   C5     4 #> 5886       2         3  18       62121   E1     2 #> 5887       2         3  18       62121   E2     2 #> 5888       2         3  18       62121   E3     4 #> 5889       2         3  18       62121   E4     4 #> 5890       2         3  18       62121   E5     4 #> 5891       2         3  18       62121   N1     5 #> 5892       2         3  18       62121   N2     5 #> 5893       2         3  18       62121   N3     4 #> 5894       2         3  18       62121   N4     4 #> 5895       2         3  18       62121   N5     4 #> 5896       2         3  18       62121   O1     3 #> 5897       2         3  18       62121   O2     5 #> 5898       2         3  18       62121   O3     4 #> 5899       2         3  18       62121   O4     3 #> 5900       2         3  18       62121   O5     4 #> 5901       2         5  32       62124   A1     1 #> 5902       2         5  32       62124   A2    NA #> 5903       2         5  32       62124   A3     4 #> 5904       2         5  32       62124   A4     6 #> 5905       2         5  32       62124   A5     5 #> 5906       2         5  32       62124   C1     5 #> 5907       2         5  32       62124   C2     5 #> 5908       2         5  32       62124   C3     2 #> 5909       2         5  32       62124   C4     3 #> 5910       2         5  32       62124   C5     3 #> 5911       2         5  32       62124   E1     2 #> 5912       2         5  32       62124   E2     2 #> 5913       2         5  32       62124   E3     4 #> 5914       2         5  32       62124   E4     2 #> 5915       2         5  32       62124   E5     4 #> 5916       2         5  32       62124   N1     2 #> 5917       2         5  32       62124   N2     3 #> 5918       2         5  32       62124   N3     3 #> 5919       2         5  32       62124   N4     4 #> 5920       2         5  32       62124   N5     3 #> 5921       2         5  32       62124   O1     4 #> 5922       2         5  32       62124   O2     3 #> 5923       2         5  32       62124   O3     5 #> 5924       2         5  32       62124   O4     6 #> 5925       2         5  32       62124   O5     2 #> 5926       1         3  19       62128   A1     1 #> 5927       1         3  19       62128   A2     6 #> 5928       1         3  19       62128   A3     6 #> 5929       1         3  19       62128   A4     6 #> 5930       1         3  19       62128   A5     5 #> 5931       1         3  19       62128   C1     4 #> 5932       1         3  19       62128   C2     3 #> 5933       1         3  19       62128   C3     4 #> 5934       1         3  19       62128   C4     5 #> 5935       1         3  19       62128   C5     6 #> 5936       1         3  19       62128   E1     2 #> 5937       1         3  19       62128   E2     2 #> 5938       1         3  19       62128   E3     4 #> 5939       1         3  19       62128   E4     5 #> 5940       1         3  19       62128   E5     3 #> 5941       1         3  19       62128   N1     1 #> 5942       1         3  19       62128   N2     2 #> 5943       1         3  19       62128   N3     4 #> 5944       1         3  19       62128   N4     4 #> 5945       1         3  19       62128   N5     4 #> 5946       1         3  19       62128   O1     4 #> 5947       1         3  19       62128   O2     3 #> 5948       1         3  19       62128   O3     5 #> 5949       1         3  19       62128   O4     5 #> 5950       1         3  19       62128   O5     3 #> 5951       1         3  19       62130   A1     3 #> 5952       1         3  19       62130   A2     4 #> 5953       1         3  19       62130   A3     4 #> 5954       1         3  19       62130   A4     6 #> 5955       1         3  19       62130   A5     3 #> 5956       1         3  19       62130   C1     5 #> 5957       1         3  19       62130   C2     5 #> 5958       1         3  19       62130   C3     5 #> 5959       1         3  19       62130   C4     2 #> 5960       1         3  19       62130   C5     4 #> 5961       1         3  19       62130   E1     6 #> 5962       1         3  19       62130   E2     4 #> 5963       1         3  19       62130   E3     3 #> 5964       1         3  19       62130   E4     3 #> 5965       1         3  19       62130   E5     4 #> 5966       1         3  19       62130   N1     1 #> 5967       1         3  19       62130   N2     3 #> 5968       1         3  19       62130   N3     2 #> 5969       1         3  19       62130   N4     2 #> 5970       1         3  19       62130   N5     1 #> 5971       1         3  19       62130   O1     6 #> 5972       1         3  19       62130   O2     2 #> 5973       1         3  19       62130   O3     3 #> 5974       1         3  19       62130   O4     5 #> 5975       1         3  19       62130   O5     2 #> 5976       1         3  21       62132   A1     3 #> 5977       1         3  21       62132   A2     5 #> 5978       1         3  21       62132   A3     6 #> 5979       1         3  21       62132   A4     5 #> 5980       1         3  21       62132   A5     6 #> 5981       1         3  21       62132   C1     5 #> 5982       1         3  21       62132   C2     6 #> 5983       1         3  21       62132   C3     5 #> 5984       1         3  21       62132   C4     2 #> 5985       1         3  21       62132   C5     1 #> 5986       1         3  21       62132   E1     3 #> 5987       1         3  21       62132   E2     1 #> 5988       1         3  21       62132   E3     4 #> 5989       1         3  21       62132   E4     6 #> 5990       1         3  21       62132   E5     5 #> 5991       1         3  21       62132   N1     3 #> 5992       1         3  21       62132   N2     5 #> 5993       1         3  21       62132   N3     3 #> 5994       1         3  21       62132   N4     3 #> 5995       1         3  21       62132   N5     1 #> 5996       1         3  21       62132   O1     4 #> 5997       1         3  21       62132   O2     1 #> 5998       1         3  21       62132   O3     4 #> 5999       1         3  21       62132   O4     4 #> 6000       1         3  21       62132   O5     3 #> 6001       2         3  22       62133   A1     1 #> 6002       2         3  22       62133   A2     5 #> 6003       2         3  22       62133   A3    NA #> 6004       2         3  22       62133   A4     6 #> 6005       2         3  22       62133   A5     5 #> 6006       2         3  22       62133   C1     5 #> 6007       2         3  22       62133   C2     5 #> 6008       2         3  22       62133   C3     6 #> 6009       2         3  22       62133   C4     1 #> 6010       2         3  22       62133   C5     1 #> 6011       2         3  22       62133   E1     1 #> 6012       2         3  22       62133   E2     2 #> 6013       2         3  22       62133   E3     4 #> 6014       2         3  22       62133   E4     6 #> 6015       2         3  22       62133   E5     6 #> 6016       2         3  22       62133   N1     4 #> 6017       2         3  22       62133   N2     4 #> 6018       2         3  22       62133   N3     3 #> 6019       2         3  22       62133   N4     2 #> 6020       2         3  22       62133   N5     2 #> 6021       2         3  22       62133   O1     6 #> 6022       2         3  22       62133   O2     1 #> 6023       2         3  22       62133   O3     5 #> 6024       2         3  22       62133   O4     6 #> 6025       2         3  22       62133   O5     2 #> 6026       1         3  19       62136   A1     2 #> 6027       1         3  19       62136   A2     4 #> 6028       1         3  19       62136   A3     4 #> 6029       1         3  19       62136   A4     6 #> 6030       1         3  19       62136   A5     3 #> 6031       1         3  19       62136   C1     4 #> 6032       1         3  19       62136   C2     5 #> 6033       1         3  19       62136   C3     4 #> 6034       1         3  19       62136   C4     2 #> 6035       1         3  19       62136   C5     4 #> 6036       1         3  19       62136   E1     6 #> 6037       1         3  19       62136   E2     4 #> 6038       1         3  19       62136   E3     2 #> 6039       1         3  19       62136   E4     3 #> 6040       1         3  19       62136   E5     4 #> 6041       1         3  19       62136   N1     1 #> 6042       1         3  19       62136   N2     1 #> 6043       1         3  19       62136   N3     1 #> 6044       1         3  19       62136   N4     1 #> 6045       1         3  19       62136   N5     1 #> 6046       1         3  19       62136   O1     6 #> 6047       1         3  19       62136   O2     2 #> 6048       1         3  19       62136   O3    NA #> 6049       1         3  19       62136   O4     5 #> 6050       1         3  19       62136   O5     2 #> 6051       2         5  41       62137   A1     4 #> 6052       2         5  41       62137   A2     5 #> 6053       2         5  41       62137   A3     5 #> 6054       2         5  41       62137   A4     5 #> 6055       2         5  41       62137   A5     5 #> 6056       2         5  41       62137   C1     5 #> 6057       2         5  41       62137   C2     4 #> 6058       2         5  41       62137   C3     5 #> 6059       2         5  41       62137   C4     2 #> 6060       2         5  41       62137   C5     4 #> 6061       2         5  41       62137   E1     2 #> 6062       2         5  41       62137   E2     2 #> 6063       2         5  41       62137   E3     4 #> 6064       2         5  41       62137   E4     5 #> 6065       2         5  41       62137   E5     5 #> 6066       2         5  41       62137   N1     2 #> 6067       2         5  41       62137   N2     3 #> 6068       2         5  41       62137   N3     2 #> 6069       2         5  41       62137   N4     1 #> 6070       2         5  41       62137   N5     1 #> 6071       2         5  41       62137   O1     5 #> 6072       2         5  41       62137   O2     4 #> 6073       2         5  41       62137   O3     4 #> 6074       2         5  41       62137   O4     4 #> 6075       2         5  41       62137   O5     1 #> 6076       1         3  40       62142   A1     1 #> 6077       1         3  40       62142   A2     6 #> 6078       1         3  40       62142   A3     4 #> 6079       1         3  40       62142   A4     6 #> 6080       1         3  40       62142   A5     6 #> 6081       1         3  40       62142   C1     5 #> 6082       1         3  40       62142   C2     4 #> 6083       1         3  40       62142   C3     5 #> 6084       1         3  40       62142   C4     1 #> 6085       1         3  40       62142   C5     2 #> 6086       1         3  40       62142   E1     5 #> 6087       1         3  40       62142   E2     1 #> 6088       1         3  40       62142   E3     5 #> 6089       1         3  40       62142   E4     4 #> 6090       1         3  40       62142   E5     6 #> 6091       1         3  40       62142   N1     1 #> 6092       1         3  40       62142   N2     2 #> 6093       1         3  40       62142   N3     2 #> 6094       1         3  40       62142   N4     1 #> 6095       1         3  40       62142   N5     1 #> 6096       1         3  40       62142   O1     4 #> 6097       1         3  40       62142   O2     2 #> 6098       1         3  40       62142   O3     5 #> 6099       1         3  40       62142   O4     5 #> 6100       1         3  40       62142   O5     3 #> 6101       2         5  44       62144   A1     4 #> 6102       2         5  44       62144   A2     3 #> 6103       2         5  44       62144   A3     4 #> 6104       2         5  44       62144   A4     2 #> 6105       2         5  44       62144   A5     5 #> 6106       2         5  44       62144   C1     5 #> 6107       2         5  44       62144   C2     5 #> 6108       2         5  44       62144   C3     5 #> 6109       2         5  44       62144   C4     2 #> 6110       2         5  44       62144   C5     2 #> 6111       2         5  44       62144   E1     2 #> 6112       2         5  44       62144   E2     2 #> 6113       2         5  44       62144   E3     3 #> 6114       2         5  44       62144   E4     3 #> 6115       2         5  44       62144   E5     6 #> 6116       2         5  44       62144   N1     4 #> 6117       2         5  44       62144   N2     4 #> 6118       2         5  44       62144   N3     3 #> 6119       2         5  44       62144   N4     3 #> 6120       2         5  44       62144   N5     2 #> 6121       2         5  44       62144   O1     6 #> 6122       2         5  44       62144   O2     1 #> 6123       2         5  44       62144   O3     4 #> 6124       2         5  44       62144   O4     6 #> 6125       2         5  44       62144   O5     1 #> 6126       1         3  25       62147   A1     3 #> 6127       1         3  25       62147   A2     5 #> 6128       1         3  25       62147   A3     5 #> 6129       1         3  25       62147   A4     5 #> 6130       1         3  25       62147   A5     5 #> 6131       1         3  25       62147   C1     4 #> 6132       1         3  25       62147   C2     3 #> 6133       1         3  25       62147   C3     3 #> 6134       1         3  25       62147   C4     3 #> 6135       1         3  25       62147   C5     5 #> 6136       1         3  25       62147   E1     3 #> 6137       1         3  25       62147   E2     4 #> 6138       1         3  25       62147   E3     3 #> 6139       1         3  25       62147   E4     5 #> 6140       1         3  25       62147   E5     5 #> 6141       1         3  25       62147   N1     4 #> 6142       1         3  25       62147   N2     5 #> 6143       1         3  25       62147   N3     2 #> 6144       1         3  25       62147   N4     2 #> 6145       1         3  25       62147   N5     2 #> 6146       1         3  25       62147   O1     5 #> 6147       1         3  25       62147   O2     4 #> 6148       1         3  25       62147   O3     4 #> 6149       1         3  25       62147   O4     5 #> 6150       1         3  25       62147   O5     5 #> 6151       2        NA  15       62151   A1     5 #> 6152       2        NA  15       62151   A2     2 #> 6153       2        NA  15       62151   A3     1 #> 6154       2        NA  15       62151   A4     1 #> 6155       2        NA  15       62151   A5     2 #> 6156       2        NA  15       62151   C1     2 #> 6157       2        NA  15       62151   C2     1 #> 6158       2        NA  15       62151   C3     2 #> 6159       2        NA  15       62151   C4     5 #> 6160       2        NA  15       62151   C5     6 #> 6161       2        NA  15       62151   E1     2 #> 6162       2        NA  15       62151   E2     5 #> 6163       2        NA  15       62151   E3     2 #> 6164       2        NA  15       62151   E4     4 #> 6165       2        NA  15       62151   E5     2 #> 6166       2        NA  15       62151   N1     3 #> 6167       2        NA  15       62151   N2     6 #> 6168       2        NA  15       62151   N3     4 #> 6169       2        NA  15       62151   N4     5 #> 6170       2        NA  15       62151   N5     3 #> 6171       2        NA  15       62151   O1     5 #> 6172       2        NA  15       62151   O2     6 #> 6173       2        NA  15       62151   O3     2 #> 6174       2        NA  15       62151   O4     6 #> 6175       2        NA  15       62151   O5     4 #> 6176       1         3  24       62156   A1     3 #> 6177       1         3  24       62156   A2     3 #> 6178       1         3  24       62156   A3     3 #> 6179       1         3  24       62156   A4     4 #> 6180       1         3  24       62156   A5     4 #> 6181       1         3  24       62156   C1     5 #> 6182       1         3  24       62156   C2     2 #> 6183       1         3  24       62156   C3     5 #> 6184       1         3  24       62156   C4     2 #> 6185       1         3  24       62156   C5     3 #> 6186       1         3  24       62156   E1     4 #> 6187       1         3  24       62156   E2     2 #> 6188       1         3  24       62156   E3     3 #> 6189       1         3  24       62156   E4     5 #> 6190       1         3  24       62156   E5     4 #> 6191       1         3  24       62156   N1     2 #> 6192       1         3  24       62156   N2     2 #> 6193       1         3  24       62156   N3     2 #> 6194       1         3  24       62156   N4     3 #> 6195       1         3  24       62156   N5     2 #> 6196       1         3  24       62156   O1     2 #> 6197       1         3  24       62156   O2     3 #> 6198       1         3  24       62156   O3     2 #> 6199       1         3  24       62156   O4     4 #> 6200       1         3  24       62156   O5     5 #> 6201       2         3  23       62160   A1     4 #> 6202       2         3  23       62160   A2     2 #> 6203       2         3  23       62160   A3     4 #> 6204       2         3  23       62160   A4     1 #> 6205       2         3  23       62160   A5     4 #> 6206       2         3  23       62160   C1     5 #> 6207       2         3  23       62160   C2     4 #> 6208       2         3  23       62160   C3     4 #> 6209       2         3  23       62160   C4     1 #> 6210       2         3  23       62160   C5     5 #> 6211       2         3  23       62160   E1     2 #> 6212       2         3  23       62160   E2     2 #> 6213       2         3  23       62160   E3     4 #> 6214       2         3  23       62160   E4     3 #> 6215       2         3  23       62160   E5     5 #> 6216       2         3  23       62160   N1     2 #> 6217       2         3  23       62160   N2     4 #> 6218       2         3  23       62160   N3     1 #> 6219       2         3  23       62160   N4     2 #> 6220       2         3  23       62160   N5     1 #> 6221       2         3  23       62160   O1     4 #> 6222       2         3  23       62160   O2     4 #> 6223       2         3  23       62160   O3    NA #> 6224       2         3  23       62160   O4     4 #> 6225       2         3  23       62160   O5     2 #> 6226       2         1  22       62161   A1     3 #> 6227       2         1  22       62161   A2     5 #> 6228       2         1  22       62161   A3     4 #> 6229       2         1  22       62161   A4     5 #> 6230       2         1  22       62161   A5     5 #> 6231       2         1  22       62161   C1     4 #> 6232       2         1  22       62161   C2     2 #> 6233       2         1  22       62161   C3     4 #> 6234       2         1  22       62161   C4     4 #> 6235       2         1  22       62161   C5     4 #> 6236       2         1  22       62161   E1     3 #> 6237       2         1  22       62161   E2     4 #> 6238       2         1  22       62161   E3     6 #> 6239       2         1  22       62161   E4     6 #> 6240       2         1  22       62161   E5     6 #> 6241       2         1  22       62161   N1     3 #> 6242       2         1  22       62161   N2     4 #> 6243       2         1  22       62161   N3     2 #> 6244       2         1  22       62161   N4     2 #> 6245       2         1  22       62161   N5     1 #> 6246       2         1  22       62161   O1     5 #> 6247       2         1  22       62161   O2     1 #> 6248       2         1  22       62161   O3     3 #> 6249       2         1  22       62161   O4     5 #> 6250       2         1  22       62161   O5     3 #> 6251       2         3  19       62162   A1     3 #> 6252       2         3  19       62162   A2     5 #> 6253       2         3  19       62162   A3     6 #> 6254       2         3  19       62162   A4     6 #> 6255       2         3  19       62162   A5     5 #> 6256       2         3  19       62162   C1     4 #> 6257       2         3  19       62162   C2     4 #> 6258       2         3  19       62162   C3     6 #> 6259       2         3  19       62162   C4     3 #> 6260       2         3  19       62162   C5     3 #> 6261       2         3  19       62162   E1     3 #> 6262       2         3  19       62162   E2     4 #> 6263       2         3  19       62162   E3     4 #> 6264       2         3  19       62162   E4     5 #> 6265       2         3  19       62162   E5     4 #> 6266       2         3  19       62162   N1     1 #> 6267       2         3  19       62162   N2     1 #> 6268       2         3  19       62162   N3     2 #> 6269       2         3  19       62162   N4     3 #> 6270       2         3  19       62162   N5     2 #> 6271       2         3  19       62162   O1     5 #> 6272       2         3  19       62162   O2     4 #> 6273       2         3  19       62162   O3     4 #> 6274       2         3  19       62162   O4     5 #> 6275       2         3  19       62162   O5     3 #> 6276       2         3  23       62163   A1     4 #> 6277       2         3  23       62163   A2     5 #> 6278       2         3  23       62163   A3     5 #> 6279       2         3  23       62163   A4     6 #> 6280       2         3  23       62163   A5     4 #> 6281       2         3  23       62163   C1     6 #> 6282       2         3  23       62163   C2     5 #> 6283       2         3  23       62163   C3     5 #> 6284       2         3  23       62163   C4     2 #> 6285       2         3  23       62163   C5     2 #> 6286       2         3  23       62163   E1     5 #> 6287       2         3  23       62163   E2     5 #> 6288       2         3  23       62163   E3     4 #> 6289       2         3  23       62163   E4     4 #> 6290       2         3  23       62163   E5     4 #> 6291       2         3  23       62163   N1     2 #> 6292       2         3  23       62163   N2     4 #> 6293       2         3  23       62163   N3     2 #> 6294       2         3  23       62163   N4     6 #> 6295       2         3  23       62163   N5     2 #> 6296       2         3  23       62163   O1     5 #> 6297       2         3  23       62163   O2     2 #> 6298       2         3  23       62163   O3     4 #> 6299       2         3  23       62163   O4     4 #> 6300       2         3  23       62163   O5     2 #> 6301       2         3  38       62164   A1     6 #> 6302       2         3  38       62164   A2     1 #> 6303       2         3  38       62164   A3     1 #> 6304       2         3  38       62164   A4     4 #> 6305       2         3  38       62164   A5     6 #> 6306       2         3  38       62164   C1     5 #> 6307       2         3  38       62164   C2     6 #> 6308       2         3  38       62164   C3     5 #> 6309       2         3  38       62164   C4     1 #> 6310       2         3  38       62164   C5     1 #> 6311       2         3  38       62164   E1     1 #> 6312       2         3  38       62164   E2     1 #> 6313       2         3  38       62164   E3     5 #> 6314       2         3  38       62164   E4     6 #> 6315       2         3  38       62164   E5     6 #> 6316       2         3  38       62164   N1     1 #> 6317       2         3  38       62164   N2     1 #> 6318       2         3  38       62164   N3     1 #> 6319       2         3  38       62164   N4     1 #> 6320       2         3  38       62164   N5     1 #> 6321       2         3  38       62164   O1     5 #> 6322       2         3  38       62164   O2     1 #> 6323       2         3  38       62164   O3     6 #> 6324       2         3  38       62164   O4     1 #> 6325       2         3  38       62164   O5     1 #> 6326       1         2  26       62165   A1     3 #> 6327       1         2  26       62165   A2     5 #> 6328       1         2  26       62165   A3     5 #> 6329       1         2  26       62165   A4     6 #> 6330       1         2  26       62165   A5     6 #> 6331       1         2  26       62165   C1     6 #> 6332       1         2  26       62165   C2     6 #> 6333       1         2  26       62165   C3     6 #> 6334       1         2  26       62165   C4     1 #> 6335       1         2  26       62165   C5     1 #> 6336       1         2  26       62165   E1     1 #> 6337       1         2  26       62165   E2     2 #> 6338       1         2  26       62165   E3     4 #> 6339       1         2  26       62165   E4     4 #> 6340       1         2  26       62165   E5     6 #> 6341       1         2  26       62165   N1     1 #> 6342       1         2  26       62165   N2     1 #> 6343       1         2  26       62165   N3     1 #> 6344       1         2  26       62165   N4     2 #> 6345       1         2  26       62165   N5     2 #> 6346       1         2  26       62165   O1     3 #> 6347       1         2  26       62165   O2     2 #> 6348       1         2  26       62165   O3     4 #> 6349       1         2  26       62165   O4     2 #> 6350       1         2  26       62165   O5     1 #> 6351       2         3  46       62166   A1     5 #> 6352       2         3  46       62166   A2     5 #> 6353       2         3  46       62166   A3     5 #> 6354       2         3  46       62166   A4     6 #> 6355       2         3  46       62166   A5     6 #> 6356       2         3  46       62166   C1     5 #> 6357       2         3  46       62166   C2     4 #> 6358       2         3  46       62166   C3     5 #> 6359       2         3  46       62166   C4     2 #> 6360       2         3  46       62166   C5     2 #> 6361       2         3  46       62166   E1     4 #> 6362       2         3  46       62166   E2     1 #> 6363       2         3  46       62166   E3     5 #> 6364       2         3  46       62166   E4     6 #> 6365       2         3  46       62166   E5     5 #> 6366       2         3  46       62166   N1     1 #> 6367       2         3  46       62166   N2     1 #> 6368       2         3  46       62166   N3     2 #> 6369       2         3  46       62166   N4     5 #> 6370       2         3  46       62166   N5     1 #> 6371       2         3  46       62166   O1     6 #> 6372       2         3  46       62166   O2     5 #> 6373       2         3  46       62166   O3     4 #> 6374       2         3  46       62166   O4     4 #> 6375       2         3  46       62166   O5     4 #> 6376       2         2  24       62168   A1     3 #> 6377       2         2  24       62168   A2     4 #> 6378       2         2  24       62168   A3     4 #> 6379       2         2  24       62168   A4     5 #> 6380       2         2  24       62168   A5     3 #> 6381       2         2  24       62168   C1     4 #> 6382       2         2  24       62168   C2     4 #> 6383       2         2  24       62168   C3     4 #> 6384       2         2  24       62168   C4     2 #> 6385       2         2  24       62168   C5     2 #> 6386       2         2  24       62168   E1     4 #> 6387       2         2  24       62168   E2     3 #> 6388       2         2  24       62168   E3     3 #> 6389       2         2  24       62168   E4     3 #> 6390       2         2  24       62168   E5     5 #> 6391       2         2  24       62168   N1     3 #> 6392       2         2  24       62168   N2     3 #> 6393       2         2  24       62168   N3     3 #> 6394       2         2  24       62168   N4     2 #> 6395       2         2  24       62168   N5     2 #> 6396       2         2  24       62168   O1     5 #> 6397       2         2  24       62168   O2     4 #> 6398       2         2  24       62168   O3     4 #> 6399       2         2  24       62168   O4     4 #> 6400       2         2  24       62168   O5     3 #> 6401       2         1  18       62170   A1     5 #> 6402       2         1  18       62170   A2     6 #> 6403       2         1  18       62170   A3     5 #> 6404       2         1  18       62170   A4     5 #> 6405       2         1  18       62170   A5     6 #> 6406       2         1  18       62170   C1     5 #> 6407       2         1  18       62170   C2     4 #> 6408       2         1  18       62170   C3     6 #> 6409       2         1  18       62170   C4     1 #> 6410       2         1  18       62170   C5     2 #> 6411       2         1  18       62170   E1     1 #> 6412       2         1  18       62170   E2     3 #> 6413       2         1  18       62170   E3     5 #> 6414       2         1  18       62170   E4     6 #> 6415       2         1  18       62170   E5     5 #> 6416       2         1  18       62170   N1     1 #> 6417       2         1  18       62170   N2     4 #> 6418       2         1  18       62170   N3     1 #> 6419       2         1  18       62170   N4     3 #> 6420       2         1  18       62170   N5     2 #> 6421       2         1  18       62170   O1     4 #> 6422       2         1  18       62170   O2     5 #> 6423       2         1  18       62170   O3     4 #> 6424       2         1  18       62170   O4     6 #> 6425       2         1  18       62170   O5     2 #> 6426       1         2  48       62171   A1     1 #> 6427       1         2  48       62171   A2     6 #> 6428       1         2  48       62171   A3     5 #> 6429       1         2  48       62171   A4     6 #> 6430       1         2  48       62171   A5     5 #> 6431       1         2  48       62171   C1     3 #> 6432       1         2  48       62171   C2     3 #> 6433       1         2  48       62171   C3     5 #> 6434       1         2  48       62171   C4     4 #> 6435       1         2  48       62171   C5     4 #> 6436       1         2  48       62171   E1     4 #> 6437       1         2  48       62171   E2     5 #> 6438       1         2  48       62171   E3     4 #> 6439       1         2  48       62171   E4     3 #> 6440       1         2  48       62171   E5     5 #> 6441       1         2  48       62171   N1     2 #> 6442       1         2  48       62171   N2     2 #> 6443       1         2  48       62171   N3     3 #> 6444       1         2  48       62171   N4     5 #> 6445       1         2  48       62171   N5     4 #> 6446       1         2  48       62171   O1     5 #> 6447       1         2  48       62171   O2     3 #> 6448       1         2  48       62171   O3     5 #> 6449       1         2  48       62171   O4     6 #> 6450       1         2  48       62171   O5     2 #> 6451       2         3  30       62173   A1     1 #> 6452       2         3  30       62173   A2     6 #> 6453       2         3  30       62173   A3     6 #> 6454       2         3  30       62173   A4     6 #> 6455       2         3  30       62173   A5     5 #> 6456       2         3  30       62173   C1     6 #> 6457       2         3  30       62173   C2     4 #> 6458       2         3  30       62173   C3     4 #> 6459       2         3  30       62173   C4     1 #> 6460       2         3  30       62173   C5     5 #> 6461       2         3  30       62173   E1     1 #> 6462       2         3  30       62173   E2     1 #> 6463       2         3  30       62173   E3     6 #> 6464       2         3  30       62173   E4     3 #> 6465       2         3  30       62173   E5     3 #> 6466       2         3  30       62173   N1     3 #> 6467       2         3  30       62173   N2     5 #> 6468       2         3  30       62173   N3     5 #> 6469       2         3  30       62173   N4     2 #> 6470       2         3  30       62173   N5     4 #> 6471       2         3  30       62173   O1     6 #> 6472       2         3  30       62173   O2     6 #> 6473       2         3  30       62173   O3     4 #> 6474       2         3  30       62173   O4     6 #> 6475       2         3  30       62173   O5     1 #> 6476       1         2  22       62176   A1     4 #> 6477       1         2  22       62176   A2     4 #> 6478       1         2  22       62176   A3     6 #> 6479       1         2  22       62176   A4     5 #> 6480       1         2  22       62176   A5     4 #> 6481       1         2  22       62176   C1     4 #> 6482       1         2  22       62176   C2     5 #> 6483       1         2  22       62176   C3     3 #> 6484       1         2  22       62176   C4     5 #> 6485       1         2  22       62176   C5     4 #> 6486       1         2  22       62176   E1     2 #> 6487       1         2  22       62176   E2     4 #> 6488       1         2  22       62176   E3     4 #> 6489       1         2  22       62176   E4     5 #> 6490       1         2  22       62176   E5     3 #> 6491       1         2  22       62176   N1     2 #> 6492       1         2  22       62176   N2     4 #> 6493       1         2  22       62176   N3     2 #> 6494       1         2  22       62176   N4     3 #> 6495       1         2  22       62176   N5     3 #> 6496       1         2  22       62176   O1     5 #> 6497       1         2  22       62176   O2     4 #> 6498       1         2  22       62176   O3     4 #> 6499       1         2  22       62176   O4     4 #> 6500       1         2  22       62176   O5     4 #> 6501       1         3  20       62179   A1     5 #> 6502       1         3  20       62179   A2     5 #> 6503       1         3  20       62179   A3     5 #> 6504       1         3  20       62179   A4     1 #> 6505       1         3  20       62179   A5     5 #> 6506       1         3  20       62179   C1     6 #> 6507       1         3  20       62179   C2     5 #> 6508       1         3  20       62179   C3     2 #> 6509       1         3  20       62179   C4     6 #> 6510       1         3  20       62179   C5     6 #> 6511       1         3  20       62179   E1     4 #> 6512       1         3  20       62179   E2     4 #> 6513       1         3  20       62179   E3     6 #> 6514       1         3  20       62179   E4     3 #> 6515       1         3  20       62179   E5     3 #> 6516       1         3  20       62179   N1     5 #> 6517       1         3  20       62179   N2     4 #> 6518       1         3  20       62179   N3     4 #> 6519       1         3  20       62179   N4     5 #> 6520       1         3  20       62179   N5     4 #> 6521       1         3  20       62179   O1     6 #> 6522       1         3  20       62179   O2     6 #> 6523       1         3  20       62179   O3     6 #> 6524       1         3  20       62179   O4     6 #> 6525       1         3  20       62179   O5     4 #> 6526       1         5  36       62180   A1     3 #> 6527       1         5  36       62180   A2     4 #> 6528       1         5  36       62180   A3     4 #> 6529       1         5  36       62180   A4     3 #> 6530       1         5  36       62180   A5     2 #> 6531       1         5  36       62180   C1     6 #> 6532       1         5  36       62180   C2     5 #> 6533       1         5  36       62180   C3     5 #> 6534       1         5  36       62180   C4     1 #> 6535       1         5  36       62180   C5     3 #> 6536       1         5  36       62180   E1     2 #> 6537       1         5  36       62180   E2     5 #> 6538       1         5  36       62180   E3     5 #> 6539       1         5  36       62180   E4     2 #> 6540       1         5  36       62180   E5     5 #> 6541       1         5  36       62180   N1     4 #> 6542       1         5  36       62180   N2     5 #> 6543       1         5  36       62180   N3     3 #> 6544       1         5  36       62180   N4     6 #> 6545       1         5  36       62180   N5     1 #> 6546       1         5  36       62180   O1     6 #> 6547       1         5  36       62180   O2     1 #> 6548       1         5  36       62180   O3     6 #> 6549       1         5  36       62180   O4     6 #> 6550       1         5  36       62180   O5     1 #> 6551       1         1  18       62181   A1     6 #> 6552       1         1  18       62181   A2     6 #> 6553       1         1  18       62181   A3     6 #> 6554       1         1  18       62181   A4     6 #> 6555       1         1  18       62181   A5     6 #> 6556       1         1  18       62181   C1     5 #> 6557       1         1  18       62181   C2     5 #> 6558       1         1  18       62181   C3     6 #> 6559       1         1  18       62181   C4     6 #> 6560       1         1  18       62181   C5     1 #> 6561       1         1  18       62181   E1     1 #> 6562       1         1  18       62181   E2     1 #> 6563       1         1  18       62181   E3     6 #> 6564       1         1  18       62181   E4     6 #> 6565       1         1  18       62181   E5     6 #> 6566       1         1  18       62181   N1     6 #> 6567       1         1  18       62181   N2     3 #> 6568       1         1  18       62181   N3     2 #> 6569       1         1  18       62181   N4     2 #> 6570       1         1  18       62181   N5     5 #> 6571       1         1  18       62181   O1     1 #> 6572       1         1  18       62181   O2     2 #> 6573       1         1  18       62181   O3     6 #> 6574       1         1  18       62181   O4     6 #> 6575       1         1  18       62181   O5     6 #> 6576       1         3  27       62182   A1     2 #> 6577       1         3  27       62182   A2     6 #> 6578       1         3  27       62182   A3     5 #> 6579       1         3  27       62182   A4     6 #> 6580       1         3  27       62182   A5     5 #> 6581       1         3  27       62182   C1     6 #> 6582       1         3  27       62182   C2     5 #> 6583       1         3  27       62182   C3     5 #> 6584       1         3  27       62182   C4     1 #> 6585       1         3  27       62182   C5     1 #> 6586       1         3  27       62182   E1     5 #> 6587       1         3  27       62182   E2     1 #> 6588       1         3  27       62182   E3     5 #> 6589       1         3  27       62182   E4     5 #> 6590       1         3  27       62182   E5     6 #> 6591       1         3  27       62182   N1     5 #> 6592       1         3  27       62182   N2     4 #> 6593       1         3  27       62182   N3     2 #> 6594       1         3  27       62182   N4     2 #> 6595       1         3  27       62182   N5     1 #> 6596       1         3  27       62182   O1     6 #> 6597       1         3  27       62182   O2     1 #> 6598       1         3  27       62182   O3     5 #> 6599       1         3  27       62182   O4     6 #> 6600       1         3  27       62182   O5     2 #> 6601       2         2  30       62183   A1     4 #> 6602       2         2  30       62183   A2     1 #> 6603       2         2  30       62183   A3     4 #> 6604       2         2  30       62183   A4     6 #> 6605       2         2  30       62183   A5     6 #> 6606       2         2  30       62183   C1     6 #> 6607       2         2  30       62183   C2     4 #> 6608       2         2  30       62183   C3     4 #> 6609       2         2  30       62183   C4     2 #> 6610       2         2  30       62183   C5     6 #> 6611       2         2  30       62183   E1     1 #> 6612       2         2  30       62183   E2     1 #> 6613       2         2  30       62183   E3     6 #> 6614       2         2  30       62183   E4     6 #> 6615       2         2  30       62183   E5     5 #> 6616       2         2  30       62183   N1     1 #> 6617       2         2  30       62183   N2     2 #> 6618       2         2  30       62183   N3     1 #> 6619       2         2  30       62183   N4     1 #> 6620       2         2  30       62183   N5     1 #> 6621       2         2  30       62183   O1     4 #> 6622       2         2  30       62183   O2     3 #> 6623       2         2  30       62183   O3     4 #> 6624       2         2  30       62183   O4     2 #> 6625       2         2  30       62183   O5     5 #> 6626       1         3  18       62189   A1     3 #> 6627       1         3  18       62189   A2     5 #> 6628       1         3  18       62189   A3     4 #> 6629       1         3  18       62189   A4     5 #> 6630       1         3  18       62189   A5     5 #> 6631       1         3  18       62189   C1     5 #> 6632       1         3  18       62189   C2     6 #> 6633       1         3  18       62189   C3     5 #> 6634       1         3  18       62189   C4     2 #> 6635       1         3  18       62189   C5     1 #> 6636       1         3  18       62189   E1     3 #> 6637       1         3  18       62189   E2     3 #> 6638       1         3  18       62189   E3     4 #> 6639       1         3  18       62189   E4     6 #> 6640       1         3  18       62189   E5     5 #> 6641       1         3  18       62189   N1     1 #> 6642       1         3  18       62189   N2     2 #> 6643       1         3  18       62189   N3     2 #> 6644       1         3  18       62189   N4     1 #> 6645       1         3  18       62189   N5     2 #> 6646       1         3  18       62189   O1     4 #> 6647       1         3  18       62189   O2     5 #> 6648       1         3  18       62189   O3     3 #> 6649       1         3  18       62189   O4     4 #> 6650       1         3  18       62189   O5     3 #> 6651       1         3  20       62192   A1     2 #> 6652       1         3  20       62192   A2     4 #> 6653       1         3  20       62192   A3     5 #> 6654       1         3  20       62192   A4     5 #> 6655       1         3  20       62192   A5     5 #> 6656       1         3  20       62192   C1     4 #> 6657       1         3  20       62192   C2     4 #> 6658       1         3  20       62192   C3     4 #> 6659       1         3  20       62192   C4     2 #> 6660       1         3  20       62192   C5     3 #> 6661       1         3  20       62192   E1     3 #> 6662       1         3  20       62192   E2     4 #> 6663       1         3  20       62192   E3     3 #> 6664       1         3  20       62192   E4     5 #> 6665       1         3  20       62192   E5     3 #> 6666       1         3  20       62192   N1     5 #> 6667       1         3  20       62192   N2     5 #> 6668       1         3  20       62192   N3     2 #> 6669       1         3  20       62192   N4     4 #> 6670       1         3  20       62192   N5     4 #> 6671       1         3  20       62192   O1     4 #> 6672       1         3  20       62192   O2     4 #> 6673       1         3  20       62192   O3     4 #> 6674       1         3  20       62192   O4     2 #> 6675       1         3  20       62192   O5     2 #> 6676       2         3  55       62197   A1     1 #> 6677       2         3  55       62197   A2     5 #> 6678       2         3  55       62197   A3     5 #> 6679       2         3  55       62197   A4     5 #> 6680       2         3  55       62197   A5     5 #> 6681       2         3  55       62197   C1     5 #> 6682       2         3  55       62197   C2     4 #> 6683       2         3  55       62197   C3     5 #> 6684       2         3  55       62197   C4     1 #> 6685       2         3  55       62197   C5     1 #> 6686       2         3  55       62197   E1     2 #> 6687       2         3  55       62197   E2     2 #> 6688       2         3  55       62197   E3     3 #> 6689       2         3  55       62197   E4     5 #> 6690       2         3  55       62197   E5     5 #> 6691       2         3  55       62197   N1     1 #> 6692       2         3  55       62197   N2     2 #> 6693       2         3  55       62197   N3     2 #> 6694       2         3  55       62197   N4     2 #> 6695       2         3  55       62197   N5     2 #> 6696       2         3  55       62197   O1     2 #> 6697       2         3  55       62197   O2     2 #> 6698       2         3  55       62197   O3     5 #> 6699       2         3  55       62197   O4     4 #> 6700       2         3  55       62197   O5     2 #> 6701       2         3  17       62198   A1     1 #> 6702       2         3  17       62198   A2     6 #> 6703       2         3  17       62198   A3     6 #> 6704       2         3  17       62198   A4     5 #> 6705       2         3  17       62198   A5     5 #> 6706       2         3  17       62198   C1     5 #> 6707       2         3  17       62198   C2     6 #> 6708       2         3  17       62198   C3     5 #> 6709       2         3  17       62198   C4     2 #> 6710       2         3  17       62198   C5     5 #> 6711       2         3  17       62198   E1     4 #> 6712       2         3  17       62198   E2     4 #> 6713       2         3  17       62198   E3     3 #> 6714       2         3  17       62198   E4     6 #> 6715       2         3  17       62198   E5     5 #> 6716       2         3  17       62198   N1     3 #> 6717       2         3  17       62198   N2     4 #> 6718       2         3  17       62198   N3     2 #> 6719       2         3  17       62198   N4     2 #> 6720       2         3  17       62198   N5     6 #> 6721       2         3  17       62198   O1     4 #> 6722       2         3  17       62198   O2     2 #> 6723       2         3  17       62198   O3     5 #> 6724       2         3  17       62198   O4     5 #> 6725       2         3  17       62198   O5     3 #> 6726       1         4  28       62199   A1     4 #> 6727       1         4  28       62199   A2     5 #> 6728       1         4  28       62199   A3     4 #> 6729       1         4  28       62199   A4     3 #> 6730       1         4  28       62199   A5     4 #> 6731       1         4  28       62199   C1     4 #> 6732       1         4  28       62199   C2     4 #> 6733       1         4  28       62199   C3     5 #> 6734       1         4  28       62199   C4     4 #> 6735       1         4  28       62199   C5     5 #> 6736       1         4  28       62199   E1     4 #> 6737       1         4  28       62199   E2     4 #> 6738       1         4  28       62199   E3     4 #> 6739       1         4  28       62199   E4     4 #> 6740       1         4  28       62199   E5     5 #> 6741       1         4  28       62199   N1     2 #> 6742       1         4  28       62199   N2     2 #> 6743       1         4  28       62199   N3     5 #> 6744       1         4  28       62199   N4     5 #> 6745       1         4  28       62199   N5     3 #> 6746       1         4  28       62199   O1     6 #> 6747       1         4  28       62199   O2     2 #> 6748       1         4  28       62199   O3     3 #> 6749       1         4  28       62199   O4     6 #> 6750       1         4  28       62199   O5     6 #> 6751       2         3  19       62201   A1     3 #> 6752       2         3  19       62201   A2     5 #> 6753       2         3  19       62201   A3     4 #> 6754       2         3  19       62201   A4     5 #> 6755       2         3  19       62201   A5     5 #> 6756       2         3  19       62201   C1     5 #> 6757       2         3  19       62201   C2     5 #> 6758       2         3  19       62201   C3     3 #> 6759       2         3  19       62201   C4     4 #> 6760       2         3  19       62201   C5     6 #> 6761       2         3  19       62201   E1     1 #> 6762       2         3  19       62201   E2     6 #> 6763       2         3  19       62201   E3     5 #> 6764       2         3  19       62201   E4     6 #> 6765       2         3  19       62201   E5     6 #> 6766       2         3  19       62201   N1     6 #> 6767       2         3  19       62201   N2     6 #> 6768       2         3  19       62201   N3     5 #> 6769       2         3  19       62201   N4     4 #> 6770       2         3  19       62201   N5     5 #> 6771       2         3  19       62201   O1     6 #> 6772       2         3  19       62201   O2     6 #> 6773       2         3  19       62201   O3     3 #> 6774       2         3  19       62201   O4     4 #> 6775       2         3  19       62201   O5     2 #> 6776       2         3  19       62202   A1     4 #> 6777       2         3  19       62202   A2     5 #> 6778       2         3  19       62202   A3     6 #> 6779       2         3  19       62202   A4     4 #> 6780       2         3  19       62202   A5     6 #> 6781       2         3  19       62202   C1     4 #> 6782       2         3  19       62202   C2     5 #> 6783       2         3  19       62202   C3     5 #> 6784       2         3  19       62202   C4     3 #> 6785       2         3  19       62202   C5     3 #> 6786       2         3  19       62202   E1     1 #> 6787       2         3  19       62202   E2     1 #> 6788       2         3  19       62202   E3     5 #> 6789       2         3  19       62202   E4     5 #> 6790       2         3  19       62202   E5     6 #> 6791       2         3  19       62202   N1     2 #> 6792       2         3  19       62202   N2     4 #> 6793       2         3  19       62202   N3     4 #> 6794       2         3  19       62202   N4    NA #> 6795       2         3  19       62202   N5     1 #> 6796       2         3  19       62202   O1     6 #> 6797       2         3  19       62202   O2     2 #> 6798       2         3  19       62202   O3     5 #> 6799       2         3  19       62202   O4     5 #> 6800       2         3  19       62202   O5     3 #> 6801       2        NA  16       62203   A1     1 #> 6802       2        NA  16       62203   A2     6 #> 6803       2        NA  16       62203   A3     6 #> 6804       2        NA  16       62203   A4     6 #> 6805       2        NA  16       62203   A5     6 #> 6806       2        NA  16       62203   C1     6 #> 6807       2        NA  16       62203   C2     5 #> 6808       2        NA  16       62203   C3     6 #> 6809       2        NA  16       62203   C4     1 #> 6810       2        NA  16       62203   C5     3 #> 6811       2        NA  16       62203   E1     1 #> 6812       2        NA  16       62203   E2     1 #> 6813       2        NA  16       62203   E3     6 #> 6814       2        NA  16       62203   E4     6 #> 6815       2        NA  16       62203   E5     6 #> 6816       2        NA  16       62203   N1     1 #> 6817       2        NA  16       62203   N2     1 #> 6818       2        NA  16       62203   N3     1 #> 6819       2        NA  16       62203   N4     3 #> 6820       2        NA  16       62203   N5     3 #> 6821       2        NA  16       62203   O1     6 #> 6822       2        NA  16       62203   O2     1 #> 6823       2        NA  16       62203   O3     6 #> 6824       2        NA  16       62203   O4     5 #> 6825       2        NA  16       62203   O5     3 #> 6826       1         1  31       62204   A1     1 #> 6827       1         1  31       62204   A2     5 #> 6828       1         1  31       62204   A3     6 #> 6829       1         1  31       62204   A4     6 #> 6830       1         1  31       62204   A5     6 #> 6831       1         1  31       62204   C1     5 #> 6832       1         1  31       62204   C2     6 #> 6833       1         1  31       62204   C3     6 #> 6834       1         1  31       62204   C4     5 #> 6835       1         1  31       62204   C5     1 #> 6836       1         1  31       62204   E1     4 #> 6837       1         1  31       62204   E2     1 #> 6838       1         1  31       62204   E3     6 #> 6839       1         1  31       62204   E4     6 #> 6840       1         1  31       62204   E5     5 #> 6841       1         1  31       62204   N1     5 #> 6842       1         1  31       62204   N2     1 #> 6843       1         1  31       62204   N3     5 #> 6844       1         1  31       62204   N4     5 #> 6845       1         1  31       62204   N5     2 #> 6846       1         1  31       62204   O1     6 #> 6847       1         1  31       62204   O2     5 #> 6848       1         1  31       62204   O3     5 #> 6849       1         1  31       62204   O4     5 #> 6850       1         1  31       62204   O5     2 #> 6851       2         3  50       62205   A1     2 #> 6852       2         3  50       62205   A2     4 #> 6853       2         3  50       62205   A3     4 #> 6854       2         3  50       62205   A4     4 #> 6855       2         3  50       62205   A5     5 #> 6856       2         3  50       62205   C1     4 #> 6857       2         3  50       62205   C2     5 #> 6858       2         3  50       62205   C3     4 #> 6859       2         3  50       62205   C4     2 #> 6860       2         3  50       62205   C5     4 #> 6861       2         3  50       62205   E1     1 #> 6862       2         3  50       62205   E2     1 #> 6863       2         3  50       62205   E3     5 #> 6864       2         3  50       62205   E4     6 #> 6865       2         3  50       62205   E5     6 #> 6866       2         3  50       62205   N1     4 #> 6867       2         3  50       62205   N2     5 #> 6868       2         3  50       62205   N3     6 #> 6869       2         3  50       62205   N4     4 #> 6870       2         3  50       62205   N5     4 #> 6871       2         3  50       62205   O1     6 #> 6872       2         3  50       62205   O2     3 #> 6873       2         3  50       62205   O3     5 #> 6874       2         3  50       62205   O4     4 #> 6875       2         3  50       62205   O5     1 #> 6876       2         3  31       62206   A1     3 #> 6877       2         3  31       62206   A2     5 #> 6878       2         3  31       62206   A3     6 #> 6879       2         3  31       62206   A4     6 #> 6880       2         3  31       62206   A5     6 #> 6881       2         3  31       62206   C1     4 #> 6882       2         3  31       62206   C2     5 #> 6883       2         3  31       62206   C3     6 #> 6884       2         3  31       62206   C4     3 #> 6885       2         3  31       62206   C5     1 #> 6886       2         3  31       62206   E1     2 #> 6887       2         3  31       62206   E2     1 #> 6888       2         3  31       62206   E3     5 #> 6889       2         3  31       62206   E4     2 #> 6890       2         3  31       62206   E5     6 #> 6891       2         3  31       62206   N1     5 #> 6892       2         3  31       62206   N2     4 #> 6893       2         3  31       62206   N3     1 #> 6894       2         3  31       62206   N4     2 #> 6895       2         3  31       62206   N5     3 #> 6896       2         3  31       62206   O1     4 #> 6897       2         3  31       62206   O2     5 #> 6898       2         3  31       62206   O3     5 #> 6899       2         3  31       62206   O4     6 #> 6900       2         3  31       62206   O5     4 #> 6901       2         5  27       62208   A1     1 #> 6902       2         5  27       62208   A2     5 #> 6903       2         5  27       62208   A3     6 #> 6904       2         5  27       62208   A4     5 #> 6905       2         5  27       62208   A5     6 #> 6906       2         5  27       62208   C1     6 #> 6907       2         5  27       62208   C2     5 #> 6908       2         5  27       62208   C3     5 #> 6909       2         5  27       62208   C4     1 #> 6910       2         5  27       62208   C5     2 #> 6911       2         5  27       62208   E1     2 #> 6912       2         5  27       62208   E2     1 #> 6913       2         5  27       62208   E3     5 #> 6914       2         5  27       62208   E4     5 #> 6915       2         5  27       62208   E5     6 #> 6916       2         5  27       62208   N1     2 #> 6917       2         5  27       62208   N2     2 #> 6918       2         5  27       62208   N3     2 #> 6919       2         5  27       62208   N4     2 #> 6920       2         5  27       62208   N5     1 #> 6921       2         5  27       62208   O1     5 #> 6922       2         5  27       62208   O2     1 #> 6923       2         5  27       62208   O3     6 #> 6924       2         5  27       62208   O4     4 #> 6925       2         5  27       62208   O5     2 #> 6926       1         3  16       62209   A1     1 #> 6927       1         3  16       62209   A2     4 #> 6928       1         3  16       62209   A3     4 #> 6929       1         3  16       62209   A4     2 #> 6930       1         3  16       62209   A5     5 #> 6931       1         3  16       62209   C1     5 #> 6932       1         3  16       62209   C2     4 #> 6933       1         3  16       62209   C3     3 #> 6934       1         3  16       62209   C4     2 #> 6935       1         3  16       62209   C5     4 #> 6936       1         3  16       62209   E1     5 #> 6937       1         3  16       62209   E2     5 #> 6938       1         3  16       62209   E3     4 #> 6939       1         3  16       62209   E4     2 #> 6940       1         3  16       62209   E5     3 #> 6941       1         3  16       62209   N1     4 #> 6942       1         3  16       62209   N2     4 #> 6943       1         3  16       62209   N3     4 #> 6944       1         3  16       62209   N4     5 #> 6945       1         3  16       62209   N5     4 #> 6946       1         3  16       62209   O1     4 #> 6947       1         3  16       62209   O2     2 #> 6948       1         3  16       62209   O3     2 #> 6949       1         3  16       62209   O4     5 #> 6950       1         3  16       62209   O5     4 #> 6951       1         3  18       62212   A1     4 #> 6952       1         3  18       62212   A2     6 #> 6953       1         3  18       62212   A3     6 #> 6954       1         3  18       62212   A4     6 #> 6955       1         3  18       62212   A5     5 #> 6956       1         3  18       62212   C1     4 #> 6957       1         3  18       62212   C2     4 #> 6958       1         3  18       62212   C3     5 #> 6959       1         3  18       62212   C4     2 #> 6960       1         3  18       62212   C5     5 #> 6961       1         3  18       62212   E1     1 #> 6962       1         3  18       62212   E2     1 #> 6963       1         3  18       62212   E3     4 #> 6964       1         3  18       62212   E4     5 #> 6965       1         3  18       62212   E5     5 #> 6966       1         3  18       62212   N1     4 #> 6967       1         3  18       62212   N2     5 #> 6968       1         3  18       62212   N3     4 #> 6969       1         3  18       62212   N4     4 #> 6970       1         3  18       62212   N5     5 #> 6971       1         3  18       62212   O1     5 #> 6972       1         3  18       62212   O2     1 #> 6973       1         3  18       62212   O3     6 #> 6974       1         3  18       62212   O4     6 #> 6975       1         3  18       62212   O5     1 #> 6976       2         3  20       62213   A1     2 #> 6977       2         3  20       62213   A2     6 #> 6978       2         3  20       62213   A3     5 #> 6979       2         3  20       62213   A4     6 #> 6980       2         3  20       62213   A5     3 #> 6981       2         3  20       62213   C1     3 #> 6982       2         3  20       62213   C2     3 #> 6983       2         3  20       62213   C3     5 #> 6984       2         3  20       62213   C4     3 #> 6985       2         3  20       62213   C5     5 #> 6986       2         3  20       62213   E1     1 #> 6987       2         3  20       62213   E2     4 #> 6988       2         3  20       62213   E3     3 #> 6989       2         3  20       62213   E4     5 #> 6990       2         3  20       62213   E5     2 #> 6991       2         3  20       62213   N1     4 #> 6992       2         3  20       62213   N2     5 #> 6993       2         3  20       62213   N3     6 #> 6994       2         3  20       62213   N4     4 #> 6995       2         3  20       62213   N5     4 #> 6996       2         3  20       62213   O1     2 #> 6997       2         3  20       62213   O2     4 #> 6998       2         3  20       62213   O3     3 #> 6999       2         3  20       62213   O4     6 #> 7000       2         3  20       62213   O5     3 #> 7001       1         3  21       62214   A1     3 #> 7002       1         3  21       62214   A2     4 #> 7003       1         3  21       62214   A3     5 #> 7004       1         3  21       62214   A4     4 #> 7005       1         3  21       62214   A5     4 #> 7006       1         3  21       62214   C1     4 #> 7007       1         3  21       62214   C2     4 #> 7008       1         3  21       62214   C3     3 #> 7009       1         3  21       62214   C4     4 #> 7010       1         3  21       62214   C5     5 #> 7011       1         3  21       62214   E1     5 #> 7012       1         3  21       62214   E2     4 #> 7013       1         3  21       62214   E3     3 #> 7014       1         3  21       62214   E4     3 #> 7015       1         3  21       62214   E5     4 #> 7016       1         3  21       62214   N1     5 #> 7017       1         3  21       62214   N2     5 #> 7018       1         3  21       62214   N3     3 #> 7019       1         3  21       62214   N4     3 #> 7020       1         3  21       62214   N5     3 #> 7021       1         3  21       62214   O1     4 #> 7022       1         3  21       62214   O2     3 #> 7023       1         3  21       62214   O3     5 #> 7024       1         3  21       62214   O4     5 #> 7025       1         3  21       62214   O5     3 #> 7026       2         5  32       62215   A1     2 #> 7027       2         5  32       62215   A2     6 #> 7028       2         5  32       62215   A3     6 #> 7029       2         5  32       62215   A4     6 #> 7030       2         5  32       62215   A5     6 #> 7031       2         5  32       62215   C1     5 #> 7032       2         5  32       62215   C2     4 #> 7033       2         5  32       62215   C3     5 #> 7034       2         5  32       62215   C4     1 #> 7035       2         5  32       62215   C5     1 #> 7036       2         5  32       62215   E1     1 #> 7037       2         5  32       62215   E2     1 #> 7038       2         5  32       62215   E3     5 #> 7039       2         5  32       62215   E4     5 #> 7040       2         5  32       62215   E5     5 #> 7041       2         5  32       62215   N1     4 #> 7042       2         5  32       62215   N2     3 #> 7043       2         5  32       62215   N3     2 #> 7044       2         5  32       62215   N4     2 #> 7045       2         5  32       62215   N5     3 #> 7046       2         5  32       62215   O1     5 #> 7047       2         5  32       62215   O2     1 #> 7048       2         5  32       62215   O3     5 #> 7049       2         5  32       62215   O4     4 #> 7050       2         5  32       62215   O5     2 #> 7051       2         3  30       62216   A1     2 #> 7052       2         3  30       62216   A2     2 #> 7053       2         3  30       62216   A3     2 #> 7054       2         3  30       62216   A4     4 #> 7055       2         3  30       62216   A5     3 #> 7056       2         3  30       62216   C1     3 #> 7057       2         3  30       62216   C2     2 #> 7058       2         3  30       62216   C3     4 #> 7059       2         3  30       62216   C4     4 #> 7060       2         3  30       62216   C5     6 #> 7061       2         3  30       62216   E1     4 #> 7062       2         3  30       62216   E2     4 #> 7063       2         3  30       62216   E3     3 #> 7064       2         3  30       62216   E4     3 #> 7065       2         3  30       62216   E5     3 #> 7066       2         3  30       62216   N1     1 #> 7067       2         3  30       62216   N2     3 #> 7068       2         3  30       62216   N3     3 #> 7069       2         3  30       62216   N4     5 #> 7070       2         3  30       62216   N5     1 #> 7071       2         3  30       62216   O1     4 #> 7072       2         3  30       62216   O2     6 #> 7073       2         3  30       62216   O3     3 #> 7074       2         3  30       62216   O4     4 #> 7075       2         3  30       62216   O5     4 #> 7076       2         3  24       62219   A1     2 #> 7077       2         3  24       62219   A2     5 #> 7078       2         3  24       62219   A3     4 #> 7079       2         3  24       62219   A4     6 #> 7080       2         3  24       62219   A5     4 #> 7081       2         3  24       62219   C1     4 #> 7082       2         3  24       62219   C2     5 #> 7083       2         3  24       62219   C3     4 #> 7084       2         3  24       62219   C4     3 #> 7085       2         3  24       62219   C5     4 #> 7086       2         3  24       62219   E1     1 #> 7087       2         3  24       62219   E2     3 #> 7088       2         3  24       62219   E3     3 #> 7089       2         3  24       62219   E4     4 #> 7090       2         3  24       62219   E5     4 #> 7091       2         3  24       62219   N1     4 #> 7092       2         3  24       62219   N2     4 #> 7093       2         3  24       62219   N3     5 #> 7094       2         3  24       62219   N4     3 #> 7095       2         3  24       62219   N5     5 #> 7096       2         3  24       62219   O1     4 #> 7097       2         3  24       62219   O2     4 #> 7098       2         3  24       62219   O3     4 #> 7099       2         3  24       62219   O4     4 #> 7100       2         3  24       62219   O5     2 #> 7101       2         1  35       62220   A1     3 #> 7102       2         1  35       62220   A2     4 #> 7103       2         1  35       62220   A3     6 #> 7104       2         1  35       62220   A4     6 #> 7105       2         1  35       62220   A5     4 #> 7106       2         1  35       62220   C1     4 #> 7107       2         1  35       62220   C2     4 #> 7108       2         1  35       62220   C3     5 #> 7109       2         1  35       62220   C4     5 #> 7110       2         1  35       62220   C5     4 #> 7111       2         1  35       62220   E1     2 #> 7112       2         1  35       62220   E2     4 #> 7113       2         1  35       62220   E3     4 #> 7114       2         1  35       62220   E4     3 #> 7115       2         1  35       62220   E5     3 #> 7116       2         1  35       62220   N1     6 #> 7117       2         1  35       62220   N2     6 #> 7118       2         1  35       62220   N3     6 #> 7119       2         1  35       62220   N4     4 #> 7120       2         1  35       62220   N5     5 #> 7121       2         1  35       62220   O1     3 #> 7122       2         1  35       62220   O2     4 #> 7123       2         1  35       62220   O3     5 #> 7124       2         1  35       62220   O4     4 #> 7125       2         1  35       62220   O5     4 #> 7126       2         3  19       62224   A1     1 #> 7127       2         3  19       62224   A2     5 #> 7128       2         3  19       62224   A3     5 #> 7129       2         3  19       62224   A4     6 #> 7130       2         3  19       62224   A5     5 #> 7131       2         3  19       62224   C1     5 #> 7132       2         3  19       62224   C2     4 #> 7133       2         3  19       62224   C3     5 #> 7134       2         3  19       62224   C4     2 #> 7135       2         3  19       62224   C5     1 #> 7136       2         3  19       62224   E1     1 #> 7137       2         3  19       62224   E2     3 #> 7138       2         3  19       62224   E3     5 #> 7139       2         3  19       62224   E4     6 #> 7140       2         3  19       62224   E5     5 #> 7141       2         3  19       62224   N1     3 #> 7142       2         3  19       62224   N2     4 #> 7143       2         3  19       62224   N3     4 #> 7144       2         3  19       62224   N4     3 #> 7145       2         3  19       62224   N5     4 #> 7146       2         3  19       62224   O1     5 #> 7147       2         3  19       62224   O2     3 #> 7148       2         3  19       62224   O3     4 #> 7149       2         3  19       62224   O4     4 #> 7150       2         3  19       62224   O5     2 #> 7151       2         3  23       62225   A1     2 #> 7152       2         3  23       62225   A2     5 #> 7153       2         3  23       62225   A3     6 #> 7154       2         3  23       62225   A4     5 #> 7155       2         3  23       62225   A5     4 #> 7156       2         3  23       62225   C1     3 #> 7157       2         3  23       62225   C2     5 #> 7158       2         3  23       62225   C3     5 #> 7159       2         3  23       62225   C4     2 #> 7160       2         3  23       62225   C5     1 #> 7161       2         3  23       62225   E1     4 #> 7162       2         3  23       62225   E2     5 #> 7163       2         3  23       62225   E3     4 #> 7164       2         3  23       62225   E4     1 #> 7165       2         3  23       62225   E5     6 #> 7166       2         3  23       62225   N1     6 #> 7167       2         3  23       62225   N2     6 #> 7168       2         3  23       62225   N3     6 #> 7169       2         3  23       62225   N4     6 #> 7170       2         3  23       62225   N5     4 #> 7171       2         3  23       62225   O1     4 #> 7172       2         3  23       62225   O2     2 #> 7173       2         3  23       62225   O3     6 #> 7174       2         3  23       62225   O4     6 #> 7175       2         3  23       62225   O5     2 #> 7176       2        NA  16       62226   A1     2 #> 7177       2        NA  16       62226   A2     4 #> 7178       2        NA  16       62226   A3     2 #> 7179       2        NA  16       62226   A4     4 #> 7180       2        NA  16       62226   A5     4 #> 7181       2        NA  16       62226   C1     5 #> 7182       2        NA  16       62226   C2     3 #> 7183       2        NA  16       62226   C3     5 #> 7184       2        NA  16       62226   C4     2 #> 7185       2        NA  16       62226   C5     3 #> 7186       2        NA  16       62226   E1     6 #> 7187       2        NA  16       62226   E2     4 #> 7188       2        NA  16       62226   E3     4 #> 7189       2        NA  16       62226   E4     5 #> 7190       2        NA  16       62226   E5     5 #> 7191       2        NA  16       62226   N1     4 #> 7192       2        NA  16       62226   N2     4 #> 7193       2        NA  16       62226   N3     3 #> 7194       2        NA  16       62226   N4     3 #> 7195       2        NA  16       62226   N5     2 #> 7196       2        NA  16       62226   O1     5 #> 7197       2        NA  16       62226   O2     2 #> 7198       2        NA  16       62226   O3     5 #> 7199       2        NA  16       62226   O4     5 #> 7200       2        NA  16       62226   O5     4 #> 7201       1         4  21       62227   A1     1 #> 7202       1         4  21       62227   A2     6 #> 7203       1         4  21       62227   A3     6 #> 7204       1         4  21       62227   A4     6 #> 7205       1         4  21       62227   A5     6 #> 7206       1         4  21       62227   C1     6 #> 7207       1         4  21       62227   C2     4 #> 7208       1         4  21       62227   C3     4 #> 7209       1         4  21       62227   C4     3 #> 7210       1         4  21       62227   C5     4 #> 7211       1         4  21       62227   E1     1 #> 7212       1         4  21       62227   E2     1 #> 7213       1         4  21       62227   E3     6 #> 7214       1         4  21       62227   E4     6 #> 7215       1         4  21       62227   E5     6 #> 7216       1         4  21       62227   N1     1 #> 7217       1         4  21       62227   N2     2 #> 7218       1         4  21       62227   N3     4 #> 7219       1         4  21       62227   N4     2 #> 7220       1         4  21       62227   N5     2 #> 7221       1         4  21       62227   O1     6 #> 7222       1         4  21       62227   O2     1 #> 7223       1         4  21       62227   O3     6 #> 7224       1         4  21       62227   O4     6 #> 7225       1         4  21       62227   O5     1 #> 7226       2         3  22       62228   A1     1 #> 7227       2         3  22       62228   A2     6 #> 7228       2         3  22       62228   A3     6 #> 7229       2         3  22       62228   A4     6 #> 7230       2         3  22       62228   A5     5 #> 7231       2         3  22       62228   C1     5 #> 7232       2         3  22       62228   C2     4 #> 7233       2         3  22       62228   C3     6 #> 7234       2         3  22       62228   C4     2 #> 7235       2         3  22       62228   C5     2 #> 7236       2         3  22       62228   E1     2 #> 7237       2         3  22       62228   E2     4 #> 7238       2         3  22       62228   E3     4 #> 7239       2         3  22       62228   E4     6 #> 7240       2         3  22       62228   E5     3 #> 7241       2         3  22       62228   N1     5 #> 7242       2         3  22       62228   N2     6 #> 7243       2         3  22       62228   N3     3 #> 7244       2         3  22       62228   N4     2 #> 7245       2         3  22       62228   N5     4 #> 7246       2         3  22       62228   O1     3 #> 7247       2         3  22       62228   O2     5 #> 7248       2         3  22       62228   O3     3 #> 7249       2         3  22       62228   O4     3 #> 7250       2         3  22       62228   O5     1 #> 7251       2         3  20       62231   A1     1 #> 7252       2         3  20       62231   A2     5 #> 7253       2         3  20       62231   A3     5 #> 7254       2         3  20       62231   A4     6 #> 7255       2         3  20       62231   A5     5 #> 7256       2         3  20       62231   C1     2 #> 7257       2         3  20       62231   C2     4 #> 7258       2         3  20       62231   C3     5 #> 7259       2         3  20       62231   C4     5 #> 7260       2         3  20       62231   C5     5 #> 7261       2         3  20       62231   E1     3 #> 7262       2         3  20       62231   E2     5 #> 7263       2         3  20       62231   E3     5 #> 7264       2         3  20       62231   E4     5 #> 7265       2         3  20       62231   E5     2 #> 7266       2         3  20       62231   N1     3 #> 7267       2         3  20       62231   N2     4 #> 7268       2         3  20       62231   N3     4 #> 7269       2         3  20       62231   N4     4 #> 7270       2         3  20       62231   N5     2 #> 7271       2         3  20       62231   O1     1 #> 7272       2         3  20       62231   O2     4 #> 7273       2         3  20       62231   O3     3 #> 7274       2         3  20       62231   O4     5 #> 7275       2         3  20       62231   O5     4 #> 7276       1         4  26       62233   A1     2 #> 7277       1         4  26       62233   A2     5 #> 7278       1         4  26       62233   A3     5 #> 7279       1         4  26       62233   A4     5 #> 7280       1         4  26       62233   A5     5 #> 7281       1         4  26       62233   C1     5 #> 7282       1         4  26       62233   C2     5 #> 7283       1         4  26       62233   C3     5 #> 7284       1         4  26       62233   C4     2 #> 7285       1         4  26       62233   C5     1 #> 7286       1         4  26       62233   E1     2 #> 7287       1         4  26       62233   E2     2 #> 7288       1         4  26       62233   E3     4 #> 7289       1         4  26       62233   E4     5 #> 7290       1         4  26       62233   E5     5 #> 7291       1         4  26       62233   N1     1 #> 7292       1         4  26       62233   N2     1 #> 7293       1         4  26       62233   N3     1 #> 7294       1         4  26       62233   N4     1 #> 7295       1         4  26       62233   N5     1 #> 7296       1         4  26       62233   O1     5 #> 7297       1         4  26       62233   O2     1 #> 7298       1         4  26       62233   O3     5 #> 7299       1         4  26       62233   O4     2 #> 7300       1         4  26       62233   O5     2 #> 7301       1         3  19       62237   A1     3 #> 7302       1         3  19       62237   A2     4 #> 7303       1         3  19       62237   A3     5 #> 7304       1         3  19       62237   A4     5 #> 7305       1         3  19       62237   A5     5 #> 7306       1         3  19       62237   C1     4 #> 7307       1         3  19       62237   C2     4 #> 7308       1         3  19       62237   C3     5 #> 7309       1         3  19       62237   C4     4 #> 7310       1         3  19       62237   C5     4 #> 7311       1         3  19       62237   E1     5 #> 7312       1         3  19       62237   E2     4 #> 7313       1         3  19       62237   E3     3 #> 7314       1         3  19       62237   E4    NA #> 7315       1         3  19       62237   E5     4 #> 7316       1         3  19       62237   N1     3 #> 7317       1         3  19       62237   N2     4 #> 7318       1         3  19       62237   N3     2 #> 7319       1         3  19       62237   N4     4 #> 7320       1         3  19       62237   N5     4 #> 7321       1         3  19       62237   O1     3 #> 7322       1         3  19       62237   O2     5 #> 7323       1         3  19       62237   O3     3 #> 7324       1         3  19       62237   O4     3 #> 7325       1         3  19       62237   O5     4 #> 7326       2         5  25       62239   A1     3 #> 7327       2         5  25       62239   A2     6 #> 7328       2         5  25       62239   A3     5 #> 7329       2         5  25       62239   A4     5 #> 7330       2         5  25       62239   A5     5 #> 7331       2         5  25       62239   C1     4 #> 7332       2         5  25       62239   C2     4 #> 7333       2         5  25       62239   C3     5 #> 7334       2         5  25       62239   C4     4 #> 7335       2         5  25       62239   C5     4 #> 7336       2         5  25       62239   E1     1 #> 7337       2         5  25       62239   E2     2 #> 7338       2         5  25       62239   E3     5 #> 7339       2         5  25       62239   E4     6 #> 7340       2         5  25       62239   E5     6 #> 7341       2         5  25       62239   N1     4 #> 7342       2         5  25       62239   N2     4 #> 7343       2         5  25       62239   N3     4 #> 7344       2         5  25       62239   N4     3 #> 7345       2         5  25       62239   N5     5 #> 7346       2         5  25       62239   O1     6 #> 7347       2         5  25       62239   O2     2 #> 7348       2         5  25       62239   O3     5 #> 7349       2         5  25       62239   O4     4 #> 7350       2         5  25       62239   O5     3 #> 7351       2         5  28       62240   A1     1 #> 7352       2         5  28       62240   A2     5 #> 7353       2         5  28       62240   A3     6 #> 7354       2         5  28       62240   A4     4 #> 7355       2         5  28       62240   A5     6 #> 7356       2         5  28       62240   C1     5 #> 7357       2         5  28       62240   C2     3 #> 7358       2         5  28       62240   C3     3 #> 7359       2         5  28       62240   C4     4 #> 7360       2         5  28       62240   C5     5 #> 7361       2         5  28       62240   E1     1 #> 7362       2         5  28       62240   E2     1 #> 7363       2         5  28       62240   E3     5 #> 7364       2         5  28       62240   E4     6 #> 7365       2         5  28       62240   E5     5 #> 7366       2         5  28       62240   N1     4 #> 7367       2         5  28       62240   N2     6 #> 7368       2         5  28       62240   N3     6 #> 7369       2         5  28       62240   N4     5 #> 7370       2         5  28       62240   N5     6 #> 7371       2         5  28       62240   O1     6 #> 7372       2         5  28       62240   O2     2 #> 7373       2         5  28       62240   O3     6 #> 7374       2         5  28       62240   O4     5 #> 7375       2         5  28       62240   O5     1 #> 7376       2        NA  17       62242   A1     5 #> 7377       2        NA  17       62242   A2     6 #> 7378       2        NA  17       62242   A3     6 #> 7379       2        NA  17       62242   A4     6 #> 7380       2        NA  17       62242   A5     6 #> 7381       2        NA  17       62242   C1     4 #> 7382       2        NA  17       62242   C2     5 #> 7383       2        NA  17       62242   C3     4 #> 7384       2        NA  17       62242   C4     3 #> 7385       2        NA  17       62242   C5     5 #> 7386       2        NA  17       62242   E1     1 #> 7387       2        NA  17       62242   E2     2 #> 7388       2        NA  17       62242   E3     4 #> 7389       2        NA  17       62242   E4     6 #> 7390       2        NA  17       62242   E5     6 #> 7391       2        NA  17       62242   N1     4 #> 7392       2        NA  17       62242   N2     5 #> 7393       2        NA  17       62242   N3     1 #> 7394       2        NA  17       62242   N4     2 #> 7395       2        NA  17       62242   N5     3 #> 7396       2        NA  17       62242   O1     6 #> 7397       2        NA  17       62242   O2     6 #> 7398       2        NA  17       62242   O3     5 #> 7399       2        NA  17       62242   O4     6 #> 7400       2        NA  17       62242   O5     2 #> 7401       2        NA  16       62244   A1     1 #> 7402       2        NA  16       62244   A2     6 #> 7403       2        NA  16       62244   A3     6 #> 7404       2        NA  16       62244   A4     1 #> 7405       2        NA  16       62244   A5     5 #> 7406       2        NA  16       62244   C1     5 #> 7407       2        NA  16       62244   C2     6 #> 7408       2        NA  16       62244   C3     6 #> 7409       2        NA  16       62244   C4     5 #> 7410       2        NA  16       62244   C5     6 #> 7411       2        NA  16       62244   E1     1 #> 7412       2        NA  16       62244   E2     3 #> 7413       2        NA  16       62244   E3     6 #> 7414       2        NA  16       62244   E4     5 #> 7415       2        NA  16       62244   E5     5 #> 7416       2        NA  16       62244   N1     4 #> 7417       2        NA  16       62244   N2     4 #> 7418       2        NA  16       62244   N3     1 #> 7419       2        NA  16       62244   N4     2 #> 7420       2        NA  16       62244   N5     2 #> 7421       2        NA  16       62244   O1     5 #> 7422       2        NA  16       62244   O2     3 #> 7423       2        NA  16       62244   O3     4 #> 7424       2        NA  16       62244   O4     5 #> 7425       2        NA  16       62244   O5     5 #> 7426       1        NA  17       62245   A1     3 #> 7427       1        NA  17       62245   A2     4 #> 7428       1        NA  17       62245   A3     4 #> 7429       1        NA  17       62245   A4     4 #> 7430       1        NA  17       62245   A5     3 #> 7431       1        NA  17       62245   C1     3 #> 7432       1        NA  17       62245   C2     2 #> 7433       1        NA  17       62245   C3     2 #> 7434       1        NA  17       62245   C4     4 #> 7435       1        NA  17       62245   C5     6 #> 7436       1        NA  17       62245   E1     5 #> 7437       1        NA  17       62245   E2     3 #> 7438       1        NA  17       62245   E3     4 #> 7439       1        NA  17       62245   E4     4 #> 7440       1        NA  17       62245   E5     5 #> 7441       1        NA  17       62245   N1     4 #> 7442       1        NA  17       62245   N2     5 #> 7443       1        NA  17       62245   N3     2 #> 7444       1        NA  17       62245   N4     3 #> 7445       1        NA  17       62245   N5     1 #> 7446       1        NA  17       62245   O1     4 #> 7447       1        NA  17       62245   O2     2 #> 7448       1        NA  17       62245   O3     5 #> 7449       1        NA  17       62245   O4     5 #> 7450       1        NA  17       62245   O5     3 #> 7451       1         1  29       62246   A1     4 #> 7452       1         1  29       62246   A2     5 #> 7453       1         1  29       62246   A3     5 #> 7454       1         1  29       62246   A4     6 #> 7455       1         1  29       62246   A5     6 #> 7456       1         1  29       62246   C1     3 #> 7457       1         1  29       62246   C2     5 #> 7458       1         1  29       62246   C3     6 #> 7459       1         1  29       62246   C4     2 #> 7460       1         1  29       62246   C5     1 #> 7461       1         1  29       62246   E1     6 #> 7462       1         1  29       62246   E2     6 #> 7463       1         1  29       62246   E3     1 #> 7464       1         1  29       62246   E4     5 #> 7465       1         1  29       62246   E5     6 #> 7466       1         1  29       62246   N1     2 #> 7467       1         1  29       62246   N2     3 #> 7468       1         1  29       62246   N3     3 #> 7469       1         1  29       62246   N4     2 #> 7470       1         1  29       62246   N5     1 #> 7471       1         1  29       62246   O1     5 #> 7472       1         1  29       62246   O2     3 #> 7473       1         1  29       62246   O3     1 #> 7474       1         1  29       62246   O4     3 #> 7475       1         1  29       62246   O5     4 #> 7476       2         2  19       62252   A1     3 #> 7477       2         2  19       62252   A2     4 #> 7478       2         2  19       62252   A3     4 #> 7479       2         2  19       62252   A4     3 #> 7480       2         2  19       62252   A5     4 #> 7481       2         2  19       62252   C1     4 #> 7482       2         2  19       62252   C2     4 #> 7483       2         2  19       62252   C3     4 #> 7484       2         2  19       62252   C4     4 #> 7485       2         2  19       62252   C5     3 #> 7486       2         2  19       62252   E1     3 #> 7487       2         2  19       62252   E2     4 #> 7488       2         2  19       62252   E3     3 #> 7489       2         2  19       62252   E4     4 #> 7490       2         2  19       62252   E5     3 #> 7491       2         2  19       62252   N1     4 #> 7492       2         2  19       62252   N2     4 #> 7493       2         2  19       62252   N3     3 #> 7494       2         2  19       62252   N4     3 #> 7495       2         2  19       62252   N5     3 #> 7496       2         2  19       62252   O1     4 #> 7497       2         2  19       62252   O2     3 #> 7498       2         2  19       62252   O3     4 #> 7499       2         2  19       62252   O4     4 #> 7500       2         2  19       62252   O5     3 #> 7501       2         4  47       62259   A1     1 #> 7502       2         4  47       62259   A2     5 #> 7503       2         4  47       62259   A3     5 #> 7504       2         4  47       62259   A4     6 #> 7505       2         4  47       62259   A5     5 #> 7506       2         4  47       62259   C1     5 #> 7507       2         4  47       62259   C2     6 #> 7508       2         4  47       62259   C3     2 #> 7509       2         4  47       62259   C4     1 #> 7510       2         4  47       62259   C5     5 #> 7511       2         4  47       62259   E1     3 #> 7512       2         4  47       62259   E2     3 #> 7513       2         4  47       62259   E3     5 #> 7514       2         4  47       62259   E4     6 #> 7515       2         4  47       62259   E5     4 #> 7516       2         4  47       62259   N1     2 #> 7517       2         4  47       62259   N2     4 #> 7518       2         4  47       62259   N3     5 #> 7519       2         4  47       62259   N4     4 #> 7520       2         4  47       62259   N5     4 #> 7521       2         4  47       62259   O1     6 #> 7522       2         4  47       62259   O2     1 #> 7523       2         4  47       62259   O3     6 #> 7524       2         4  47       62259   O4     6 #> 7525       2         4  47       62259   O5     1 #> 7526       2         4  52       62260   A1     1 #> 7527       2         4  52       62260   A2     5 #> 7528       2         4  52       62260   A3     5 #> 7529       2         4  52       62260   A4     5 #> 7530       2         4  52       62260   A5     4 #> 7531       2         4  52       62260   C1     4 #> 7532       2         4  52       62260   C2     4 #> 7533       2         4  52       62260   C3     4 #> 7534       2         4  52       62260   C4     2 #> 7535       2         4  52       62260   C5     2 #> 7536       2         4  52       62260   E1     4 #> 7537       2         4  52       62260   E2     4 #> 7538       2         4  52       62260   E3     4 #> 7539       2         4  52       62260   E4     2 #> 7540       2         4  52       62260   E5     5 #> 7541       2         4  52       62260   N1     1 #> 7542       2         4  52       62260   N2     2 #> 7543       2         4  52       62260   N3     1 #> 7544       2         4  52       62260   N4     1 #> 7545       2         4  52       62260   N5     2 #> 7546       2         4  52       62260   O1     4 #> 7547       2         4  52       62260   O2     2 #> 7548       2         4  52       62260   O3     5 #> 7549       2         4  52       62260   O4     6 #> 7550       2         4  52       62260   O5     2 #> 7551       2         4  22       62261   A1     2 #> 7552       2         4  22       62261   A2     6 #> 7553       2         4  22       62261   A3     6 #> 7554       2         4  22       62261   A4     5 #> 7555       2         4  22       62261   A5     5 #> 7556       2         4  22       62261   C1     5 #> 7557       2         4  22       62261   C2     2 #> 7558       2         4  22       62261   C3     6 #> 7559       2         4  22       62261   C4     5 #> 7560       2         4  22       62261   C5     5 #> 7561       2         4  22       62261   E1     6 #> 7562       2         4  22       62261   E2     5 #> 7563       2         4  22       62261   E3     3 #> 7564       2         4  22       62261   E4     4 #> 7565       2         4  22       62261   E5     2 #> 7566       2         4  22       62261   N1     2 #> 7567       2         4  22       62261   N2     2 #> 7568       2         4  22       62261   N3     2 #> 7569       2         4  22       62261   N4     4 #> 7570       2         4  22       62261   N5     2 #> 7571       2         4  22       62261   O1     4 #> 7572       2         4  22       62261   O2     5 #> 7573       2         4  22       62261   O3     3 #> 7574       2         4  22       62261   O4     4 #> 7575       2         4  22       62261   O5     3 #> 7576       1         3  18       62263   A1     3 #> 7577       1         3  18       62263   A2     5 #> 7578       1         3  18       62263   A3     6 #> 7579       1         3  18       62263   A4     4 #> 7580       1         3  18       62263   A5     5 #> 7581       1         3  18       62263   C1     4 #> 7582       1         3  18       62263   C2     3 #> 7583       1         3  18       62263   C3     5 #> 7584       1         3  18       62263   C4     3 #> 7585       1         3  18       62263   C5     4 #> 7586       1         3  18       62263   E1     1 #> 7587       1         3  18       62263   E2     2 #> 7588       1         3  18       62263   E3     4 #> 7589       1         3  18       62263   E4     5 #> 7590       1         3  18       62263   E5     4 #> 7591       1         3  18       62263   N1     2 #> 7592       1         3  18       62263   N2     3 #> 7593       1         3  18       62263   N3     5 #> 7594       1         3  18       62263   N4     4 #> 7595       1         3  18       62263   N5     3 #> 7596       1         3  18       62263   O1     4 #> 7597       1         3  18       62263   O2     3 #> 7598       1         3  18       62263   O3     5 #> 7599       1         3  18       62263   O4     6 #> 7600       1         3  18       62263   O5     5 #> 7601       2         3  30       62264   A1     4 #> 7602       2         3  30       62264   A2     5 #> 7603       2         3  30       62264   A3     4 #> 7604       2         3  30       62264   A4     5 #> 7605       2         3  30       62264   A5     4 #> 7606       2         3  30       62264   C1     5 #> 7607       2         3  30       62264   C2     5 #> 7608       2         3  30       62264   C3     4 #> 7609       2         3  30       62264   C4     1 #> 7610       2         3  30       62264   C5     1 #> 7611       2         3  30       62264   E1     2 #> 7612       2         3  30       62264   E2     2 #> 7613       2         3  30       62264   E3     4 #> 7614       2         3  30       62264   E4     5 #> 7615       2         3  30       62264   E5     5 #> 7616       2         3  30       62264   N1     3 #> 7617       2         3  30       62264   N2     4 #> 7618       2         3  30       62264   N3     4 #> 7619       2         3  30       62264   N4     4 #> 7620       2         3  30       62264   N5     4 #> 7621       2         3  30       62264   O1     5 #> 7622       2         3  30       62264   O2     1 #> 7623       2         3  30       62264   O3     5 #> 7624       2         3  30       62264   O4     4 #> 7625       2         3  30       62264   O5     1 #> 7626       2        NA  17       62265   A1     2 #> 7627       2        NA  17       62265   A2     6 #> 7628       2        NA  17       62265   A3     4 #> 7629       2        NA  17       62265   A4     2 #> 7630       2        NA  17       62265   A5     5 #> 7631       2        NA  17       62265   C1     5 #> 7632       2        NA  17       62265   C2     5 #> 7633       2        NA  17       62265   C3     4 #> 7634       2        NA  17       62265   C4     2 #> 7635       2        NA  17       62265   C5     4 #> 7636       2        NA  17       62265   E1     2 #> 7637       2        NA  17       62265   E2     4 #> 7638       2        NA  17       62265   E3     3 #> 7639       2        NA  17       62265   E4     2 #> 7640       2        NA  17       62265   E5     6 #> 7641       2        NA  17       62265   N1     2 #> 7642       2        NA  17       62265   N2     5 #> 7643       2        NA  17       62265   N3     6 #> 7644       2        NA  17       62265   N4     5 #> 7645       2        NA  17       62265   N5     2 #> 7646       2        NA  17       62265   O1     6 #> 7647       2        NA  17       62265   O2     1 #> 7648       2        NA  17       62265   O3     6 #> 7649       2        NA  17       62265   O4    NA #> 7650       2        NA  17       62265   O5     1 #> 7651       1         3  31       62266   A1     5 #> 7652       1         3  31       62266   A2     2 #> 7653       1         3  31       62266   A3     3 #> 7654       1         3  31       62266   A4     2 #> 7655       1         3  31       62266   A5     3 #> 7656       1         3  31       62266   C1     5 #> 7657       1         3  31       62266   C2     4 #> 7658       1         3  31       62266   C3     3 #> 7659       1         3  31       62266   C4     2 #> 7660       1         3  31       62266   C5     3 #> 7661       1         3  31       62266   E1     4 #> 7662       1         3  31       62266   E2     5 #> 7663       1         3  31       62266   E3     4 #> 7664       1         3  31       62266   E4     4 #> 7665       1         3  31       62266   E5     1 #> 7666       1         3  31       62266   N1     2 #> 7667       1         3  31       62266   N2     4 #> 7668       1         3  31       62266   N3     5 #> 7669       1         3  31       62266   N4     5 #> 7670       1         3  31       62266   N5     4 #> 7671       1         3  31       62266   O1     5 #> 7672       1         3  31       62266   O2     1 #> 7673       1         3  31       62266   O3     5 #> 7674       1         3  31       62266   O4     6 #> 7675       1         3  31       62266   O5     3 #> 7676       1         2  56       62267   A1     1 #> 7677       1         2  56       62267   A2     6 #> 7678       1         2  56       62267   A3     5 #> 7679       1         2  56       62267   A4     6 #> 7680       1         2  56       62267   A5     5 #> 7681       1         2  56       62267   C1     5 #> 7682       1         2  56       62267   C2     6 #> 7683       1         2  56       62267   C3     6 #> 7684       1         2  56       62267   C4     2 #> 7685       1         2  56       62267   C5     1 #> 7686       1         2  56       62267   E1     5 #> 7687       1         2  56       62267   E2     2 #> 7688       1         2  56       62267   E3     5 #> 7689       1         2  56       62267   E4     1 #> 7690       1         2  56       62267   E5     6 #> 7691       1         2  56       62267   N1     5 #> 7692       1         2  56       62267   N2     4 #> 7693       1         2  56       62267   N3     5 #> 7694       1         2  56       62267   N4     4 #> 7695       1         2  56       62267   N5     2 #> 7696       1         2  56       62267   O1     6 #> 7697       1         2  56       62267   O2     5 #> 7698       1         2  56       62267   O3     3 #> 7699       1         2  56       62267   O4     6 #> 7700       1         2  56       62267   O5     5 #> 7701       2         4  28       62272   A1     3 #> 7702       2         4  28       62272   A2     4 #> 7703       2         4  28       62272   A3     4 #> 7704       2         4  28       62272   A4     4 #> 7705       2         4  28       62272   A5     4 #> 7706       2         4  28       62272   C1     6 #> 7707       2         4  28       62272   C2     6 #> 7708       2         4  28       62272   C3     5 #> 7709       2         4  28       62272   C4     1 #> 7710       2         4  28       62272   C5     2 #> 7711       2         4  28       62272   E1     2 #> 7712       2         4  28       62272   E2     2 #> 7713       2         4  28       62272   E3     5 #> 7714       2         4  28       62272   E4     5 #> 7715       2         4  28       62272   E5     6 #> 7716       2         4  28       62272   N1     2 #> 7717       2         4  28       62272   N2     2 #> 7718       2         4  28       62272   N3     2 #> 7719       2         4  28       62272   N4     2 #> 7720       2         4  28       62272   N5     4 #> 7721       2         4  28       62272   O1     5 #> 7722       2         4  28       62272   O2     6 #> 7723       2         4  28       62272   O3     6 #> 7724       2         4  28       62272   O4     6 #> 7725       2         4  28       62272   O5     4 #> 7726       2         4  33       62276   A1     1 #> 7727       2         4  33       62276   A2     6 #> 7728       2         4  33       62276   A3     6 #> 7729       2         4  33       62276   A4     4 #> 7730       2         4  33       62276   A5     6 #> 7731       2         4  33       62276   C1     4 #> 7732       2         4  33       62276   C2     5 #> 7733       2         4  33       62276   C3     5 #> 7734       2         4  33       62276   C4     2 #> 7735       2         4  33       62276   C5     3 #> 7736       2         4  33       62276   E1     1 #> 7737       2         4  33       62276   E2     1 #> 7738       2         4  33       62276   E3     6 #> 7739       2         4  33       62276   E4     6 #> 7740       2         4  33       62276   E5     5 #> 7741       2         4  33       62276   N1     3 #> 7742       2         4  33       62276   N2     5 #> 7743       2         4  33       62276   N3     5 #> 7744       2         4  33       62276   N4     2 #> 7745       2         4  33       62276   N5     2 #> 7746       2         4  33       62276   O1     5 #> 7747       2         4  33       62276   O2     5 #> 7748       2         4  33       62276   O3     6 #> 7749       2         4  33       62276   O4     6 #> 7750       2         4  33       62276   O5    NA #> 7751       2         3  30       62278   A1     3 #> 7752       2         3  30       62278   A2     5 #> 7753       2         3  30       62278   A3     6 #> 7754       2         3  30       62278   A4     6 #> 7755       2         3  30       62278   A5     6 #> 7756       2         3  30       62278   C1     3 #> 7757       2         3  30       62278   C2     5 #> 7758       2         3  30       62278   C3     3 #> 7759       2         3  30       62278   C4     2 #> 7760       2         3  30       62278   C5     2 #> 7761       2         3  30       62278   E1     2 #> 7762       2         3  30       62278   E2     4 #> 7763       2         3  30       62278   E3     4 #> 7764       2         3  30       62278   E4     4 #> 7765       2         3  30       62278   E5     4 #> 7766       2         3  30       62278   N1     2 #> 7767       2         3  30       62278   N2     3 #> 7768       2         3  30       62278   N3     1 #> 7769       2         3  30       62278   N4     2 #> 7770       2         3  30       62278   N5     3 #> 7771       2         3  30       62278   O1     5 #> 7772       2         3  30       62278   O2     5 #> 7773       2         3  30       62278   O3     4 #> 7774       2         3  30       62278   O4     5 #> 7775       2         3  30       62278   O5    NA #> 7776       2         3  23       62279   A1     1 #> 7777       2         3  23       62279   A2     6 #> 7778       2         3  23       62279   A3     5 #> 7779       2         3  23       62279   A4     4 #> 7780       2         3  23       62279   A5     5 #> 7781       2         3  23       62279   C1     6 #> 7782       2         3  23       62279   C2     5 #> 7783       2         3  23       62279   C3     5 #> 7784       2         3  23       62279   C4     1 #> 7785       2         3  23       62279   C5     2 #> 7786       2         3  23       62279   E1     1 #> 7787       2         3  23       62279   E2     4 #> 7788       2         3  23       62279   E3     1 #> 7789       2         3  23       62279   E4     5 #> 7790       2         3  23       62279   E5     4 #> 7791       2         3  23       62279   N1     2 #> 7792       2         3  23       62279   N2     4 #> 7793       2         3  23       62279   N3     4 #> 7794       2         3  23       62279   N4     2 #> 7795       2         3  23       62279   N5     6 #> 7796       2         3  23       62279   O1     2 #> 7797       2         3  23       62279   O2     1 #> 7798       2         3  23       62279   O3     4 #> 7799       2         3  23       62279   O4     6 #> 7800       2         3  23       62279   O5     4 #> 7801       2         3  20       62280   A1     2 #> 7802       2         3  20       62280   A2     5 #> 7803       2         3  20       62280   A3     5 #> 7804       2         3  20       62280   A4     6 #> 7805       2         3  20       62280   A5     5 #> 7806       2         3  20       62280   C1     4 #> 7807       2         3  20       62280   C2     4 #> 7808       2         3  20       62280   C3     4 #> 7809       2         3  20       62280   C4     1 #> 7810       2         3  20       62280   C5     2 #> 7811       2         3  20       62280   E1     3 #> 7812       2         3  20       62280   E2     2 #> 7813       2         3  20       62280   E3     4 #> 7814       2         3  20       62280   E4     5 #> 7815       2         3  20       62280   E5     5 #> 7816       2         3  20       62280   N1     1 #> 7817       2         3  20       62280   N2     1 #> 7818       2         3  20       62280   N3     1 #> 7819       2         3  20       62280   N4     2 #> 7820       2         3  20       62280   N5     1 #> 7821       2         3  20       62280   O1     5 #> 7822       2         3  20       62280   O2     2 #> 7823       2         3  20       62280   O3     4 #> 7824       2         3  20       62280   O4     5 #> 7825       2         3  20       62280   O5     2 #> 7826       2         2  27       62281   A1     1 #> 7827       2         2  27       62281   A2     6 #> 7828       2         2  27       62281   A3     6 #> 7829       2         2  27       62281   A4     6 #> 7830       2         2  27       62281   A5     6 #> 7831       2         2  27       62281   C1     5 #> 7832       2         2  27       62281   C2     4 #> 7833       2         2  27       62281   C3     6 #> 7834       2         2  27       62281   C4     1 #> 7835       2         2  27       62281   C5     2 #> 7836       2         2  27       62281   E1     1 #> 7837       2         2  27       62281   E2     2 #> 7838       2         2  27       62281   E3     6 #> 7839       2         2  27       62281   E4     6 #> 7840       2         2  27       62281   E5     6 #> 7841       2         2  27       62281   N1     1 #> 7842       2         2  27       62281   N2     4 #> 7843       2         2  27       62281   N3     4 #> 7844       2         2  27       62281   N4     1 #> 7845       2         2  27       62281   N5     1 #> 7846       2         2  27       62281   O1     5 #> 7847       2         2  27       62281   O2     4 #> 7848       2         2  27       62281   O3     6 #> 7849       2         2  27       62281   O4     4 #> 7850       2         2  27       62281   O5     4 #> 7851       1         2  25       62282   A1     3 #> 7852       1         2  25       62282   A2     5 #> 7853       1         2  25       62282   A3     5 #> 7854       1         2  25       62282   A4     6 #> 7855       1         2  25       62282   A5     1 #> 7856       1         2  25       62282   C1     6 #> 7857       1         2  25       62282   C2     5 #> 7858       1         2  25       62282   C3     5 #> 7859       1         2  25       62282   C4     4 #> 7860       1         2  25       62282   C5     6 #> 7861       1         2  25       62282   E1     1 #> 7862       1         2  25       62282   E2     1 #> 7863       1         2  25       62282   E3     6 #> 7864       1         2  25       62282   E4     3 #> 7865       1         2  25       62282   E5     6 #> 7866       1         2  25       62282   N1     6 #> 7867       1         2  25       62282   N2     6 #> 7868       1         2  25       62282   N3     6 #> 7869       1         2  25       62282   N4     5 #> 7870       1         2  25       62282   N5     1 #> 7871       1         2  25       62282   O1     6 #> 7872       1         2  25       62282   O2     1 #> 7873       1         2  25       62282   O3     6 #> 7874       1         2  25       62282   O4     3 #> 7875       1         2  25       62282   O5     1 #> 7876       2         2  27       62287   A1     2 #> 7877       2         2  27       62287   A2     4 #> 7878       2         2  27       62287   A3     4 #> 7879       2         2  27       62287   A4     6 #> 7880       2         2  27       62287   A5     2 #> 7881       2         2  27       62287   C1     5 #> 7882       2         2  27       62287   C2     5 #> 7883       2         2  27       62287   C3     5 #> 7884       2         2  27       62287   C4     2 #> 7885       2         2  27       62287   C5     1 #> 7886       2         2  27       62287   E1     3 #> 7887       2         2  27       62287   E2     6 #> 7888       2         2  27       62287   E3     5 #> 7889       2         2  27       62287   E4     4 #> 7890       2         2  27       62287   E5     5 #> 7891       2         2  27       62287   N1     5 #> 7892       2         2  27       62287   N2     5 #> 7893       2         2  27       62287   N3     6 #> 7894       2         2  27       62287   N4     5 #> 7895       2         2  27       62287   N5     5 #> 7896       2         2  27       62287   O1     5 #> 7897       2         2  27       62287   O2     2 #> 7898       2         2  27       62287   O3     4 #> 7899       2         2  27       62287   O4     6 #> 7900       2         2  27       62287   O5     1 #> 7901       2        NA  56       62288   A1     3 #> 7902       2        NA  56       62288   A2     5 #> 7903       2        NA  56       62288   A3     6 #> 7904       2        NA  56       62288   A4     4 #> 7905       2        NA  56       62288   A5     6 #> 7906       2        NA  56       62288   C1     2 #> 7907       2        NA  56       62288   C2     1 #> 7908       2        NA  56       62288   C3     5 #> 7909       2        NA  56       62288   C4     6 #> 7910       2        NA  56       62288   C5     6 #> 7911       2        NA  56       62288   E1     1 #> 7912       2        NA  56       62288   E2     1 #> 7913       2        NA  56       62288   E3     5 #> 7914       2        NA  56       62288   E4     5 #> 7915       2        NA  56       62288   E5     1 #> 7916       2        NA  56       62288   N1     1 #> 7917       2        NA  56       62288   N2     1 #> 7918       2        NA  56       62288   N3     3 #> 7919       2        NA  56       62288   N4     6 #> 7920       2        NA  56       62288   N5     4 #> 7921       2        NA  56       62288   O1     6 #> 7922       2        NA  56       62288   O2     2 #> 7923       2        NA  56       62288   O3     2 #> 7924       2        NA  56       62288   O4     4 #> 7925       2        NA  56       62288   O5     1 #> 7926       2         3  18       62289   A1     3 #> 7927       2         3  18       62289   A2     5 #> 7928       2         3  18       62289   A3     4 #> 7929       2         3  18       62289   A4     2 #> 7930       2         3  18       62289   A5     2 #> 7931       2         3  18       62289   C1     4 #> 7932       2         3  18       62289   C2     4 #> 7933       2         3  18       62289   C3     4 #> 7934       2         3  18       62289   C4     2 #> 7935       2         3  18       62289   C5     2 #> 7936       2         3  18       62289   E1     4 #> 7937       2         3  18       62289   E2     4 #> 7938       2         3  18       62289   E3     2 #> 7939       2         3  18       62289   E4     4 #> 7940       2         3  18       62289   E5     4 #> 7941       2         3  18       62289   N1     5 #> 7942       2         3  18       62289   N2     6 #> 7943       2         3  18       62289   N3     6 #> 7944       2         3  18       62289   N4     6 #> 7945       2         3  18       62289   N5     6 #> 7946       2         3  18       62289   O1     5 #> 7947       2         3  18       62289   O2     1 #> 7948       2         3  18       62289   O3     3 #> 7949       2         3  18       62289   O4     6 #> 7950       2         3  18       62289   O5     2 #> 7951       1         1  18       62290   A1     2 #> 7952       1         1  18       62290   A2     5 #> 7953       1         1  18       62290   A3     3 #> 7954       1         1  18       62290   A4     2 #> 7955       1         1  18       62290   A5     2 #> 7956       1         1  18       62290   C1     5 #> 7957       1         1  18       62290   C2     3 #> 7958       1         1  18       62290   C3     4 #> 7959       1         1  18       62290   C4     4 #> 7960       1         1  18       62290   C5     4 #> 7961       1         1  18       62290   E1     2 #> 7962       1         1  18       62290   E2     2 #> 7963       1         1  18       62290   E3     4 #> 7964       1         1  18       62290   E4     4 #> 7965       1         1  18       62290   E5     5 #> 7966       1         1  18       62290   N1     2 #> 7967       1         1  18       62290   N2     2 #> 7968       1         1  18       62290   N3     4 #> 7969       1         1  18       62290   N4     2 #> 7970       1         1  18       62290   N5     2 #> 7971       1         1  18       62290   O1     4 #> 7972       1         1  18       62290   O2     2 #> 7973       1         1  18       62290   O3     5 #> 7974       1         1  18       62290   O4     6 #> 7975       1         1  18       62290   O5     1 #> 7976       1         3  21       62293   A1     2 #> 7977       1         3  21       62293   A2     6 #> 7978       1         3  21       62293   A3     6 #> 7979       1         3  21       62293   A4     6 #> 7980       1         3  21       62293   A5     6 #> 7981       1         3  21       62293   C1     4 #> 7982       1         3  21       62293   C2     5 #> 7983       1         3  21       62293   C3     5 #> 7984       1         3  21       62293   C4     1 #> 7985       1         3  21       62293   C5     2 #> 7986       1         3  21       62293   E1     2 #> 7987       1         3  21       62293   E2     2 #> 7988       1         3  21       62293   E3     3 #> 7989       1         3  21       62293   E4     5 #> 7990       1         3  21       62293   E5     6 #> 7991       1         3  21       62293   N1     2 #> 7992       1         3  21       62293   N2     3 #> 7993       1         3  21       62293   N3     3 #> 7994       1         3  21       62293   N4     1 #> 7995       1         3  21       62293   N5     1 #> 7996       1         3  21       62293   O1     5 #> 7997       1         3  21       62293   O2     2 #> 7998       1         3  21       62293   O3     3 #> 7999       1         3  21       62293   O4     2 #> 8000       1         3  21       62293   O5     1 #> 8001       1         2  19       62295   A1     2 #> 8002       1         2  19       62295   A2     2 #> 8003       1         2  19       62295   A3     6 #> 8004       1         2  19       62295   A4     2 #> 8005       1         2  19       62295   A5     2 #> 8006       1         2  19       62295   C1     6 #> 8007       1         2  19       62295   C2     6 #> 8008       1         2  19       62295   C3     6 #> 8009       1         2  19       62295   C4     1 #> 8010       1         2  19       62295   C5     2 #> 8011       1         2  19       62295   E1     6 #> 8012       1         2  19       62295   E2     2 #> 8013       1         2  19       62295   E3     6 #> 8014       1         2  19       62295   E4     1 #> 8015       1         2  19       62295   E5     6 #> 8016       1         2  19       62295   N1     5 #> 8017       1         2  19       62295   N2     5 #> 8018       1         2  19       62295   N3     5 #> 8019       1         2  19       62295   N4     5 #> 8020       1         2  19       62295   N5     1 #> 8021       1         2  19       62295   O1     6 #> 8022       1         2  19       62295   O2     1 #> 8023       1         2  19       62295   O3     6 #> 8024       1         2  19       62295   O4     6 #> 8025       1         2  19       62295   O5     1 #> 8026       2         2  18       62296   A1     2 #> 8027       2         2  18       62296   A2     6 #> 8028       2         2  18       62296   A3     6 #> 8029       2         2  18       62296   A4     6 #> 8030       2         2  18       62296   A5     6 #> 8031       2         2  18       62296   C1     4 #> 8032       2         2  18       62296   C2     5 #> 8033       2         2  18       62296   C3     4 #> 8034       2         2  18       62296   C4     2 #> 8035       2         2  18       62296   C5     2 #> 8036       2         2  18       62296   E1     1 #> 8037       2         2  18       62296   E2     1 #> 8038       2         2  18       62296   E3     5 #> 8039       2         2  18       62296   E4     6 #> 8040       2         2  18       62296   E5     5 #> 8041       2         2  18       62296   N1     2 #> 8042       2         2  18       62296   N2     2 #> 8043       2         2  18       62296   N3     2 #> 8044       2         2  18       62296   N4     1 #> 8045       2         2  18       62296   N5     2 #> 8046       2         2  18       62296   O1     4 #> 8047       2         2  18       62296   O2     1 #> 8048       2         2  18       62296   O3     5 #> 8049       2         2  18       62296   O4     5 #> 8050       2         2  18       62296   O5     4 #> 8051       1         4  29       62298   A1     5 #> 8052       1         4  29       62298   A2     2 #> 8053       1         4  29       62298   A3     2 #> 8054       1         4  29       62298   A4     2 #> 8055       1         4  29       62298   A5     5 #> 8056       1         4  29       62298   C1     5 #> 8057       1         4  29       62298   C2     5 #> 8058       1         4  29       62298   C3     6 #> 8059       1         4  29       62298   C4     2 #> 8060       1         4  29       62298   C5     5 #> 8061       1         4  29       62298   E1     4 #> 8062       1         4  29       62298   E2     5 #> 8063       1         4  29       62298   E3     3 #> 8064       1         4  29       62298   E4     3 #> 8065       1         4  29       62298   E5     3 #> 8066       1         4  29       62298   N1     5 #> 8067       1         4  29       62298   N2     5 #> 8068       1         4  29       62298   N3     5 #> 8069       1         4  29       62298   N4     6 #> 8070       1         4  29       62298   N5     5 #> 8071       1         4  29       62298   O1     5 #> 8072       1         4  29       62298   O2     1 #> 8073       1         4  29       62298   O3     5 #> 8074       1         4  29       62298   O4     6 #> 8075       1         4  29       62298   O5     1 #> 8076       1         4  33       62299   A1     4 #> 8077       1         4  33       62299   A2     4 #> 8078       1         4  33       62299   A3     4 #> 8079       1         4  33       62299   A4     4 #> 8080       1         4  33       62299   A5     4 #> 8081       1         4  33       62299   C1     5 #> 8082       1         4  33       62299   C2     5 #> 8083       1         4  33       62299   C3     5 #> 8084       1         4  33       62299   C4     5 #> 8085       1         4  33       62299   C5     5 #> 8086       1         4  33       62299   E1     3 #> 8087       1         4  33       62299   E2     3 #> 8088       1         4  33       62299   E3     3 #> 8089       1         4  33       62299   E4     3 #> 8090       1         4  33       62299   E5     3 #> 8091       1         4  33       62299   N1     4 #> 8092       1         4  33       62299   N2     4 #> 8093       1         4  33       62299   N3     4 #> 8094       1         4  33       62299   N4     4 #> 8095       1         4  33       62299   N5     4 #> 8096       1         4  33       62299   O1     3 #> 8097       1         4  33       62299   O2     3 #> 8098       1         4  33       62299   O3     3 #> 8099       1         4  33       62299   O4     3 #> 8100       1         4  33       62299   O5     3 #> 8101       1         3  31       62300   A1     3 #> 8102       1         3  31       62300   A2     3 #> 8103       1         3  31       62300   A3     4 #> 8104       1         3  31       62300   A4     6 #> 8105       1         3  31       62300   A5     3 #> 8106       1         3  31       62300   C1     4 #> 8107       1         3  31       62300   C2     4 #> 8108       1         3  31       62300   C3     3 #> 8109       1         3  31       62300   C4     4 #> 8110       1         3  31       62300   C5     4 #> 8111       1         3  31       62300   E1     5 #> 8112       1         3  31       62300   E2     5 #> 8113       1         3  31       62300   E3     2 #> 8114       1         3  31       62300   E4     3 #> 8115       1         3  31       62300   E5     5 #> 8116       1         3  31       62300   N1     4 #> 8117       1         3  31       62300   N2     5 #> 8118       1         3  31       62300   N3     4 #> 8119       1         3  31       62300   N4     4 #> 8120       1         3  31       62300   N5     2 #> 8121       1         3  31       62300   O1     5 #> 8122       1         3  31       62300   O2     2 #> 8123       1         3  31       62300   O3     3 #> 8124       1         3  31       62300   O4     4 #> 8125       1         3  31       62300   O5     3 #> 8126       2         5  33       62301   A1     1 #> 8127       2         5  33       62301   A2     6 #> 8128       2         5  33       62301   A3     5 #> 8129       2         5  33       62301   A4     6 #> 8130       2         5  33       62301   A5     5 #> 8131       2         5  33       62301   C1     5 #> 8132       2         5  33       62301   C2     5 #> 8133       2         5  33       62301   C3     5 #> 8134       2         5  33       62301   C4     2 #> 8135       2         5  33       62301   C5     4 #> 8136       2         5  33       62301   E1     2 #> 8137       2         5  33       62301   E2     2 #> 8138       2         5  33       62301   E3     5 #> 8139       2         5  33       62301   E4     5 #> 8140       2         5  33       62301   E5     5 #> 8141       2         5  33       62301   N1     2 #> 8142       2         5  33       62301   N2     4 #> 8143       2         5  33       62301   N3     2 #> 8144       2         5  33       62301   N4     4 #> 8145       2         5  33       62301   N5     1 #> 8146       2         5  33       62301   O1     5 #> 8147       2         5  33       62301   O2     1 #> 8148       2         5  33       62301   O3     6 #> 8149       2         5  33       62301   O4     6 #> 8150       2         5  33       62301   O5     1 #> 8151       2         5  28       62303   A1     2 #> 8152       2         5  28       62303   A2    NA #> 8153       2         5  28       62303   A3     5 #> 8154       2         5  28       62303   A4     5 #> 8155       2         5  28       62303   A5     2 #> 8156       2         5  28       62303   C1     5 #> 8157       2         5  28       62303   C2     5 #> 8158       2         5  28       62303   C3     6 #> 8159       2         5  28       62303   C4     1 #> 8160       2         5  28       62303   C5     2 #> 8161       2         5  28       62303   E1     6 #> 8162       2         5  28       62303   E2     5 #> 8163       2         5  28       62303   E3     2 #> 8164       2         5  28       62303   E4     4 #> 8165       2         5  28       62303   E5     5 #> 8166       2         5  28       62303   N1     2 #> 8167       2         5  28       62303   N2     2 #> 8168       2         5  28       62303   N3     4 #> 8169       2         5  28       62303   N4     2 #> 8170       2         5  28       62303   N5     4 #> 8171       2         5  28       62303   O1     4 #> 8172       2         5  28       62303   O2     1 #> 8173       2         5  28       62303   O3    NA #> 8174       2         5  28       62303   O4     4 #> 8175       2         5  28       62303   O5     4 #> 8176       2         3  21       62305   A1     4 #> 8177       2         3  21       62305   A2     5 #> 8178       2         3  21       62305   A3     5 #> 8179       2         3  21       62305   A4     6 #> 8180       2         3  21       62305   A5     4 #> 8181       2         3  21       62305   C1     4 #> 8182       2         3  21       62305   C2     4 #> 8183       2         3  21       62305   C3     3 #> 8184       2         3  21       62305   C4     3 #> 8185       2         3  21       62305   C5     4 #> 8186       2         3  21       62305   E1     6 #> 8187       2         3  21       62305   E2     6 #> 8188       2         3  21       62305   E3     3 #> 8189       2         3  21       62305   E4     2 #> 8190       2         3  21       62305   E5     3 #> 8191       2         3  21       62305   N1     4 #> 8192       2         3  21       62305   N2     4 #> 8193       2         3  21       62305   N3     3 #> 8194       2         3  21       62305   N4     3 #> 8195       2         3  21       62305   N5     5 #> 8196       2         3  21       62305   O1    NA #> 8197       2         3  21       62305   O2     4 #> 8198       2         3  21       62305   O3     3 #> 8199       2         3  21       62305   O4     6 #> 8200       2         3  21       62305   O5     2 #> 8201       2         4  45       62307   A1     1 #> 8202       2         4  45       62307   A2     5 #> 8203       2         4  45       62307   A3     4 #> 8204       2         4  45       62307   A4     5 #> 8205       2         4  45       62307   A5     6 #> 8206       2         4  45       62307   C1     6 #> 8207       2         4  45       62307   C2     6 #> 8208       2         4  45       62307   C3     2 #> 8209       2         4  45       62307   C4     6 #> 8210       2         4  45       62307   C5     6 #> 8211       2         4  45       62307   E1     1 #> 8212       2         4  45       62307   E2     1 #> 8213       2         4  45       62307   E3     6 #> 8214       2         4  45       62307   E4     6 #> 8215       2         4  45       62307   E5     6 #> 8216       2         4  45       62307   N1     6 #> 8217       2         4  45       62307   N2     6 #> 8218       2         4  45       62307   N3     6 #> 8219       2         4  45       62307   N4     6 #> 8220       2         4  45       62307   N5     5 #> 8221       2         4  45       62307   O1     6 #> 8222       2         4  45       62307   O2     5 #> 8223       2         4  45       62307   O3     6 #> 8224       2         4  45       62307   O4     6 #> 8225       2         4  45       62307   O5     1 #> 8226       2         5  44       62312   A1     2 #> 8227       2         5  44       62312   A2     2 #> 8228       2         5  44       62312   A3     5 #> 8229       2         5  44       62312   A4     4 #> 8230       2         5  44       62312   A5     4 #> 8231       2         5  44       62312   C1     5 #> 8232       2         5  44       62312   C2     5 #> 8233       2         5  44       62312   C3     4 #> 8234       2         5  44       62312   C4     3 #> 8235       2         5  44       62312   C5     5 #> 8236       2         5  44       62312   E1     5 #> 8237       2         5  44       62312   E2     5 #> 8238       2         5  44       62312   E3     4 #> 8239       2         5  44       62312   E4     5 #> 8240       2         5  44       62312   E5     4 #> 8241       2         5  44       62312   N1     3 #> 8242       2         5  44       62312   N2     2 #> 8243       2         5  44       62312   N3     4 #> 8244       2         5  44       62312   N4     4 #> 8245       2         5  44       62312   N5     2 #> 8246       2         5  44       62312   O1     6 #> 8247       2         5  44       62312   O2     2 #> 8248       2         5  44       62312   O3     6 #> 8249       2         5  44       62312   O4     6 #> 8250       2         5  44       62312   O5     2 #> 8251       2         3  18       62313   A1     1 #> 8252       2         3  18       62313   A2     6 #> 8253       2         3  18       62313   A3     6 #> 8254       2         3  18       62313   A4     6 #> 8255       2         3  18       62313   A5     4 #> 8256       2         3  18       62313   C1     5 #> 8257       2         3  18       62313   C2     5 #> 8258       2         3  18       62313   C3     3 #> 8259       2         3  18       62313   C4     4 #> 8260       2         3  18       62313   C5     5 #> 8261       2         3  18       62313   E1     4 #> 8262       2         3  18       62313   E2     4 #> 8263       2         3  18       62313   E3     4 #> 8264       2         3  18       62313   E4     5 #> 8265       2         3  18       62313   E5     3 #> 8266       2         3  18       62313   N1     4 #> 8267       2         3  18       62313   N2     4 #> 8268       2         3  18       62313   N3     5 #> 8269       2         3  18       62313   N4     6 #> 8270       2         3  18       62313   N5     4 #> 8271       2         3  18       62313   O1     6 #> 8272       2         3  18       62313   O2     1 #> 8273       2         3  18       62313   O3     6 #> 8274       2         3  18       62313   O4     6 #> 8275       2         3  18       62313   O5     1 #> 8276       2         5  27       62316   A1     2 #> 8277       2         5  27       62316   A2     6 #> 8278       2         5  27       62316   A3     5 #> 8279       2         5  27       62316   A4     5 #> 8280       2         5  27       62316   A5     6 #> 8281       2         5  27       62316   C1     4 #> 8282       2         5  27       62316   C2     6 #> 8283       2         5  27       62316   C3     5 #> 8284       2         5  27       62316   C4     1 #> 8285       2         5  27       62316   C5     2 #> 8286       2         5  27       62316   E1     3 #> 8287       2         5  27       62316   E2     2 #> 8288       2         5  27       62316   E3     5 #> 8289       2         5  27       62316   E4     5 #> 8290       2         5  27       62316   E5     5 #> 8291       2         5  27       62316   N1     1 #> 8292       2         5  27       62316   N2     2 #> 8293       2         5  27       62316   N3     3 #> 8294       2         5  27       62316   N4    NA #> 8295       2         5  27       62316   N5     2 #> 8296       2         5  27       62316   O1     5 #> 8297       2         5  27       62316   O2     2 #> 8298       2         5  27       62316   O3     6 #> 8299       2         5  27       62316   O4     4 #> 8300       2         5  27       62316   O5     2 #> 8301       2         4  45       62317   A1     4 #> 8302       2         4  45       62317   A2     4 #> 8303       2         4  45       62317   A3     3 #> 8304       2         4  45       62317   A4     2 #> 8305       2         4  45       62317   A5     3 #> 8306       2         4  45       62317   C1     4 #> 8307       2         4  45       62317   C2     4 #> 8308       2         4  45       62317   C3     5 #> 8309       2         4  45       62317   C4     2 #> 8310       2         4  45       62317   C5     3 #> 8311       2         4  45       62317   E1     2 #> 8312       2         4  45       62317   E2     4 #> 8313       2         4  45       62317   E3     4 #> 8314       2         4  45       62317   E4     1 #> 8315       2         4  45       62317   E5     5 #> 8316       2         4  45       62317   N1     6 #> 8317       2         4  45       62317   N2     6 #> 8318       2         4  45       62317   N3     5 #> 8319       2         4  45       62317   N4     5 #> 8320       2         4  45       62317   N5     5 #> 8321       2         4  45       62317   O1     4 #> 8322       2         4  45       62317   O2     1 #> 8323       2         4  45       62317   O3     5 #> 8324       2         4  45       62317   O4     5 #> 8325       2         4  45       62317   O5     4 #> 8326       2         2  18       62325   A1     1 #> 8327       2         2  18       62325   A2     4 #> 8328       2         2  18       62325   A3     6 #> 8329       2         2  18       62325   A4     6 #> 8330       2         2  18       62325   A5     6 #> 8331       2         2  18       62325   C1     5 #> 8332       2         2  18       62325   C2     6 #> 8333       2         2  18       62325   C3     4 #> 8334       2         2  18       62325   C4     1 #> 8335       2         2  18       62325   C5     1 #> 8336       2         2  18       62325   E1     3 #> 8337       2         2  18       62325   E2     4 #> 8338       2         2  18       62325   E3     6 #> 8339       2         2  18       62325   E4     6 #> 8340       2         2  18       62325   E5     6 #> 8341       2         2  18       62325   N1     1 #> 8342       2         2  18       62325   N2     3 #> 8343       2         2  18       62325   N3     3 #> 8344       2         2  18       62325   N4     4 #> 8345       2         2  18       62325   N5     2 #> 8346       2         2  18       62325   O1     6 #> 8347       2         2  18       62325   O2     1 #> 8348       2         2  18       62325   O3     6 #> 8349       2         2  18       62325   O4     5 #> 8350       2         2  18       62325   O5     1 #> 8351       2         3  24       62327   A1     2 #> 8352       2         3  24       62327   A2     6 #> 8353       2         3  24       62327   A3     6 #> 8354       2         3  24       62327   A4     6 #> 8355       2         3  24       62327   A5     5 #> 8356       2         3  24       62327   C1     4 #> 8357       2         3  24       62327   C2     6 #> 8358       2         3  24       62327   C3     6 #> 8359       2         3  24       62327   C4     2 #> 8360       2         3  24       62327   C5     5 #> 8361       2         3  24       62327   E1     6 #> 8362       2         3  24       62327   E2     6 #> 8363       2         3  24       62327   E3     2 #> 8364       2         3  24       62327   E4     3 #> 8365       2         3  24       62327   E5     4 #> 8366       2         3  24       62327   N1     5 #> 8367       2         3  24       62327   N2    NA #> 8368       2         3  24       62327   N3     5 #> 8369       2         3  24       62327   N4     5 #> 8370       2         3  24       62327   N5     6 #> 8371       2         3  24       62327   O1     6 #> 8372       2         3  24       62327   O2     4 #> 8373       2         3  24       62327   O3     1 #> 8374       2         3  24       62327   O4     6 #> 8375       2         3  24       62327   O5     4 #> 8376       2         2  48       62328   A1     3 #> 8377       2         2  48       62328   A2     2 #> 8378       2         2  48       62328   A3     6 #> 8379       2         2  48       62328   A4     1 #> 8380       2         2  48       62328   A5     6 #> 8381       2         2  48       62328   C1     3 #> 8382       2         2  48       62328   C2     1 #> 8383       2         2  48       62328   C3     5 #> 8384       2         2  48       62328   C4     2 #> 8385       2         2  48       62328   C5     5 #> 8386       2         2  48       62328   E1     3 #> 8387       2         2  48       62328   E2     5 #> 8388       2         2  48       62328   E3     2 #> 8389       2         2  48       62328   E4     4 #> 8390       2         2  48       62328   E5     5 #> 8391       2         2  48       62328   N1     5 #> 8392       2         2  48       62328   N2     6 #> 8393       2         2  48       62328   N3     6 #> 8394       2         2  48       62328   N4     6 #> 8395       2         2  48       62328   N5     5 #> 8396       2         2  48       62328   O1    NA #> 8397       2         2  48       62328   O2     2 #> 8398       2         2  48       62328   O3     1 #> 8399       2         2  48       62328   O4     6 #> 8400       2         2  48       62328   O5     1 #> 8401       2         3  29       62330   A1     3 #> 8402       2         3  29       62330   A2     5 #> 8403       2         3  29       62330   A3     5 #> 8404       2         3  29       62330   A4     5 #> 8405       2         3  29       62330   A5     5 #> 8406       2         3  29       62330   C1     3 #> 8407       2         3  29       62330   C2     5 #> 8408       2         3  29       62330   C3     5 #> 8409       2         3  29       62330   C4     2 #> 8410       2         3  29       62330   C5     1 #> 8411       2         3  29       62330   E1     1 #> 8412       2         3  29       62330   E2     1 #> 8413       2         3  29       62330   E3     5 #> 8414       2         3  29       62330   E4     6 #> 8415       2         3  29       62330   E5     5 #> 8416       2         3  29       62330   N1     6 #> 8417       2         3  29       62330   N2     6 #> 8418       2         3  29       62330   N3     5 #> 8419       2         3  29       62330   N4     5 #> 8420       2         3  29       62330   N5     3 #> 8421       2         3  29       62330   O1     5 #> 8422       2         3  29       62330   O2     5 #> 8423       2         3  29       62330   O3     6 #> 8424       2         3  29       62330   O4     6 #> 8425       2         3  29       62330   O5     4 #> 8426       2         1  51       62333   A1     1 #> 8427       2         1  51       62333   A2     6 #> 8428       2         1  51       62333   A3     6 #> 8429       2         1  51       62333   A4     6 #> 8430       2         1  51       62333   A5     6 #> 8431       2         1  51       62333   C1     6 #> 8432       2         1  51       62333   C2     6 #> 8433       2         1  51       62333   C3     6 #> 8434       2         1  51       62333   C4     1 #> 8435       2         1  51       62333   C5     1 #> 8436       2         1  51       62333   E1     5 #> 8437       2         1  51       62333   E2     1 #> 8438       2         1  51       62333   E3     6 #> 8439       2         1  51       62333   E4     6 #> 8440       2         1  51       62333   E5     6 #> 8441       2         1  51       62333   N1     1 #> 8442       2         1  51       62333   N2     6 #> 8443       2         1  51       62333   N3     1 #> 8444       2         1  51       62333   N4     1 #> 8445       2         1  51       62333   N5     1 #> 8446       2         1  51       62333   O1     6 #> 8447       2         1  51       62333   O2     1 #> 8448       2         1  51       62333   O3     3 #> 8449       2         1  51       62333   O4     6 #> 8450       2         1  51       62333   O5     1 #> 8451       2         2  50       62335   A1     2 #> 8452       2         2  50       62335   A2     5 #> 8453       2         2  50       62335   A3     5 #> 8454       2         2  50       62335   A4     6 #> 8455       2         2  50       62335   A5     4 #> 8456       2         2  50       62335   C1     3 #> 8457       2         2  50       62335   C2     3 #> 8458       2         2  50       62335   C3     4 #> 8459       2         2  50       62335   C4     4 #> 8460       2         2  50       62335   C5     2 #> 8461       2         2  50       62335   E1     5 #> 8462       2         2  50       62335   E2     5 #> 8463       2         2  50       62335   E3     4 #> 8464       2         2  50       62335   E4     3 #> 8465       2         2  50       62335   E5     3 #> 8466       2         2  50       62335   N1     2 #> 8467       2         2  50       62335   N2     4 #> 8468       2         2  50       62335   N3     5 #> 8469       2         2  50       62335   N4     6 #> 8470       2         2  50       62335   N5     4 #> 8471       2         2  50       62335   O1     4 #> 8472       2         2  50       62335   O2     5 #> 8473       2         2  50       62335   O3     4 #> 8474       2         2  50       62335   O4     5 #> 8475       2         2  50       62335   O5     4 #> 8476       2         2  36       62336   A1     1 #> 8477       2         2  36       62336   A2     5 #> 8478       2         2  36       62336   A3     4 #> 8479       2         2  36       62336   A4     3 #> 8480       2         2  36       62336   A5     2 #> 8481       2         2  36       62336   C1     6 #> 8482       2         2  36       62336   C2     5 #> 8483       2         2  36       62336   C3     2 #> 8484       2         2  36       62336   C4     1 #> 8485       2         2  36       62336   C5     5 #> 8486       2         2  36       62336   E1     2 #> 8487       2         2  36       62336   E2     2 #> 8488       2         2  36       62336   E3     1 #> 8489       2         2  36       62336   E4     2 #> 8490       2         2  36       62336   E5     2 #> 8491       2         2  36       62336   N1     4 #> 8492       2         2  36       62336   N2     4 #> 8493       2         2  36       62336   N3     5 #> 8494       2         2  36       62336   N4     5 #> 8495       2         2  36       62336   N5     2 #> 8496       2         2  36       62336   O1     4 #> 8497       2         2  36       62336   O2     3 #> 8498       2         2  36       62336   O3     4 #> 8499       2         2  36       62336   O4     5 #> 8500       2         2  36       62336   O5     3 #> 8501       2        NA  18       62339   A1     3 #> 8502       2        NA  18       62339   A2     5 #> 8503       2        NA  18       62339   A3     3 #> 8504       2        NA  18       62339   A4     4 #> 8505       2        NA  18       62339   A5     4 #> 8506       2        NA  18       62339   C1     3 #> 8507       2        NA  18       62339   C2     2 #> 8508       2        NA  18       62339   C3     5 #> 8509       2        NA  18       62339   C4     4 #> 8510       2        NA  18       62339   C5     5 #> 8511       2        NA  18       62339   E1     2 #> 8512       2        NA  18       62339   E2     5 #> 8513       2        NA  18       62339   E3     3 #> 8514       2        NA  18       62339   E4     3 #> 8515       2        NA  18       62339   E5     4 #> 8516       2        NA  18       62339   N1     4 #> 8517       2        NA  18       62339   N2     5 #> 8518       2        NA  18       62339   N3     4 #> 8519       2        NA  18       62339   N4     2 #> 8520       2        NA  18       62339   N5     2 #> 8521       2        NA  18       62339   O1     4 #> 8522       2        NA  18       62339   O2     5 #> 8523       2        NA  18       62339   O3     3 #> 8524       2        NA  18       62339   O4     5 #> 8525       2        NA  18       62339   O5     4 #> 8526       2         4  52       62342   A1     1 #> 8527       2         4  52       62342   A2     5 #> 8528       2         4  52       62342   A3     5 #> 8529       2         4  52       62342   A4     5 #> 8530       2         4  52       62342   A5     5 #> 8531       2         4  52       62342   C1     4 #> 8532       2         4  52       62342   C2     1 #> 8533       2         4  52       62342   C3     2 #> 8534       2         4  52       62342   C4     6 #> 8535       2         4  52       62342   C5     3 #> 8536       2         4  52       62342   E1     2 #> 8537       2         4  52       62342   E2     1 #> 8538       2         4  52       62342   E3     5 #> 8539       2         4  52       62342   E4     3 #> 8540       2         4  52       62342   E5     6 #> 8541       2         4  52       62342   N1     6 #> 8542       2         4  52       62342   N2     5 #> 8543       2         4  52       62342   N3     2 #> 8544       2         4  52       62342   N4     4 #> 8545       2         4  52       62342   N5    NA #> 8546       2         4  52       62342   O1     6 #> 8547       2         4  52       62342   O2     1 #> 8548       2         4  52       62342   O3     6 #> 8549       2         4  52       62342   O4     6 #> 8550       2         4  52       62342   O5     1 #> 8551       2         5  38       62343   A1     2 #> 8552       2         5  38       62343   A2     4 #> 8553       2         5  38       62343   A3     2 #> 8554       2         5  38       62343   A4     6 #> 8555       2         5  38       62343   A5     5 #> 8556       2         5  38       62343   C1     5 #> 8557       2         5  38       62343   C2     4 #> 8558       2         5  38       62343   C3     4 #> 8559       2         5  38       62343   C4     6 #> 8560       2         5  38       62343   C5     5 #> 8561       2         5  38       62343   E1     5 #> 8562       2         5  38       62343   E2     6 #> 8563       2         5  38       62343   E3     3 #> 8564       2         5  38       62343   E4     3 #> 8565       2         5  38       62343   E5     5 #> 8566       2         5  38       62343   N1     4 #> 8567       2         5  38       62343   N2     4 #> 8568       2         5  38       62343   N3     4 #> 8569       2         5  38       62343   N4     3 #> 8570       2         5  38       62343   N5     4 #> 8571       2         5  38       62343   O1     3 #> 8572       2         5  38       62343   O2     2 #> 8573       2         5  38       62343   O3     2 #> 8574       2         5  38       62343   O4     2 #> 8575       2         5  38       62343   O5     4 #> 8576       2         3  17       62344   A1     1 #> 8577       2         3  17       62344   A2     6 #> 8578       2         3  17       62344   A3     5 #> 8579       2         3  17       62344   A4     6 #> 8580       2         3  17       62344   A5     5 #> 8581       2         3  17       62344   C1     5 #> 8582       2         3  17       62344   C2     6 #> 8583       2         3  17       62344   C3     4 #> 8584       2         3  17       62344   C4     2 #> 8585       2         3  17       62344   C5     4 #> 8586       2         3  17       62344   E1     1 #> 8587       2         3  17       62344   E2     1 #> 8588       2         3  17       62344   E3     5 #> 8589       2         3  17       62344   E4     6 #> 8590       2         3  17       62344   E5     6 #> 8591       2         3  17       62344   N1     2 #> 8592       2         3  17       62344   N2     2 #> 8593       2         3  17       62344   N3     4 #> 8594       2         3  17       62344   N4     2 #> 8595       2         3  17       62344   N5     2 #> 8596       2         3  17       62344   O1     4 #> 8597       2         3  17       62344   O2     5 #> 8598       2         3  17       62344   O3     6 #> 8599       2         3  17       62344   O4     5 #> 8600       2         3  17       62344   O5     1 #> 8601       2         5  53       62345   A1     2 #> 8602       2         5  53       62345   A2     4 #> 8603       2         5  53       62345   A3     4 #> 8604       2         5  53       62345   A4     6 #> 8605       2         5  53       62345   A5     5 #> 8606       2         5  53       62345   C1     4 #> 8607       2         5  53       62345   C2     4 #> 8608       2         5  53       62345   C3     4 #> 8609       2         5  53       62345   C4     3 #> 8610       2         5  53       62345   C5     4 #> 8611       2         5  53       62345   E1     1 #> 8612       2         5  53       62345   E2     1 #> 8613       2         5  53       62345   E3     5 #> 8614       2         5  53       62345   E4     5 #> 8615       2         5  53       62345   E5     6 #> 8616       2         5  53       62345   N1     2 #> 8617       2         5  53       62345   N2     4 #> 8618       2         5  53       62345   N3     3 #> 8619       2         5  53       62345   N4     3 #> 8620       2         5  53       62345   N5     1 #> 8621       2         5  53       62345   O1     6 #> 8622       2         5  53       62345   O2     1 #> 8623       2         5  53       62345   O3     6 #> 8624       2         5  53       62345   O4     4 #> 8625       2         5  53       62345   O5     2 #> 8626       2         5  35       62346   A1     1 #> 8627       2         5  35       62346   A2     5 #> 8628       2         5  35       62346   A3     5 #> 8629       2         5  35       62346   A4     5 #> 8630       2         5  35       62346   A5     5 #> 8631       2         5  35       62346   C1     5 #> 8632       2         5  35       62346   C2     4 #> 8633       2         5  35       62346   C3     5 #> 8634       2         5  35       62346   C4     3 #> 8635       2         5  35       62346   C5     3 #> 8636       2         5  35       62346   E1     3 #> 8637       2         5  35       62346   E2     2 #> 8638       2         5  35       62346   E3     5 #> 8639       2         5  35       62346   E4     5 #> 8640       2         5  35       62346   E5     5 #> 8641       2         5  35       62346   N1     5 #> 8642       2         5  35       62346   N2     5 #> 8643       2         5  35       62346   N3     4 #> 8644       2         5  35       62346   N4     4 #> 8645       2         5  35       62346   N5     3 #> 8646       2         5  35       62346   O1     6 #> 8647       2         5  35       62346   O2     4 #> 8648       2         5  35       62346   O3     6 #> 8649       2         5  35       62346   O4     6 #> 8650       2         5  35       62346   O5     2 #> 8651       2         3  21       62347   A1     2 #> 8652       2         3  21       62347   A2     4 #> 8653       2         3  21       62347   A3     5 #> 8654       2         3  21       62347   A4     6 #> 8655       2         3  21       62347   A5     5 #> 8656       2         3  21       62347   C1     4 #> 8657       2         3  21       62347   C2     3 #> 8658       2         3  21       62347   C3     3 #> 8659       2         3  21       62347   C4     5 #> 8660       2         3  21       62347   C5     5 #> 8661       2         3  21       62347   E1     5 #> 8662       2         3  21       62347   E2     4 #> 8663       2         3  21       62347   E3     5 #> 8664       2         3  21       62347   E4     3 #> 8665       2         3  21       62347   E5     1 #> 8666       2         3  21       62347   N1     3 #> 8667       2         3  21       62347   N2     4 #> 8668       2         3  21       62347   N3     4 #> 8669       2         3  21       62347   N4     5 #> 8670       2         3  21       62347   N5     5 #> 8671       2         3  21       62347   O1     4 #> 8672       2         3  21       62347   O2     2 #> 8673       2         3  21       62347   O3     4 #> 8674       2         3  21       62347   O4     5 #> 8675       2         3  21       62347   O5     2 #> 8676       1         3  29       62348   A1     5 #> 8677       1         3  29       62348   A2     5 #> 8678       1         3  29       62348   A3     5 #> 8679       1         3  29       62348   A4     6 #> 8680       1         3  29       62348   A5     5 #> 8681       1         3  29       62348   C1     4 #> 8682       1         3  29       62348   C2     5 #> 8683       1         3  29       62348   C3     6 #> 8684       1         3  29       62348   C4     4 #> 8685       1         3  29       62348   C5     3 #> 8686       1         3  29       62348   E1     5 #> 8687       1         3  29       62348   E2     4 #> 8688       1         3  29       62348   E3     4 #> 8689       1         3  29       62348   E4     4 #> 8690       1         3  29       62348   E5     6 #> 8691       1         3  29       62348   N1     6 #> 8692       1         3  29       62348   N2     6 #> 8693       1         3  29       62348   N3     6 #> 8694       1         3  29       62348   N4     5 #> 8695       1         3  29       62348   N5     4 #> 8696       1         3  29       62348   O1     3 #> 8697       1         3  29       62348   O2     6 #> 8698       1         3  29       62348   O3     3 #> 8699       1         3  29       62348   O4     5 #> 8700       1         3  29       62348   O5     4 #> 8701       2         5  36       62349   A1     2 #> 8702       2         5  36       62349   A2     5 #> 8703       2         5  36       62349   A3     5 #> 8704       2         5  36       62349   A4     6 #> 8705       2         5  36       62349   A5     5 #> 8706       2         5  36       62349   C1     6 #> 8707       2         5  36       62349   C2     6 #> 8708       2         5  36       62349   C3     5 #> 8709       2         5  36       62349   C4     1 #> 8710       2         5  36       62349   C5     3 #> 8711       2         5  36       62349   E1     1 #> 8712       2         5  36       62349   E2     1 #> 8713       2         5  36       62349   E3     4 #> 8714       2         5  36       62349   E4     4 #> 8715       2         5  36       62349   E5     6 #> 8716       2         5  36       62349   N1     4 #> 8717       2         5  36       62349   N2     4 #> 8718       2         5  36       62349   N3     1 #> 8719       2         5  36       62349   N4     5 #> 8720       2         5  36       62349   N5     3 #> 8721       2         5  36       62349   O1     5 #> 8722       2         5  36       62349   O2     2 #> 8723       2         5  36       62349   O3     4 #> 8724       2         5  36       62349   O4     6 #> 8725       2         5  36       62349   O5     1 #> 8726       2         3  19       62350   A1     2 #> 8727       2         3  19       62350   A2     5 #> 8728       2         3  19       62350   A3     1 #> 8729       2         3  19       62350   A4     6 #> 8730       2         3  19       62350   A5     4 #> 8731       2         3  19       62350   C1     4 #> 8732       2         3  19       62350   C2     4 #> 8733       2         3  19       62350   C3     6 #> 8734       2         3  19       62350   C4     4 #> 8735       2         3  19       62350   C5     2 #> 8736       2         3  19       62350   E1     4 #> 8737       2         3  19       62350   E2     3 #> 8738       2         3  19       62350   E3     4 #> 8739       2         3  19       62350   E4     5 #> 8740       2         3  19       62350   E5     4 #> 8741       2         3  19       62350   N1     3 #> 8742       2         3  19       62350   N2     5 #> 8743       2         3  19       62350   N3     4 #> 8744       2         3  19       62350   N4     4 #> 8745       2         3  19       62350   N5     4 #> 8746       2         3  19       62350   O1     4 #> 8747       2         3  19       62350   O2     4 #> 8748       2         3  19       62350   O3     5 #> 8749       2         3  19       62350   O4     5 #> 8750       2         3  19       62350   O5     4 #> 8751       2         3  23       62351   A1     4 #> 8752       2         3  23       62351   A2     6 #> 8753       2         3  23       62351   A3     6 #> 8754       2         3  23       62351   A4     4 #> 8755       2         3  23       62351   A5     4 #> 8756       2         3  23       62351   C1     6 #> 8757       2         3  23       62351   C2     4 #> 8758       2         3  23       62351   C3     6 #> 8759       2         3  23       62351   C4     1 #> 8760       2         3  23       62351   C5     4 #> 8761       2         3  23       62351   E1     1 #> 8762       2         3  23       62351   E2     5 #> 8763       2         3  23       62351   E3     5 #> 8764       2         3  23       62351   E4     2 #> 8765       2         3  23       62351   E5     5 #> 8766       2         3  23       62351   N1     4 #> 8767       2         3  23       62351   N2     6 #> 8768       2         3  23       62351   N3     4 #> 8769       2         3  23       62351   N4     2 #> 8770       2         3  23       62351   N5     5 #> 8771       2         3  23       62351   O1     6 #> 8772       2         3  23       62351   O2     3 #> 8773       2         3  23       62351   O3     4 #> 8774       2         3  23       62351   O4     6 #> 8775       2         3  23       62351   O5     2 #> 8776       2         2  39       62352   A1     2 #> 8777       2         2  39       62352   A2     5 #> 8778       2         2  39       62352   A3     5 #> 8779       2         2  39       62352   A4     6 #> 8780       2         2  39       62352   A5     5 #> 8781       2         2  39       62352   C1     5 #> 8782       2         2  39       62352   C2     5 #> 8783       2         2  39       62352   C3     5 #> 8784       2         2  39       62352   C4     1 #> 8785       2         2  39       62352   C5     1 #> 8786       2         2  39       62352   E1     2 #> 8787       2         2  39       62352   E2     2 #> 8788       2         2  39       62352   E3     4 #> 8789       2         2  39       62352   E4     5 #> 8790       2         2  39       62352   E5     4 #> 8791       2         2  39       62352   N1     3 #> 8792       2         2  39       62352   N2     4 #> 8793       2         2  39       62352   N3     4 #> 8794       2         2  39       62352   N4     4 #> 8795       2         2  39       62352   N5     4 #> 8796       2         2  39       62352   O1     4 #> 8797       2         2  39       62352   O2     3 #> 8798       2         2  39       62352   O3     4 #> 8799       2         2  39       62352   O4     5 #> 8800       2         2  39       62352   O5     3 #> 8801       2         3  19       62353   A1     3 #> 8802       2         3  19       62353   A2     5 #> 8803       2         3  19       62353   A3     1 #> 8804       2         3  19       62353   A4     4 #> 8805       2         3  19       62353   A5     4 #> 8806       2         3  19       62353   C1     2 #> 8807       2         3  19       62353   C2     4 #> 8808       2         3  19       62353   C3     4 #> 8809       2         3  19       62353   C4     4 #> 8810       2         3  19       62353   C5     4 #> 8811       2         3  19       62353   E1     4 #> 8812       2         3  19       62353   E2     2 #> 8813       2         3  19       62353   E3     5 #> 8814       2         3  19       62353   E4     4 #> 8815       2         3  19       62353   E5     5 #> 8816       2         3  19       62353   N1     4 #> 8817       2         3  19       62353   N2     4 #> 8818       2         3  19       62353   N3     5 #> 8819       2         3  19       62353   N4     2 #> 8820       2         3  19       62353   N5     1 #> 8821       2         3  19       62353   O1     5 #> 8822       2         3  19       62353   O2     1 #> 8823       2         3  19       62353   O3     6 #> 8824       2         3  19       62353   O4     5 #> 8825       2         3  19       62353   O5     1 #> 8826       2        NA  17       62354   A1     2 #> 8827       2        NA  17       62354   A2     5 #> 8828       2        NA  17       62354   A3     5 #> 8829       2        NA  17       62354   A4     4 #> 8830       2        NA  17       62354   A5     5 #> 8831       2        NA  17       62354   C1     4 #> 8832       2        NA  17       62354   C2     4 #> 8833       2        NA  17       62354   C3     5 #> 8834       2        NA  17       62354   C4     3 #> 8835       2        NA  17       62354   C5     2 #> 8836       2        NA  17       62354   E1     4 #> 8837       2        NA  17       62354   E2     5 #> 8838       2        NA  17       62354   E3     5 #> 8839       2        NA  17       62354   E4     6 #> 8840       2        NA  17       62354   E5     3 #> 8841       2        NA  17       62354   N1     5 #> 8842       2        NA  17       62354   N2     6 #> 8843       2        NA  17       62354   N3     2 #> 8844       2        NA  17       62354   N4     1 #> 8845       2        NA  17       62354   N5     4 #> 8846       2        NA  17       62354   O1     4 #> 8847       2        NA  17       62354   O2     5 #> 8848       2        NA  17       62354   O3     4 #> 8849       2        NA  17       62354   O4     3 #> 8850       2        NA  17       62354   O5     3 #> 8851       2         3  18       62358   A1     2 #> 8852       2         3  18       62358   A2     3 #> 8853       2         3  18       62358   A3     6 #> 8854       2         3  18       62358   A4     4 #> 8855       2         3  18       62358   A5     5 #> 8856       2         3  18       62358   C1     3 #> 8857       2         3  18       62358   C2     3 #> 8858       2         3  18       62358   C3     5 #> 8859       2         3  18       62358   C4     3 #> 8860       2         3  18       62358   C5     4 #> 8861       2         3  18       62358   E1     3 #> 8862       2         3  18       62358   E2     1 #> 8863       2         3  18       62358   E3     6 #> 8864       2         3  18       62358   E4     5 #> 8865       2         3  18       62358   E5     6 #> 8866       2         3  18       62358   N1     1 #> 8867       2         3  18       62358   N2     2 #> 8868       2         3  18       62358   N3     4 #> 8869       2         3  18       62358   N4     4 #> 8870       2         3  18       62358   N5     1 #> 8871       2         3  18       62358   O1     6 #> 8872       2         3  18       62358   O2     5 #> 8873       2         3  18       62358   O3     6 #> 8874       2         3  18       62358   O4     6 #> 8875       2         3  18       62358   O5     4 #> 8876       2         3  25       62359   A1     1 #> 8877       2         3  25       62359   A2     5 #> 8878       2         3  25       62359   A3     5 #> 8879       2         3  25       62359   A4     6 #> 8880       2         3  25       62359   A5     5 #> 8881       2         3  25       62359   C1     5 #> 8882       2         3  25       62359   C2     5 #> 8883       2         3  25       62359   C3     5 #> 8884       2         3  25       62359   C4     2 #> 8885       2         3  25       62359   C5     4 #> 8886       2         3  25       62359   E1     4 #> 8887       2         3  25       62359   E2    NA #> 8888       2         3  25       62359   E3     4 #> 8889       2         3  25       62359   E4     5 #> 8890       2         3  25       62359   E5     3 #> 8891       2         3  25       62359   N1     3 #> 8892       2         3  25       62359   N2     5 #> 8893       2         3  25       62359   N3     4 #> 8894       2         3  25       62359   N4     4 #> 8895       2         3  25       62359   N5     4 #> 8896       2         3  25       62359   O1     5 #> 8897       2         3  25       62359   O2     5 #> 8898       2         3  25       62359   O3     4 #> 8899       2         3  25       62359   O4     5 #> 8900       2         3  25       62359   O5     3 #> 8901       2        NA  15       62360   A1     4 #> 8902       2        NA  15       62360   A2     2 #> 8903       2        NA  15       62360   A3     5 #> 8904       2        NA  15       62360   A4     5 #> 8905       2        NA  15       62360   A5     5 #> 8906       2        NA  15       62360   C1     4 #> 8907       2        NA  15       62360   C2     4 #> 8908       2        NA  15       62360   C3     3 #> 8909       2        NA  15       62360   C4     4 #> 8910       2        NA  15       62360   C5     6 #> 8911       2        NA  15       62360   E1     4 #> 8912       2        NA  15       62360   E2     2 #> 8913       2        NA  15       62360   E3     3 #> 8914       2        NA  15       62360   E4     3 #> 8915       2        NA  15       62360   E5     4 #> 8916       2        NA  15       62360   N1     2 #> 8917       2        NA  15       62360   N2     2 #> 8918       2        NA  15       62360   N3     4 #> 8919       2        NA  15       62360   N4     3 #> 8920       2        NA  15       62360   N5     2 #> 8921       2        NA  15       62360   O1     4 #> 8922       2        NA  15       62360   O2     3 #> 8923       2        NA  15       62360   O3     3 #> 8924       2        NA  15       62360   O4     5 #> 8925       2        NA  15       62360   O5     4 #> 8926       1         3  20       62362   A1     1 #> 8927       1         3  20       62362   A2     5 #> 8928       1         3  20       62362   A3     5 #> 8929       1         3  20       62362   A4     5 #> 8930       1         3  20       62362   A5     4 #> 8931       1         3  20       62362   C1     5 #> 8932       1         3  20       62362   C2     4 #> 8933       1         3  20       62362   C3     5 #> 8934       1         3  20       62362   C4     4 #> 8935       1         3  20       62362   C5     5 #> 8936       1         3  20       62362   E1     5 #> 8937       1         3  20       62362   E2     5 #> 8938       1         3  20       62362   E3     1 #> 8939       1         3  20       62362   E4     2 #> 8940       1         3  20       62362   E5     4 #> 8941       1         3  20       62362   N1     5 #> 8942       1         3  20       62362   N2     5 #> 8943       1         3  20       62362   N3     2 #> 8944       1         3  20       62362   N4     3 #> 8945       1         3  20       62362   N5     3 #> 8946       1         3  20       62362   O1     4 #> 8947       1         3  20       62362   O2     2 #> 8948       1         3  20       62362   O3     5 #> 8949       1         3  20       62362   O4     6 #> 8950       1         3  20       62362   O5     1 #> 8951       2        NA  14       62363   A1     2 #> 8952       2        NA  14       62363   A2     5 #> 8953       2        NA  14       62363   A3     5 #> 8954       2        NA  14       62363   A4     6 #> 8955       2        NA  14       62363   A5     6 #> 8956       2        NA  14       62363   C1     4 #> 8957       2        NA  14       62363   C2     5 #> 8958       2        NA  14       62363   C3     2 #> 8959       2        NA  14       62363   C4     4 #> 8960       2        NA  14       62363   C5     4 #> 8961       2        NA  14       62363   E1     6 #> 8962       2        NA  14       62363   E2     5 #> 8963       2        NA  14       62363   E3     5 #> 8964       2        NA  14       62363   E4     4 #> 8965       2        NA  14       62363   E5     4 #> 8966       2        NA  14       62363   N1     1 #> 8967       2        NA  14       62363   N2     3 #> 8968       2        NA  14       62363   N3     4 #> 8969       2        NA  14       62363   N4     4 #> 8970       2        NA  14       62363   N5     6 #> 8971       2        NA  14       62363   O1     5 #> 8972       2        NA  14       62363   O2     2 #> 8973       2        NA  14       62363   O3     5 #> 8974       2        NA  14       62363   O4     5 #> 8975       2        NA  14       62363   O5     3 #> 8976       2         3  20       62366   A1     4 #> 8977       2         3  20       62366   A2     3 #> 8978       2         3  20       62366   A3     6 #> 8979       2         3  20       62366   A4     6 #> 8980       2         3  20       62366   A5     6 #> 8981       2         3  20       62366   C1     5 #> 8982       2         3  20       62366   C2     6 #> 8983       2         3  20       62366   C3     5 #> 8984       2         3  20       62366   C4     1 #> 8985       2         3  20       62366   C5     3 #> 8986       2         3  20       62366   E1     1 #> 8987       2         3  20       62366   E2     1 #> 8988       2         3  20       62366   E3     3 #> 8989       2         3  20       62366   E4     5 #> 8990       2         3  20       62366   E5     6 #> 8991       2         3  20       62366   N1     1 #> 8992       2         3  20       62366   N2     6 #> 8993       2         3  20       62366   N3     4 #> 8994       2         3  20       62366   N4     2 #> 8995       2         3  20       62366   N5     1 #> 8996       2         3  20       62366   O1     5 #> 8997       2         3  20       62366   O2     1 #> 8998       2         3  20       62366   O3     5 #> 8999       2         3  20       62366   O4     1 #> 9000       2         3  20       62366   O5     2 #> 9001       1         1  25       62367   A1     3 #> 9002       1         1  25       62367   A2     4 #> 9003       1         1  25       62367   A3     6 #> 9004       1         1  25       62367   A4     6 #> 9005       1         1  25       62367   A5     6 #> 9006       1         1  25       62367   C1     6 #> 9007       1         1  25       62367   C2     4 #> 9008       1         1  25       62367   C3     5 #> 9009       1         1  25       62367   C4     1 #> 9010       1         1  25       62367   C5     2 #> 9011       1         1  25       62367   E1     4 #> 9012       1         1  25       62367   E2     1 #> 9013       1         1  25       62367   E3     5 #> 9014       1         1  25       62367   E4     4 #> 9015       1         1  25       62367   E5     5 #> 9016       1         1  25       62367   N1     5 #> 9017       1         1  25       62367   N2     5 #> 9018       1         1  25       62367   N3     4 #> 9019       1         1  25       62367   N4     4 #> 9020       1         1  25       62367   N5     6 #> 9021       1         1  25       62367   O1     4 #> 9022       1         1  25       62367   O2     4 #> 9023       1         1  25       62367   O3     5 #> 9024       1         1  25       62367   O4     5 #> 9025       1         1  25       62367   O5     2 #> 9026       2         5  64       62368   A1     1 #> 9027       2         5  64       62368   A2     6 #> 9028       2         5  64       62368   A3     1 #> 9029       2         5  64       62368   A4     6 #> 9030       2         5  64       62368   A5     5 #> 9031       2         5  64       62368   C1     4 #> 9032       2         5  64       62368   C2     1 #> 9033       2         5  64       62368   C3     2 #> 9034       2         5  64       62368   C4     5 #> 9035       2         5  64       62368   C5     6 #> 9036       2         5  64       62368   E1     1 #> 9037       2         5  64       62368   E2     1 #> 9038       2         5  64       62368   E3     5 #> 9039       2         5  64       62368   E4     5 #> 9040       2         5  64       62368   E5     6 #> 9041       2         5  64       62368   N1     2 #> 9042       2         5  64       62368   N2     4 #> 9043       2         5  64       62368   N3     6 #> 9044       2         5  64       62368   N4     6 #> 9045       2         5  64       62368   N5     5 #> 9046       2         5  64       62368   O1     6 #> 9047       2         5  64       62368   O2     1 #> 9048       2         5  64       62368   O3     6 #> 9049       2         5  64       62368   O4     6 #> 9050       2         5  64       62368   O5     1 #> 9051       2         2  18       62369   A1     1 #> 9052       2         2  18       62369   A2     5 #> 9053       2         2  18       62369   A3     3 #> 9054       2         2  18       62369   A4     6 #> 9055       2         2  18       62369   A5     5 #> 9056       2         2  18       62369   C1     2 #> 9057       2         2  18       62369   C2     5 #> 9058       2         2  18       62369   C3     5 #> 9059       2         2  18       62369   C4     1 #> 9060       2         2  18       62369   C5     2 #> 9061       2         2  18       62369   E1     2 #> 9062       2         2  18       62369   E2     4 #> 9063       2         2  18       62369   E3     6 #> 9064       2         2  18       62369   E4     5 #> 9065       2         2  18       62369   E5     5 #> 9066       2         2  18       62369   N1     1 #> 9067       2         2  18       62369   N2     2 #> 9068       2         2  18       62369   N3     2 #> 9069       2         2  18       62369   N4     3 #> 9070       2         2  18       62369   N5     3 #> 9071       2         2  18       62369   O1     6 #> 9072       2         2  18       62369   O2     1 #> 9073       2         2  18       62369   O3     5 #> 9074       2         2  18       62369   O4     6 #> 9075       2         2  18       62369   O5     1 #> 9076       2         3  20       62370   A1     1 #> 9077       2         3  20       62370   A2     6 #> 9078       2         3  20       62370   A3     6 #> 9079       2         3  20       62370   A4     4 #> 9080       2         3  20       62370   A5     5 #> 9081       2         3  20       62370   C1     4 #> 9082       2         3  20       62370   C2     5 #> 9083       2         3  20       62370   C3     2 #> 9084       2         3  20       62370   C4     3 #> 9085       2         3  20       62370   C5     6 #> 9086       2         3  20       62370   E1     3 #> 9087       2         3  20       62370   E2     4 #> 9088       2         3  20       62370   E3     4 #> 9089       2         3  20       62370   E4     6 #> 9090       2         3  20       62370   E5     4 #> 9091       2         3  20       62370   N1     2 #> 9092       2         3  20       62370   N2     4 #> 9093       2         3  20       62370   N3     4 #> 9094       2         3  20       62370   N4     2 #> 9095       2         3  20       62370   N5     5 #> 9096       2         3  20       62370   O1     5 #> 9097       2         3  20       62370   O2     1 #> 9098       2         3  20       62370   O3     4 #> 9099       2         3  20       62370   O4     5 #> 9100       2         3  20       62370   O5     2 #> 9101       2         1  19       62371   A1     4 #> 9102       2         1  19       62371   A2     5 #> 9103       2         1  19       62371   A3     2 #> 9104       2         1  19       62371   A4     6 #> 9105       2         1  19       62371   A5     5 #> 9106       2         1  19       62371   C1     3 #> 9107       2         1  19       62371   C2     2 #> 9108       2         1  19       62371   C3     4 #> 9109       2         1  19       62371   C4     3 #> 9110       2         1  19       62371   C5     6 #> 9111       2         1  19       62371   E1     1 #> 9112       2         1  19       62371   E2     5 #> 9113       2         1  19       62371   E3     4 #> 9114       2         1  19       62371   E4     3 #> 9115       2         1  19       62371   E5     2 #> 9116       2         1  19       62371   N1     5 #> 9117       2         1  19       62371   N2     6 #> 9118       2         1  19       62371   N3     2 #> 9119       2         1  19       62371   N4     2 #> 9120       2         1  19       62371   N5     5 #> 9121       2         1  19       62371   O1     4 #> 9122       2         1  19       62371   O2     2 #> 9123       2         1  19       62371   O3     4 #> 9124       2         1  19       62371   O4     5 #> 9125       2         1  19       62371   O5     3 #> 9126       2         5  32       62375   A1     1 #> 9127       2         5  32       62375   A2     6 #> 9128       2         5  32       62375   A3     5 #> 9129       2         5  32       62375   A4     3 #> 9130       2         5  32       62375   A5     6 #> 9131       2         5  32       62375   C1     5 #> 9132       2         5  32       62375   C2     6 #> 9133       2         5  32       62375   C3     6 #> 9134       2         5  32       62375   C4     1 #> 9135       2         5  32       62375   C5     2 #> 9136       2         5  32       62375   E1     4 #> 9137       2         5  32       62375   E2     4 #> 9138       2         5  32       62375   E3     4 #> 9139       2         5  32       62375   E4     6 #> 9140       2         5  32       62375   E5     5 #> 9141       2         5  32       62375   N1     4 #> 9142       2         5  32       62375   N2     4 #> 9143       2         5  32       62375   N3     3 #> 9144       2         5  32       62375   N4     2 #> 9145       2         5  32       62375   N5     1 #> 9146       2         5  32       62375   O1     6 #> 9147       2         5  32       62375   O2     1 #> 9148       2         5  32       62375   O3     5 #> 9149       2         5  32       62375   O4     6 #> 9150       2         5  32       62375   O5     1 #> 9151       1         2  49       62376   A1    NA #> 9152       1         2  49       62376   A2     5 #> 9153       1         2  49       62376   A3     4 #> 9154       1         2  49       62376   A4     3 #> 9155       1         2  49       62376   A5     4 #> 9156       1         2  49       62376   C1     4 #> 9157       1         2  49       62376   C2     3 #> 9158       1         2  49       62376   C3     3 #> 9159       1         2  49       62376   C4     3 #> 9160       1         2  49       62376   C5     4 #> 9161       1         2  49       62376   E1     4 #> 9162       1         2  49       62376   E2     2 #> 9163       1         2  49       62376   E3     4 #> 9164       1         2  49       62376   E4     4 #> 9165       1         2  49       62376   E5     4 #> 9166       1         2  49       62376   N1     3 #> 9167       1         2  49       62376   N2     3 #> 9168       1         2  49       62376   N3     4 #> 9169       1         2  49       62376   N4     3 #> 9170       1         2  49       62376   N5     2 #> 9171       1         2  49       62376   O1     5 #> 9172       1         2  49       62376   O2     2 #> 9173       1         2  49       62376   O3     4 #> 9174       1         2  49       62376   O4     4 #> 9175       1         2  49       62376   O5     3 #> 9176       1         2  45       62377   A1     4 #> 9177       1         2  45       62377   A2     6 #> 9178       1         2  45       62377   A3     5 #> 9179       1         2  45       62377   A4     6 #> 9180       1         2  45       62377   A5     6 #> 9181       1         2  45       62377   C1     5 #> 9182       1         2  45       62377   C2     5 #> 9183       1         2  45       62377   C3     5 #> 9184       1         2  45       62377   C4     5 #> 9185       1         2  45       62377   C5     4 #> 9186       1         2  45       62377   E1     1 #> 9187       1         2  45       62377   E2     1 #> 9188       1         2  45       62377   E3     6 #> 9189       1         2  45       62377   E4     6 #> 9190       1         2  45       62377   E5     6 #> 9191       1         2  45       62377   N1     4 #> 9192       1         2  45       62377   N2     4 #> 9193       1         2  45       62377   N3     6 #> 9194       1         2  45       62377   N4     4 #> 9195       1         2  45       62377   N5     1 #> 9196       1         2  45       62377   O1     6 #> 9197       1         2  45       62377   O2     2 #> 9198       1         2  45       62377   O3     6 #> 9199       1         2  45       62377   O4     6 #> 9200       1         2  45       62377   O5     2 #> 9201       1         1  18       62380   A1     1 #> 9202       1         1  18       62380   A2     6 #> 9203       1         1  18       62380   A3     5 #> 9204       1         1  18       62380   A4     2 #> 9205       1         1  18       62380   A5     2 #> 9206       1         1  18       62380   C1     4 #> 9207       1         1  18       62380   C2     1 #> 9208       1         1  18       62380   C3     5 #> 9209       1         1  18       62380   C4     6 #> 9210       1         1  18       62380   C5     6 #> 9211       1         1  18       62380   E1     6 #> 9212       1         1  18       62380   E2     6 #> 9213       1         1  18       62380   E3     3 #> 9214       1         1  18       62380   E4     2 #> 9215       1         1  18       62380   E5     1 #> 9216       1         1  18       62380   N1     4 #> 9217       1         1  18       62380   N2     5 #> 9218       1         1  18       62380   N3     5 #> 9219       1         1  18       62380   N4     5 #> 9220       1         1  18       62380   N5     2 #> 9221       1         1  18       62380   O1     5 #> 9222       1         1  18       62380   O2     6 #> 9223       1         1  18       62380   O3     5 #> 9224       1         1  18       62380   O4     6 #> 9225       1         1  18       62380   O5     2 #> 9226       2         5  28       62382   A1     6 #> 9227       2         5  28       62382   A2     6 #> 9228       2         5  28       62382   A3     6 #> 9229       2         5  28       62382   A4     6 #> 9230       2         5  28       62382   A5     6 #> 9231       2         5  28       62382   C1     3 #> 9232       2         5  28       62382   C2     6 #> 9233       2         5  28       62382   C3     2 #> 9234       2         5  28       62382   C4     5 #> 9235       2         5  28       62382   C5     5 #> 9236       2         5  28       62382   E1     2 #> 9237       2         5  28       62382   E2     2 #> 9238       2         5  28       62382   E3     6 #> 9239       2         5  28       62382   E4     6 #> 9240       2         5  28       62382   E5     6 #> 9241       2         5  28       62382   N1     6 #> 9242       2         5  28       62382   N2     6 #> 9243       2         5  28       62382   N3     6 #> 9244       2         5  28       62382   N4     6 #> 9245       2         5  28       62382   N5     6 #> 9246       2         5  28       62382   O1     6 #> 9247       2         5  28       62382   O2     5 #> 9248       2         5  28       62382   O3     5 #> 9249       2         5  28       62382   O4     6 #> 9250       2         5  28       62382   O5     2 #> 9251       2         5  22       62384   A1     1 #> 9252       2         5  22       62384   A2     4 #> 9253       2         5  22       62384   A3     2 #> 9254       2         5  22       62384   A4     5 #> 9255       2         5  22       62384   A5     3 #> 9256       2         5  22       62384   C1     5 #> 9257       2         5  22       62384   C2     5 #> 9258       2         5  22       62384   C3     5 #> 9259       2         5  22       62384   C4     3 #> 9260       2         5  22       62384   C5     4 #> 9261       2         5  22       62384   E1     4 #> 9262       2         5  22       62384   E2     5 #> 9263       2         5  22       62384   E3     3 #> 9264       2         5  22       62384   E4     3 #> 9265       2         5  22       62384   E5     2 #> 9266       2         5  22       62384   N1     2 #> 9267       2         5  22       62384   N2     2 #> 9268       2         5  22       62384   N3     3 #> 9269       2         5  22       62384   N4     4 #> 9270       2         5  22       62384   N5     5 #> 9271       2         5  22       62384   O1     4 #> 9272       2         5  22       62384   O2     2 #> 9273       2         5  22       62384   O3     4 #> 9274       2         5  22       62384   O4     5 #> 9275       2         5  22       62384   O5     2 #> 9276       2         4  25       62387   A1     3 #> 9277       2         4  25       62387   A2     5 #> 9278       2         4  25       62387   A3     4 #> 9279       2         4  25       62387   A4     4 #> 9280       2         4  25       62387   A5     3 #> 9281       2         4  25       62387   C1     5 #> 9282       2         4  25       62387   C2     5 #> 9283       2         4  25       62387   C3     4 #> 9284       2         4  25       62387   C4     2 #> 9285       2         4  25       62387   C5     4 #> 9286       2         4  25       62387   E1     5 #> 9287       2         4  25       62387   E2     2 #> 9288       2         4  25       62387   E3     3 #> 9289       2         4  25       62387   E4     4 #> 9290       2         4  25       62387   E5     4 #> 9291       2         4  25       62387   N1     4 #> 9292       2         4  25       62387   N2     4 #> 9293       2         4  25       62387   N3     4 #> 9294       2         4  25       62387   N4     3 #> 9295       2         4  25       62387   N5     2 #> 9296       2         4  25       62387   O1     6 #> 9297       2         4  25       62387   O2     1 #> 9298       2         4  25       62387   O3     5 #> 9299       2         4  25       62387   O4     5 #> 9300       2         4  25       62387   O5     2 #> 9301       2         5  34       62390   A1     1 #> 9302       2         5  34       62390   A2     6 #> 9303       2         5  34       62390   A3     6 #> 9304       2         5  34       62390   A4     6 #> 9305       2         5  34       62390   A5     6 #> 9306       2         5  34       62390   C1     6 #> 9307       2         5  34       62390   C2     5 #> 9308       2         5  34       62390   C3     4 #> 9309       2         5  34       62390   C4     1 #> 9310       2         5  34       62390   C5     1 #> 9311       2         5  34       62390   E1     1 #> 9312       2         5  34       62390   E2     1 #> 9313       2         5  34       62390   E3     6 #> 9314       2         5  34       62390   E4     6 #> 9315       2         5  34       62390   E5     6 #> 9316       2         5  34       62390   N1     1 #> 9317       2         5  34       62390   N2     1 #> 9318       2         5  34       62390   N3     1 #> 9319       2         5  34       62390   N4     1 #> 9320       2         5  34       62390   N5     1 #> 9321       2         5  34       62390   O1     6 #> 9322       2         5  34       62390   O2     1 #> 9323       2         5  34       62390   O3     6 #> 9324       2         5  34       62390   O4     6 #> 9325       2         5  34       62390   O5     1 #> 9326       2         5  42       62391   A1     2 #> 9327       2         5  42       62391   A2     5 #> 9328       2         5  42       62391   A3     5 #> 9329       2         5  42       62391   A4     5 #> 9330       2         5  42       62391   A5     3 #> 9331       2         5  42       62391   C1     5 #> 9332       2         5  42       62391   C2     5 #> 9333       2         5  42       62391   C3     5 #> 9334       2         5  42       62391   C4     1 #> 9335       2         5  42       62391   C5     4 #> 9336       2         5  42       62391   E1     4 #> 9337       2         5  42       62391   E2     4 #> 9338       2         5  42       62391   E3     3 #> 9339       2         5  42       62391   E4     3 #> 9340       2         5  42       62391   E5     5 #> 9341       2         5  42       62391   N1     4 #> 9342       2         5  42       62391   N2     4 #> 9343       2         5  42       62391   N3     4 #> 9344       2         5  42       62391   N4     4 #> 9345       2         5  42       62391   N5     2 #> 9346       2         5  42       62391   O1     6 #> 9347       2         5  42       62391   O2     1 #> 9348       2         5  42       62391   O3     5 #> 9349       2         5  42       62391   O4     4 #> 9350       2         5  42       62391   O5     1 #> 9351       2         3  37       62394   A1     1 #> 9352       2         3  37       62394   A2     5 #> 9353       2         3  37       62394   A3     3 #> 9354       2         3  37       62394   A4     5 #> 9355       2         3  37       62394   A5     4 #> 9356       2         3  37       62394   C1     5 #> 9357       2         3  37       62394   C2     4 #> 9358       2         3  37       62394   C3     3 #> 9359       2         3  37       62394   C4     1 #> 9360       2         3  37       62394   C5     4 #> 9361       2         3  37       62394   E1     3 #> 9362       2         3  37       62394   E2     2 #> 9363       2         3  37       62394   E3     4 #> 9364       2         3  37       62394   E4     5 #> 9365       2         3  37       62394   E5     5 #> 9366       2         3  37       62394   N1     3 #> 9367       2         3  37       62394   N2     4 #> 9368       2         3  37       62394   N3     2 #> 9369       2         3  37       62394   N4     3 #> 9370       2         3  37       62394   N5     2 #> 9371       2         3  37       62394   O1     5 #> 9372       2         3  37       62394   O2     4 #> 9373       2         3  37       62394   O3     4 #> 9374       2         3  37       62394   O4     5 #> 9375       2         3  37       62394   O5     1 #> 9376       2         3  42       62397   A1     1 #> 9377       2         3  42       62397   A2     6 #> 9378       2         3  42       62397   A3     5 #> 9379       2         3  42       62397   A4     6 #> 9380       2         3  42       62397   A5     6 #> 9381       2         3  42       62397   C1     6 #> 9382       2         3  42       62397   C2     6 #> 9383       2         3  42       62397   C3     6 #> 9384       2         3  42       62397   C4     1 #> 9385       2         3  42       62397   C5     1 #> 9386       2         3  42       62397   E1     1 #> 9387       2         3  42       62397   E2     1 #> 9388       2         3  42       62397   E3     4 #> 9389       2         3  42       62397   E4     6 #> 9390       2         3  42       62397   E5     5 #> 9391       2         3  42       62397   N1     1 #> 9392       2         3  42       62397   N2     1 #> 9393       2         3  42       62397   N3     1 #> 9394       2         3  42       62397   N4     1 #> 9395       2         3  42       62397   N5     1 #> 9396       2         3  42       62397   O1     6 #> 9397       2         3  42       62397   O2     1 #> 9398       2         3  42       62397   O3     6 #> 9399       2         3  42       62397   O4     6 #> 9400       2         3  42       62397   O5     1 #> 9401       2         5  23       62401   A1     1 #> 9402       2         5  23       62401   A2     5 #> 9403       2         5  23       62401   A3     5 #> 9404       2         5  23       62401   A4     6 #> 9405       2         5  23       62401   A5     4 #> 9406       2         5  23       62401   C1     5 #> 9407       2         5  23       62401   C2     5 #> 9408       2         5  23       62401   C3     4 #> 9409       2         5  23       62401   C4     1 #> 9410       2         5  23       62401   C5     1 #> 9411       2         5  23       62401   E1     3 #> 9412       2         5  23       62401   E2     3 #> 9413       2         5  23       62401   E3     3 #> 9414       2         5  23       62401   E4     4 #> 9415       2         5  23       62401   E5     5 #> 9416       2         5  23       62401   N1     2 #> 9417       2         5  23       62401   N2     2 #> 9418       2         5  23       62401   N3     2 #> 9419       2         5  23       62401   N4     2 #> 9420       2         5  23       62401   N5     4 #> 9421       2         5  23       62401   O1     5 #> 9422       2         5  23       62401   O2     1 #> 9423       2         5  23       62401   O3     5 #> 9424       2         5  23       62401   O4     5 #> 9425       2         5  23       62401   O5     1 #> 9426       2         4  29       62408   A1     4 #> 9427       2         4  29       62408   A2     4 #> 9428       2         4  29       62408   A3     5 #> 9429       2         4  29       62408   A4     1 #> 9430       2         4  29       62408   A5     5 #> 9431       2         4  29       62408   C1     5 #> 9432       2         4  29       62408   C2     4 #> 9433       2         4  29       62408   C3     4 #> 9434       2         4  29       62408   C4     2 #> 9435       2         4  29       62408   C5     5 #> 9436       2         4  29       62408   E1     1 #> 9437       2         4  29       62408   E2     4 #> 9438       2         4  29       62408   E3     6 #> 9439       2         4  29       62408   E4     6 #> 9440       2         4  29       62408   E5     5 #> 9441       2         4  29       62408   N1     3 #> 9442       2         4  29       62408   N2     4 #> 9443       2         4  29       62408   N3     4 #> 9444       2         4  29       62408   N4     2 #> 9445       2         4  29       62408   N5     5 #> 9446       2         4  29       62408   O1     3 #> 9447       2         4  29       62408   O2     3 #> 9448       2         4  29       62408   O3     5 #> 9449       2         4  29       62408   O4     6 #> 9450       2         4  29       62408   O5     5 #> 9451       1         2  18       62412   A1     2 #> 9452       1         2  18       62412   A2     3 #> 9453       1         2  18       62412   A3     4 #> 9454       1         2  18       62412   A4     1 #> 9455       1         2  18       62412   A5     2 #> 9456       1         2  18       62412   C1     3 #> 9457       1         2  18       62412   C2     5 #> 9458       1         2  18       62412   C3     3 #> 9459       1         2  18       62412   C4     1 #> 9460       1         2  18       62412   C5     1 #> 9461       1         2  18       62412   E1     6 #> 9462       1         2  18       62412   E2     6 #> 9463       1         2  18       62412   E3     1 #> 9464       1         2  18       62412   E4     2 #> 9465       1         2  18       62412   E5     3 #> 9466       1         2  18       62412   N1     1 #> 9467       1         2  18       62412   N2     1 #> 9468       1         2  18       62412   N3     2 #> 9469       1         2  18       62412   N4     2 #> 9470       1         2  18       62412   N5     2 #> 9471       1         2  18       62412   O1     5 #> 9472       1         2  18       62412   O2     2 #> 9473       1         2  18       62412   O3     3 #> 9474       1         2  18       62412   O4     5 #> 9475       1         2  18       62412   O5     3 #> 9476       2         3  22       62416   A1     2 #> 9477       2         3  22       62416   A2     6 #> 9478       2         3  22       62416   A3     6 #> 9479       2         3  22       62416   A4     4 #> 9480       2         3  22       62416   A5     6 #> 9481       2         3  22       62416   C1     6 #> 9482       2         3  22       62416   C2     5 #> 9483       2         3  22       62416   C3     5 #> 9484       2         3  22       62416   C4     3 #> 9485       2         3  22       62416   C5     2 #> 9486       2         3  22       62416   E1     1 #> 9487       2         3  22       62416   E2     1 #> 9488       2         3  22       62416   E3     3 #> 9489       2         3  22       62416   E4     6 #> 9490       2         3  22       62416   E5     5 #> 9491       2         3  22       62416   N1     1 #> 9492       2         3  22       62416   N2     1 #> 9493       2         3  22       62416   N3     1 #> 9494       2         3  22       62416   N4     1 #> 9495       2         3  22       62416   N5     1 #> 9496       2         3  22       62416   O1     5 #> 9497       2         3  22       62416   O2     1 #> 9498       2         3  22       62416   O3     5 #> 9499       2         3  22       62416   O4     1 #> 9500       2         3  22       62416   O5     6 #> 9501       2         3  30       62419   A1     4 #> 9502       2         3  30       62419   A2     6 #> 9503       2         3  30       62419   A3     6 #> 9504       2         3  30       62419   A4     6 #> 9505       2         3  30       62419   A5     5 #> 9506       2         3  30       62419   C1     4 #> 9507       2         3  30       62419   C2     5 #> 9508       2         3  30       62419   C3     6 #> 9509       2         3  30       62419   C4    NA #> 9510       2         3  30       62419   C5     1 #> 9511       2         3  30       62419   E1     1 #> 9512       2         3  30       62419   E2     3 #> 9513       2         3  30       62419   E3     6 #> 9514       2         3  30       62419   E4     6 #> 9515       2         3  30       62419   E5     6 #> 9516       2         3  30       62419   N1     1 #> 9517       2         3  30       62419   N2     6 #> 9518       2         3  30       62419   N3     6 #> 9519       2         3  30       62419   N4     3 #> 9520       2         3  30       62419   N5     4 #> 9521       2         3  30       62419   O1     6 #> 9522       2         3  30       62419   O2     1 #> 9523       2         3  30       62419   O3     6 #> 9524       2         3  30       62419   O4     5 #> 9525       2         3  30       62419   O5     1 #> 9526       2         3  38       62421   A1     4 #> 9527       2         3  38       62421   A2     4 #> 9528       2         3  38       62421   A3     4 #> 9529       2         3  38       62421   A4     4 #> 9530       2         3  38       62421   A5     5 #> 9531       2         3  38       62421   C1     5 #> 9532       2         3  38       62421   C2     5 #> 9533       2         3  38       62421   C3     4 #> 9534       2         3  38       62421   C4     2 #> 9535       2         3  38       62421   C5     3 #> 9536       2         3  38       62421   E1     4 #> 9537       2         3  38       62421   E2     2 #> 9538       2         3  38       62421   E3     4 #> 9539       2         3  38       62421   E4     4 #> 9540       2         3  38       62421   E5     5 #> 9541       2         3  38       62421   N1     2 #> 9542       2         3  38       62421   N2     4 #> 9543       2         3  38       62421   N3     5 #> 9544       2         3  38       62421   N4     4 #> 9545       2         3  38       62421   N5     5 #> 9546       2         3  38       62421   O1     4 #> 9547       2         3  38       62421   O2     1 #> 9548       2         3  38       62421   O3     4 #> 9549       2         3  38       62421   O4     5 #> 9550       2         3  38       62421   O5     3 #> 9551       2         2  53       62423   A1     2 #> 9552       2         2  53       62423   A2     6 #> 9553       2         2  53       62423   A3     5 #> 9554       2         2  53       62423   A4     6 #> 9555       2         2  53       62423   A5     3 #> 9556       2         2  53       62423   C1     5 #> 9557       2         2  53       62423   C2     6 #> 9558       2         2  53       62423   C3     5 #> 9559       2         2  53       62423   C4     2 #> 9560       2         2  53       62423   C5     3 #> 9561       2         2  53       62423   E1     3 #> 9562       2         2  53       62423   E2     2 #> 9563       2         2  53       62423   E3     5 #> 9564       2         2  53       62423   E4     5 #> 9565       2         2  53       62423   E5     5 #> 9566       2         2  53       62423   N1     3 #> 9567       2         2  53       62423   N2     5 #> 9568       2         2  53       62423   N3     5 #> 9569       2         2  53       62423   N4     1 #> 9570       2         2  53       62423   N5     4 #> 9571       2         2  53       62423   O1     4 #> 9572       2         2  53       62423   O2     2 #> 9573       2         2  53       62423   O3     4 #> 9574       2         2  53       62423   O4     6 #> 9575       2         2  53       62423   O5     2 #> 9576       1         3  28       62426   A1     2 #> 9577       1         3  28       62426   A2     4 #> 9578       1         3  28       62426   A3     4 #> 9579       1         3  28       62426   A4     6 #> 9580       1         3  28       62426   A5    NA #> 9581       1         3  28       62426   C1     5 #> 9582       1         3  28       62426   C2     4 #> 9583       1         3  28       62426   C3     5 #> 9584       1         3  28       62426   C4     4 #> 9585       1         3  28       62426   C5     4 #> 9586       1         3  28       62426   E1     6 #> 9587       1         3  28       62426   E2     4 #> 9588       1         3  28       62426   E3     4 #> 9589       1         3  28       62426   E4     5 #> 9590       1         3  28       62426   E5     2 #> 9591       1         3  28       62426   N1     1 #> 9592       1         3  28       62426   N2     2 #> 9593       1         3  28       62426   N3     3 #> 9594       1         3  28       62426   N4     4 #> 9595       1         3  28       62426   N5     2 #> 9596       1         3  28       62426   O1     3 #> 9597       1         3  28       62426   O2     1 #> 9598       1         3  28       62426   O3     2 #> 9599       1         3  28       62426   O4     4 #> 9600       1         3  28       62426   O5     5 #> 9601       1         3  18       62433   A1     5 #> 9602       1         3  18       62433   A2     6 #> 9603       1         3  18       62433   A3     6 #> 9604       1         3  18       62433   A4     5 #> 9605       1         3  18       62433   A5     6 #> 9606       1         3  18       62433   C1     5 #> 9607       1         3  18       62433   C2     6 #> 9608       1         3  18       62433   C3     2 #> 9609       1         3  18       62433   C4     2 #> 9610       1         3  18       62433   C5     4 #> 9611       1         3  18       62433   E1     3 #> 9612       1         3  18       62433   E2     4 #> 9613       1         3  18       62433   E3     5 #> 9614       1         3  18       62433   E4     5 #> 9615       1         3  18       62433   E5     5 #> 9616       1         3  18       62433   N1     3 #> 9617       1         3  18       62433   N2     3 #> 9618       1         3  18       62433   N3     2 #> 9619       1         3  18       62433   N4     4 #> 9620       1         3  18       62433   N5     3 #> 9621       1         3  18       62433   O1     6 #> 9622       1         3  18       62433   O2     4 #> 9623       1         3  18       62433   O3     5 #> 9624       1         3  18       62433   O4     6 #> 9625       1         3  18       62433   O5     4 #> 9626       2         4  31       62434   A1     1 #> 9627       2         4  31       62434   A2     5 #> 9628       2         4  31       62434   A3     6 #> 9629       2         4  31       62434   A4     6 #> 9630       2         4  31       62434   A5     6 #> 9631       2         4  31       62434   C1     6 #> 9632       2         4  31       62434   C2     5 #> 9633       2         4  31       62434   C3     5 #> 9634       2         4  31       62434   C4     1 #> 9635       2         4  31       62434   C5     1 #> 9636       2         4  31       62434   E1     2 #> 9637       2         4  31       62434   E2     4 #> 9638       2         4  31       62434   E3     5 #> 9639       2         4  31       62434   E4     6 #> 9640       2         4  31       62434   E5     5 #> 9641       2         4  31       62434   N1     2 #> 9642       2         4  31       62434   N2     3 #> 9643       2         4  31       62434   N3     2 #> 9644       2         4  31       62434   N4     2 #> 9645       2         4  31       62434   N5     4 #> 9646       2         4  31       62434   O1     5 #> 9647       2         4  31       62434   O2     1 #> 9648       2         4  31       62434   O3     4 #> 9649       2         4  31       62434   O4     5 #> 9650       2         4  31       62434   O5     2 #> 9651       2         1  32       62435   A1     2 #> 9652       2         1  32       62435   A2     5 #> 9653       2         1  32       62435   A3     5 #> 9654       2         1  32       62435   A4     4 #> 9655       2         1  32       62435   A5     6 #> 9656       2         1  32       62435   C1     5 #> 9657       2         1  32       62435   C2     5 #> 9658       2         1  32       62435   C3     5 #> 9659       2         1  32       62435   C4     2 #> 9660       2         1  32       62435   C5     2 #> 9661       2         1  32       62435   E1     5 #> 9662       2         1  32       62435   E2     4 #> 9663       2         1  32       62435   E3     5 #> 9664       2         1  32       62435   E4     4 #> 9665       2         1  32       62435   E5     5 #> 9666       2         1  32       62435   N1     1 #> 9667       2         1  32       62435   N2     1 #> 9668       2         1  32       62435   N3     1 #> 9669       2         1  32       62435   N4     1 #> 9670       2         1  32       62435   N5     3 #> 9671       2         1  32       62435   O1     5 #> 9672       2         1  32       62435   O2     4 #> 9673       2         1  32       62435   O3     4 #> 9674       2         1  32       62435   O4     4 #> 9675       2         1  32       62435   O5     2 #> 9676       2         5  40       62438   A1     4 #> 9677       2         5  40       62438   A2     4 #> 9678       2         5  40       62438   A3     4 #> 9679       2         5  40       62438   A4    NA #> 9680       2         5  40       62438   A5     5 #> 9681       2         5  40       62438   C1     4 #> 9682       2         5  40       62438   C2     4 #> 9683       2         5  40       62438   C3     4 #> 9684       2         5  40       62438   C4    NA #> 9685       2         5  40       62438   C5     3 #> 9686       2         5  40       62438   E1     4 #> 9687       2         5  40       62438   E2     2 #> 9688       2         5  40       62438   E3     4 #> 9689       2         5  40       62438   E4    NA #> 9690       2         5  40       62438   E5     5 #> 9691       2         5  40       62438   N1     4 #> 9692       2         5  40       62438   N2     4 #> 9693       2         5  40       62438   N3     4 #> 9694       2         5  40       62438   N4     4 #> 9695       2         5  40       62438   N5     4 #> 9696       2         5  40       62438   O1     5 #> 9697       2         5  40       62438   O2     4 #> 9698       2         5  40       62438   O3    NA #> 9699       2         5  40       62438   O4     5 #> 9700       2         5  40       62438   O5     2 #> 9701       2        NA  15       62440   A1     1 #> 9702       2        NA  15       62440   A2     6 #> 9703       2        NA  15       62440   A3     5 #> 9704       2        NA  15       62440   A4     5 #> 9705       2        NA  15       62440   A5     6 #> 9706       2        NA  15       62440   C1     3 #> 9707       2        NA  15       62440   C2     4 #> 9708       2        NA  15       62440   C3     5 #> 9709       2        NA  15       62440   C4     5 #> 9710       2        NA  15       62440   C5     6 #> 9711       2        NA  15       62440   E1     4 #> 9712       2        NA  15       62440   E2     5 #> 9713       2        NA  15       62440   E3     6 #> 9714       2        NA  15       62440   E4     6 #> 9715       2        NA  15       62440   E5     4 #> 9716       2        NA  15       62440   N1     4 #> 9717       2        NA  15       62440   N2     4 #> 9718       2        NA  15       62440   N3     4 #> 9719       2        NA  15       62440   N4     6 #> 9720       2        NA  15       62440   N5     4 #> 9721       2        NA  15       62440   O1     6 #> 9722       2        NA  15       62440   O2     1 #> 9723       2        NA  15       62440   O3     6 #> 9724       2        NA  15       62440   O4     6 #> 9725       2        NA  15       62440   O5     3 #> 9726       2         2  23       62443   A1     4 #> 9727       2         2  23       62443   A2     2 #> 9728       2         2  23       62443   A3     4 #> 9729       2         2  23       62443   A4     6 #> 9730       2         2  23       62443   A5     5 #> 9731       2         2  23       62443   C1     5 #> 9732       2         2  23       62443   C2     5 #> 9733       2         2  23       62443   C3     4 #> 9734       2         2  23       62443   C4     1 #> 9735       2         2  23       62443   C5     2 #> 9736       2         2  23       62443   E1     3 #> 9737       2         2  23       62443   E2     2 #> 9738       2         2  23       62443   E3     3 #> 9739       2         2  23       62443   E4     5 #> 9740       2         2  23       62443   E5     5 #> 9741       2         2  23       62443   N1     3 #> 9742       2         2  23       62443   N2     4 #> 9743       2         2  23       62443   N3     2 #> 9744       2         2  23       62443   N4     1 #> 9745       2         2  23       62443   N5     1 #> 9746       2         2  23       62443   O1     3 #> 9747       2         2  23       62443   O2     4 #> 9748       2         2  23       62443   O3     1 #> 9749       2         2  23       62443   O4     4 #> 9750       2         2  23       62443   O5     2 #> 9751       2         4  34       62444   A1     2 #> 9752       2         4  34       62444   A2     5 #> 9753       2         4  34       62444   A3     5 #> 9754       2         4  34       62444   A4     4 #> 9755       2         4  34       62444   A5     4 #> 9756       2         4  34       62444   C1     2 #> 9757       2         4  34       62444   C2     3 #> 9758       2         4  34       62444   C3     3 #> 9759       2         4  34       62444   C4     4 #> 9760       2         4  34       62444   C5     3 #> 9761       2         4  34       62444   E1     1 #> 9762       2         4  34       62444   E2     6 #> 9763       2         4  34       62444   E3     4 #> 9764       2         4  34       62444   E4     3 #> 9765       2         4  34       62444   E5     6 #> 9766       2         4  34       62444   N1     4 #> 9767       2         4  34       62444   N2     5 #> 9768       2         4  34       62444   N3     4 #> 9769       2         4  34       62444   N4     4 #> 9770       2         4  34       62444   N5     2 #> 9771       2         4  34       62444   O1     6 #> 9772       2         4  34       62444   O2     2 #> 9773       2         4  34       62444   O3     5 #> 9774       2         4  34       62444   O4     6 #> 9775       2         4  34       62444   O5     2 #> 9776       1         5  29       62447   A1     2 #> 9777       1         5  29       62447   A2     6 #> 9778       1         5  29       62447   A3     6 #> 9779       1         5  29       62447   A4     6 #> 9780       1         5  29       62447   A5     6 #> 9781       1         5  29       62447   C1     5 #> 9782       1         5  29       62447   C2     5 #> 9783       1         5  29       62447   C3     5 #> 9784       1         5  29       62447   C4     1 #> 9785       1         5  29       62447   C5     5 #> 9786       1         5  29       62447   E1     5 #> 9787       1         5  29       62447   E2     1 #> 9788       1         5  29       62447   E3     5 #> 9789       1         5  29       62447   E4     5 #> 9790       1         5  29       62447   E5     6 #> 9791       1         5  29       62447   N1     5 #> 9792       1         5  29       62447   N2     5 #> 9793       1         5  29       62447   N3     5 #> 9794       1         5  29       62447   N4     4 #> 9795       1         5  29       62447   N5     2 #> 9796       1         5  29       62447   O1     6 #> 9797       1         5  29       62447   O2     2 #> 9798       1         5  29       62447   O3     6 #> 9799       1         5  29       62447   O4     5 #> 9800       1         5  29       62447   O5     1 #> 9801       2         3  51       62448   A1     1 #> 9802       2         3  51       62448   A2     6 #> 9803       2         3  51       62448   A3     2 #> 9804       2         3  51       62448   A4     6 #> 9805       2         3  51       62448   A5     5 #> 9806       2         3  51       62448   C1     5 #> 9807       2         3  51       62448   C2     5 #> 9808       2         3  51       62448   C3     5 #> 9809       2         3  51       62448   C4     4 #> 9810       2         3  51       62448   C5     2 #> 9811       2         3  51       62448   E1     2 #> 9812       2         3  51       62448   E2     6 #> 9813       2         3  51       62448   E3     1 #> 9814       2         3  51       62448   E4     4 #> 9815       2         3  51       62448   E5     5 #> 9816       2         3  51       62448   N1     4 #> 9817       2         3  51       62448   N2     2 #> 9818       2         3  51       62448   N3     1 #> 9819       2         3  51       62448   N4     2 #> 9820       2         3  51       62448   N5     3 #> 9821       2         3  51       62448   O1     6 #> 9822       2         3  51       62448   O2     5 #> 9823       2         3  51       62448   O3     3 #> 9824       2         3  51       62448   O4     6 #> 9825       2         3  51       62448   O5     5 #> 9826       2         3  22       62450   A1     1 #> 9827       2         3  22       62450   A2     6 #> 9828       2         3  22       62450   A3     6 #> 9829       2         3  22       62450   A4     6 #> 9830       2         3  22       62450   A5     6 #> 9831       2         3  22       62450   C1     6 #> 9832       2         3  22       62450   C2     5 #> 9833       2         3  22       62450   C3     3 #> 9834       2         3  22       62450   C4     3 #> 9835       2         3  22       62450   C5     4 #> 9836       2         3  22       62450   E1     2 #> 9837       2         3  22       62450   E2     1 #> 9838       2         3  22       62450   E3     5 #> 9839       2         3  22       62450   E4     5 #> 9840       2         3  22       62450   E5     6 #> 9841       2         3  22       62450   N1     2 #> 9842       2         3  22       62450   N2     4 #> 9843       2         3  22       62450   N3     4 #> 9844       2         3  22       62450   N4     3 #> 9845       2         3  22       62450   N5     1 #> 9846       2         3  22       62450   O1     6 #> 9847       2         3  22       62450   O2     1 #> 9848       2         3  22       62450   O3     6 #> 9849       2         3  22       62450   O4     6 #> 9850       2         3  22       62450   O5     2 #> 9851       2         3  38       62453   A1     2 #> 9852       2         3  38       62453   A2     5 #> 9853       2         3  38       62453   A3     5 #> 9854       2         3  38       62453   A4     6 #> 9855       2         3  38       62453   A5     4 #> 9856       2         3  38       62453   C1     4 #> 9857       2         3  38       62453   C2     5 #> 9858       2         3  38       62453   C3     4 #> 9859       2         3  38       62453   C4     2 #> 9860       2         3  38       62453   C5     3 #> 9861       2         3  38       62453   E1     2 #> 9862       2         3  38       62453   E2     3 #> 9863       2         3  38       62453   E3     4 #> 9864       2         3  38       62453   E4     4 #> 9865       2         3  38       62453   E5     5 #> 9866       2         3  38       62453   N1     3 #> 9867       2         3  38       62453   N2     3 #> 9868       2         3  38       62453   N3     1 #> 9869       2         3  38       62453   N4     1 #> 9870       2         3  38       62453   N5     2 #> 9871       2         3  38       62453   O1     5 #> 9872       2         3  38       62453   O2     1 #> 9873       2         3  38       62453   O3     4 #> 9874       2         3  38       62453   O4     4 #> 9875       2         3  38       62453   O5     2 #> 9876       2         5  26       62454   A1     3 #> 9877       2         5  26       62454   A2     3 #> 9878       2         5  26       62454   A3     6 #> 9879       2         5  26       62454   A4     2 #> 9880       2         5  26       62454   A5     6 #> 9881       2         5  26       62454   C1     3 #> 9882       2         5  26       62454   C2     4 #> 9883       2         5  26       62454   C3     3 #> 9884       2         5  26       62454   C4     4 #> 9885       2         5  26       62454   C5     4 #> 9886       2         5  26       62454   E1     1 #> 9887       2         5  26       62454   E2     1 #> 9888       2         5  26       62454   E3     6 #> 9889       2         5  26       62454   E4     6 #> 9890       2         5  26       62454   E5     5 #> 9891       2         5  26       62454   N1     3 #> 9892       2         5  26       62454   N2     3 #> 9893       2         5  26       62454   N3     4 #> 9894       2         5  26       62454   N4     1 #> 9895       2         5  26       62454   N5     2 #> 9896       2         5  26       62454   O1    NA #> 9897       2         5  26       62454   O2     4 #> 9898       2         5  26       62454   O3     6 #> 9899       2         5  26       62454   O4     5 #> 9900       2         5  26       62454   O5     2 #> 9901       2         2  36       62457   A1     4 #> 9902       2         2  36       62457   A2     4 #> 9903       2         2  36       62457   A3    NA #> 9904       2         2  36       62457   A4     3 #> 9905       2         2  36       62457   A5     4 #> 9906       2         2  36       62457   C1     5 #> 9907       2         2  36       62457   C2     3 #> 9908       2         2  36       62457   C3     4 #> 9909       2         2  36       62457   C4     3 #> 9910       2         2  36       62457   C5     3 #> 9911       2         2  36       62457   E1     2 #> 9912       2         2  36       62457   E2     2 #> 9913       2         2  36       62457   E3     3 #> 9914       2         2  36       62457   E4     3 #> 9915       2         2  36       62457   E5     5 #> 9916       2         2  36       62457   N1     3 #> 9917       2         2  36       62457   N2     3 #> 9918       2         2  36       62457   N3     2 #> 9919       2         2  36       62457   N4     2 #> 9920       2         2  36       62457   N5     2 #> 9921       2         2  36       62457   O1     5 #> 9922       2         2  36       62457   O2     2 #> 9923       2         2  36       62457   O3     3 #> 9924       2         2  36       62457   O4     4 #> 9925       2         2  36       62457   O5     5 #> 9926       2         3  19       62462   A1     1 #> 9927       2         3  19       62462   A2     5 #> 9928       2         3  19       62462   A3     3 #> 9929       2         3  19       62462   A4     4 #> 9930       2         3  19       62462   A5     4 #> 9931       2         3  19       62462   C1     6 #> 9932       2         3  19       62462   C2     4 #> 9933       2         3  19       62462   C3     4 #> 9934       2         3  19       62462   C4     1 #> 9935       2         3  19       62462   C5     1 #> 9936       2         3  19       62462   E1     3 #> 9937       2         3  19       62462   E2     4 #> 9938       2         3  19       62462   E3     3 #> 9939       2         3  19       62462   E4     5 #> 9940       2         3  19       62462   E5     5 #> 9941       2         3  19       62462   N1     3 #> 9942       2         3  19       62462   N2     3 #> 9943       2         3  19       62462   N3     3 #> 9944       2         3  19       62462   N4     1 #> 9945       2         3  19       62462   N5     3 #> 9946       2         3  19       62462   O1     4 #> 9947       2         3  19       62462   O2     4 #> 9948       2         3  19       62462   O3     3 #> 9949       2         3  19       62462   O4     4 #> 9950       2         3  19       62462   O5     3 #> 9951       2         3  33       62463   A1     2 #> 9952       2         3  33       62463   A2     6 #> 9953       2         3  33       62463   A3     5 #> 9954       2         3  33       62463   A4     4 #> 9955       2         3  33       62463   A5     5 #> 9956       2         3  33       62463   C1     5 #> 9957       2         3  33       62463   C2     2 #> 9958       2         3  33       62463   C3     5 #> 9959       2         3  33       62463   C4     3 #> 9960       2         3  33       62463   C5     2 #> 9961       2         3  33       62463   E1     2 #> 9962       2         3  33       62463   E2     4 #> 9963       2         3  33       62463   E3     4 #> 9964       2         3  33       62463   E4     6 #> 9965       2         3  33       62463   E5     4 #> 9966       2         3  33       62463   N1     5 #> 9967       2         3  33       62463   N2     5 #> 9968       2         3  33       62463   N3     4 #> 9969       2         3  33       62463   N4     4 #> 9970       2         3  33       62463   N5     5 #> 9971       2         3  33       62463   O1     2 #> 9972       2         3  33       62463   O2     4 #> 9973       2         3  33       62463   O3     4 #> 9974       2         3  33       62463   O4     5 #> 9975       2         3  33       62463   O5     3 #> 9976       2         1  40       62464   A1     1 #> 9977       2         1  40       62464   A2     5 #> 9978       2         1  40       62464   A3     5 #> 9979       2         1  40       62464   A4     6 #> 9980       2         1  40       62464   A5     5 #> 9981       2         1  40       62464   C1     5 #> 9982       2         1  40       62464   C2     4 #> 9983       2         1  40       62464   C3     4 #> 9984       2         1  40       62464   C4     4 #> 9985       2         1  40       62464   C5     4 #> 9986       2         1  40       62464   E1     1 #> 9987       2         1  40       62464   E2     2 #> 9988       2         1  40       62464   E3     4 #> 9989       2         1  40       62464   E4     6 #> 9990       2         1  40       62464   E5     4 #> 9991       2         1  40       62464   N1     4 #> 9992       2         1  40       62464   N2     5 #> 9993       2         1  40       62464   N3     5 #> 9994       2         1  40       62464   N4     3 #> 9995       2         1  40       62464   N5     5 #> 9996       2         1  40       62464   O1     2 #> 9997       2         1  40       62464   O2     2 #> 9998       2         1  40       62464   O3     6 #> 9999       2         1  40       62464   O4     5 #> 10000      2         1  40       62464   O5     2 #> 10001      2         5  26       62467   A1     3 #> 10002      2         5  26       62467   A2     4 #> 10003      2         5  26       62467   A3     3 #> 10004      2         5  26       62467   A4     5 #> 10005      2         5  26       62467   A5     5 #> 10006      2         5  26       62467   C1     3 #> 10007      2         5  26       62467   C2     2 #> 10008      2         5  26       62467   C3     2 #> 10009      2         5  26       62467   C4     3 #> 10010      2         5  26       62467   C5     2 #> 10011      2         5  26       62467   E1     4 #> 10012      2         5  26       62467   E2     2 #> 10013      2         5  26       62467   E3     3 #> 10014      2         5  26       62467   E4     4 #> 10015      2         5  26       62467   E5     3 #> 10016      2         5  26       62467   N1     2 #> 10017      2         5  26       62467   N2     3 #> 10018      2         5  26       62467   N3     2 #> 10019      2         5  26       62467   N4     3 #> 10020      2         5  26       62467   N5     1 #> 10021      2         5  26       62467   O1     5 #> 10022      2         5  26       62467   O2     3 #> 10023      2         5  26       62467   O3     4 #> 10024      2         5  26       62467   O4     4 #> 10025      2         5  26       62467   O5     4 #> 10026      2        NA  12       62468   A1     1 #> 10027      2        NA  12       62468   A2     6 #> 10028      2        NA  12       62468   A3     6 #> 10029      2        NA  12       62468   A4     5 #> 10030      2        NA  12       62468   A5     2 #> 10031      2        NA  12       62468   C1     4 #> 10032      2        NA  12       62468   C2     2 #> 10033      2        NA  12       62468   C3     2 #> 10034      2        NA  12       62468   C4     5 #> 10035      2        NA  12       62468   C5     4 #> 10036      2        NA  12       62468   E1     6 #> 10037      2        NA  12       62468   E2     1 #> 10038      2        NA  12       62468   E3     5 #> 10039      2        NA  12       62468   E4     6 #> 10040      2        NA  12       62468   E5     1 #> 10041      2        NA  12       62468   N1     3 #> 10042      2        NA  12       62468   N2     5 #> 10043      2        NA  12       62468   N3     4 #> 10044      2        NA  12       62468   N4     4 #> 10045      2        NA  12       62468   N5     2 #> 10046      2        NA  12       62468   O1     6 #> 10047      2        NA  12       62468   O2     1 #> 10048      2        NA  12       62468   O3     4 #> 10049      2        NA  12       62468   O4     6 #> 10050      2        NA  12       62468   O5     3 #> 10051      2         3  30       62469   A1     3 #> 10052      2         3  30       62469   A2     5 #> 10053      2         3  30       62469   A3     5 #> 10054      2         3  30       62469   A4     4 #> 10055      2         3  30       62469   A5     4 #> 10056      2         3  30       62469   C1     5 #> 10057      2         3  30       62469   C2     5 #> 10058      2         3  30       62469   C3     6 #> 10059      2         3  30       62469   C4     1 #> 10060      2         3  30       62469   C5     1 #> 10061      2         3  30       62469   E1     2 #> 10062      2         3  30       62469   E2     1 #> 10063      2         3  30       62469   E3     4 #> 10064      2         3  30       62469   E4     5 #> 10065      2         3  30       62469   E5     5 #> 10066      2         3  30       62469   N1     4 #> 10067      2         3  30       62469   N2     4 #> 10068      2         3  30       62469   N3     2 #> 10069      2         3  30       62469   N4     1 #> 10070      2         3  30       62469   N5     2 #> 10071      2         3  30       62469   O1     5 #> 10072      2         3  30       62469   O2     1 #> 10073      2         3  30       62469   O3     5 #> 10074      2         3  30       62469   O4     5 #> 10075      2         3  30       62469   O5     1 #> 10076      2         1  59       62470   A1     2 #> 10077      2         1  59       62470   A2     6 #> 10078      2         1  59       62470   A3     5 #> 10079      2         1  59       62470   A4     5 #> 10080      2         1  59       62470   A5     6 #> 10081      2         1  59       62470   C1     5 #> 10082      2         1  59       62470   C2     4 #> 10083      2         1  59       62470   C3     2 #> 10084      2         1  59       62470   C4     2 #> 10085      2         1  59       62470   C5     4 #> 10086      2         1  59       62470   E1     2 #> 10087      2         1  59       62470   E2     4 #> 10088      2         1  59       62470   E3     5 #> 10089      2         1  59       62470   E4     5 #> 10090      2         1  59       62470   E5     3 #> 10091      2         1  59       62470   N1     1 #> 10092      2         1  59       62470   N2    NA #> 10093      2         1  59       62470   N3     1 #> 10094      2         1  59       62470   N4     1 #> 10095      2         1  59       62470   N5     2 #> 10096      2         1  59       62470   O1     4 #> 10097      2         1  59       62470   O2     2 #> 10098      2         1  59       62470   O3     4 #> 10099      2         1  59       62470   O4     5 #> 10100      2         1  59       62470   O5     3 #> 10101      2         3  32       62474   A1     4 #> 10102      2         3  32       62474   A2     5 #> 10103      2         3  32       62474   A3     5 #> 10104      2         3  32       62474   A4     4 #> 10105      2         3  32       62474   A5     5 #> 10106      2         3  32       62474   C1     2 #> 10107      2         3  32       62474   C2     6 #> 10108      2         3  32       62474   C3     6 #> 10109      2         3  32       62474   C4     1 #> 10110      2         3  32       62474   C5     1 #> 10111      2         3  32       62474   E1     6 #> 10112      2         3  32       62474   E2     1 #> 10113      2         3  32       62474   E3     5 #> 10114      2         3  32       62474   E4     5 #> 10115      2         3  32       62474   E5     5 #> 10116      2         3  32       62474   N1     4 #> 10117      2         3  32       62474   N2     5 #> 10118      2         3  32       62474   N3     3 #> 10119      2         3  32       62474   N4     4 #> 10120      2         3  32       62474   N5     5 #> 10121      2         3  32       62474   O1     6 #> 10122      2         3  32       62474   O2     1 #> 10123      2         3  32       62474   O3     6 #> 10124      2         3  32       62474   O4     5 #> 10125      2         3  32       62474   O5     1 #> 10126      1         2  30       62476   A1     4 #> 10127      1         2  30       62476   A2     3 #> 10128      1         2  30       62476   A3     2 #> 10129      1         2  30       62476   A4     5 #> 10130      1         2  30       62476   A5     3 #> 10131      1         2  30       62476   C1     4 #> 10132      1         2  30       62476   C2     2 #> 10133      1         2  30       62476   C3     4 #> 10134      1         2  30       62476   C4     4 #> 10135      1         2  30       62476   C5     5 #> 10136      1         2  30       62476   E1     5 #> 10137      1         2  30       62476   E2     4 #> 10138      1         2  30       62476   E3     4 #> 10139      1         2  30       62476   E4     4 #> 10140      1         2  30       62476   E5     2 #> 10141      1         2  30       62476   N1     5 #> 10142      1         2  30       62476   N2     4 #> 10143      1         2  30       62476   N3     5 #> 10144      1         2  30       62476   N4     5 #> 10145      1         2  30       62476   N5     3 #> 10146      1         2  30       62476   O1     6 #> 10147      1         2  30       62476   O2     1 #> 10148      1         2  30       62476   O3     6 #> 10149      1         2  30       62476   O4     6 #> 10150      1         2  30       62476   O5     3 #> 10151      2         4  32       62479   A1     1 #> 10152      2         4  32       62479   A2     4 #> 10153      2         4  32       62479   A3     6 #> 10154      2         4  32       62479   A4     5 #> 10155      2         4  32       62479   A5     6 #> 10156      2         4  32       62479   C1     5 #> 10157      2         4  32       62479   C2     6 #> 10158      2         4  32       62479   C3     5 #> 10159      2         4  32       62479   C4     1 #> 10160      2         4  32       62479   C5     1 #> 10161      2         4  32       62479   E1     2 #> 10162      2         4  32       62479   E2     2 #> 10163      2         4  32       62479   E3     6 #> 10164      2         4  32       62479   E4     6 #> 10165      2         4  32       62479   E5     4 #> 10166      2         4  32       62479   N1     4 #> 10167      2         4  32       62479   N2     4 #> 10168      2         4  32       62479   N3     2 #> 10169      2         4  32       62479   N4     1 #> 10170      2         4  32       62479   N5     1 #> 10171      2         4  32       62479   O1     5 #> 10172      2         4  32       62479   O2     1 #> 10173      2         4  32       62479   O3     6 #> 10174      2         4  32       62479   O4     5 #> 10175      2         4  32       62479   O5     2 #> 10176      2         5  29       62480   A1     1 #> 10177      2         5  29       62480   A2     5 #> 10178      2         5  29       62480   A3     6 #> 10179      2         5  29       62480   A4     6 #> 10180      2         5  29       62480   A5     3 #> 10181      2         5  29       62480   C1     5 #> 10182      2         5  29       62480   C2     5 #> 10183      2         5  29       62480   C3     4 #> 10184      2         5  29       62480   C4     2 #> 10185      2         5  29       62480   C5     4 #> 10186      2         5  29       62480   E1     1 #> 10187      2         5  29       62480   E2     3 #> 10188      2         5  29       62480   E3     4 #> 10189      2         5  29       62480   E4     5 #> 10190      2         5  29       62480   E5     5 #> 10191      2         5  29       62480   N1     5 #> 10192      2         5  29       62480   N2     5 #> 10193      2         5  29       62480   N3     4 #> 10194      2         5  29       62480   N4     5 #> 10195      2         5  29       62480   N5     5 #> 10196      2         5  29       62480   O1     5 #> 10197      2         5  29       62480   O2     2 #> 10198      2         5  29       62480   O3     5 #> 10199      2         5  29       62480   O4     6 #> 10200      2         5  29       62480   O5     1 #> 10201      1         3  27       62481   A1     3 #> 10202      1         3  27       62481   A2     4 #> 10203      1         3  27       62481   A3     4 #> 10204      1         3  27       62481   A4     5 #> 10205      1         3  27       62481   A5     4 #> 10206      1         3  27       62481   C1     5 #> 10207      1         3  27       62481   C2     5 #> 10208      1         3  27       62481   C3     6 #> 10209      1         3  27       62481   C4     1 #> 10210      1         3  27       62481   C5     2 #> 10211      1         3  27       62481   E1     3 #> 10212      1         3  27       62481   E2     3 #> 10213      1         3  27       62481   E3     2 #> 10214      1         3  27       62481   E4     4 #> 10215      1         3  27       62481   E5     5 #> 10216      1         3  27       62481   N1     4 #> 10217      1         3  27       62481   N2     4 #> 10218      1         3  27       62481   N3     4 #> 10219      1         3  27       62481   N4     2 #> 10220      1         3  27       62481   N5     1 #> 10221      1         3  27       62481   O1     5 #> 10222      1         3  27       62481   O2     3 #> 10223      1         3  27       62481   O3     2 #> 10224      1         3  27       62481   O4     3 #> 10225      1         3  27       62481   O5     4 #> 10226      2         3  48       62486   A1     3 #> 10227      2         3  48       62486   A2     4 #> 10228      2         3  48       62486   A3     5 #> 10229      2         3  48       62486   A4     2 #> 10230      2         3  48       62486   A5     3 #> 10231      2         3  48       62486   C1     5 #> 10232      2         3  48       62486   C2     3 #> 10233      2         3  48       62486   C3     4 #> 10234      2         3  48       62486   C4     1 #> 10235      2         3  48       62486   C5     1 #> 10236      2         3  48       62486   E1     6 #> 10237      2         3  48       62486   E2     5 #> 10238      2         3  48       62486   E3     2 #> 10239      2         3  48       62486   E4     1 #> 10240      2         3  48       62486   E5     3 #> 10241      2         3  48       62486   N1     3 #> 10242      2         3  48       62486   N2     3 #> 10243      2         3  48       62486   N3     2 #> 10244      2         3  48       62486   N4     3 #> 10245      2         3  48       62486   N5     2 #> 10246      2         3  48       62486   O1     5 #> 10247      2         3  48       62486   O2     5 #> 10248      2         3  48       62486   O3     2 #> 10249      2         3  48       62486   O4     2 #> 10250      2         3  48       62486   O5     5 #> 10251      2         4  39       62489   A1     1 #> 10252      2         4  39       62489   A2     6 #> 10253      2         4  39       62489   A3     5 #> 10254      2         4  39       62489   A4     6 #> 10255      2         4  39       62489   A5     5 #> 10256      2         4  39       62489   C1     4 #> 10257      2         4  39       62489   C2     4 #> 10258      2         4  39       62489   C3     5 #> 10259      2         4  39       62489   C4     2 #> 10260      2         4  39       62489   C5     5 #> 10261      2         4  39       62489   E1     3 #> 10262      2         4  39       62489   E2     2 #> 10263      2         4  39       62489   E3     5 #> 10264      2         4  39       62489   E4     5 #> 10265      2         4  39       62489   E5     4 #> 10266      2         4  39       62489   N1     1 #> 10267      2         4  39       62489   N2     1 #> 10268      2         4  39       62489   N3     1 #> 10269      2         4  39       62489   N4     1 #> 10270      2         4  39       62489   N5     1 #> 10271      2         4  39       62489   O1     6 #> 10272      2         4  39       62489   O2     1 #> 10273      2         4  39       62489   O3     4 #> 10274      2         4  39       62489   O4     5 #> 10275      2         4  39       62489   O5     2 #> 10276      2         3  22       62491   A1     3 #> 10277      2         3  22       62491   A2     6 #> 10278      2         3  22       62491   A3     6 #> 10279      2         3  22       62491   A4     6 #> 10280      2         3  22       62491   A5     4 #> 10281      2         3  22       62491   C1     5 #> 10282      2         3  22       62491   C2     2 #> 10283      2         3  22       62491   C3     5 #> 10284      2         3  22       62491   C4     4 #> 10285      2         3  22       62491   C5     3 #> 10286      2         3  22       62491   E1     4 #> 10287      2         3  22       62491   E2     2 #> 10288      2         3  22       62491   E3     3 #> 10289      2         3  22       62491   E4     4 #> 10290      2         3  22       62491   E5     5 #> 10291      2         3  22       62491   N1     4 #> 10292      2         3  22       62491   N2     4 #> 10293      2         3  22       62491   N3     6 #> 10294      2         3  22       62491   N4     5 #> 10295      2         3  22       62491   N5     4 #> 10296      2         3  22       62491   O1     3 #> 10297      2         3  22       62491   O2     6 #> 10298      2         3  22       62491   O3     1 #> 10299      2         3  22       62491   O4     5 #> 10300      2         3  22       62491   O5     4 #> 10301      2         3  26       62493   A1     1 #> 10302      2         3  26       62493   A2     6 #> 10303      2         3  26       62493   A3     3 #> 10304      2         3  26       62493   A4     5 #> 10305      2         3  26       62493   A5     3 #> 10306      2         3  26       62493   C1     2 #> 10307      2         3  26       62493   C2     3 #> 10308      2         3  26       62493   C3     2 #> 10309      2         3  26       62493   C4     3 #> 10310      2         3  26       62493   C5     4 #> 10311      2         3  26       62493   E1     4 #> 10312      2         3  26       62493   E2     2 #> 10313      2         3  26       62493   E3     3 #> 10314      2         3  26       62493   E4     3 #> 10315      2         3  26       62493   E5     4 #> 10316      2         3  26       62493   N1     4 #> 10317      2         3  26       62493   N2     6 #> 10318      2         3  26       62493   N3     6 #> 10319      2         3  26       62493   N4     6 #> 10320      2         3  26       62493   N5     6 #> 10321      2         3  26       62493   O1     6 #> 10322      2         3  26       62493   O2     1 #> 10323      2         3  26       62493   O3     4 #> 10324      2         3  26       62493   O4     6 #> 10325      2         3  26       62493   O5     1 #> 10326      2         3  26       62494   A1     1 #> 10327      2         3  26       62494   A2     2 #> 10328      2         3  26       62494   A3     1 #> 10329      2         3  26       62494   A4     4 #> 10330      2         3  26       62494   A5     2 #> 10331      2         3  26       62494   C1     6 #> 10332      2         3  26       62494   C2     5 #> 10333      2         3  26       62494   C3     5 #> 10334      2         3  26       62494   C4     1 #> 10335      2         3  26       62494   C5     1 #> 10336      2         3  26       62494   E1     6 #> 10337      2         3  26       62494   E2     6 #> 10338      2         3  26       62494   E3     4 #> 10339      2         3  26       62494   E4     1 #> 10340      2         3  26       62494   E5     3 #> 10341      2         3  26       62494   N1     2 #> 10342      2         3  26       62494   N2     5 #> 10343      2         3  26       62494   N3     1 #> 10344      2         3  26       62494   N4     5 #> 10345      2         3  26       62494   N5     5 #> 10346      2         3  26       62494   O1     6 #> 10347      2         3  26       62494   O2     1 #> 10348      2         3  26       62494   O3     5 #> 10349      2         3  26       62494   O4     6 #> 10350      2         3  26       62494   O5     1 #> 10351      2         2  39       62496   A1     3 #> 10352      2         2  39       62496   A2     5 #> 10353      2         2  39       62496   A3     5 #> 10354      2         2  39       62496   A4     6 #> 10355      2         2  39       62496   A5     3 #> 10356      2         2  39       62496   C1     5 #> 10357      2         2  39       62496   C2     2 #> 10358      2         2  39       62496   C3     4 #> 10359      2         2  39       62496   C4     1 #> 10360      2         2  39       62496   C5     5 #> 10361      2         2  39       62496   E1     1 #> 10362      2         2  39       62496   E2     1 #> 10363      2         2  39       62496   E3     3 #> 10364      2         2  39       62496   E4     3 #> 10365      2         2  39       62496   E5     5 #> 10366      2         2  39       62496   N1     5 #> 10367      2         2  39       62496   N2     5 #> 10368      2         2  39       62496   N3     2 #> 10369      2         2  39       62496   N4     2 #> 10370      2         2  39       62496   N5     2 #> 10371      2         2  39       62496   O1     6 #> 10372      2         2  39       62496   O2     1 #> 10373      2         2  39       62496   O3     6 #> 10374      2         2  39       62496   O4     4 #> 10375      2         2  39       62496   O5     1 #> 10376      2         3  32       62497   A1     4 #> 10377      2         3  32       62497   A2     6 #> 10378      2         3  32       62497   A3     5 #> 10379      2         3  32       62497   A4     6 #> 10380      2         3  32       62497   A5     6 #> 10381      2         3  32       62497   C1     5 #> 10382      2         3  32       62497   C2     3 #> 10383      2         3  32       62497   C3     2 #> 10384      2         3  32       62497   C4     3 #> 10385      2         3  32       62497   C5     5 #> 10386      2         3  32       62497   E1     1 #> 10387      2         3  32       62497   E2     2 #> 10388      2         3  32       62497   E3     3 #> 10389      2         3  32       62497   E4     4 #> 10390      2         3  32       62497   E5     5 #> 10391      2         3  32       62497   N1     4 #> 10392      2         3  32       62497   N2     4 #> 10393      2         3  32       62497   N3     5 #> 10394      2         3  32       62497   N4     1 #> 10395      2         3  32       62497   N5     5 #> 10396      2         3  32       62497   O1     6 #> 10397      2         3  32       62497   O2     3 #> 10398      2         3  32       62497   O3     4 #> 10399      2         3  32       62497   O4     4 #> 10400      2         3  32       62497   O5     5 #> 10401      2         3  52       62498   A1     1 #> 10402      2         3  52       62498   A2     5 #> 10403      2         3  52       62498   A3     6 #> 10404      2         3  52       62498   A4     6 #> 10405      2         3  52       62498   A5     6 #> 10406      2         3  52       62498   C1     1 #> 10407      2         3  52       62498   C2     5 #> 10408      2         3  52       62498   C3     6 #> 10409      2         3  52       62498   C4     4 #> 10410      2         3  52       62498   C5     1 #> 10411      2         3  52       62498   E1     6 #> 10412      2         3  52       62498   E2     6 #> 10413      2         3  52       62498   E3     3 #> 10414      2         3  52       62498   E4     2 #> 10415      2         3  52       62498   E5     5 #> 10416      2         3  52       62498   N1     4 #> 10417      2         3  52       62498   N2     1 #> 10418      2         3  52       62498   N3     2 #> 10419      2         3  52       62498   N4     6 #> 10420      2         3  52       62498   N5     6 #> 10421      2         3  52       62498   O1     6 #> 10422      2         3  52       62498   O2     5 #> 10423      2         3  52       62498   O3     2 #> 10424      2         3  52       62498   O4     6 #> 10425      2         3  52       62498   O5     5 #> 10426      2         3  40       62499   A1     3 #> 10427      2         3  40       62499   A2     5 #> 10428      2         3  40       62499   A3     5 #> 10429      2         3  40       62499   A4     6 #> 10430      2         3  40       62499   A5     5 #> 10431      2         3  40       62499   C1     5 #> 10432      2         3  40       62499   C2     4 #> 10433      2         3  40       62499   C3     5 #> 10434      2         3  40       62499   C4     2 #> 10435      2         3  40       62499   C5     3 #> 10436      2         3  40       62499   E1     2 #> 10437      2         3  40       62499   E2     4 #> 10438      2         3  40       62499   E3     4 #> 10439      2         3  40       62499   E4     5 #> 10440      2         3  40       62499   E5     4 #> 10441      2         3  40       62499   N1     4 #> 10442      2         3  40       62499   N2     5 #> 10443      2         3  40       62499   N3     5 #> 10444      2         3  40       62499   N4     5 #> 10445      2         3  40       62499   N5     4 #> 10446      2         3  40       62499   O1     5 #> 10447      2         3  40       62499   O2     2 #> 10448      2         3  40       62499   O3     3 #> 10449      2         3  40       62499   O4     5 #> 10450      2         3  40       62499   O5     4 #> 10451      2         2  40       62500   A1     3 #> 10452      2         2  40       62500   A2     6 #> 10453      2         2  40       62500   A3     5 #> 10454      2         2  40       62500   A4     6 #> 10455      2         2  40       62500   A5     6 #> 10456      2         2  40       62500   C1     5 #> 10457      2         2  40       62500   C2     6 #> 10458      2         2  40       62500   C3     5 #> 10459      2         2  40       62500   C4     1 #> 10460      2         2  40       62500   C5     3 #> 10461      2         2  40       62500   E1     1 #> 10462      2         2  40       62500   E2     2 #> 10463      2         2  40       62500   E3     5 #> 10464      2         2  40       62500   E4     6 #> 10465      2         2  40       62500   E5     6 #> 10466      2         2  40       62500   N1     2 #> 10467      2         2  40       62500   N2     4 #> 10468      2         2  40       62500   N3     3 #> 10469      2         2  40       62500   N4     2 #> 10470      2         2  40       62500   N5     2 #> 10471      2         2  40       62500   O1     5 #> 10472      2         2  40       62500   O2     3 #> 10473      2         2  40       62500   O3     5 #> 10474      2         2  40       62500   O4     4 #> 10475      2         2  40       62500   O5     3 #> 10476      2         3  40       62502   A1     1 #> 10477      2         3  40       62502   A2     6 #> 10478      2         3  40       62502   A3     6 #> 10479      2         3  40       62502   A4     6 #> 10480      2         3  40       62502   A5     6 #> 10481      2         3  40       62502   C1     1 #> 10482      2         3  40       62502   C2     5 #> 10483      2         3  40       62502   C3     6 #> 10484      2         3  40       62502   C4     1 #> 10485      2         3  40       62502   C5     1 #> 10486      2         3  40       62502   E1     1 #> 10487      2         3  40       62502   E2     6 #> 10488      2         3  40       62502   E3     5 #> 10489      2         3  40       62502   E4     5 #> 10490      2         3  40       62502   E5     1 #> 10491      2         3  40       62502   N1     1 #> 10492      2         3  40       62502   N2     3 #> 10493      2         3  40       62502   N3     1 #> 10494      2         3  40       62502   N4     2 #> 10495      2         3  40       62502   N5     1 #> 10496      2         3  40       62502   O1     6 #> 10497      2         3  40       62502   O2     1 #> 10498      2         3  40       62502   O3     5 #> 10499      2         3  40       62502   O4     6 #> 10500      2         3  40       62502   O5     1 #> 10501      2         3  40       62505   A1     1 #> 10502      2         3  40       62505   A2     6 #> 10503      2         3  40       62505   A3     4 #> 10504      2         3  40       62505   A4     6 #> 10505      2         3  40       62505   A5     6 #> 10506      2         3  40       62505   C1     5 #> 10507      2         3  40       62505   C2     6 #> 10508      2         3  40       62505   C3     4 #> 10509      2         3  40       62505   C4     3 #> 10510      2         3  40       62505   C5     4 #> 10511      2         3  40       62505   E1     3 #> 10512      2         3  40       62505   E2     2 #> 10513      2         3  40       62505   E3     5 #> 10514      2         3  40       62505   E4     6 #> 10515      2         3  40       62505   E5     2 #> 10516      2         3  40       62505   N1     5 #> 10517      2         3  40       62505   N2     6 #> 10518      2         3  40       62505   N3     6 #> 10519      2         3  40       62505   N4     6 #> 10520      2         3  40       62505   N5     6 #> 10521      2         3  40       62505   O1     6 #> 10522      2         3  40       62505   O2     6 #> 10523      2         3  40       62505   O3     6 #> 10524      2         3  40       62505   O4     6 #> 10525      2         3  40       62505   O5     1 #> 10526      2         2  24       62508   A1     3 #> 10527      2         2  24       62508   A2     6 #> 10528      2         2  24       62508   A3     5 #> 10529      2         2  24       62508   A4     6 #> 10530      2         2  24       62508   A5     5 #> 10531      2         2  24       62508   C1     5 #> 10532      2         2  24       62508   C2     6 #> 10533      2         2  24       62508   C3     6 #> 10534      2         2  24       62508   C4     1 #> 10535      2         2  24       62508   C5     2 #> 10536      2         2  24       62508   E1     1 #> 10537      2         2  24       62508   E2     2 #> 10538      2         2  24       62508   E3     5 #> 10539      2         2  24       62508   E4     6 #> 10540      2         2  24       62508   E5     6 #> 10541      2         2  24       62508   N1     5 #> 10542      2         2  24       62508   N2     2 #> 10543      2         2  24       62508   N3     1 #> 10544      2         2  24       62508   N4    NA #> 10545      2         2  24       62508   N5     4 #> 10546      2         2  24       62508   O1     4 #> 10547      2         2  24       62508   O2     5 #> 10548      2         2  24       62508   O3     6 #> 10549      2         2  24       62508   O4     5 #> 10550      2         2  24       62508   O5     4 #> 10551      2         3  23       62509   A1     1 #> 10552      2         3  23       62509   A2     6 #> 10553      2         3  23       62509   A3     6 #> 10554      2         3  23       62509   A4     5 #> 10555      2         3  23       62509   A5     6 #> 10556      2         3  23       62509   C1     6 #> 10557      2         3  23       62509   C2     5 #> 10558      2         3  23       62509   C3     6 #> 10559      2         3  23       62509   C4     2 #> 10560      2         3  23       62509   C5     2 #> 10561      2         3  23       62509   E1     5 #> 10562      2         3  23       62509   E2     4 #> 10563      2         3  23       62509   E3     5 #> 10564      2         3  23       62509   E4     3 #> 10565      2         3  23       62509   E5     3 #> 10566      2         3  23       62509   N1     1 #> 10567      2         3  23       62509   N2     5 #> 10568      2         3  23       62509   N3     5 #> 10569      2         3  23       62509   N4     5 #> 10570      2         3  23       62509   N5     6 #> 10571      2         3  23       62509   O1     6 #> 10572      2         3  23       62509   O2     5 #> 10573      2         3  23       62509   O3     3 #> 10574      2         3  23       62509   O4     6 #> 10575      2         3  23       62509   O5     1 #> 10576      2         3  26       62512   A1     5 #> 10577      2         3  26       62512   A2    NA #> 10578      2         3  26       62512   A3     5 #> 10579      2         3  26       62512   A4     6 #> 10580      2         3  26       62512   A5     5 #> 10581      2         3  26       62512   C1     6 #> 10582      2         3  26       62512   C2     6 #> 10583      2         3  26       62512   C3     5 #> 10584      2         3  26       62512   C4    NA #> 10585      2         3  26       62512   C5     2 #> 10586      2         3  26       62512   E1     5 #> 10587      2         3  26       62512   E2     2 #> 10588      2         3  26       62512   E3     6 #> 10589      2         3  26       62512   E4     3 #> 10590      2         3  26       62512   E5     6 #> 10591      2         3  26       62512   N1     4 #> 10592      2         3  26       62512   N2    NA #> 10593      2         3  26       62512   N3    NA #> 10594      2         3  26       62512   N4     4 #> 10595      2         3  26       62512   N5     1 #> 10596      2         3  26       62512   O1     6 #> 10597      2         3  26       62512   O2     1 #> 10598      2         3  26       62512   O3     3 #> 10599      2         3  26       62512   O4     2 #> 10600      2         3  26       62512   O5     1 #> 10601      2         3  26       62514   A1     5 #> 10602      2         3  26       62514   A2    NA #> 10603      2         3  26       62514   A3     5 #> 10604      2         3  26       62514   A4     6 #> 10605      2         3  26       62514   A5     5 #> 10606      2         3  26       62514   C1     6 #> 10607      2         3  26       62514   C2     6 #> 10608      2         3  26       62514   C3     5 #> 10609      2         3  26       62514   C4     1 #> 10610      2         3  26       62514   C5     2 #> 10611      2         3  26       62514   E1     5 #> 10612      2         3  26       62514   E2     2 #> 10613      2         3  26       62514   E3     6 #> 10614      2         3  26       62514   E4     3 #> 10615      2         3  26       62514   E5     6 #> 10616      2         3  26       62514   N1     4 #> 10617      2         3  26       62514   N2     3 #> 10618      2         3  26       62514   N3     2 #> 10619      2         3  26       62514   N4     4 #> 10620      2         3  26       62514   N5     1 #> 10621      2         3  26       62514   O1     6 #> 10622      2         3  26       62514   O2     1 #> 10623      2         3  26       62514   O3     3 #> 10624      2         3  26       62514   O4     2 #> 10625      2         3  26       62514   O5     1 #> 10626      2         2  51       62518   A1     2 #> 10627      2         2  51       62518   A2     6 #> 10628      2         2  51       62518   A3     6 #> 10629      2         2  51       62518   A4     6 #> 10630      2         2  51       62518   A5     6 #> 10631      2         2  51       62518   C1     6 #> 10632      2         2  51       62518   C2     6 #> 10633      2         2  51       62518   C3     5 #> 10634      2         2  51       62518   C4     1 #> 10635      2         2  51       62518   C5     1 #> 10636      2         2  51       62518   E1     1 #> 10637      2         2  51       62518   E2     1 #> 10638      2         2  51       62518   E3     6 #> 10639      2         2  51       62518   E4     5 #> 10640      2         2  51       62518   E5     6 #> 10641      2         2  51       62518   N1     1 #> 10642      2         2  51       62518   N2     1 #> 10643      2         2  51       62518   N3     2 #> 10644      2         2  51       62518   N4     2 #> 10645      2         2  51       62518   N5     1 #> 10646      2         2  51       62518   O1     6 #> 10647      2         2  51       62518   O2     1 #> 10648      2         2  51       62518   O3     6 #> 10649      2         2  51       62518   O4     5 #> 10650      2         2  51       62518   O5     1 #> 10651      2         1  39       62520   A1    NA #> 10652      2         1  39       62520   A2     6 #> 10653      2         1  39       62520   A3     6 #> 10654      2         1  39       62520   A4     6 #> 10655      2         1  39       62520   A5     6 #> 10656      2         1  39       62520   C1     4 #> 10657      2         1  39       62520   C2     5 #> 10658      2         1  39       62520   C3     4 #> 10659      2         1  39       62520   C4     1 #> 10660      2         1  39       62520   C5     1 #> 10661      2         1  39       62520   E1     4 #> 10662      2         1  39       62520   E2     1 #> 10663      2         1  39       62520   E3     6 #> 10664      2         1  39       62520   E4     6 #> 10665      2         1  39       62520   E5     6 #> 10666      2         1  39       62520   N1     1 #> 10667      2         1  39       62520   N2     3 #> 10668      2         1  39       62520   N3     3 #> 10669      2         1  39       62520   N4     2 #> 10670      2         1  39       62520   N5    NA #> 10671      2         1  39       62520   O1     6 #> 10672      2         1  39       62520   O2     1 #> 10673      2         1  39       62520   O3     3 #> 10674      2         1  39       62520   O4     4 #> 10675      2         1  39       62520   O5     1 #> 10676      2         3  20       62522   A1     1 #> 10677      2         3  20       62522   A2     6 #> 10678      2         3  20       62522   A3     6 #> 10679      2         3  20       62522   A4     6 #> 10680      2         3  20       62522   A5     6 #> 10681      2         3  20       62522   C1     6 #> 10682      2         3  20       62522   C2     5 #> 10683      2         3  20       62522   C3     4 #> 10684      2         3  20       62522   C4     2 #> 10685      2         3  20       62522   C5     3 #> 10686      2         3  20       62522   E1     1 #> 10687      2         3  20       62522   E2     1 #> 10688      2         3  20       62522   E3     6 #> 10689      2         3  20       62522   E4     6 #> 10690      2         3  20       62522   E5     5 #> 10691      2         3  20       62522   N1     1 #> 10692      2         3  20       62522   N2     2 #> 10693      2         3  20       62522   N3     2 #> 10694      2         3  20       62522   N4     1 #> 10695      2         3  20       62522   N5     2 #> 10696      2         3  20       62522   O1     5 #> 10697      2         3  20       62522   O2     1 #> 10698      2         3  20       62522   O3     6 #> 10699      2         3  20       62522   O4     5 #> 10700      2         3  20       62522   O5     3 #> 10701      2         1  19       62526   A1     2 #> 10702      2         1  19       62526   A2     6 #> 10703      2         1  19       62526   A3     5 #> 10704      2         1  19       62526   A4     6 #> 10705      2         1  19       62526   A5     5 #> 10706      2         1  19       62526   C1     5 #> 10707      2         1  19       62526   C2     4 #> 10708      2         1  19       62526   C3     1 #> 10709      2         1  19       62526   C4     3 #> 10710      2         1  19       62526   C5     1 #> 10711      2         1  19       62526   E1     4 #> 10712      2         1  19       62526   E2     1 #> 10713      2         1  19       62526   E3     6 #> 10714      2         1  19       62526   E4     6 #> 10715      2         1  19       62526   E5     5 #> 10716      2         1  19       62526   N1     5 #> 10717      2         1  19       62526   N2     5 #> 10718      2         1  19       62526   N3     5 #> 10719      2         1  19       62526   N4     5 #> 10720      2         1  19       62526   N5     5 #> 10721      2         1  19       62526   O1     4 #> 10722      2         1  19       62526   O2     1 #> 10723      2         1  19       62526   O3     6 #> 10724      2         1  19       62526   O4     5 #> 10725      2         1  19       62526   O5     5 #> 10726      1         5  28       62527   A1     2 #> 10727      1         5  28       62527   A2     5 #> 10728      1         5  28       62527   A3     2 #> 10729      1         5  28       62527   A4     4 #> 10730      1         5  28       62527   A5     2 #> 10731      1         5  28       62527   C1     2 #> 10732      1         5  28       62527   C2     3 #> 10733      1         5  28       62527   C3     2 #> 10734      1         5  28       62527   C4     3 #> 10735      1         5  28       62527   C5     5 #> 10736      1         5  28       62527   E1     5 #> 10737      1         5  28       62527   E2     5 #> 10738      1         5  28       62527   E3     4 #> 10739      1         5  28       62527   E4     2 #> 10740      1         5  28       62527   E5     5 #> 10741      1         5  28       62527   N1     3 #> 10742      1         5  28       62527   N2     4 #> 10743      1         5  28       62527   N3     4 #> 10744      1         5  28       62527   N4     4 #> 10745      1         5  28       62527   N5     4 #> 10746      1         5  28       62527   O1     5 #> 10747      1         5  28       62527   O2     3 #> 10748      1         5  28       62527   O3     5 #> 10749      1         5  28       62527   O4     5 #> 10750      1         5  28       62527   O5     2 #> 10751      1         1  35       62528   A1     2 #> 10752      1         1  35       62528   A2     5 #> 10753      1         1  35       62528   A3     2 #> 10754      1         1  35       62528   A4     5 #> 10755      1         1  35       62528   A5     5 #> 10756      1         1  35       62528   C1     5 #> 10757      1         1  35       62528   C2     6 #> 10758      1         1  35       62528   C3     5 #> 10759      1         1  35       62528   C4     1 #> 10760      1         1  35       62528   C5     3 #> 10761      1         1  35       62528   E1     2 #> 10762      1         1  35       62528   E2     2 #> 10763      1         1  35       62528   E3     5 #> 10764      1         1  35       62528   E4     2 #> 10765      1         1  35       62528   E5     4 #> 10766      1         1  35       62528   N1     2 #> 10767      1         1  35       62528   N2     5 #> 10768      1         1  35       62528   N3     3 #> 10769      1         1  35       62528   N4     5 #> 10770      1         1  35       62528   N5     2 #> 10771      1         1  35       62528   O1     5 #> 10772      1         1  35       62528   O2     2 #> 10773      1         1  35       62528   O3     5 #> 10774      1         1  35       62528   O4     6 #> 10775      1         1  35       62528   O5     1 #> 10776      1         1  35       62529   A1     2 #> 10777      1         1  35       62529   A2     5 #> 10778      1         1  35       62529   A3     2 #> 10779      1         1  35       62529   A4     5 #> 10780      1         1  35       62529   A5     5 #> 10781      1         1  35       62529   C1     5 #> 10782      1         1  35       62529   C2     6 #> 10783      1         1  35       62529   C3     5 #> 10784      1         1  35       62529   C4     1 #> 10785      1         1  35       62529   C5     3 #> 10786      1         1  35       62529   E1     2 #> 10787      1         1  35       62529   E2     2 #> 10788      1         1  35       62529   E3     5 #> 10789      1         1  35       62529   E4     2 #> 10790      1         1  35       62529   E5     4 #> 10791      1         1  35       62529   N1     1 #> 10792      1         1  35       62529   N2     4 #> 10793      1         1  35       62529   N3     2 #> 10794      1         1  35       62529   N4     5 #> 10795      1         1  35       62529   N5     2 #> 10796      1         1  35       62529   O1     5 #> 10797      1         1  35       62529   O2     2 #> 10798      1         1  35       62529   O3     5 #> 10799      1         1  35       62529   O4     6 #> 10800      1         1  35       62529   O5     1 #> 10801      1         1  35       62530   A1     2 #> 10802      1         1  35       62530   A2     5 #> 10803      1         1  35       62530   A3     2 #> 10804      1         1  35       62530   A4     5 #> 10805      1         1  35       62530   A5     5 #> 10806      1         1  35       62530   C1     5 #> 10807      1         1  35       62530   C2     6 #> 10808      1         1  35       62530   C3     5 #> 10809      1         1  35       62530   C4     1 #> 10810      1         1  35       62530   C5     3 #> 10811      1         1  35       62530   E1     2 #> 10812      1         1  35       62530   E2     2 #> 10813      1         1  35       62530   E3     5 #> 10814      1         1  35       62530   E4     2 #> 10815      1         1  35       62530   E5     4 #> 10816      1         1  35       62530   N1     1 #> 10817      1         1  35       62530   N2     4 #> 10818      1         1  35       62530   N3     3 #> 10819      1         1  35       62530   N4     4 #> 10820      1         1  35       62530   N5     2 #> 10821      1         1  35       62530   O1     5 #> 10822      1         1  35       62530   O2     2 #> 10823      1         1  35       62530   O3     5 #> 10824      1         1  35       62530   O4     6 #> 10825      1         1  35       62530   O5     1 #> 10826      1         1  35       62531   A1     2 #> 10827      1         1  35       62531   A2     5 #> 10828      1         1  35       62531   A3     2 #> 10829      1         1  35       62531   A4     5 #> 10830      1         1  35       62531   A5     5 #> 10831      1         1  35       62531   C1     5 #> 10832      1         1  35       62531   C2     6 #> 10833      1         1  35       62531   C3     5 #> 10834      1         1  35       62531   C4     1 #> 10835      1         1  35       62531   C5     3 #> 10836      1         1  35       62531   E1     2 #> 10837      1         1  35       62531   E2     2 #> 10838      1         1  35       62531   E3     5 #> 10839      1         1  35       62531   E4     2 #> 10840      1         1  35       62531   E5     4 #> 10841      1         1  35       62531   N1     1 #> 10842      1         1  35       62531   N2     5 #> 10843      1         1  35       62531   N3     4 #> 10844      1         1  35       62531   N4     4 #> 10845      1         1  35       62531   N5     2 #> 10846      1         1  35       62531   O1     5 #> 10847      1         1  35       62531   O2     2 #> 10848      1         1  35       62531   O3     5 #> 10849      1         1  35       62531   O4     6 #> 10850      1         1  35       62531   O5     1 #> 10851      1         1  35       62532   A1     2 #> 10852      1         1  35       62532   A2     5 #> 10853      1         1  35       62532   A3     5 #> 10854      1         1  35       62532   A4     5 #> 10855      1         1  35       62532   A5     5 #> 10856      1         1  35       62532   C1     5 #> 10857      1         1  35       62532   C2     6 #> 10858      1         1  35       62532   C3     4 #> 10859      1         1  35       62532   C4     2 #> 10860      1         1  35       62532   C5     4 #> 10861      1         1  35       62532   E1     2 #> 10862      1         1  35       62532   E2     2 #> 10863      1         1  35       62532   E3     4 #> 10864      1         1  35       62532   E4     2 #> 10865      1         1  35       62532   E5     4 #> 10866      1         1  35       62532   N1     1 #> 10867      1         1  35       62532   N2     5 #> 10868      1         1  35       62532   N3     4 #> 10869      1         1  35       62532   N4     4 #> 10870      1         1  35       62532   N5     2 #> 10871      1         1  35       62532   O1     5 #> 10872      1         1  35       62532   O2     2 #> 10873      1         1  35       62532   O3     5 #> 10874      1         1  35       62532   O4     6 #> 10875      1         1  35       62532   O5     1 #> 10876      1         1  35       62533   A1     2 #> 10877      1         1  35       62533   A2     5 #> 10878      1         1  35       62533   A3     5 #> 10879      1         1  35       62533   A4     5 #> 10880      1         1  35       62533   A5     5 #> 10881      1         1  35       62533   C1     5 #> 10882      1         1  35       62533   C2     6 #> 10883      1         1  35       62533   C3     4 #> 10884      1         1  35       62533   C4     2 #> 10885      1         1  35       62533   C5     4 #> 10886      1         1  35       62533   E1     2 #> 10887      1         1  35       62533   E2     2 #> 10888      1         1  35       62533   E3     4 #> 10889      1         1  35       62533   E4     3 #> 10890      1         1  35       62533   E5     5 #> 10891      1         1  35       62533   N1     1 #> 10892      1         1  35       62533   N2     5 #> 10893      1         1  35       62533   N3     4 #> 10894      1         1  35       62533   N4     4 #> 10895      1         1  35       62533   N5     2 #> 10896      1         1  35       62533   O1     5 #> 10897      1         1  35       62533   O2     2 #> 10898      1         1  35       62533   O3     5 #> 10899      1         1  35       62533   O4     6 #> 10900      1         1  35       62533   O5     1 #> 10901      1         1  35       62535   A1     2 #> 10902      1         1  35       62535   A2     5 #> 10903      1         1  35       62535   A3     5 #> 10904      1         1  35       62535   A4     5 #> 10905      1         1  35       62535   A5     5 #> 10906      1         1  35       62535   C1     5 #> 10907      1         1  35       62535   C2     6 #> 10908      1         1  35       62535   C3     4 #> 10909      1         1  35       62535   C4     2 #> 10910      1         1  35       62535   C5     4 #> 10911      1         1  35       62535   E1     2 #> 10912      1         1  35       62535   E2     2 #> 10913      1         1  35       62535   E3     4 #> 10914      1         1  35       62535   E4     3 #> 10915      1         1  35       62535   E5     5 #> 10916      1         1  35       62535   N1     1 #> 10917      1         1  35       62535   N2     5 #> 10918      1         1  35       62535   N3     4 #> 10919      1         1  35       62535   N4     4 #> 10920      1         1  35       62535   N5     2 #> 10921      1         1  35       62535   O1     5 #> 10922      1         1  35       62535   O2     3 #> 10923      1         1  35       62535   O3     5 #> 10924      1         1  35       62535   O4     6 #> 10925      1         1  35       62535   O5     2 #> 10926      2         4  32       62537   A1     2 #> 10927      2         4  32       62537   A2     5 #> 10928      2         4  32       62537   A3     2 #> 10929      2         4  32       62537   A4     6 #> 10930      2         4  32       62537   A5     5 #> 10931      2         4  32       62537   C1     2 #> 10932      2         4  32       62537   C2     2 #> 10933      2         4  32       62537   C3     2 #> 10934      2         4  32       62537   C4     4 #> 10935      2         4  32       62537   C5     5 #> 10936      2         4  32       62537   E1     2 #> 10937      2         4  32       62537   E2     1 #> 10938      2         4  32       62537   E3     4 #> 10939      2         4  32       62537   E4     5 #> 10940      2         4  32       62537   E5     2 #> 10941      2         4  32       62537   N1     2 #> 10942      2         4  32       62537   N2     2 #> 10943      2         4  32       62537   N3     2 #> 10944      2         4  32       62537   N4     2 #> 10945      2         4  32       62537   N5     1 #> 10946      2         4  32       62537   O1     6 #> 10947      2         4  32       62537   O2     5 #> 10948      2         4  32       62537   O3     2 #> 10949      2         4  32       62537   O4     3 #> 10950      2         4  32       62537   O5     4 #> 10951      2         3  24       62538   A1     4 #> 10952      2         3  24       62538   A2     5 #> 10953      2         3  24       62538   A3     6 #> 10954      2         3  24       62538   A4     6 #> 10955      2         3  24       62538   A5     3 #> 10956      2         3  24       62538   C1     6 #> 10957      2         3  24       62538   C2     6 #> 10958      2         3  24       62538   C3     6 #> 10959      2         3  24       62538   C4     1 #> 10960      2         3  24       62538   C5     1 #> 10961      2         3  24       62538   E1     2 #> 10962      2         3  24       62538   E2     4 #> 10963      2         3  24       62538   E3     4 #> 10964      2         3  24       62538   E4     5 #> 10965      2         3  24       62538   E5     6 #> 10966      2         3  24       62538   N1     6 #> 10967      2         3  24       62538   N2     6 #> 10968      2         3  24       62538   N3     6 #> 10969      2         3  24       62538   N4     3 #> 10970      2         3  24       62538   N5     2 #> 10971      2         3  24       62538   O1     6 #> 10972      2         3  24       62538   O2     1 #> 10973      2         3  24       62538   O3     6 #> 10974      2         3  24       62538   O4     6 #> 10975      2         3  24       62538   O5     1 #> 10976      2         1  23       62541   A1     2 #> 10977      2         1  23       62541   A2     6 #> 10978      2         1  23       62541   A3     6 #> 10979      2         1  23       62541   A4     6 #> 10980      2         1  23       62541   A5     5 #> 10981      2         1  23       62541   C1     6 #> 10982      2         1  23       62541   C2     5 #> 10983      2         1  23       62541   C3     6 #> 10984      2         1  23       62541   C4     4 #> 10985      2         1  23       62541   C5     4 #> 10986      2         1  23       62541   E1     4 #> 10987      2         1  23       62541   E2     1 #> 10988      2         1  23       62541   E3     4 #> 10989      2         1  23       62541   E4     6 #> 10990      2         1  23       62541   E5     5 #> 10991      2         1  23       62541   N1     1 #> 10992      2         1  23       62541   N2     1 #> 10993      2         1  23       62541   N3     1 #> 10994      2         1  23       62541   N4     5 #> 10995      2         1  23       62541   N5     1 #> 10996      2         1  23       62541   O1     6 #> 10997      2         1  23       62541   O2     1 #> 10998      2         1  23       62541   O3     6 #> 10999      2         1  23       62541   O4     6 #> 11000      2         1  23       62541   O5     1 #> 11001      1         3  39       62542   A1     5 #> 11002      1         3  39       62542   A2     4 #> 11003      1         3  39       62542   A3     5 #> 11004      1         3  39       62542   A4     6 #> 11005      1         3  39       62542   A5     4 #> 11006      1         3  39       62542   C1     6 #> 11007      1         3  39       62542   C2     5 #> 11008      1         3  39       62542   C3     6 #> 11009      1         3  39       62542   C4     1 #> 11010      1         3  39       62542   C5     3 #> 11011      1         3  39       62542   E1     5 #> 11012      1         3  39       62542   E2     5 #> 11013      1         3  39       62542   E3     5 #> 11014      1         3  39       62542   E4     5 #> 11015      1         3  39       62542   E5     6 #> 11016      1         3  39       62542   N1     6 #> 11017      1         3  39       62542   N2     6 #> 11018      1         3  39       62542   N3     4 #> 11019      1         3  39       62542   N4     6 #> 11020      1         3  39       62542   N5     5 #> 11021      1         3  39       62542   O1     5 #> 11022      1         3  39       62542   O2     6 #> 11023      1         3  39       62542   O3     4 #> 11024      1         3  39       62542   O4     6 #> 11025      1         3  39       62542   O5     1 #> 11026      1         4  48       62543   A1     2 #> 11027      1         4  48       62543   A2     5 #> 11028      1         4  48       62543   A3     6 #> 11029      1         4  48       62543   A4     5 #> 11030      1         4  48       62543   A5     5 #> 11031      1         4  48       62543   C1     4 #> 11032      1         4  48       62543   C2     5 #> 11033      1         4  48       62543   C3     5 #> 11034      1         4  48       62543   C4     1 #> 11035      1         4  48       62543   C5    NA #> 11036      1         4  48       62543   E1     6 #> 11037      1         4  48       62543   E2     5 #> 11038      1         4  48       62543   E3     3 #> 11039      1         4  48       62543   E4     2 #> 11040      1         4  48       62543   E5     5 #> 11041      1         4  48       62543   N1     4 #> 11042      1         4  48       62543   N2     4 #> 11043      1         4  48       62543   N3     1 #> 11044      1         4  48       62543   N4     1 #> 11045      1         4  48       62543   N5     1 #> 11046      1         4  48       62543   O1     6 #> 11047      1         4  48       62543   O2     1 #> 11048      1         4  48       62543   O3     4 #> 11049      1         4  48       62543   O4     6 #> 11050      1         4  48       62543   O5     1 #> 11051      2         1  26       62545   A1     1 #> 11052      2         1  26       62545   A2     4 #> 11053      2         1  26       62545   A3     4 #> 11054      2         1  26       62545   A4     5 #> 11055      2         1  26       62545   A5     4 #> 11056      2         1  26       62545   C1     2 #> 11057      2         1  26       62545   C2     5 #> 11058      2         1  26       62545   C3     2 #> 11059      2         1  26       62545   C4     4 #> 11060      2         1  26       62545   C5     3 #> 11061      2         1  26       62545   E1     6 #> 11062      2         1  26       62545   E2     5 #> 11063      2         1  26       62545   E3     3 #> 11064      2         1  26       62545   E4     5 #> 11065      2         1  26       62545   E5     5 #> 11066      2         1  26       62545   N1     6 #> 11067      2         1  26       62545   N2     6 #> 11068      2         1  26       62545   N3     6 #> 11069      2         1  26       62545   N4     5 #> 11070      2         1  26       62545   N5     6 #> 11071      2         1  26       62545   O1     3 #> 11072      2         1  26       62545   O2     6 #> 11073      2         1  26       62545   O3     4 #> 11074      2         1  26       62545   O4     5 #> 11075      2         1  26       62545   O5     2 #> 11076      1         3  28       62546   A1     4 #> 11077      1         3  28       62546   A2     4 #> 11078      1         3  28       62546   A3     4 #> 11079      1         3  28       62546   A4     5 #> 11080      1         3  28       62546   A5     5 #> 11081      1         3  28       62546   C1     4 #> 11082      1         3  28       62546   C2     4 #> 11083      1         3  28       62546   C3     4 #> 11084      1         3  28       62546   C4     2 #> 11085      1         3  28       62546   C5     4 #> 11086      1         3  28       62546   E1     4 #> 11087      1         3  28       62546   E2     5 #> 11088      1         3  28       62546   E3     4 #> 11089      1         3  28       62546   E4     5 #> 11090      1         3  28       62546   E5     5 #> 11091      1         3  28       62546   N1     4 #> 11092      1         3  28       62546   N2     4 #> 11093      1         3  28       62546   N3     5 #> 11094      1         3  28       62546   N4     3 #> 11095      1         3  28       62546   N5     2 #> 11096      1         3  28       62546   O1     4 #> 11097      1         3  28       62546   O2     5 #> 11098      1         3  28       62546   O3     5 #> 11099      1         3  28       62546   O4     4 #> 11100      1         3  28       62546   O5     2 #> 11101      2         1  23       62547   A1     1 #> 11102      2         1  23       62547   A2     6 #> 11103      2         1  23       62547   A3     6 #> 11104      2         1  23       62547   A4     6 #> 11105      2         1  23       62547   A5     5 #> 11106      2         1  23       62547   C1     6 #> 11107      2         1  23       62547   C2     5 #> 11108      2         1  23       62547   C3     6 #> 11109      2         1  23       62547   C4     5 #> 11110      2         1  23       62547   C5     4 #> 11111      2         1  23       62547   E1     5 #> 11112      2         1  23       62547   E2     1 #> 11113      2         1  23       62547   E3     5 #> 11114      2         1  23       62547   E4     6 #> 11115      2         1  23       62547   E5     5 #> 11116      2         1  23       62547   N1     1 #> 11117      2         1  23       62547   N2     1 #> 11118      2         1  23       62547   N3     2 #> 11119      2         1  23       62547   N4     5 #> 11120      2         1  23       62547   N5     1 #> 11121      2         1  23       62547   O1     6 #> 11122      2         1  23       62547   O2     4 #> 11123      2         1  23       62547   O3     6 #> 11124      2         1  23       62547   O4     6 #> 11125      2         1  23       62547   O5     2 #> 11126      1         3  24       62548   A1     2 #> 11127      1         3  24       62548   A2     6 #> 11128      1         3  24       62548   A3     6 #> 11129      1         3  24       62548   A4     5 #> 11130      1         3  24       62548   A5     4 #> 11131      1         3  24       62548   C1     4 #> 11132      1         3  24       62548   C2     3 #> 11133      1         3  24       62548   C3     5 #> 11134      1         3  24       62548   C4     1 #> 11135      1         3  24       62548   C5     2 #> 11136      1         3  24       62548   E1     5 #> 11137      1         3  24       62548   E2     2 #> 11138      1         3  24       62548   E3     4 #> 11139      1         3  24       62548   E4     2 #> 11140      1         3  24       62548   E5     5 #> 11141      1         3  24       62548   N1     1 #> 11142      1         3  24       62548   N2     1 #> 11143      1         3  24       62548   N3     1 #> 11144      1         3  24       62548   N4     5 #> 11145      1         3  24       62548   N5     1 #> 11146      1         3  24       62548   O1     5 #> 11147      1         3  24       62548   O2     6 #> 11148      1         3  24       62548   O3     2 #> 11149      1         3  24       62548   O4     5 #> 11150      1         3  24       62548   O5     2 #> 11151      2         3  29       62550   A1     2 #> 11152      2         3  29       62550   A2     5 #> 11153      2         3  29       62550   A3     6 #> 11154      2         3  29       62550   A4     6 #> 11155      2         3  29       62550   A5     5 #> 11156      2         3  29       62550   C1     4 #> 11157      2         3  29       62550   C2     4 #> 11158      2         3  29       62550   C3     4 #> 11159      2         3  29       62550   C4     2 #> 11160      2         3  29       62550   C5     4 #> 11161      2         3  29       62550   E1     3 #> 11162      2         3  29       62550   E2     3 #> 11163      2         3  29       62550   E3     5 #> 11164      2         3  29       62550   E4     5 #> 11165      2         3  29       62550   E5     5 #> 11166      2         3  29       62550   N1     2 #> 11167      2         3  29       62550   N2     3 #> 11168      2         3  29       62550   N3     2 #> 11169      2         3  29       62550   N4     2 #> 11170      2         3  29       62550   N5     1 #> 11171      2         3  29       62550   O1     4 #> 11172      2         3  29       62550   O2     2 #> 11173      2         3  29       62550   O3     5 #> 11174      2         3  29       62550   O4     5 #> 11175      2         3  29       62550   O5     3 #> 11176      2        NA  19       62551   A1     5 #> 11177      2        NA  19       62551   A2     4 #> 11178      2        NA  19       62551   A3     2 #> 11179      2        NA  19       62551   A4     2 #> 11180      2        NA  19       62551   A5     1 #> 11181      2        NA  19       62551   C1     4 #> 11182      2        NA  19       62551   C2     4 #> 11183      2        NA  19       62551   C3     3 #> 11184      2        NA  19       62551   C4     6 #> 11185      2        NA  19       62551   C5     6 #> 11186      2        NA  19       62551   E1     4 #> 11187      2        NA  19       62551   E2     1 #> 11188      2        NA  19       62551   E3     4 #> 11189      2        NA  19       62551   E4     2 #> 11190      2        NA  19       62551   E5     6 #> 11191      2        NA  19       62551   N1     6 #> 11192      2        NA  19       62551   N2     6 #> 11193      2        NA  19       62551   N3     6 #> 11194      2        NA  19       62551   N4     6 #> 11195      2        NA  19       62551   N5     6 #> 11196      2        NA  19       62551   O1     6 #> 11197      2        NA  19       62551   O2     3 #> 11198      2        NA  19       62551   O3     1 #> 11199      2        NA  19       62551   O4     6 #> 11200      2        NA  19       62551   O5     1 #> 11201      1         3  29       62552   A1     6 #> 11202      1         3  29       62552   A2     1 #> 11203      1         3  29       62552   A3     1 #> 11204      1         3  29       62552   A4     4 #> 11205      1         3  29       62552   A5     1 #> 11206      1         3  29       62552   C1     6 #> 11207      1         3  29       62552   C2     6 #> 11208      1         3  29       62552   C3     6 #> 11209      1         3  29       62552   C4     1 #> 11210      1         3  29       62552   C5     1 #> 11211      1         3  29       62552   E1     6 #> 11212      1         3  29       62552   E2     6 #> 11213      1         3  29       62552   E3     1 #> 11214      1         3  29       62552   E4     1 #> 11215      1         3  29       62552   E5     2 #> 11216      1         3  29       62552   N1     1 #> 11217      1         3  29       62552   N2     1 #> 11218      1         3  29       62552   N3     1 #> 11219      1         3  29       62552   N4     1 #> 11220      1         3  29       62552   N5     1 #> 11221      1         3  29       62552   O1     6 #> 11222      1         3  29       62552   O2     5 #> 11223      1         3  29       62552   O3     6 #> 11224      1         3  29       62552   O4     6 #> 11225      1         3  29       62552   O5     4 #> 11226      2         3  26       62553   A1     1 #> 11227      2         3  26       62553   A2     6 #> 11228      2         3  26       62553   A3     6 #> 11229      2         3  26       62553   A4     6 #> 11230      2         3  26       62553   A5     5 #> 11231      2         3  26       62553   C1     6 #> 11232      2         3  26       62553   C2     6 #> 11233      2         3  26       62553   C3     6 #> 11234      2         3  26       62553   C4     1 #> 11235      2         3  26       62553   C5     1 #> 11236      2         3  26       62553   E1     2 #> 11237      2         3  26       62553   E2     3 #> 11238      2         3  26       62553   E3     6 #> 11239      2         3  26       62553   E4     5 #> 11240      2         3  26       62553   E5     6 #> 11241      2         3  26       62553   N1     3 #> 11242      2         3  26       62553   N2     3 #> 11243      2         3  26       62553   N3     3 #> 11244      2         3  26       62553   N4     1 #> 11245      2         3  26       62553   N5     2 #> 11246      2         3  26       62553   O1     6 #> 11247      2         3  26       62553   O2     1 #> 11248      2         3  26       62553   O3     6 #> 11249      2         3  26       62553   O4     5 #> 11250      2         3  26       62553   O5     1 #> 11251      2        NA  15       62555   A1     4 #> 11252      2        NA  15       62555   A2     4 #> 11253      2        NA  15       62555   A3     3 #> 11254      2        NA  15       62555   A4     4 #> 11255      2        NA  15       62555   A5     2 #> 11256      2        NA  15       62555   C1     3 #> 11257      2        NA  15       62555   C2     4 #> 11258      2        NA  15       62555   C3     4 #> 11259      2        NA  15       62555   C4     5 #> 11260      2        NA  15       62555   C5     5 #> 11261      2        NA  15       62555   E1     6 #> 11262      2        NA  15       62555   E2     5 #> 11263      2        NA  15       62555   E3     2 #> 11264      2        NA  15       62555   E4     2 #> 11265      2        NA  15       62555   E5     2 #> 11266      2        NA  15       62555   N1     5 #> 11267      2        NA  15       62555   N2     4 #> 11268      2        NA  15       62555   N3     4 #> 11269      2        NA  15       62555   N4     5 #> 11270      2        NA  15       62555   N5     5 #> 11271      2        NA  15       62555   O1     5 #> 11272      2        NA  15       62555   O2     5 #> 11273      2        NA  15       62555   O3     4 #> 11274      2        NA  15       62555   O4     4 #> 11275      2        NA  15       62555   O5     3 #> 11276      2         3  37       62556   A1     4 #> 11277      2         3  37       62556   A2     5 #> 11278      2         3  37       62556   A3     5 #> 11279      2         3  37       62556   A4     5 #> 11280      2         3  37       62556   A5     4 #> 11281      2         3  37       62556   C1     5 #> 11282      2         3  37       62556   C2     5 #> 11283      2         3  37       62556   C3     4 #> 11284      2         3  37       62556   C4     3 #> 11285      2         3  37       62556   C5     3 #> 11286      2         3  37       62556   E1     3 #> 11287      2         3  37       62556   E2     2 #> 11288      2         3  37       62556   E3     5 #> 11289      2         3  37       62556   E4     5 #> 11290      2         3  37       62556   E5     6 #> 11291      2         3  37       62556   N1     4 #> 11292      2         3  37       62556   N2     2 #> 11293      2         3  37       62556   N3     3 #> 11294      2         3  37       62556   N4     1 #> 11295      2         3  37       62556   N5     3 #> 11296      2         3  37       62556   O1     4 #> 11297      2         3  37       62556   O2     1 #> 11298      2         3  37       62556   O3     5 #> 11299      2         3  37       62556   O4    NA #> 11300      2         3  37       62556   O5     2 #> 11301      1         3  30       62557   A1     3 #> 11302      1         3  30       62557   A2     6 #> 11303      1         3  30       62557   A3     6 #> 11304      1         3  30       62557   A4     6 #> 11305      1         3  30       62557   A5     5 #> 11306      1         3  30       62557   C1     5 #> 11307      1         3  30       62557   C2     5 #> 11308      1         3  30       62557   C3     5 #> 11309      1         3  30       62557   C4     1 #> 11310      1         3  30       62557   C5     2 #> 11311      1         3  30       62557   E1     1 #> 11312      1         3  30       62557   E2     1 #> 11313      1         3  30       62557   E3     5 #> 11314      1         3  30       62557   E4     5 #> 11315      1         3  30       62557   E5     6 #> 11316      1         3  30       62557   N1     4 #> 11317      1         3  30       62557   N2     4 #> 11318      1         3  30       62557   N3     3 #> 11319      1         3  30       62557   N4     2 #> 11320      1         3  30       62557   N5     1 #> 11321      1         3  30       62557   O1     6 #> 11322      1         3  30       62557   O2     1 #> 11323      1         3  30       62557   O3     5 #> 11324      1         3  30       62557   O4     5 #> 11325      1         3  30       62557   O5     1 #> 11326      2         3  36       62559   A1     5 #> 11327      2         3  36       62559   A2     6 #> 11328      2         3  36       62559   A3     5 #> 11329      2         3  36       62559   A4     6 #> 11330      2         3  36       62559   A5     5 #> 11331      2         3  36       62559   C1     5 #> 11332      2         3  36       62559   C2     5 #> 11333      2         3  36       62559   C3     5 #> 11334      2         3  36       62559   C4     4 #> 11335      2         3  36       62559   C5     3 #> 11336      2         3  36       62559   E1     3 #> 11337      2         3  36       62559   E2     2 #> 11338      2         3  36       62559   E3     5 #> 11339      2         3  36       62559   E4     6 #> 11340      2         3  36       62559   E5     5 #> 11341      2         3  36       62559   N1     2 #> 11342      2         3  36       62559   N2     3 #> 11343      2         3  36       62559   N3     2 #> 11344      2         3  36       62559   N4     5 #> 11345      2         3  36       62559   N5     4 #> 11346      2         3  36       62559   O1     6 #> 11347      2         3  36       62559   O2     1 #> 11348      2         3  36       62559   O3     5 #> 11349      2         3  36       62559   O4     2 #> 11350      2         3  36       62559   O5     1 #> 11351      2         3  33       62561   A1     1 #> 11352      2         3  33       62561   A2     6 #> 11353      2         3  33       62561   A3     5 #> 11354      2         3  33       62561   A4     6 #> 11355      2         3  33       62561   A5     5 #> 11356      2         3  33       62561   C1     6 #> 11357      2         3  33       62561   C2     4 #> 11358      2         3  33       62561   C3     5 #> 11359      2         3  33       62561   C4     1 #> 11360      2         3  33       62561   C5     1 #> 11361      2         3  33       62561   E1     1 #> 11362      2         3  33       62561   E2     1 #> 11363      2         3  33       62561   E3     5 #> 11364      2         3  33       62561   E4     5 #> 11365      2         3  33       62561   E5     6 #> 11366      2         3  33       62561   N1     4 #> 11367      2         3  33       62561   N2     5 #> 11368      2         3  33       62561   N3     4 #> 11369      2         3  33       62561   N4     1 #> 11370      2         3  33       62561   N5     5 #> 11371      2         3  33       62561   O1     5 #> 11372      2         3  33       62561   O2     6 #> 11373      2         3  33       62561   O3     5 #> 11374      2         3  33       62561   O4     5 #> 11375      2         3  33       62561   O5     2 #> 11376      1         3  23       62562   A1     2 #> 11377      1         3  23       62562   A2     5 #> 11378      1         3  23       62562   A3     4 #> 11379      1         3  23       62562   A4     6 #> 11380      1         3  23       62562   A5     5 #> 11381      1         3  23       62562   C1     3 #> 11382      1         3  23       62562   C2     5 #> 11383      1         3  23       62562   C3     5 #> 11384      1         3  23       62562   C4     1 #> 11385      1         3  23       62562   C5     2 #> 11386      1         3  23       62562   E1     4 #> 11387      1         3  23       62562   E2     3 #> 11388      1         3  23       62562   E3     2 #> 11389      1         3  23       62562   E4     5 #> 11390      1         3  23       62562   E5     6 #> 11391      1         3  23       62562   N1     2 #> 11392      1         3  23       62562   N2     2 #> 11393      1         3  23       62562   N3     1 #> 11394      1         3  23       62562   N4     1 #> 11395      1         3  23       62562   N5     2 #> 11396      1         3  23       62562   O1     5 #> 11397      1         3  23       62562   O2     2 #> 11398      1         3  23       62562   O3     4 #> 11399      1         3  23       62562   O4     3 #> 11400      1         3  23       62562   O5     4 #> 11401      2         2  22       62565   A1     1 #> 11402      2         2  22       62565   A2     6 #> 11403      2         2  22       62565   A3     6 #> 11404      2         2  22       62565   A4     6 #> 11405      2         2  22       62565   A5     6 #> 11406      2         2  22       62565   C1     1 #> 11407      2         2  22       62565   C2     6 #> 11408      2         2  22       62565   C3     6 #> 11409      2         2  22       62565   C4     1 #> 11410      2         2  22       62565   C5     1 #> 11411      2         2  22       62565   E1     1 #> 11412      2         2  22       62565   E2     1 #> 11413      2         2  22       62565   E3     6 #> 11414      2         2  22       62565   E4     6 #> 11415      2         2  22       62565   E5     6 #> 11416      2         2  22       62565   N1     1 #> 11417      2         2  22       62565   N2     4 #> 11418      2         2  22       62565   N3     5 #> 11419      2         2  22       62565   N4     1 #> 11420      2         2  22       62565   N5     1 #> 11421      2         2  22       62565   O1     6 #> 11422      2         2  22       62565   O2     1 #> 11423      2         2  22       62565   O3     6 #> 11424      2         2  22       62565   O4     6 #> 11425      2         2  22       62565   O5     1 #> 11426      2         3  17       62567   A1     2 #> 11427      2         3  17       62567   A2     5 #> 11428      2         3  17       62567   A3     6 #> 11429      2         3  17       62567   A4     5 #> 11430      2         3  17       62567   A5     4 #> 11431      2         3  17       62567   C1     6 #> 11432      2         3  17       62567   C2     6 #> 11433      2         3  17       62567   C3     4 #> 11434      2         3  17       62567   C4     1 #> 11435      2         3  17       62567   C5     5 #> 11436      2         3  17       62567   E1     1 #> 11437      2         3  17       62567   E2     2 #> 11438      2         3  17       62567   E3     4 #> 11439      2         3  17       62567   E4     5 #> 11440      2         3  17       62567   E5     5 #> 11441      2         3  17       62567   N1     5 #> 11442      2         3  17       62567   N2     5 #> 11443      2         3  17       62567   N3     5 #> 11444      2         3  17       62567   N4     2 #> 11445      2         3  17       62567   N5     2 #> 11446      2         3  17       62567   O1     6 #> 11447      2         3  17       62567   O2     1 #> 11448      2         3  17       62567   O3     6 #> 11449      2         3  17       62567   O4     6 #> 11450      2         3  17       62567   O5     1 #> 11451      1         2  28       62570   A1     2 #> 11452      1         2  28       62570   A2     2 #> 11453      1         2  28       62570   A3     2 #> 11454      1         2  28       62570   A4     1 #> 11455      1         2  28       62570   A5     3 #> 11456      1         2  28       62570   C1     3 #> 11457      1         2  28       62570   C2     4 #> 11458      1         2  28       62570   C3     2 #> 11459      1         2  28       62570   C4     5 #> 11460      1         2  28       62570   C5     5 #> 11461      1         2  28       62570   E1     5 #> 11462      1         2  28       62570   E2     6 #> 11463      1         2  28       62570   E3     2 #> 11464      1         2  28       62570   E4     1 #> 11465      1         2  28       62570   E5     1 #> 11466      1         2  28       62570   N1     2 #> 11467      1         2  28       62570   N2     3 #> 11468      1         2  28       62570   N3     1 #> 11469      1         2  28       62570   N4     2 #> 11470      1         2  28       62570   N5     5 #> 11471      1         2  28       62570   O1     6 #> 11472      1         2  28       62570   O2     2 #> 11473      1         2  28       62570   O3     6 #> 11474      1         2  28       62570   O4     6 #> 11475      1         2  28       62570   O5     1 #> 11476      2         3  36       62573   A1     5 #> 11477      2         3  36       62573   A2     4 #> 11478      2         3  36       62573   A3     4 #> 11479      2         3  36       62573   A4     4 #> 11480      2         3  36       62573   A5     3 #> 11481      2         3  36       62573   C1     6 #> 11482      2         3  36       62573   C2     6 #> 11483      2         3  36       62573   C3     6 #> 11484      2         3  36       62573   C4     1 #> 11485      2         3  36       62573   C5     6 #> 11486      2         3  36       62573   E1     3 #> 11487      2         3  36       62573   E2     2 #> 11488      2         3  36       62573   E3     4 #> 11489      2         3  36       62573   E4     4 #> 11490      2         3  36       62573   E5     5 #> 11491      2         3  36       62573   N1     6 #> 11492      2         3  36       62573   N2     6 #> 11493      2         3  36       62573   N3     4 #> 11494      2         3  36       62573   N4     4 #> 11495      2         3  36       62573   N5     3 #> 11496      2         3  36       62573   O1     6 #> 11497      2         3  36       62573   O2     3 #> 11498      2         3  36       62573   O3     5 #> 11499      2         3  36       62573   O4     4 #> 11500      2         3  36       62573   O5     3 #> 11501      2         3  29       62574   A1     2 #> 11502      2         3  29       62574   A2     5 #> 11503      2         3  29       62574   A3     4 #> 11504      2         3  29       62574   A4     4 #> 11505      2         3  29       62574   A5     6 #> 11506      2         3  29       62574   C1     4 #> 11507      2         3  29       62574   C2     3 #> 11508      2         3  29       62574   C3     3 #> 11509      2         3  29       62574   C4     2 #> 11510      2         3  29       62574   C5     4 #> 11511      2         3  29       62574   E1     3 #> 11512      2         3  29       62574   E2     2 #> 11513      2         3  29       62574   E3     4 #> 11514      2         3  29       62574   E4     5 #> 11515      2         3  29       62574   E5     3 #> 11516      2         3  29       62574   N1     1 #> 11517      2         3  29       62574   N2     1 #> 11518      2         3  29       62574   N3     2 #> 11519      2         3  29       62574   N4     4 #> 11520      2         3  29       62574   N5     2 #> 11521      2         3  29       62574   O1     3 #> 11522      2         3  29       62574   O2     4 #> 11523      2         3  29       62574   O3     5 #> 11524      2         3  29       62574   O4     5 #> 11525      2         3  29       62574   O5     3 #> 11526      2         5  59       62577   A1     2 #> 11527      2         5  59       62577   A2     5 #> 11528      2         5  59       62577   A3     5 #> 11529      2         5  59       62577   A4     5 #> 11530      2         5  59       62577   A5     5 #> 11531      2         5  59       62577   C1     2 #> 11532      2         5  59       62577   C2     1 #> 11533      2         5  59       62577   C3     2 #> 11534      2         5  59       62577   C4     5 #> 11535      2         5  59       62577   C5     4 #> 11536      2         5  59       62577   E1     5 #> 11537      2         5  59       62577   E2     5 #> 11538      2         5  59       62577   E3     5 #> 11539      2         5  59       62577   E4     4 #> 11540      2         5  59       62577   E5     2 #> 11541      2         5  59       62577   N1     5 #> 11542      2         5  59       62577   N2     5 #> 11543      2         5  59       62577   N3     5 #> 11544      2         5  59       62577   N4     6 #> 11545      2         5  59       62577   N5     4 #> 11546      2         5  59       62577   O1     5 #> 11547      2         5  59       62577   O2     6 #> 11548      2         5  59       62577   O3     4 #> 11549      2         5  59       62577   O4     6 #> 11550      2         5  59       62577   O5     2 #> 11551      2         3  19       62578   A1     1 #> 11552      2         3  19       62578   A2     5 #> 11553      2         3  19       62578   A3     6 #> 11554      2         3  19       62578   A4     6 #> 11555      2         3  19       62578   A5     5 #> 11556      2         3  19       62578   C1     5 #> 11557      2         3  19       62578   C2     5 #> 11558      2         3  19       62578   C3     6 #> 11559      2         3  19       62578   C4     1 #> 11560      2         3  19       62578   C5     3 #> 11561      2         3  19       62578   E1     3 #> 11562      2         3  19       62578   E2     3 #> 11563      2         3  19       62578   E3     5 #> 11564      2         3  19       62578   E4     6 #> 11565      2         3  19       62578   E5     5 #> 11566      2         3  19       62578   N1     1 #> 11567      2         3  19       62578   N2     2 #> 11568      2         3  19       62578   N3     1 #> 11569      2         3  19       62578   N4     2 #> 11570      2         3  19       62578   N5     2 #> 11571      2         3  19       62578   O1     6 #> 11572      2         3  19       62578   O2     2 #> 11573      2         3  19       62578   O3     5 #> 11574      2         3  19       62578   O4     6 #> 11575      2         3  19       62578   O5     2 #> 11576      1         3  27       62582   A1     3 #> 11577      1         3  27       62582   A2     6 #> 11578      1         3  27       62582   A3     5 #> 11579      1         3  27       62582   A4     6 #> 11580      1         3  27       62582   A5     6 #> 11581      1         3  27       62582   C1     5 #> 11582      1         3  27       62582   C2     3 #> 11583      1         3  27       62582   C3     3 #> 11584      1         3  27       62582   C4     2 #> 11585      1         3  27       62582   C5     2 #> 11586      1         3  27       62582   E1     1 #> 11587      1         3  27       62582   E2     3 #> 11588      1         3  27       62582   E3     6 #> 11589      1         3  27       62582   E4     4 #> 11590      1         3  27       62582   E5     6 #> 11591      1         3  27       62582   N1     5 #> 11592      1         3  27       62582   N2     6 #> 11593      1         3  27       62582   N3     4 #> 11594      1         3  27       62582   N4     5 #> 11595      1         3  27       62582   N5     5 #> 11596      1         3  27       62582   O1     6 #> 11597      1         3  27       62582   O2     1 #> 11598      1         3  27       62582   O3     5 #> 11599      1         3  27       62582   O4     6 #> 11600      1         3  27       62582   O5     1 #> 11601      2         4  41       62589   A1     1 #> 11602      2         4  41       62589   A2     5 #> 11603      2         4  41       62589   A3     1 #> 11604      2         4  41       62589   A4     5 #> 11605      2         4  41       62589   A5     5 #> 11606      2         4  41       62589   C1     5 #> 11607      2         4  41       62589   C2     4 #> 11608      2         4  41       62589   C3     3 #> 11609      2         4  41       62589   C4     2 #> 11610      2         4  41       62589   C5     2 #> 11611      2         4  41       62589   E1     2 #> 11612      2         4  41       62589   E2     4 #> 11613      2         4  41       62589   E3     3 #> 11614      2         4  41       62589   E4     5 #> 11615      2         4  41       62589   E5     4 #> 11616      2         4  41       62589   N1     1 #> 11617      2         4  41       62589   N2     3 #> 11618      2         4  41       62589   N3     5 #> 11619      2         4  41       62589   N4     2 #> 11620      2         4  41       62589   N5     4 #> 11621      2         4  41       62589   O1     4 #> 11622      2         4  41       62589   O2     2 #> 11623      2         4  41       62589   O3     4 #> 11624      2         4  41       62589   O4     5 #> 11625      2         4  41       62589   O5     2 #> 11626      2         4  27       62590   A1     1 #> 11627      2         4  27       62590   A2     5 #> 11628      2         4  27       62590   A3     6 #> 11629      2         4  27       62590   A4     5 #> 11630      2         4  27       62590   A5     5 #> 11631      2         4  27       62590   C1     5 #> 11632      2         4  27       62590   C2     4 #> 11633      2         4  27       62590   C3     4 #> 11634      2         4  27       62590   C4     1 #> 11635      2         4  27       62590   C5     4 #> 11636      2         4  27       62590   E1     1 #> 11637      2         4  27       62590   E2     2 #> 11638      2         4  27       62590   E3     3 #> 11639      2         4  27       62590   E4     6 #> 11640      2         4  27       62590   E5     4 #> 11641      2         4  27       62590   N1     1 #> 11642      2         4  27       62590   N2     1 #> 11643      2         4  27       62590   N3     1 #> 11644      2         4  27       62590   N4     1 #> 11645      2         4  27       62590   N5     2 #> 11646      2         4  27       62590   O1     4 #> 11647      2         4  27       62590   O2     1 #> 11648      2         4  27       62590   O3     4 #> 11649      2         4  27       62590   O4     5 #> 11650      2         4  27       62590   O5     2 #> 11651      2         2  24       62594   A1     2 #> 11652      2         2  24       62594   A2     6 #> 11653      2         2  24       62594   A3    NA #> 11654      2         2  24       62594   A4     5 #> 11655      2         2  24       62594   A5     5 #> 11656      2         2  24       62594   C1     6 #> 11657      2         2  24       62594   C2     5 #> 11658      2         2  24       62594   C3     6 #> 11659      2         2  24       62594   C4     2 #> 11660      2         2  24       62594   C5     3 #> 11661      2         2  24       62594   E1     1 #> 11662      2         2  24       62594   E2     1 #> 11663      2         2  24       62594   E3     6 #> 11664      2         2  24       62594   E4     6 #> 11665      2         2  24       62594   E5     6 #> 11666      2         2  24       62594   N1     2 #> 11667      2         2  24       62594   N2     3 #> 11668      2         2  24       62594   N3     4 #> 11669      2         2  24       62594   N4     1 #> 11670      2         2  24       62594   N5     2 #> 11671      2         2  24       62594   O1     6 #> 11672      2         2  24       62594   O2     6 #> 11673      2         2  24       62594   O3     5 #> 11674      2         2  24       62594   O4     4 #> 11675      2         2  24       62594   O5     2 #> 11676      2         5  60       62597   A1     2 #> 11677      2         5  60       62597   A2     5 #> 11678      2         5  60       62597   A3     5 #> 11679      2         5  60       62597   A4     5 #> 11680      2         5  60       62597   A5     4 #> 11681      2         5  60       62597   C1     6 #> 11682      2         5  60       62597   C2     2 #> 11683      2         5  60       62597   C3     4 #> 11684      2         5  60       62597   C4     2 #> 11685      2         5  60       62597   C5     5 #> 11686      2         5  60       62597   E1     1 #> 11687      2         5  60       62597   E2     2 #> 11688      2         5  60       62597   E3     5 #> 11689      2         5  60       62597   E4     5 #> 11690      2         5  60       62597   E5     5 #> 11691      2         5  60       62597   N1     4 #> 11692      2         5  60       62597   N2     5 #> 11693      2         5  60       62597   N3     5 #> 11694      2         5  60       62597   N4     5 #> 11695      2         5  60       62597   N5     4 #> 11696      2         5  60       62597   O1     6 #> 11697      2         5  60       62597   O2     2 #> 11698      2         5  60       62597   O3     5 #> 11699      2         5  60       62597   O4     6 #> 11700      2         5  60       62597   O5     1 #> 11701      2         3  52       62599   A1     1 #> 11702      2         3  52       62599   A2     5 #> 11703      2         3  52       62599   A3     5 #> 11704      2         3  52       62599   A4     6 #> 11705      2         3  52       62599   A5     6 #> 11706      2         3  52       62599   C1     6 #> 11707      2         3  52       62599   C2     1 #> 11708      2         3  52       62599   C3     5 #> 11709      2         3  52       62599   C4     3 #> 11710      2         3  52       62599   C5     1 #> 11711      2         3  52       62599   E1     6 #> 11712      2         3  52       62599   E2     6 #> 11713      2         3  52       62599   E3     1 #> 11714      2         3  52       62599   E4     5 #> 11715      2         3  52       62599   E5     1 #> 11716      2         3  52       62599   N1     4 #> 11717      2         3  52       62599   N2     5 #> 11718      2         3  52       62599   N3     5 #> 11719      2         3  52       62599   N4     6 #> 11720      2         3  52       62599   N5     1 #> 11721      2         3  52       62599   O1     6 #> 11722      2         3  52       62599   O2     5 #> 11723      2         3  52       62599   O3     1 #> 11724      2         3  52       62599   O4     6 #> 11725      2         3  52       62599   O5     4 #> 11726      2         3  21       62604   A1     1 #> 11727      2         3  21       62604   A2     5 #> 11728      2         3  21       62604   A3     6 #> 11729      2         3  21       62604   A4     6 #> 11730      2         3  21       62604   A5     6 #> 11731      2         3  21       62604   C1     6 #> 11732      2         3  21       62604   C2     5 #> 11733      2         3  21       62604   C3     6 #> 11734      2         3  21       62604   C4     1 #> 11735      2         3  21       62604   C5     2 #> 11736      2         3  21       62604   E1     1 #> 11737      2         3  21       62604   E2     1 #> 11738      2         3  21       62604   E3     5 #> 11739      2         3  21       62604   E4     6 #> 11740      2         3  21       62604   E5    NA #> 11741      2         3  21       62604   N1     2 #> 11742      2         3  21       62604   N2     4 #> 11743      2         3  21       62604   N3     2 #> 11744      2         3  21       62604   N4     3 #> 11745      2         3  21       62604   N5     5 #> 11746      2         3  21       62604   O1     6 #> 11747      2         3  21       62604   O2     4 #> 11748      2         3  21       62604   O3     6 #> 11749      2         3  21       62604   O4     6 #> 11750      2         3  21       62604   O5     1 #> 11751      2         3  20       62605   A1     3 #> 11752      2         3  20       62605   A2     5 #> 11753      2         3  20       62605   A3     5 #> 11754      2         3  20       62605   A4     6 #> 11755      2         3  20       62605   A5     5 #> 11756      2         3  20       62605   C1     5 #> 11757      2         3  20       62605   C2     5 #> 11758      2         3  20       62605   C3     3 #> 11759      2         3  20       62605   C4     4 #> 11760      2         3  20       62605   C5     1 #> 11761      2         3  20       62605   E1     4 #> 11762      2         3  20       62605   E2     3 #> 11763      2         3  20       62605   E3     4 #> 11764      2         3  20       62605   E4     4 #> 11765      2         3  20       62605   E5     5 #> 11766      2         3  20       62605   N1     5 #> 11767      2         3  20       62605   N2     6 #> 11768      2         3  20       62605   N3     6 #> 11769      2         3  20       62605   N4     2 #> 11770      2         3  20       62605   N5     2 #> 11771      2         3  20       62605   O1     5 #> 11772      2         3  20       62605   O2     2 #> 11773      2         3  20       62605   O3     5 #> 11774      2         3  20       62605   O4     6 #> 11775      2         3  20       62605   O5     3 #> 11776      2         3  25       62606   A1     2 #> 11777      2         3  25       62606   A2     6 #> 11778      2         3  25       62606   A3     6 #> 11779      2         3  25       62606   A4     6 #> 11780      2         3  25       62606   A5     6 #> 11781      2         3  25       62606   C1     5 #> 11782      2         3  25       62606   C2     5 #> 11783      2         3  25       62606   C3     3 #> 11784      2         3  25       62606   C4     1 #> 11785      2         3  25       62606   C5     1 #> 11786      2         3  25       62606   E1     1 #> 11787      2         3  25       62606   E2     4 #> 11788      2         3  25       62606   E3     5 #> 11789      2         3  25       62606   E4     6 #> 11790      2         3  25       62606   E5     4 #> 11791      2         3  25       62606   N1     1 #> 11792      2         3  25       62606   N2     1 #> 11793      2         3  25       62606   N3     1 #> 11794      2         3  25       62606   N4     1 #> 11795      2         3  25       62606   N5     1 #> 11796      2         3  25       62606   O1     5 #> 11797      2         3  25       62606   O2     1 #> 11798      2         3  25       62606   O3     5 #> 11799      2         3  25       62606   O4     3 #> 11800      2         3  25       62606   O5     4 #> 11801      2         3  24       62610   A1     1 #> 11802      2         3  24       62610   A2     6 #> 11803      2         3  24       62610   A3     5 #> 11804      2         3  24       62610   A4     6 #> 11805      2         3  24       62610   A5     6 #> 11806      2         3  24       62610   C1     5 #> 11807      2         3  24       62610   C2     6 #> 11808      2         3  24       62610   C3     5 #> 11809      2         3  24       62610   C4     1 #> 11810      2         3  24       62610   C5     1 #> 11811      2         3  24       62610   E1     3 #> 11812      2         3  24       62610   E2     4 #> 11813      2         3  24       62610   E3     3 #> 11814      2         3  24       62610   E4     6 #> 11815      2         3  24       62610   E5     4 #> 11816      2         3  24       62610   N1     3 #> 11817      2         3  24       62610   N2     4 #> 11818      2         3  24       62610   N3     4 #> 11819      2         3  24       62610   N4     3 #> 11820      2         3  24       62610   N5     5 #> 11821      2         3  24       62610   O1     3 #> 11822      2         3  24       62610   O2     3 #> 11823      2         3  24       62610   O3     4 #> 11824      2         3  24       62610   O4     5 #> 11825      2         3  24       62610   O5     4 #> 11826      2         2  25       62611   A1     2 #> 11827      2         2  25       62611   A2     2 #> 11828      2         2  25       62611   A3     4 #> 11829      2         2  25       62611   A4     3 #> 11830      2         2  25       62611   A5     2 #> 11831      2         2  25       62611   C1     4 #> 11832      2         2  25       62611   C2     3 #> 11833      2         2  25       62611   C3     4 #> 11834      2         2  25       62611   C4     3 #> 11835      2         2  25       62611   C5     4 #> 11836      2         2  25       62611   E1     4 #> 11837      2         2  25       62611   E2     2 #> 11838      2         2  25       62611   E3     1 #> 11839      2         2  25       62611   E4     5 #> 11840      2         2  25       62611   E5     4 #> 11841      2         2  25       62611   N1     4 #> 11842      2         2  25       62611   N2     4 #> 11843      2         2  25       62611   N3     4 #> 11844      2         2  25       62611   N4     5 #> 11845      2         2  25       62611   N5     1 #> 11846      2         2  25       62611   O1     3 #> 11847      2         2  25       62611   O2     2 #> 11848      2         2  25       62611   O3     2 #> 11849      2         2  25       62611   O4     4 #> 11850      2         2  25       62611   O5     4 #> 11851      2         2  30       62612   A1     2 #> 11852      2         2  30       62612   A2     4 #> 11853      2         2  30       62612   A3     4 #> 11854      2         2  30       62612   A4     3 #> 11855      2         2  30       62612   A5     3 #> 11856      2         2  30       62612   C1     4 #> 11857      2         2  30       62612   C2     2 #> 11858      2         2  30       62612   C3     4 #> 11859      2         2  30       62612   C4     4 #> 11860      2         2  30       62612   C5     2 #> 11861      2         2  30       62612   E1     5 #> 11862      2         2  30       62612   E2     3 #> 11863      2         2  30       62612   E3     3 #> 11864      2         2  30       62612   E4     4 #> 11865      2         2  30       62612   E5     4 #> 11866      2         2  30       62612   N1     2 #> 11867      2         2  30       62612   N2     3 #> 11868      2         2  30       62612   N3     3 #> 11869      2         2  30       62612   N4     3 #> 11870      2         2  30       62612   N5     3 #> 11871      2         2  30       62612   O1     4 #> 11872      2         2  30       62612   O2     2 #> 11873      2         2  30       62612   O3     3 #> 11874      2         2  30       62612   O4     3 #> 11875      2         2  30       62612   O5     3 #> 11876      2         3  19       62613   A1     4 #> 11877      2         3  19       62613   A2     2 #> 11878      2         3  19       62613   A3     1 #> 11879      2         3  19       62613   A4     3 #> 11880      2         3  19       62613   A5     1 #> 11881      2         3  19       62613   C1     3 #> 11882      2         3  19       62613   C2     1 #> 11883      2         3  19       62613   C3     2 #> 11884      2         3  19       62613   C4     4 #> 11885      2         3  19       62613   C5     2 #> 11886      2         3  19       62613   E1     3 #> 11887      2         3  19       62613   E2     4 #> 11888      2         3  19       62613   E3     1 #> 11889      2         3  19       62613   E4     3 #> 11890      2         3  19       62613   E5     2 #> 11891      2         3  19       62613   N1     4 #> 11892      2         3  19       62613   N2     5 #> 11893      2         3  19       62613   N3     4 #> 11894      2         3  19       62613   N4     3 #> 11895      2         3  19       62613   N5     3 #> 11896      2         3  19       62613   O1     2 #> 11897      2         3  19       62613   O2     4 #> 11898      2         3  19       62613   O3     2 #> 11899      2         3  19       62613   O4     6 #> 11900      2         3  19       62613   O5     2 #> 11901      1         3  17       62615   A1     6 #> 11902      1         3  17       62615   A2     5 #> 11903      1         3  17       62615   A3     5 #> 11904      1         3  17       62615   A4     6 #> 11905      1         3  17       62615   A5     6 #> 11906      1         3  17       62615   C1     3 #> 11907      1         3  17       62615   C2     1 #> 11908      1         3  17       62615   C3     4 #> 11909      1         3  17       62615   C4     5 #> 11910      1         3  17       62615   C5     2 #> 11911      1         3  17       62615   E1     3 #> 11912      1         3  17       62615   E2     1 #> 11913      1         3  17       62615   E3     6 #> 11914      1         3  17       62615   E4     5 #> 11915      1         3  17       62615   E5     3 #> 11916      1         3  17       62615   N1     1 #> 11917      1         3  17       62615   N2     1 #> 11918      1         3  17       62615   N3     4 #> 11919      1         3  17       62615   N4     3 #> 11920      1         3  17       62615   N5     1 #> 11921      1         3  17       62615   O1     6 #> 11922      1         3  17       62615   O2     6 #> 11923      1         3  17       62615   O3     6 #> 11924      1         3  17       62615   O4     6 #> 11925      1         3  17       62615   O5     3 #> 11926      2         3  52       62617   A1     2 #> 11927      2         3  52       62617   A2     6 #> 11928      2         3  52       62617   A3     6 #> 11929      2         3  52       62617   A4     6 #> 11930      2         3  52       62617   A5     6 #> 11931      2         3  52       62617   C1     6 #> 11932      2         3  52       62617   C2     6 #> 11933      2         3  52       62617   C3     6 #> 11934      2         3  52       62617   C4     1 #> 11935      2         3  52       62617   C5     1 #> 11936      2         3  52       62617   E1     3 #> 11937      2         3  52       62617   E2     6 #> 11938      2         3  52       62617   E3     6 #> 11939      2         3  52       62617   E4     1 #> 11940      2         3  52       62617   E5     6 #> 11941      2         3  52       62617   N1     4 #> 11942      2         3  52       62617   N2     4 #> 11943      2         3  52       62617   N3     1 #> 11944      2         3  52       62617   N4     1 #> 11945      2         3  52       62617   N5     3 #> 11946      2         3  52       62617   O1     6 #> 11947      2         3  52       62617   O2     1 #> 11948      2         3  52       62617   O3     6 #> 11949      2         3  52       62617   O4     2 #> 11950      2         3  52       62617   O5     1 #> 11951      2         3  27       62618   A1     2 #> 11952      2         3  27       62618   A2     5 #> 11953      2         3  27       62618   A3     6 #> 11954      2         3  27       62618   A4     5 #> 11955      2         3  27       62618   A5     4 #> 11956      2         3  27       62618   C1     4 #> 11957      2         3  27       62618   C2     3 #> 11958      2         3  27       62618   C3     4 #> 11959      2         3  27       62618   C4     5 #> 11960      2         3  27       62618   C5     4 #> 11961      2         3  27       62618   E1     1 #> 11962      2         3  27       62618   E2     2 #> 11963      2         3  27       62618   E3     4 #> 11964      2         3  27       62618   E4     6 #> 11965      2         3  27       62618   E5     5 #> 11966      2         3  27       62618   N1     6 #> 11967      2         3  27       62618   N2     6 #> 11968      2         3  27       62618   N3     6 #> 11969      2         3  27       62618   N4     3 #> 11970      2         3  27       62618   N5     2 #> 11971      2         3  27       62618   O1     4 #> 11972      2         3  27       62618   O2     3 #> 11973      2         3  27       62618   O3     2 #> 11974      2         3  27       62618   O4     3 #> 11975      2         3  27       62618   O5     5 #> 11976      2         3  55       62622   A1     1 #> 11977      2         3  55       62622   A2     6 #> 11978      2         3  55       62622   A3     6 #> 11979      2         3  55       62622   A4     6 #> 11980      2         3  55       62622   A5     6 #> 11981      2         3  55       62622   C1     6 #> 11982      2         3  55       62622   C2     6 #> 11983      2         3  55       62622   C3     5 #> 11984      2         3  55       62622   C4     1 #> 11985      2         3  55       62622   C5     4 #> 11986      2         3  55       62622   E1     1 #> 11987      2         3  55       62622   E2     1 #> 11988      2         3  55       62622   E3     6 #> 11989      2         3  55       62622   E4     6 #> 11990      2         3  55       62622   E5     6 #> 11991      2         3  55       62622   N1     4 #> 11992      2         3  55       62622   N2     6 #> 11993      2         3  55       62622   N3     5 #> 11994      2         3  55       62622   N4     2 #> 11995      2         3  55       62622   N5     4 #> 11996      2         3  55       62622   O1     6 #> 11997      2         3  55       62622   O2     5 #> 11998      2         3  55       62622   O3     6 #> 11999      2         3  55       62622   O4     6 #> 12000      2         3  55       62622   O5     1 #> 12001      2         5  39       62623   A1     1 #> 12002      2         5  39       62623   A2     6 #> 12003      2         5  39       62623   A3     2 #> 12004      2         5  39       62623   A4     6 #> 12005      2         5  39       62623   A5     6 #> 12006      2         5  39       62623   C1     5 #> 12007      2         5  39       62623   C2     4 #> 12008      2         5  39       62623   C3     2 #> 12009      2         5  39       62623   C4     1 #> 12010      2         5  39       62623   C5     2 #> 12011      2         5  39       62623   E1     1 #> 12012      2         5  39       62623   E2     1 #> 12013      2         5  39       62623   E3     5 #> 12014      2         5  39       62623   E4     6 #> 12015      2         5  39       62623   E5     5 #> 12016      2         5  39       62623   N1     5 #> 12017      2         5  39       62623   N2     4 #> 12018      2         5  39       62623   N3     4 #> 12019      2         5  39       62623   N4     4 #> 12020      2         5  39       62623   N5     5 #> 12021      2         5  39       62623   O1     6 #> 12022      2         5  39       62623   O2     1 #> 12023      2         5  39       62623   O3     6 #> 12024      2         5  39       62623   O4     5 #> 12025      2         5  39       62623   O5     1 #> 12026      2         3  39       62625   A1     1 #> 12027      2         3  39       62625   A2     6 #> 12028      2         3  39       62625   A3     6 #> 12029      2         3  39       62625   A4     6 #> 12030      2         3  39       62625   A5     6 #> 12031      2         3  39       62625   C1     6 #> 12032      2         3  39       62625   C2     5 #> 12033      2         3  39       62625   C3     5 #> 12034      2         3  39       62625   C4     1 #> 12035      2         3  39       62625   C5     2 #> 12036      2         3  39       62625   E1     2 #> 12037      2         3  39       62625   E2     1 #> 12038      2         3  39       62625   E3     5 #> 12039      2         3  39       62625   E4     5 #> 12040      2         3  39       62625   E5     6 #> 12041      2         3  39       62625   N1     2 #> 12042      2         3  39       62625   N2     4 #> 12043      2         3  39       62625   N3     2 #> 12044      2         3  39       62625   N4     2 #> 12045      2         3  39       62625   N5     4 #> 12046      2         3  39       62625   O1     6 #> 12047      2         3  39       62625   O2     2 #> 12048      2         3  39       62625   O3     6 #> 12049      2         3  39       62625   O4     6 #> 12050      2         3  39       62625   O5     2 #> 12051      2         3  39       62627   A1     1 #> 12052      2         3  39       62627   A2     3 #> 12053      2         3  39       62627   A3     2 #> 12054      2         3  39       62627   A4     6 #> 12055      2         3  39       62627   A5     3 #> 12056      2         3  39       62627   C1     5 #> 12057      2         3  39       62627   C2     5 #> 12058      2         3  39       62627   C3     5 #> 12059      2         3  39       62627   C4     2 #> 12060      2         3  39       62627   C5     4 #> 12061      2         3  39       62627   E1     5 #> 12062      2         3  39       62627   E2     6 #> 12063      2         3  39       62627   E3     1 #> 12064      2         3  39       62627   E4     3 #> 12065      2         3  39       62627   E5     3 #> 12066      2         3  39       62627   N1     3 #> 12067      2         3  39       62627   N2     4 #> 12068      2         3  39       62627   N3     1 #> 12069      2         3  39       62627   N4     4 #> 12070      2         3  39       62627   N5     1 #> 12071      2         3  39       62627   O1     2 #> 12072      2         3  39       62627   O2     6 #> 12073      2         3  39       62627   O3     1 #> 12074      2         3  39       62627   O4     5 #> 12075      2         3  39       62627   O5     3 #> 12076      1         3  18       62635   A1     3 #> 12077      1         3  18       62635   A2     5 #> 12078      1         3  18       62635   A3     2 #> 12079      1         3  18       62635   A4     5 #> 12080      1         3  18       62635   A5     5 #> 12081      1         3  18       62635   C1     4 #> 12082      1         3  18       62635   C2     4 #> 12083      1         3  18       62635   C3    NA #> 12084      1         3  18       62635   C4     4 #> 12085      1         3  18       62635   C5     5 #> 12086      1         3  18       62635   E1     4 #> 12087      1         3  18       62635   E2     2 #> 12088      1         3  18       62635   E3     5 #> 12089      1         3  18       62635   E4     3 #> 12090      1         3  18       62635   E5     4 #> 12091      1         3  18       62635   N1     2 #> 12092      1         3  18       62635   N2     4 #> 12093      1         3  18       62635   N3     5 #> 12094      1         3  18       62635   N4     4 #> 12095      1         3  18       62635   N5     4 #> 12096      1         3  18       62635   O1     4 #> 12097      1         3  18       62635   O2     3 #> 12098      1         3  18       62635   O3     5 #> 12099      1         3  18       62635   O4     5 #> 12100      1         3  18       62635   O5     2 #> 12101      2         3  51       62638   A1     2 #> 12102      2         3  51       62638   A2     6 #> 12103      2         3  51       62638   A3     6 #> 12104      2         3  51       62638   A4     2 #> 12105      2         3  51       62638   A5     6 #> 12106      2         3  51       62638   C1     6 #> 12107      2         3  51       62638   C2     3 #> 12108      2         3  51       62638   C3     5 #> 12109      2         3  51       62638   C4     1 #> 12110      2         3  51       62638   C5     2 #> 12111      2         3  51       62638   E1     1 #> 12112      2         3  51       62638   E2     1 #> 12113      2         3  51       62638   E3     5 #> 12114      2         3  51       62638   E4     5 #> 12115      2         3  51       62638   E5     6 #> 12116      2         3  51       62638   N1     1 #> 12117      2         3  51       62638   N2     2 #> 12118      2         3  51       62638   N3     2 #> 12119      2         3  51       62638   N4     1 #> 12120      2         3  51       62638   N5     1 #> 12121      2         3  51       62638   O1     5 #> 12122      2         3  51       62638   O2     1 #> 12123      2         3  51       62638   O3     6 #> 12124      2         3  51       62638   O4     4 #> 12125      2         3  51       62638   O5     1 #> 12126      2         3  25       62640   A1     1 #> 12127      2         3  25       62640   A2     5 #> 12128      2         3  25       62640   A3     6 #> 12129      2         3  25       62640   A4     6 #> 12130      2         3  25       62640   A5     6 #> 12131      2         3  25       62640   C1     6 #> 12132      2         3  25       62640   C2     5 #> 12133      2         3  25       62640   C3     5 #> 12134      2         3  25       62640   C4     5 #> 12135      2         3  25       62640   C5     1 #> 12136      2         3  25       62640   E1     6 #> 12137      2         3  25       62640   E2     5 #> 12138      2         3  25       62640   E3     5 #> 12139      2         3  25       62640   E4     5 #> 12140      2         3  25       62640   E5     2 #> 12141      2         3  25       62640   N1     1 #> 12142      2         3  25       62640   N2     1 #> 12143      2         3  25       62640   N3     4 #> 12144      2         3  25       62640   N4     1 #> 12145      2         3  25       62640   N5     5 #> 12146      2         3  25       62640   O1     6 #> 12147      2         3  25       62640   O2     6 #> 12148      2         3  25       62640   O3     6 #> 12149      2         3  25       62640   O4     6 #> 12150      2         3  25       62640   O5     5 #> 12151      2         1  15       62642   A1     2 #> 12152      2         1  15       62642   A2     5 #> 12153      2         1  15       62642   A3     5 #> 12154      2         1  15       62642   A4     6 #> 12155      2         1  15       62642   A5     5 #> 12156      2         1  15       62642   C1     6 #> 12157      2         1  15       62642   C2     5 #> 12158      2         1  15       62642   C3     5 #> 12159      2         1  15       62642   C4     3 #> 12160      2         1  15       62642   C5     3 #> 12161      2         1  15       62642   E1     2 #> 12162      2         1  15       62642   E2     3 #> 12163      2         1  15       62642   E3     4 #> 12164      2         1  15       62642   E4     5 #> 12165      2         1  15       62642   E5     5 #> 12166      2         1  15       62642   N1     1 #> 12167      2         1  15       62642   N2     2 #> 12168      2         1  15       62642   N3     2 #> 12169      2         1  15       62642   N4     2 #> 12170      2         1  15       62642   N5     2 #> 12171      2         1  15       62642   O1     6 #> 12172      2         1  15       62642   O2     5 #> 12173      2         1  15       62642   O3     5 #> 12174      2         1  15       62642   O4     4 #> 12175      2         1  15       62642   O5     1 #> 12176      2         2  42       62643   A1     1 #> 12177      2         2  42       62643   A2     4 #> 12178      2         2  42       62643   A3     4 #> 12179      2         2  42       62643   A4     6 #> 12180      2         2  42       62643   A5     4 #> 12181      2         2  42       62643   C1     3 #> 12182      2         2  42       62643   C2     4 #> 12183      2         2  42       62643   C3     2 #> 12184      2         2  42       62643   C4     2 #> 12185      2         2  42       62643   C5     2 #> 12186      2         2  42       62643   E1     2 #> 12187      2         2  42       62643   E2     4 #> 12188      2         2  42       62643   E3     4 #> 12189      2         2  42       62643   E4     4 #> 12190      2         2  42       62643   E5     4 #> 12191      2         2  42       62643   N1     4 #> 12192      2         2  42       62643   N2     4 #> 12193      2         2  42       62643   N3     5 #> 12194      2         2  42       62643   N4     4 #> 12195      2         2  42       62643   N5     4 #> 12196      2         2  42       62643   O1     3 #> 12197      2         2  42       62643   O2     4 #> 12198      2         2  42       62643   O3     3 #> 12199      2         2  42       62643   O4     5 #> 12200      2         2  42       62643   O5     3 #> 12201      2         4  24       62644   A1     2 #> 12202      2         4  24       62644   A2     6 #> 12203      2         4  24       62644   A3     5 #> 12204      2         4  24       62644   A4     6 #> 12205      2         4  24       62644   A5     4 #> 12206      2         4  24       62644   C1     5 #> 12207      2         4  24       62644   C2     6 #> 12208      2         4  24       62644   C3     5 #> 12209      2         4  24       62644   C4     1 #> 12210      2         4  24       62644   C5     2 #> 12211      2         4  24       62644   E1     1 #> 12212      2         4  24       62644   E2     4 #> 12213      2         4  24       62644   E3     5 #> 12214      2         4  24       62644   E4     5 #> 12215      2         4  24       62644   E5     5 #> 12216      2         4  24       62644   N1     4 #> 12217      2         4  24       62644   N2     4 #> 12218      2         4  24       62644   N3     5 #> 12219      2         4  24       62644   N4     5 #> 12220      2         4  24       62644   N5     6 #> 12221      2         4  24       62644   O1     4 #> 12222      2         4  24       62644   O2     1 #> 12223      2         4  24       62644   O3     6 #> 12224      2         4  24       62644   O4     6 #> 12225      2         4  24       62644   O5     1 #> 12226      2         3  46       62645   A1     1 #> 12227      2         3  46       62645   A2     5 #> 12228      2         3  46       62645   A3     5 #> 12229      2         3  46       62645   A4     6 #> 12230      2         3  46       62645   A5     5 #> 12231      2         3  46       62645   C1     5 #> 12232      2         3  46       62645   C2     5 #> 12233      2         3  46       62645   C3     5 #> 12234      2         3  46       62645   C4     1 #> 12235      2         3  46       62645   C5     3 #> 12236      2         3  46       62645   E1     6 #> 12237      2         3  46       62645   E2     4 #> 12238      2         3  46       62645   E3     2 #> 12239      2         3  46       62645   E4     5 #> 12240      2         3  46       62645   E5     5 #> 12241      2         3  46       62645   N1     2 #> 12242      2         3  46       62645   N2     2 #> 12243      2         3  46       62645   N3     4 #> 12244      2         3  46       62645   N4     2 #> 12245      2         3  46       62645   N5     2 #> 12246      2         3  46       62645   O1     5 #> 12247      2         3  46       62645   O2     2 #> 12248      2         3  46       62645   O3     5 #> 12249      2         3  46       62645   O4     3 #> 12250      2         3  46       62645   O5     1 #> 12251      2         3  37       62646   A1     1 #> 12252      2         3  37       62646   A2     6 #> 12253      2         3  37       62646   A3     6 #> 12254      2         3  37       62646   A4     3 #> 12255      2         3  37       62646   A5     6 #> 12256      2         3  37       62646   C1     3 #> 12257      2         3  37       62646   C2     4 #> 12258      2         3  37       62646   C3     6 #> 12259      2         3  37       62646   C4     5 #> 12260      2         3  37       62646   C5     2 #> 12261      2         3  37       62646   E1     6 #> 12262      2         3  37       62646   E2     1 #> 12263      2         3  37       62646   E3     6 #> 12264      2         3  37       62646   E4     6 #> 12265      2         3  37       62646   E5     6 #> 12266      2         3  37       62646   N1     2 #> 12267      2         3  37       62646   N2     5 #> 12268      2         3  37       62646   N3     2 #> 12269      2         3  37       62646   N4     3 #> 12270      2         3  37       62646   N5     4 #> 12271      2         3  37       62646   O1     4 #> 12272      2         3  37       62646   O2     2 #> 12273      2         3  37       62646   O3     6 #> 12274      2         3  37       62646   O4     3 #> 12275      2         3  37       62646   O5     1 #> 12276      2         3  24       62647   A1     4 #> 12277      2         3  24       62647   A2     5 #> 12278      2         3  24       62647   A3     5 #> 12279      2         3  24       62647   A4     6 #> 12280      2         3  24       62647   A5     5 #> 12281      2         3  24       62647   C1     5 #> 12282      2         3  24       62647   C2     5 #> 12283      2         3  24       62647   C3     5 #> 12284      2         3  24       62647   C4     2 #> 12285      2         3  24       62647   C5     2 #> 12286      2         3  24       62647   E1     4 #> 12287      2         3  24       62647   E2     2 #> 12288      2         3  24       62647   E3     2 #> 12289      2         3  24       62647   E4     6 #> 12290      2         3  24       62647   E5     2 #> 12291      2         3  24       62647   N1     1 #> 12292      2         3  24       62647   N2     2 #> 12293      2         3  24       62647   N3     5 #> 12294      2         3  24       62647   N4     2 #> 12295      2         3  24       62647   N5     4 #> 12296      2         3  24       62647   O1     2 #> 12297      2         3  24       62647   O2     6 #> 12298      2         3  24       62647   O3     3 #> 12299      2         3  24       62647   O4     3 #> 12300      2         3  24       62647   O5     3 #> 12301      1         3  20       62648   A1     1 #> 12302      1         3  20       62648   A2     6 #> 12303      1         3  20       62648   A3     5 #> 12304      1         3  20       62648   A4     5 #> 12305      1         3  20       62648   A5     2 #> 12306      1         3  20       62648   C1     1 #> 12307      1         3  20       62648   C2     4 #> 12308      1         3  20       62648   C3     6 #> 12309      1         3  20       62648   C4     4 #> 12310      1         3  20       62648   C5    NA #> 12311      1         3  20       62648   E1     3 #> 12312      1         3  20       62648   E2     1 #> 12313      1         3  20       62648   E3     1 #> 12314      1         3  20       62648   E4     3 #> 12315      1         3  20       62648   E5     5 #> 12316      1         3  20       62648   N1     4 #> 12317      1         3  20       62648   N2     4 #> 12318      1         3  20       62648   N3     4 #> 12319      1         3  20       62648   N4     1 #> 12320      1         3  20       62648   N5     4 #> 12321      1         3  20       62648   O1     6 #> 12322      1         3  20       62648   O2     2 #> 12323      1         3  20       62648   O3     4 #> 12324      1         3  20       62648   O4     3 #> 12325      1         3  20       62648   O5     2 #> 12326      2         4  50       62650   A1     2 #> 12327      2         4  50       62650   A2     5 #> 12328      2         4  50       62650   A3     1 #> 12329      2         4  50       62650   A4     6 #> 12330      2         4  50       62650   A5     6 #> 12331      2         4  50       62650   C1     6 #> 12332      2         4  50       62650   C2     6 #> 12333      2         4  50       62650   C3     5 #> 12334      2         4  50       62650   C4     1 #> 12335      2         4  50       62650   C5     1 #> 12336      2         4  50       62650   E1     2 #> 12337      2         4  50       62650   E2     1 #> 12338      2         4  50       62650   E3     6 #> 12339      2         4  50       62650   E4     5 #> 12340      2         4  50       62650   E5     6 #> 12341      2         4  50       62650   N1     2 #> 12342      2         4  50       62650   N2     4 #> 12343      2         4  50       62650   N3     2 #> 12344      2         4  50       62650   N4     2 #> 12345      2         4  50       62650   N5     5 #> 12346      2         4  50       62650   O1     6 #> 12347      2         4  50       62650   O2     1 #> 12348      2         4  50       62650   O3     5 #> 12349      2         4  50       62650   O4     4 #> 12350      2         4  50       62650   O5     1 #> 12351      2         3  46       62652   A1     1 #> 12352      2         3  46       62652   A2     6 #> 12353      2         3  46       62652   A3     5 #> 12354      2         3  46       62652   A4     3 #> 12355      2         3  46       62652   A5     4 #> 12356      2         3  46       62652   C1     4 #> 12357      2         3  46       62652   C2     3 #> 12358      2         3  46       62652   C3     3 #> 12359      2         3  46       62652   C4     1 #> 12360      2         3  46       62652   C5     1 #> 12361      2         3  46       62652   E1     6 #> 12362      2         3  46       62652   E2     3 #> 12363      2         3  46       62652   E3     3 #> 12364      2         3  46       62652   E4     3 #> 12365      2         3  46       62652   E5     5 #> 12366      2         3  46       62652   N1     1 #> 12367      2         3  46       62652   N2     3 #> 12368      2         3  46       62652   N3    NA #> 12369      2         3  46       62652   N4     5 #> 12370      2         3  46       62652   N5     3 #> 12371      2         3  46       62652   O1     6 #> 12372      2         3  46       62652   O2     3 #> 12373      2         3  46       62652   O3     3 #> 12374      2         3  46       62652   O4     3 #> 12375      2         3  46       62652   O5     4 #> 12376      1         2  23       62653   A1     4 #> 12377      1         2  23       62653   A2     3 #> 12378      1         2  23       62653   A3     5 #> 12379      1         2  23       62653   A4     5 #> 12380      1         2  23       62653   A5     5 #> 12381      1         2  23       62653   C1     4 #> 12382      1         2  23       62653   C2     6 #> 12383      1         2  23       62653   C3     5 #> 12384      1         2  23       62653   C4     2 #> 12385      1         2  23       62653   C5     3 #> 12386      1         2  23       62653   E1     4 #> 12387      1         2  23       62653   E2     1 #> 12388      1         2  23       62653   E3     5 #> 12389      1         2  23       62653   E4     6 #> 12390      1         2  23       62653   E5     6 #> 12391      1         2  23       62653   N1     5 #> 12392      1         2  23       62653   N2     6 #> 12393      1         2  23       62653   N3     5 #> 12394      1         2  23       62653   N4     4 #> 12395      1         2  23       62653   N5     2 #> 12396      1         2  23       62653   O1     6 #> 12397      1         2  23       62653   O2     6 #> 12398      1         2  23       62653   O3     5 #> 12399      1         2  23       62653   O4     5 #> 12400      1         2  23       62653   O5     1 #> 12401      2         3  18       62654   A1     4 #> 12402      2         3  18       62654   A2     4 #> 12403      2         3  18       62654   A3     5 #> 12404      2         3  18       62654   A4     6 #> 12405      2         3  18       62654   A5     5 #> 12406      2         3  18       62654   C1     5 #> 12407      2         3  18       62654   C2     6 #> 12408      2         3  18       62654   C3     6 #> 12409      2         3  18       62654   C4     1 #> 12410      2         3  18       62654   C5     2 #> 12411      2         3  18       62654   E1     3 #> 12412      2         3  18       62654   E2     4 #> 12413      2         3  18       62654   E3     5 #> 12414      2         3  18       62654   E4     6 #> 12415      2         3  18       62654   E5     6 #> 12416      2         3  18       62654   N1     3 #> 12417      2         3  18       62654   N2     3 #> 12418      2         3  18       62654   N3     1 #> 12419      2         3  18       62654   N4     4 #> 12420      2         3  18       62654   N5     1 #> 12421      2         3  18       62654   O1     5 #> 12422      2         3  18       62654   O2     1 #> 12423      2         3  18       62654   O3     5 #> 12424      2         3  18       62654   O4     5 #> 12425      2         3  18       62654   O5     2 #> 12426      1         2  21       62657   A1     2 #> 12427      1         2  21       62657   A2     6 #> 12428      1         2  21       62657   A3     4 #> 12429      1         2  21       62657   A4     2 #> 12430      1         2  21       62657   A5     5 #> 12431      1         2  21       62657   C1     6 #> 12432      1         2  21       62657   C2     3 #> 12433      1         2  21       62657   C3     5 #> 12434      1         2  21       62657   C4     2 #> 12435      1         2  21       62657   C5     1 #> 12436      1         2  21       62657   E1     3 #> 12437      1         2  21       62657   E2     2 #> 12438      1         2  21       62657   E3     4 #> 12439      1         2  21       62657   E4     5 #> 12440      1         2  21       62657   E5     5 #> 12441      1         2  21       62657   N1     3 #> 12442      1         2  21       62657   N2     5 #> 12443      1         2  21       62657   N3     3 #> 12444      1         2  21       62657   N4     1 #> 12445      1         2  21       62657   N5     1 #> 12446      1         2  21       62657   O1     5 #> 12447      1         2  21       62657   O2     2 #> 12448      1         2  21       62657   O3     4 #> 12449      1         2  21       62657   O4     4 #> 12450      1         2  21       62657   O5     2 #> 12451      2         3  26       62662   A1     4 #> 12452      2         3  26       62662   A2     5 #> 12453      2         3  26       62662   A3     4 #> 12454      2         3  26       62662   A4     5 #> 12455      2         3  26       62662   A5     4 #> 12456      2         3  26       62662   C1     5 #> 12457      2         3  26       62662   C2     4 #> 12458      2         3  26       62662   C3     4 #> 12459      2         3  26       62662   C4     5 #> 12460      2         3  26       62662   C5     5 #> 12461      2         3  26       62662   E1     1 #> 12462      2         3  26       62662   E2     4 #> 12463      2         3  26       62662   E3     6 #> 12464      2         3  26       62662   E4     5 #> 12465      2         3  26       62662   E5     6 #> 12466      2         3  26       62662   N1     6 #> 12467      2         3  26       62662   N2     6 #> 12468      2         3  26       62662   N3     6 #> 12469      2         3  26       62662   N4     5 #> 12470      2         3  26       62662   N5     5 #> 12471      2         3  26       62662   O1     5 #> 12472      2         3  26       62662   O2     2 #> 12473      2         3  26       62662   O3     5 #> 12474      2         3  26       62662   O4     5 #> 12475      2         3  26       62662   O5     5 #> 12476      2         3  53       62664   A1     1 #> 12477      2         3  53       62664   A2     5 #> 12478      2         3  53       62664   A3     6 #> 12479      2         3  53       62664   A4     5 #> 12480      2         3  53       62664   A5     6 #> 12481      2         3  53       62664   C1     6 #> 12482      2         3  53       62664   C2     5 #> 12483      2         3  53       62664   C3     5 #> 12484      2         3  53       62664   C4     1 #> 12485      2         3  53       62664   C5     2 #> 12486      2         3  53       62664   E1     2 #> 12487      2         3  53       62664   E2     1 #> 12488      2         3  53       62664   E3     5 #> 12489      2         3  53       62664   E4     1 #> 12490      2         3  53       62664   E5     5 #> 12491      2         3  53       62664   N1     4 #> 12492      2         3  53       62664   N2     5 #> 12493      2         3  53       62664   N3     4 #> 12494      2         3  53       62664   N4     5 #> 12495      2         3  53       62664   N5     4 #> 12496      2         3  53       62664   O1     5 #> 12497      2         3  53       62664   O2     4 #> 12498      2         3  53       62664   O3     4 #> 12499      2         3  53       62664   O4     6 #> 12500      2         3  53       62664   O5     1 #> 12501      2         2  26       62665   A1     3 #> 12502      2         2  26       62665   A2     6 #> 12503      2         2  26       62665   A3     6 #> 12504      2         2  26       62665   A4     6 #> 12505      2         2  26       62665   A5     6 #> 12506      2         2  26       62665   C1     4 #> 12507      2         2  26       62665   C2     5 #> 12508      2         2  26       62665   C3     3 #> 12509      2         2  26       62665   C4     1 #> 12510      2         2  26       62665   C5     1 #> 12511      2         2  26       62665   E1     1 #> 12512      2         2  26       62665   E2     1 #> 12513      2         2  26       62665   E3     5 #> 12514      2         2  26       62665   E4     6 #> 12515      2         2  26       62665   E5     3 #> 12516      2         2  26       62665   N1     1 #> 12517      2         2  26       62665   N2     1 #> 12518      2         2  26       62665   N3     1 #> 12519      2         2  26       62665   N4     2 #> 12520      2         2  26       62665   N5     1 #> 12521      2         2  26       62665   O1     5 #> 12522      2         2  26       62665   O2     3 #> 12523      2         2  26       62665   O3     5 #> 12524      2         2  26       62665   O4     1 #> 12525      2         2  26       62665   O5     1 #> 12526      1         3  30       62667   A1     1 #> 12527      1         3  30       62667   A2     6 #> 12528      1         3  30       62667   A3     6 #> 12529      1         3  30       62667   A4     6 #> 12530      1         3  30       62667   A5     6 #> 12531      1         3  30       62667   C1     5 #> 12532      1         3  30       62667   C2     6 #> 12533      1         3  30       62667   C3     5 #> 12534      1         3  30       62667   C4     1 #> 12535      1         3  30       62667   C5     1 #> 12536      1         3  30       62667   E1     5 #> 12537      1         3  30       62667   E2     2 #> 12538      1         3  30       62667   E3     6 #> 12539      1         3  30       62667   E4     6 #> 12540      1         3  30       62667   E5     6 #> 12541      1         3  30       62667   N1     1 #> 12542      1         3  30       62667   N2     1 #> 12543      1         3  30       62667   N3     1 #> 12544      1         3  30       62667   N4     2 #> 12545      1         3  30       62667   N5     2 #> 12546      1         3  30       62667   O1     5 #> 12547      1         3  30       62667   O2     6 #> 12548      1         3  30       62667   O3     6 #> 12549      1         3  30       62667   O4     6 #> 12550      1         3  30       62667   O5     2 #> 12551      1         2  51       62668   A1     5 #> 12552      1         2  51       62668   A2     6 #> 12553      1         2  51       62668   A3     5 #> 12554      1         2  51       62668   A4     4 #> 12555      1         2  51       62668   A5     5 #> 12556      1         2  51       62668   C1     1 #> 12557      1         2  51       62668   C2     6 #> 12558      1         2  51       62668   C3     5 #> 12559      1         2  51       62668   C4     3 #> 12560      1         2  51       62668   C5     4 #> 12561      1         2  51       62668   E1     4 #> 12562      1         2  51       62668   E2     1 #> 12563      1         2  51       62668   E3     6 #> 12564      1         2  51       62668   E4     5 #> 12565      1         2  51       62668   E5     6 #> 12566      1         2  51       62668   N1     4 #> 12567      1         2  51       62668   N2     4 #> 12568      1         2  51       62668   N3     4 #> 12569      1         2  51       62668   N4     4 #> 12570      1         2  51       62668   N5    NA #> 12571      1         2  51       62668   O1     6 #> 12572      1         2  51       62668   O2     4 #> 12573      1         2  51       62668   O3     5 #> 12574      1         2  51       62668   O4     6 #> 12575      1         2  51       62668   O5     5 #> 12576      2         3  34       62669   A1     5 #> 12577      2         3  34       62669   A2     6 #> 12578      2         3  34       62669   A3     6 #> 12579      2         3  34       62669   A4     5 #> 12580      2         3  34       62669   A5     6 #> 12581      2         3  34       62669   C1     5 #> 12582      2         3  34       62669   C2     4 #> 12583      2         3  34       62669   C3     6 #> 12584      2         3  34       62669   C4     2 #> 12585      2         3  34       62669   C5     4 #> 12586      2         3  34       62669   E1     3 #> 12587      2         3  34       62669   E2     3 #> 12588      2         3  34       62669   E3     4 #> 12589      2         3  34       62669   E4     4 #> 12590      2         3  34       62669   E5     5 #> 12591      2         3  34       62669   N1     5 #> 12592      2         3  34       62669   N2     4 #> 12593      2         3  34       62669   N3     4 #> 12594      2         3  34       62669   N4     3 #> 12595      2         3  34       62669   N5     2 #> 12596      2         3  34       62669   O1     5 #> 12597      2         3  34       62669   O2     2 #> 12598      2         3  34       62669   O3     5 #> 12599      2         3  34       62669   O4     4 #> 12600      2         3  34       62669   O5     3 #> 12601      2         3  28       62670   A1     2 #> 12602      2         3  28       62670   A2     4 #> 12603      2         3  28       62670   A3     2 #> 12604      2         3  28       62670   A4     6 #> 12605      2         3  28       62670   A5     4 #> 12606      2         3  28       62670   C1     5 #> 12607      2         3  28       62670   C2     5 #> 12608      2         3  28       62670   C3     5 #> 12609      2         3  28       62670   C4     2 #> 12610      2         3  28       62670   C5     2 #> 12611      2         3  28       62670   E1     3 #> 12612      2         3  28       62670   E2     3 #> 12613      2         3  28       62670   E3     5 #> 12614      2         3  28       62670   E4     5 #> 12615      2         3  28       62670   E5     5 #> 12616      2         3  28       62670   N1     3 #> 12617      2         3  28       62670   N2     4 #> 12618      2         3  28       62670   N3     4 #> 12619      2         3  28       62670   N4     2 #> 12620      2         3  28       62670   N5     3 #> 12621      2         3  28       62670   O1     5 #> 12622      2         3  28       62670   O2     2 #> 12623      2         3  28       62670   O3     4 #> 12624      2         3  28       62670   O4     4 #> 12625      2         3  28       62670   O5     2 #> 12626      2         2  42       62673   A1     2 #> 12627      2         2  42       62673   A2     6 #> 12628      2         2  42       62673   A3     5 #> 12629      2         2  42       62673   A4     5 #> 12630      2         2  42       62673   A5     4 #> 12631      2         2  42       62673   C1     5 #> 12632      2         2  42       62673   C2     5 #> 12633      2         2  42       62673   C3     5 #> 12634      2         2  42       62673   C4    NA #> 12635      2         2  42       62673   C5     2 #> 12636      2         2  42       62673   E1     1 #> 12637      2         2  42       62673   E2     4 #> 12638      2         2  42       62673   E3     4 #> 12639      2         2  42       62673   E4     6 #> 12640      2         2  42       62673   E5     5 #> 12641      2         2  42       62673   N1     1 #> 12642      2         2  42       62673   N2     4 #> 12643      2         2  42       62673   N3     5 #> 12644      2         2  42       62673   N4     2 #> 12645      2         2  42       62673   N5     6 #> 12646      2         2  42       62673   O1     4 #> 12647      2         2  42       62673   O2     4 #> 12648      2         2  42       62673   O3     4 #> 12649      2         2  42       62673   O4     5 #> 12650      2         2  42       62673   O5     2 #> 12651      1         3  21       62675   A1     1 #> 12652      1         3  21       62675   A2     6 #> 12653      1         3  21       62675   A3     5 #> 12654      1         3  21       62675   A4     5 #> 12655      1         3  21       62675   A5     6 #> 12656      1         3  21       62675   C1     6 #> 12657      1         3  21       62675   C2     6 #> 12658      1         3  21       62675   C3     4 #> 12659      1         3  21       62675   C4     1 #> 12660      1         3  21       62675   C5     2 #> 12661      1         3  21       62675   E1     2 #> 12662      1         3  21       62675   E2     4 #> 12663      1         3  21       62675   E3     4 #> 12664      1         3  21       62675   E4     5 #> 12665      1         3  21       62675   E5     5 #> 12666      1         3  21       62675   N1     3 #> 12667      1         3  21       62675   N2     3 #> 12668      1         3  21       62675   N3     4 #> 12669      1         3  21       62675   N4     3 #> 12670      1         3  21       62675   N5     4 #> 12671      1         3  21       62675   O1     5 #> 12672      1         3  21       62675   O2     4 #> 12673      1         3  21       62675   O3     4 #> 12674      1         3  21       62675   O4     5 #> 12675      1         3  21       62675   O5     2 #> 12676      2         3  20       62677   A1     1 #> 12677      2         3  20       62677   A2     6 #> 12678      2         3  20       62677   A3     6 #> 12679      2         3  20       62677   A4     6 #> 12680      2         3  20       62677   A5     6 #> 12681      2         3  20       62677   C1     1 #> 12682      2         3  20       62677   C2     4 #> 12683      2         3  20       62677   C3     4 #> 12684      2         3  20       62677   C4     4 #> 12685      2         3  20       62677   C5     1 #> 12686      2         3  20       62677   E1     5 #> 12687      2         3  20       62677   E2     1 #> 12688      2         3  20       62677   E3     6 #> 12689      2         3  20       62677   E4     6 #> 12690      2         3  20       62677   E5     6 #> 12691      2         3  20       62677   N1     6 #> 12692      2         3  20       62677   N2     4 #> 12693      2         3  20       62677   N3     6 #> 12694      2         3  20       62677   N4     5 #> 12695      2         3  20       62677   N5     5 #> 12696      2         3  20       62677   O1     6 #> 12697      2         3  20       62677   O2     6 #> 12698      2         3  20       62677   O3     6 #> 12699      2         3  20       62677   O4     3 #> 12700      2         3  20       62677   O5     6 #> 12701      1         1  26       62679   A1     2 #> 12702      1         1  26       62679   A2     4 #> 12703      1         1  26       62679   A3     6 #> 12704      1         1  26       62679   A4     5 #> 12705      1         1  26       62679   A5     6 #> 12706      1         1  26       62679   C1     5 #> 12707      1         1  26       62679   C2     4 #> 12708      1         1  26       62679   C3     4 #> 12709      1         1  26       62679   C4     1 #> 12710      1         1  26       62679   C5     2 #> 12711      1         1  26       62679   E1     4 #> 12712      1         1  26       62679   E2     3 #> 12713      1         1  26       62679   E3     6 #> 12714      1         1  26       62679   E4     5 #> 12715      1         1  26       62679   E5     4 #> 12716      1         1  26       62679   N1     2 #> 12717      1         1  26       62679   N2     5 #> 12718      1         1  26       62679   N3     4 #> 12719      1         1  26       62679   N4     3 #> 12720      1         1  26       62679   N5     4 #> 12721      1         1  26       62679   O1     5 #> 12722      1         1  26       62679   O2     1 #> 12723      1         1  26       62679   O3     5 #> 12724      1         1  26       62679   O4     6 #> 12725      1         1  26       62679   O5     1 #> 12726      2         3  18       62681   A1     1 #> 12727      2         3  18       62681   A2     5 #> 12728      2         3  18       62681   A3     6 #> 12729      2         3  18       62681   A4     6 #> 12730      2         3  18       62681   A5     5 #> 12731      2         3  18       62681   C1     5 #> 12732      2         3  18       62681   C2     4 #> 12733      2         3  18       62681   C3     4 #> 12734      2         3  18       62681   C4     1 #> 12735      2         3  18       62681   C5     2 #> 12736      2         3  18       62681   E1     1 #> 12737      2         3  18       62681   E2     4 #> 12738      2         3  18       62681   E3     4 #> 12739      2         3  18       62681   E4     4 #> 12740      2         3  18       62681   E5     5 #> 12741      2         3  18       62681   N1     4 #> 12742      2         3  18       62681   N2     4 #> 12743      2         3  18       62681   N3     4 #> 12744      2         3  18       62681   N4     2 #> 12745      2         3  18       62681   N5     1 #> 12746      2         3  18       62681   O1     2 #> 12747      2         3  18       62681   O2     6 #> 12748      2         3  18       62681   O3     4 #> 12749      2         3  18       62681   O4     5 #> 12750      2         3  18       62681   O5     1 #> 12751      2         3  39       62682   A1     3 #> 12752      2         3  39       62682   A2     5 #> 12753      2         3  39       62682   A3     5 #> 12754      2         3  39       62682   A4     6 #> 12755      2         3  39       62682   A5     5 #> 12756      2         3  39       62682   C1     3 #> 12757      2         3  39       62682   C2     6 #> 12758      2         3  39       62682   C3     3 #> 12759      2         3  39       62682   C4     3 #> 12760      2         3  39       62682   C5     1 #> 12761      2         3  39       62682   E1     4 #> 12762      2         3  39       62682   E2     2 #> 12763      2         3  39       62682   E3     4 #> 12764      2         3  39       62682   E4     5 #> 12765      2         3  39       62682   E5     6 #> 12766      2         3  39       62682   N1     3 #> 12767      2         3  39       62682   N2     3 #> 12768      2         3  39       62682   N3     4 #> 12769      2         3  39       62682   N4     2 #> 12770      2         3  39       62682   N5     4 #> 12771      2         3  39       62682   O1     5 #> 12772      2         3  39       62682   O2     3 #> 12773      2         3  39       62682   O3     5 #> 12774      2         3  39       62682   O4     4 #> 12775      2         3  39       62682   O5     4 #> 12776      2         3  45       62683   A1     6 #> 12777      2         3  45       62683   A2     1 #> 12778      2         3  45       62683   A3     6 #> 12779      2         3  45       62683   A4     6 #> 12780      2         3  45       62683   A5     6 #> 12781      2         3  45       62683   C1     6 #> 12782      2         3  45       62683   C2     5 #> 12783      2         3  45       62683   C3     6 #> 12784      2         3  45       62683   C4     1 #> 12785      2         3  45       62683   C5     1 #> 12786      2         3  45       62683   E1     4 #> 12787      2         3  45       62683   E2     1 #> 12788      2         3  45       62683   E3     6 #> 12789      2         3  45       62683   E4     6 #> 12790      2         3  45       62683   E5     6 #> 12791      2         3  45       62683   N1     1 #> 12792      2         3  45       62683   N2     1 #> 12793      2         3  45       62683   N3     1 #> 12794      2         3  45       62683   N4     1 #> 12795      2         3  45       62683   N5     1 #> 12796      2         3  45       62683   O1     6 #> 12797      2         3  45       62683   O2     4 #> 12798      2         3  45       62683   O3     5 #> 12799      2         3  45       62683   O4     5 #> 12800      2         3  45       62683   O5     5 #> 12801      2         3  20       62684   A1     2 #> 12802      2         3  20       62684   A2     5 #> 12803      2         3  20       62684   A3     4 #> 12804      2         3  20       62684   A4     6 #> 12805      2         3  20       62684   A5     4 #> 12806      2         3  20       62684   C1     5 #> 12807      2         3  20       62684   C2     5 #> 12808      2         3  20       62684   C3     5 #> 12809      2         3  20       62684   C4     1 #> 12810      2         3  20       62684   C5     2 #> 12811      2         3  20       62684   E1     1 #> 12812      2         3  20       62684   E2     1 #> 12813      2         3  20       62684   E3     5 #> 12814      2         3  20       62684   E4     6 #> 12815      2         3  20       62684   E5     6 #> 12816      2         3  20       62684   N1     5 #> 12817      2         3  20       62684   N2     4 #> 12818      2         3  20       62684   N3     6 #> 12819      2         3  20       62684   N4     3 #> 12820      2         3  20       62684   N5     4 #> 12821      2         3  20       62684   O1     5 #> 12822      2         3  20       62684   O2     1 #> 12823      2         3  20       62684   O3     4 #> 12824      2         3  20       62684   O4     6 #> 12825      2         3  20       62684   O5     2 #> 12826      1         3  43       62685   A1     3 #> 12827      1         3  43       62685   A2     2 #> 12828      1         3  43       62685   A3     4 #> 12829      1         3  43       62685   A4     6 #> 12830      1         3  43       62685   A5     6 #> 12831      1         3  43       62685   C1     6 #> 12832      1         3  43       62685   C2     2 #> 12833      1         3  43       62685   C3     5 #> 12834      1         3  43       62685   C4     1 #> 12835      1         3  43       62685   C5     2 #> 12836      1         3  43       62685   E1     3 #> 12837      1         3  43       62685   E2     2 #> 12838      1         3  43       62685   E3     5 #> 12839      1         3  43       62685   E4     6 #> 12840      1         3  43       62685   E5     4 #> 12841      1         3  43       62685   N1     1 #> 12842      1         3  43       62685   N2     1 #> 12843      1         3  43       62685   N3     2 #> 12844      1         3  43       62685   N4     3 #> 12845      1         3  43       62685   N5     1 #> 12846      1         3  43       62685   O1     6 #> 12847      1         3  43       62685   O2     2 #> 12848      1         3  43       62685   O3     6 #> 12849      1         3  43       62685   O4     5 #> 12850      1         3  43       62685   O5     3 #> 12851      2         3  23       62686   A1     2 #> 12852      2         3  23       62686   A2     4 #> 12853      2         3  23       62686   A3     4 #> 12854      2         3  23       62686   A4     6 #> 12855      2         3  23       62686   A5     4 #> 12856      2         3  23       62686   C1     6 #> 12857      2         3  23       62686   C2     5 #> 12858      2         3  23       62686   C3     3 #> 12859      2         3  23       62686   C4     2 #> 12860      2         3  23       62686   C5     2 #> 12861      2         3  23       62686   E1     5 #> 12862      2         3  23       62686   E2     5 #> 12863      2         3  23       62686   E3     2 #> 12864      2         3  23       62686   E4     3 #> 12865      2         3  23       62686   E5     4 #> 12866      2         3  23       62686   N1     3 #> 12867      2         3  23       62686   N2     3 #> 12868      2         3  23       62686   N3     2 #> 12869      2         3  23       62686   N4     4 #> 12870      2         3  23       62686   N5     2 #> 12871      2         3  23       62686   O1     3 #> 12872      2         3  23       62686   O2     5 #> 12873      2         3  23       62686   O3     5 #> 12874      2         3  23       62686   O4     5 #> 12875      2         3  23       62686   O5     2 #> 12876      2         3  25       62687   A1     2 #> 12877      2         3  25       62687   A2     4 #> 12878      2         3  25       62687   A3     6 #> 12879      2         3  25       62687   A4     5 #> 12880      2         3  25       62687   A5     5 #> 12881      2         3  25       62687   C1     2 #> 12882      2         3  25       62687   C2     6 #> 12883      2         3  25       62687   C3     5 #> 12884      2         3  25       62687   C4     3 #> 12885      2         3  25       62687   C5     2 #> 12886      2         3  25       62687   E1     4 #> 12887      2         3  25       62687   E2     2 #> 12888      2         3  25       62687   E3     4 #> 12889      2         3  25       62687   E4     5 #> 12890      2         3  25       62687   E5     6 #> 12891      2         3  25       62687   N1     4 #> 12892      2         3  25       62687   N2     4 #> 12893      2         3  25       62687   N3     4 #> 12894      2         3  25       62687   N4     4 #> 12895      2         3  25       62687   N5     2 #> 12896      2         3  25       62687   O1     5 #> 12897      2         3  25       62687   O2     2 #> 12898      2         3  25       62687   O3     5 #> 12899      2         3  25       62687   O4     5 #> 12900      2         3  25       62687   O5     4 #> 12901      2         3  51       62688   A1     1 #> 12902      2         3  51       62688   A2     6 #> 12903      2         3  51       62688   A3     5 #> 12904      2         3  51       62688   A4     6 #> 12905      2         3  51       62688   A5     4 #> 12906      2         3  51       62688   C1     6 #> 12907      2         3  51       62688   C2     6 #> 12908      2         3  51       62688   C3     5 #> 12909      2         3  51       62688   C4     1 #> 12910      2         3  51       62688   C5     2 #> 12911      2         3  51       62688   E1     1 #> 12912      2         3  51       62688   E2     5 #> 12913      2         3  51       62688   E3     2 #> 12914      2         3  51       62688   E4     5 #> 12915      2         3  51       62688   E5     6 #> 12916      2         3  51       62688   N1     5 #> 12917      2         3  51       62688   N2     6 #> 12918      2         3  51       62688   N3     4 #> 12919      2         3  51       62688   N4     4 #> 12920      2         3  51       62688   N5     3 #> 12921      2         3  51       62688   O1     4 #> 12922      2         3  51       62688   O2     5 #> 12923      2         3  51       62688   O3     5 #> 12924      2         3  51       62688   O4     5 #> 12925      2         3  51       62688   O5     2 #> 12926      2         3  26       62690   A1     1 #> 12927      2         3  26       62690   A2     5 #> 12928      2         3  26       62690   A3     4 #> 12929      2         3  26       62690   A4     6 #> 12930      2         3  26       62690   A5     4 #> 12931      2         3  26       62690   C1     4 #> 12932      2         3  26       62690   C2     6 #> 12933      2         3  26       62690   C3     5 #> 12934      2         3  26       62690   C4     4 #> 12935      2         3  26       62690   C5     1 #> 12936      2         3  26       62690   E1     6 #> 12937      2         3  26       62690   E2     4 #> 12938      2         3  26       62690   E3     5 #> 12939      2         3  26       62690   E4     4 #> 12940      2         3  26       62690   E5     6 #> 12941      2         3  26       62690   N1     4 #> 12942      2         3  26       62690   N2     6 #> 12943      2         3  26       62690   N3     5 #> 12944      2         3  26       62690   N4     4 #> 12945      2         3  26       62690   N5     4 #> 12946      2         3  26       62690   O1     5 #> 12947      2         3  26       62690   O2     1 #> 12948      2         3  26       62690   O3     5 #> 12949      2         3  26       62690   O4     4 #> 12950      2         3  26       62690   O5     1 #> 12951      1         3  28       62692   A1     1 #> 12952      1         3  28       62692   A2     6 #> 12953      1         3  28       62692   A3     6 #> 12954      1         3  28       62692   A4     6 #> 12955      1         3  28       62692   A5     6 #> 12956      1         3  28       62692   C1     6 #> 12957      1         3  28       62692   C2     6 #> 12958      1         3  28       62692   C3     5 #> 12959      1         3  28       62692   C4     1 #> 12960      1         3  28       62692   C5     1 #> 12961      1         3  28       62692   E1     1 #> 12962      1         3  28       62692   E2     1 #> 12963      1         3  28       62692   E3     5 #> 12964      1         3  28       62692   E4     6 #> 12965      1         3  28       62692   E5     6 #> 12966      1         3  28       62692   N1     1 #> 12967      1         3  28       62692   N2     2 #> 12968      1         3  28       62692   N3     1 #> 12969      1         3  28       62692   N4     1 #> 12970      1         3  28       62692   N5     1 #> 12971      1         3  28       62692   O1     6 #> 12972      1         3  28       62692   O2     1 #> 12973      1         3  28       62692   O3     6 #> 12974      1         3  28       62692   O4     5 #> 12975      1         3  28       62692   O5     1 #> 12976      2         3  23       62694   A1     3 #> 12977      2         3  23       62694   A2     5 #> 12978      2         3  23       62694   A3     5 #> 12979      2         3  23       62694   A4     6 #> 12980      2         3  23       62694   A5     5 #> 12981      2         3  23       62694   C1     6 #> 12982      2         3  23       62694   C2     6 #> 12983      2         3  23       62694   C3     5 #> 12984      2         3  23       62694   C4     1 #> 12985      2         3  23       62694   C5     1 #> 12986      2         3  23       62694   E1     3 #> 12987      2         3  23       62694   E2     2 #> 12988      2         3  23       62694   E3     6 #> 12989      2         3  23       62694   E4     6 #> 12990      2         3  23       62694   E5     6 #> 12991      2         3  23       62694   N1     4 #> 12992      2         3  23       62694   N2     4 #> 12993      2         3  23       62694   N3     4 #> 12994      2         3  23       62694   N4     2 #> 12995      2         3  23       62694   N5     1 #> 12996      2         3  23       62694   O1     6 #> 12997      2         3  23       62694   O2     4 #> 12998      2         3  23       62694   O3     5 #> 12999      2         3  23       62694   O4     2 #> 13000      2         3  23       62694   O5     2 #> 13001      2         3  34       62698   A1     4 #> 13002      2         3  34       62698   A2     6 #> 13003      2         3  34       62698   A3     6 #> 13004      2         3  34       62698   A4     6 #> 13005      2         3  34       62698   A5     5 #> 13006      2         3  34       62698   C1     4 #> 13007      2         3  34       62698   C2     3 #> 13008      2         3  34       62698   C3     4 #> 13009      2         3  34       62698   C4     4 #> 13010      2         3  34       62698   C5     3 #> 13011      2         3  34       62698   E1     2 #> 13012      2         3  34       62698   E2     4 #> 13013      2         3  34       62698   E3     4 #> 13014      2         3  34       62698   E4     5 #> 13015      2         3  34       62698   E5     4 #> 13016      2         3  34       62698   N1     4 #> 13017      2         3  34       62698   N2     5 #> 13018      2         3  34       62698   N3     5 #> 13019      2         3  34       62698   N4     3 #> 13020      2         3  34       62698   N5     2 #> 13021      2         3  34       62698   O1     4 #> 13022      2         3  34       62698   O2     5 #> 13023      2         3  34       62698   O3     4 #> 13024      2         3  34       62698   O4     6 #> 13025      2         3  34       62698   O5     2 #> 13026      1         3  23       62700   A1     1 #> 13027      1         3  23       62700   A2     4 #> 13028      1         3  23       62700   A3     4 #> 13029      1         3  23       62700   A4     4 #> 13030      1         3  23       62700   A5     6 #> 13031      1         3  23       62700   C1     4 #> 13032      1         3  23       62700   C2     4 #> 13033      1         3  23       62700   C3     6 #> 13034      1         3  23       62700   C4     3 #> 13035      1         3  23       62700   C5     3 #> 13036      1         3  23       62700   E1     4 #> 13037      1         3  23       62700   E2     2 #> 13038      1         3  23       62700   E3     4 #> 13039      1         3  23       62700   E4     6 #> 13040      1         3  23       62700   E5     2 #> 13041      1         3  23       62700   N1     2 #> 13042      1         3  23       62700   N2     3 #> 13043      1         3  23       62700   N3     2 #> 13044      1         3  23       62700   N4     1 #> 13045      1         3  23       62700   N5     1 #> 13046      1         3  23       62700   O1     6 #> 13047      1         3  23       62700   O2     2 #> 13048      1         3  23       62700   O3     5 #> 13049      1         3  23       62700   O4     4 #> 13050      1         3  23       62700   O5     2 #> 13051      2         3  29       62703   A1     2 #> 13052      2         3  29       62703   A2     5 #> 13053      2         3  29       62703   A3     5 #> 13054      2         3  29       62703   A4     4 #> 13055      2         3  29       62703   A5     6 #> 13056      2         3  29       62703   C1     4 #> 13057      2         3  29       62703   C2     3 #> 13058      2         3  29       62703   C3     5 #> 13059      2         3  29       62703   C4     2 #> 13060      2         3  29       62703   C5     3 #> 13061      2         3  29       62703   E1     3 #> 13062      2         3  29       62703   E2     2 #> 13063      2         3  29       62703   E3     5 #> 13064      2         3  29       62703   E4    NA #> 13065      2         3  29       62703   E5     3 #> 13066      2         3  29       62703   N1     1 #> 13067      2         3  29       62703   N2     2 #> 13068      2         3  29       62703   N3     1 #> 13069      2         3  29       62703   N4     3 #> 13070      2         3  29       62703   N5     2 #> 13071      2         3  29       62703   O1     4 #> 13072      2         3  29       62703   O2     4 #> 13073      2         3  29       62703   O3     5 #> 13074      2         3  29       62703   O4     4 #> 13075      2         3  29       62703   O5     3 #> 13076      2         3  35       62706   A1     1 #> 13077      2         3  35       62706   A2     6 #> 13078      2         3  35       62706   A3     5 #> 13079      2         3  35       62706   A4     6 #> 13080      2         3  35       62706   A5     5 #> 13081      2         3  35       62706   C1     4 #> 13082      2         3  35       62706   C2     5 #> 13083      2         3  35       62706   C3     6 #> 13084      2         3  35       62706   C4     1 #> 13085      2         3  35       62706   C5     1 #> 13086      2         3  35       62706   E1     1 #> 13087      2         3  35       62706   E2     1 #> 13088      2         3  35       62706   E3     5 #> 13089      2         3  35       62706   E4     5 #> 13090      2         3  35       62706   E5     6 #> 13091      2         3  35       62706   N1     5 #> 13092      2         3  35       62706   N2     5 #> 13093      2         3  35       62706   N3     6 #> 13094      2         3  35       62706   N4     1 #> 13095      2         3  35       62706   N5     5 #> 13096      2         3  35       62706   O1     3 #> 13097      2         3  35       62706   O2     6 #> 13098      2         3  35       62706   O3     5 #> 13099      2         3  35       62706   O4     6 #> 13100      2         3  35       62706   O5     5 #> 13101      2         3  40       62707   A1     2 #> 13102      2         3  40       62707   A2     4 #> 13103      2         3  40       62707   A3     2 #> 13104      2         3  40       62707   A4     6 #> 13105      2         3  40       62707   A5     5 #> 13106      2         3  40       62707   C1     3 #> 13107      2         3  40       62707   C2     5 #> 13108      2         3  40       62707   C3     4 #> 13109      2         3  40       62707   C4     2 #> 13110      2         3  40       62707   C5     2 #> 13111      2         3  40       62707   E1    NA #> 13112      2         3  40       62707   E2     4 #> 13113      2         3  40       62707   E3     3 #> 13114      2         3  40       62707   E4     4 #> 13115      2         3  40       62707   E5     4 #> 13116      2         3  40       62707   N1     4 #> 13117      2         3  40       62707   N2     4 #> 13118      2         3  40       62707   N3     4 #> 13119      2         3  40       62707   N4     5 #> 13120      2         3  40       62707   N5     6 #> 13121      2         3  40       62707   O1     5 #> 13122      2         3  40       62707   O2     4 #> 13123      2         3  40       62707   O3     3 #> 13124      2         3  40       62707   O4     4 #> 13125      2         3  40       62707   O5     4 #> 13126      2         2  27       62708   A1     6 #> 13127      2         2  27       62708   A2     6 #> 13128      2         2  27       62708   A3     6 #> 13129      2         2  27       62708   A4     6 #> 13130      2         2  27       62708   A5     6 #> 13131      2         2  27       62708   C1     5 #> 13132      2         2  27       62708   C2     6 #> 13133      2         2  27       62708   C3     6 #> 13134      2         2  27       62708   C4     1 #> 13135      2         2  27       62708   C5     1 #> 13136      2         2  27       62708   E1     1 #> 13137      2         2  27       62708   E2     1 #> 13138      2         2  27       62708   E3     6 #> 13139      2         2  27       62708   E4     6 #> 13140      2         2  27       62708   E5     6 #> 13141      2         2  27       62708   N1     6 #> 13142      2         2  27       62708   N2     6 #> 13143      2         2  27       62708   N3     6 #> 13144      2         2  27       62708   N4     1 #> 13145      2         2  27       62708   N5     6 #> 13146      2         2  27       62708   O1     6 #> 13147      2         2  27       62708   O2     6 #> 13148      2         2  27       62708   O3     6 #> 13149      2         2  27       62708   O4     6 #> 13150      2         2  27       62708   O5     1 #> 13151      1         4  45       62710   A1     1 #> 13152      1         4  45       62710   A2     6 #> 13153      1         4  45       62710   A3     4 #> 13154      1         4  45       62710   A4     3 #> 13155      1         4  45       62710   A5     5 #> 13156      1         4  45       62710   C1     5 #> 13157      1         4  45       62710   C2     4 #> 13158      1         4  45       62710   C3     4 #> 13159      1         4  45       62710   C4     3 #> 13160      1         4  45       62710   C5     4 #> 13161      1         4  45       62710   E1     1 #> 13162      1         4  45       62710   E2     2 #> 13163      1         4  45       62710   E3     4 #> 13164      1         4  45       62710   E4     5 #> 13165      1         4  45       62710   E5     3 #> 13166      1         4  45       62710   N1     3 #> 13167      1         4  45       62710   N2     3 #> 13168      1         4  45       62710   N3     4 #> 13169      1         4  45       62710   N4     3 #> 13170      1         4  45       62710   N5     2 #> 13171      1         4  45       62710   O1     5 #> 13172      1         4  45       62710   O2     2 #> 13173      1         4  45       62710   O3     4 #> 13174      1         4  45       62710   O4     6 #> 13175      1         4  45       62710   O5     2 #> 13176      1         2  60       62712   A1     1 #> 13177      1         2  60       62712   A2     6 #> 13178      1         2  60       62712   A3     5 #> 13179      1         2  60       62712   A4     5 #> 13180      1         2  60       62712   A5     6 #> 13181      1         2  60       62712   C1     6 #> 13182      1         2  60       62712   C2     5 #> 13183      1         2  60       62712   C3     5 #> 13184      1         2  60       62712   C4     1 #> 13185      1         2  60       62712   C5     3 #> 13186      1         2  60       62712   E1     1 #> 13187      1         2  60       62712   E2     1 #> 13188      1         2  60       62712   E3     5 #> 13189      1         2  60       62712   E4     6 #> 13190      1         2  60       62712   E5     6 #> 13191      1         2  60       62712   N1     1 #> 13192      1         2  60       62712   N2     2 #> 13193      1         2  60       62712   N3     1 #> 13194      1         2  60       62712   N4     1 #> 13195      1         2  60       62712   N5     1 #> 13196      1         2  60       62712   O1     6 #> 13197      1         2  60       62712   O2     1 #> 13198      1         2  60       62712   O3     6 #> 13199      1         2  60       62712   O4     6 #> 13200      1         2  60       62712   O5     1 #> 13201      2         3  27       62715   A1     2 #> 13202      2         3  27       62715   A2     4 #> 13203      2         3  27       62715   A3     5 #> 13204      2         3  27       62715   A4     6 #> 13205      2         3  27       62715   A5     5 #> 13206      2         3  27       62715   C1     5 #> 13207      2         3  27       62715   C2     5 #> 13208      2         3  27       62715   C3     5 #> 13209      2         3  27       62715   C4     2 #> 13210      2         3  27       62715   C5     4 #> 13211      2         3  27       62715   E1     3 #> 13212      2         3  27       62715   E2     4 #> 13213      2         3  27       62715   E3     4 #> 13214      2         3  27       62715   E4     5 #> 13215      2         3  27       62715   E5     1 #> 13216      2         3  27       62715   N1     2 #> 13217      2         3  27       62715   N2     4 #> 13218      2         3  27       62715   N3     3 #> 13219      2         3  27       62715   N4     5 #> 13220      2         3  27       62715   N5     4 #> 13221      2         3  27       62715   O1     4 #> 13222      2         3  27       62715   O2     5 #> 13223      2         3  27       62715   O3     4 #> 13224      2         3  27       62715   O4     4 #> 13225      2         3  27       62715   O5     3 #> 13226      1         3  20       62716   A1     4 #> 13227      1         3  20       62716   A2     5 #> 13228      1         3  20       62716   A3     4 #> 13229      1         3  20       62716   A4     5 #> 13230      1         3  20       62716   A5     4 #> 13231      1         3  20       62716   C1     3 #> 13232      1         3  20       62716   C2     1 #> 13233      1         3  20       62716   C3     1 #> 13234      1         3  20       62716   C4     3 #> 13235      1         3  20       62716   C5     6 #> 13236      1         3  20       62716   E1     2 #> 13237      1         3  20       62716   E2     3 #> 13238      1         3  20       62716   E3     4 #> 13239      1         3  20       62716   E4     5 #> 13240      1         3  20       62716   E5     3 #> 13241      1         3  20       62716   N1     2 #> 13242      1         3  20       62716   N2     4 #> 13243      1         3  20       62716   N3     3 #> 13244      1         3  20       62716   N4    NA #> 13245      1         3  20       62716   N5     1 #> 13246      1         3  20       62716   O1     6 #> 13247      1         3  20       62716   O2     2 #> 13248      1         3  20       62716   O3     5 #> 13249      1         3  20       62716   O4     4 #> 13250      1         3  20       62716   O5     2 #> 13251      2         3  21       62717   A1     1 #> 13252      2         3  21       62717   A2     5 #> 13253      2         3  21       62717   A3     5 #> 13254      2         3  21       62717   A4     6 #> 13255      2         3  21       62717   A5     4 #> 13256      2         3  21       62717   C1     5 #> 13257      2         3  21       62717   C2     4 #> 13258      2         3  21       62717   C3     5 #> 13259      2         3  21       62717   C4     4 #> 13260      2         3  21       62717   C5     2 #> 13261      2         3  21       62717   E1     4 #> 13262      2         3  21       62717   E2     4 #> 13263      2         3  21       62717   E3     4 #> 13264      2         3  21       62717   E4     5 #> 13265      2         3  21       62717   E5     3 #> 13266      2         3  21       62717   N1     3 #> 13267      2         3  21       62717   N2     4 #> 13268      2         3  21       62717   N3     4 #> 13269      2         3  21       62717   N4     3 #> 13270      2         3  21       62717   N5     4 #> 13271      2         3  21       62717   O1     5 #> 13272      2         3  21       62717   O2     2 #> 13273      2         3  21       62717   O3     4 #> 13274      2         3  21       62717   O4     6 #> 13275      2         3  21       62717   O5     2 #> 13276      2         3  25       62718   A1     3 #> 13277      2         3  25       62718   A2     4 #> 13278      2         3  25       62718   A3     4 #> 13279      2         3  25       62718   A4     5 #> 13280      2         3  25       62718   A5     5 #> 13281      2         3  25       62718   C1     5 #> 13282      2         3  25       62718   C2     4 #> 13283      2         3  25       62718   C3     3 #> 13284      2         3  25       62718   C4     3 #> 13285      2         3  25       62718   C5     4 #> 13286      2         3  25       62718   E1     2 #> 13287      2         3  25       62718   E2     2 #> 13288      2         3  25       62718   E3     5 #> 13289      2         3  25       62718   E4     6 #> 13290      2         3  25       62718   E5     4 #> 13291      2         3  25       62718   N1     5 #> 13292      2         3  25       62718   N2     5 #> 13293      2         3  25       62718   N3     6 #> 13294      2         3  25       62718   N4     4 #> 13295      2         3  25       62718   N5     4 #> 13296      2         3  25       62718   O1     4 #> 13297      2         3  25       62718   O2     2 #> 13298      2         3  25       62718   O3     4 #> 13299      2         3  25       62718   O4     4 #> 13300      2         3  25       62718   O5     4 #> 13301      2         2  51       62719   A1     1 #> 13302      2         2  51       62719   A2     4 #> 13303      2         2  51       62719   A3     6 #> 13304      2         2  51       62719   A4     5 #> 13305      2         2  51       62719   A5     6 #> 13306      2         2  51       62719   C1     5 #> 13307      2         2  51       62719   C2     2 #> 13308      2         2  51       62719   C3     6 #> 13309      2         2  51       62719   C4     1 #> 13310      2         2  51       62719   C5     1 #> 13311      2         2  51       62719   E1     1 #> 13312      2         2  51       62719   E2     3 #> 13313      2         2  51       62719   E3     5 #> 13314      2         2  51       62719   E4     1 #> 13315      2         2  51       62719   E5     5 #> 13316      2         2  51       62719   N1     6 #> 13317      2         2  51       62719   N2     6 #> 13318      2         2  51       62719   N3     6 #> 13319      2         2  51       62719   N4     6 #> 13320      2         2  51       62719   N5     6 #> 13321      2         2  51       62719   O1     4 #> 13322      2         2  51       62719   O2     1 #> 13323      2         2  51       62719   O3     5 #> 13324      2         2  51       62719   O4     6 #> 13325      2         2  51       62719   O5     1 #> 13326      2         3  27       62720   A1     2 #> 13327      2         3  27       62720   A2     5 #> 13328      2         3  27       62720   A3     5 #> 13329      2         3  27       62720   A4     6 #> 13330      2         3  27       62720   A5     5 #> 13331      2         3  27       62720   C1     1 #> 13332      2         3  27       62720   C2     5 #> 13333      2         3  27       62720   C3     2 #> 13334      2         3  27       62720   C4     1 #> 13335      2         3  27       62720   C5     2 #> 13336      2         3  27       62720   E1     1 #> 13337      2         3  27       62720   E2     5 #> 13338      2         3  27       62720   E3     5 #> 13339      2         3  27       62720   E4     5 #> 13340      2         3  27       62720   E5     3 #> 13341      2         3  27       62720   N1     3 #> 13342      2         3  27       62720   N2     5 #> 13343      2         3  27       62720   N3     1 #> 13344      2         3  27       62720   N4     1 #> 13345      2         3  27       62720   N5     3 #> 13346      2         3  27       62720   O1     3 #> 13347      2         3  27       62720   O2     1 #> 13348      2         3  27       62720   O3     3 #> 13349      2         3  27       62720   O4     4 #> 13350      2         3  27       62720   O5     5 #> 13351      1         3  24       62722   A1     1 #> 13352      1         3  24       62722   A2     5 #> 13353      1         3  24       62722   A3     4 #> 13354      1         3  24       62722   A4     1 #> 13355      1         3  24       62722   A5     5 #> 13356      1         3  24       62722   C1     5 #> 13357      1         3  24       62722   C2     4 #> 13358      1         3  24       62722   C3     5 #> 13359      1         3  24       62722   C4     2 #> 13360      1         3  24       62722   C5     5 #> 13361      1         3  24       62722   E1     6 #> 13362      1         3  24       62722   E2     6 #> 13363      1         3  24       62722   E3     3 #> 13364      1         3  24       62722   E4     1 #> 13365      1         3  24       62722   E5     2 #> 13366      1         3  24       62722   N1     4 #> 13367      1         3  24       62722   N2     4 #> 13368      1         3  24       62722   N3     6 #> 13369      1         3  24       62722   N4     6 #> 13370      1         3  24       62722   N5     5 #> 13371      1         3  24       62722   O1     5 #> 13372      1         3  24       62722   O2     2 #> 13373      1         3  24       62722   O3     5 #> 13374      1         3  24       62722   O4     6 #> 13375      1         3  24       62722   O5     2 #> 13376      2         1  21       62726   A1     1 #> 13377      2         1  21       62726   A2     5 #> 13378      2         1  21       62726   A3     6 #> 13379      2         1  21       62726   A4     6 #> 13380      2         1  21       62726   A5     5 #> 13381      2         1  21       62726   C1     2 #> 13382      2         1  21       62726   C2     5 #> 13383      2         1  21       62726   C3     4 #> 13384      2         1  21       62726   C4     2 #> 13385      2         1  21       62726   C5     1 #> 13386      2         1  21       62726   E1     1 #> 13387      2         1  21       62726   E2     1 #> 13388      2         1  21       62726   E3     4 #> 13389      2         1  21       62726   E4     6 #> 13390      2         1  21       62726   E5     4 #> 13391      2         1  21       62726   N1     3 #> 13392      2         1  21       62726   N2     4 #> 13393      2         1  21       62726   N3     1 #> 13394      2         1  21       62726   N4     1 #> 13395      2         1  21       62726   N5     1 #> 13396      2         1  21       62726   O1     5 #> 13397      2         1  21       62726   O2     6 #> 13398      2         1  21       62726   O3     4 #> 13399      2         1  21       62726   O4     2 #> 13400      2         1  21       62726   O5     3 #> 13401      2         2  55       62728   A1     3 #> 13402      2         2  55       62728   A2     4 #> 13403      2         2  55       62728   A3    NA #> 13404      2         2  55       62728   A4     5 #> 13405      2         2  55       62728   A5     3 #> 13406      2         2  55       62728   C1     5 #> 13407      2         2  55       62728   C2     5 #> 13408      2         2  55       62728   C3     4 #> 13409      2         2  55       62728   C4     2 #> 13410      2         2  55       62728   C5     5 #> 13411      2         2  55       62728   E1     3 #> 13412      2         2  55       62728   E2     5 #> 13413      2         2  55       62728   E3     1 #> 13414      2         2  55       62728   E4     2 #> 13415      2         2  55       62728   E5     3 #> 13416      2         2  55       62728   N1     2 #> 13417      2         2  55       62728   N2     5 #> 13418      2         2  55       62728   N3     3 #> 13419      2         2  55       62728   N4     3 #> 13420      2         2  55       62728   N5     2 #> 13421      2         2  55       62728   O1     4 #> 13422      2         2  55       62728   O2     2 #> 13423      2         2  55       62728   O3     6 #> 13424      2         2  55       62728   O4     5 #> 13425      2         2  55       62728   O5     1 #> 13426      1         2  30       62729   A1     5 #> 13427      1         2  30       62729   A2     4 #> 13428      1         2  30       62729   A3     3 #> 13429      1         2  30       62729   A4     4 #> 13430      1         2  30       62729   A5     3 #> 13431      1         2  30       62729   C1     3 #> 13432      1         2  30       62729   C2     2 #> 13433      1         2  30       62729   C3     3 #> 13434      1         2  30       62729   C4     4 #> 13435      1         2  30       62729   C5     4 #> 13436      1         2  30       62729   E1     3 #> 13437      1         2  30       62729   E2     3 #> 13438      1         2  30       62729   E3     5 #> 13439      1         2  30       62729   E4     4 #> 13440      1         2  30       62729   E5     3 #> 13441      1         2  30       62729   N1     5 #> 13442      1         2  30       62729   N2     5 #> 13443      1         2  30       62729   N3     4 #> 13444      1         2  30       62729   N4     5 #> 13445      1         2  30       62729   N5     4 #> 13446      1         2  30       62729   O1     5 #> 13447      1         2  30       62729   O2     1 #> 13448      1         2  30       62729   O3     5 #> 13449      1         2  30       62729   O4     6 #> 13450      1         2  30       62729   O5     3 #> 13451      1         3  31       62731   A1     5 #> 13452      1         3  31       62731   A2     4 #> 13453      1         3  31       62731   A3     4 #> 13454      1         3  31       62731   A4     4 #> 13455      1         3  31       62731   A5     2 #> 13456      1         3  31       62731   C1     5 #> 13457      1         3  31       62731   C2     2 #> 13458      1         3  31       62731   C3     2 #> 13459      1         3  31       62731   C4     4 #> 13460      1         3  31       62731   C5     2 #> 13461      1         3  31       62731   E1     3 #> 13462      1         3  31       62731   E2     4 #> 13463      1         3  31       62731   E3    NA #> 13464      1         3  31       62731   E4     4 #> 13465      1         3  31       62731   E5     3 #> 13466      1         3  31       62731   N1     4 #> 13467      1         3  31       62731   N2     4 #> 13468      1         3  31       62731   N3     5 #> 13469      1         3  31       62731   N4     5 #> 13470      1         3  31       62731   N5     2 #> 13471      1         3  31       62731   O1     2 #> 13472      1         3  31       62731   O2     2 #> 13473      1         3  31       62731   O3     2 #> 13474      1         3  31       62731   O4     5 #> 13475      1         3  31       62731   O5     4 #> 13476      2         2  22       62740   A1     3 #> 13477      2         2  22       62740   A2     5 #> 13478      2         2  22       62740   A3     5 #> 13479      2         2  22       62740   A4     5 #> 13480      2         2  22       62740   A5     5 #> 13481      2         2  22       62740   C1     5 #> 13482      2         2  22       62740   C2     4 #> 13483      2         2  22       62740   C3     3 #> 13484      2         2  22       62740   C4     2 #> 13485      2         2  22       62740   C5     4 #> 13486      2         2  22       62740   E1     2 #> 13487      2         2  22       62740   E2     2 #> 13488      2         2  22       62740   E3     4 #> 13489      2         2  22       62740   E4     4 #> 13490      2         2  22       62740   E5     4 #> 13491      2         2  22       62740   N1     5 #> 13492      2         2  22       62740   N2     6 #> 13493      2         2  22       62740   N3     4 #> 13494      2         2  22       62740   N4     3 #> 13495      2         2  22       62740   N5     4 #> 13496      2         2  22       62740   O1     5 #> 13497      2         2  22       62740   O2     4 #> 13498      2         2  22       62740   O3     4 #> 13499      2         2  22       62740   O4     5 #> 13500      2         2  22       62740   O5     2 #> 13501      2         3  28       62741   A1     3 #> 13502      2         3  28       62741   A2     4 #> 13503      2         3  28       62741   A3     4 #> 13504      2         3  28       62741   A4     6 #> 13505      2         3  28       62741   A5     5 #> 13506      2         3  28       62741   C1     5 #> 13507      2         3  28       62741   C2     4 #> 13508      2         3  28       62741   C3     5 #> 13509      2         3  28       62741   C4     2 #> 13510      2         3  28       62741   C5     1 #> 13511      2         3  28       62741   E1     5 #> 13512      2         3  28       62741   E2     3 #> 13513      2         3  28       62741   E3     4 #> 13514      2         3  28       62741   E4     4 #> 13515      2         3  28       62741   E5     4 #> 13516      2         3  28       62741   N1     2 #> 13517      2         3  28       62741   N2     3 #> 13518      2         3  28       62741   N3     3 #> 13519      2         3  28       62741   N4     2 #> 13520      2         3  28       62741   N5     2 #> 13521      2         3  28       62741   O1     4 #> 13522      2         3  28       62741   O2     4 #> 13523      2         3  28       62741   O3     4 #> 13524      2         3  28       62741   O4     5 #> 13525      2         3  28       62741   O5     3 #> 13526      2         3  24       62744   A1     4 #> 13527      2         3  24       62744   A2     5 #> 13528      2         3  24       62744   A3     4 #> 13529      2         3  24       62744   A4     5 #> 13530      2         3  24       62744   A5     2 #> 13531      2         3  24       62744   C1     5 #> 13532      2         3  24       62744   C2     5 #> 13533      2         3  24       62744   C3     5 #> 13534      2         3  24       62744   C4     3 #> 13535      2         3  24       62744   C5     2 #> 13536      2         3  24       62744   E1     3 #> 13537      2         3  24       62744   E2     2 #> 13538      2         3  24       62744   E3     6 #> 13539      2         3  24       62744   E4     2 #> 13540      2         3  24       62744   E5     6 #> 13541      2         3  24       62744   N1     5 #> 13542      2         3  24       62744   N2     5 #> 13543      2         3  24       62744   N3     5 #> 13544      2         3  24       62744   N4     3 #> 13545      2         3  24       62744   N5     5 #> 13546      2         3  24       62744   O1     6 #> 13547      2         3  24       62744   O2     3 #> 13548      2         3  24       62744   O3     6 #> 13549      2         3  24       62744   O4     6 #> 13550      2         3  24       62744   O5     1 #> 13551      2         3  31       62745   A1     1 #> 13552      2         3  31       62745   A2     6 #> 13553      2         3  31       62745   A3     6 #> 13554      2         3  31       62745   A4     6 #> 13555      2         3  31       62745   A5     5 #> 13556      2         3  31       62745   C1     2 #> 13557      2         3  31       62745   C2     5 #> 13558      2         3  31       62745   C3     6 #> 13559      2         3  31       62745   C4     4 #> 13560      2         3  31       62745   C5     6 #> 13561      2         3  31       62745   E1     6 #> 13562      2         3  31       62745   E2     5 #> 13563      2         3  31       62745   E3     5 #> 13564      2         3  31       62745   E4     5 #> 13565      2         3  31       62745   E5     6 #> 13566      2         3  31       62745   N1     4 #> 13567      2         3  31       62745   N2     5 #> 13568      2         3  31       62745   N3     4 #> 13569      2         3  31       62745   N4     4 #> 13570      2         3  31       62745   N5     6 #> 13571      2         3  31       62745   O1     2 #> 13572      2         3  31       62745   O2     6 #> 13573      2         3  31       62745   O3     4 #> 13574      2         3  31       62745   O4     6 #> 13575      2         3  31       62745   O5     3 #> 13576      2         3  43       62749   A1     1 #> 13577      2         3  43       62749   A2     6 #> 13578      2         3  43       62749   A3     4 #> 13579      2         3  43       62749   A4     6 #> 13580      2         3  43       62749   A5     5 #> 13581      2         3  43       62749   C1     2 #> 13582      2         3  43       62749   C2     5 #> 13583      2         3  43       62749   C3     6 #> 13584      2         3  43       62749   C4     1 #> 13585      2         3  43       62749   C5     2 #> 13586      2         3  43       62749   E1     1 #> 13587      2         3  43       62749   E2     3 #> 13588      2         3  43       62749   E3     4 #> 13589      2         3  43       62749   E4     3 #> 13590      2         3  43       62749   E5     5 #> 13591      2         3  43       62749   N1     2 #> 13592      2         3  43       62749   N2     2 #> 13593      2         3  43       62749   N3     1 #> 13594      2         3  43       62749   N4     6 #> 13595      2         3  43       62749   N5     4 #> 13596      2         3  43       62749   O1     4 #> 13597      2         3  43       62749   O2     1 #> 13598      2         3  43       62749   O3     4 #> 13599      2         3  43       62749   O4     5 #> 13600      2         3  43       62749   O5     2 #> 13601      1         3  60       62750   A1     2 #> 13602      1         3  60       62750   A2     4 #> 13603      1         3  60       62750   A3     4 #> 13604      1         3  60       62750   A4     6 #> 13605      1         3  60       62750   A5     5 #> 13606      1         3  60       62750   C1     5 #> 13607      1         3  60       62750   C2     2 #> 13608      1         3  60       62750   C3     5 #> 13609      1         3  60       62750   C4     1 #> 13610      1         3  60       62750   C5     2 #> 13611      1         3  60       62750   E1     2 #> 13612      1         3  60       62750   E2     2 #> 13613      1         3  60       62750   E3     3 #> 13614      1         3  60       62750   E4     5 #> 13615      1         3  60       62750   E5     4 #> 13616      1         3  60       62750   N1     2 #> 13617      1         3  60       62750   N2     5 #> 13618      1         3  60       62750   N3     2 #> 13619      1         3  60       62750   N4     2 #> 13620      1         3  60       62750   N5     1 #> 13621      1         3  60       62750   O1     4 #> 13622      1         3  60       62750   O2     3 #> 13623      1         3  60       62750   O3     4 #> 13624      1         3  60       62750   O4     4 #> 13625      1         3  60       62750   O5     3 #> 13626      1         3  36       62751   A1     2 #> 13627      1         3  36       62751   A2     4 #> 13628      1         3  36       62751   A3     5 #> 13629      1         3  36       62751   A4     3 #> 13630      1         3  36       62751   A5     5 #> 13631      1         3  36       62751   C1     4 #> 13632      1         3  36       62751   C2     4 #> 13633      1         3  36       62751   C3     5 #> 13634      1         3  36       62751   C4     4 #> 13635      1         3  36       62751   C5     5 #> 13636      1         3  36       62751   E1     5 #> 13637      1         3  36       62751   E2     6 #> 13638      1         3  36       62751   E3     3 #> 13639      1         3  36       62751   E4     4 #> 13640      1         3  36       62751   E5     3 #> 13641      1         3  36       62751   N1     3 #> 13642      1         3  36       62751   N2     4 #> 13643      1         3  36       62751   N3     4 #> 13644      1         3  36       62751   N4     4 #> 13645      1         3  36       62751   N5     3 #> 13646      1         3  36       62751   O1     5 #> 13647      1         3  36       62751   O2     5 #> 13648      1         3  36       62751   O3     4 #> 13649      1         3  36       62751   O4     6 #> 13650      1         3  36       62751   O5     5 #> 13651      2         4  40       62757   A1     5 #> 13652      2         4  40       62757   A2     4 #> 13653      2         4  40       62757   A3     4 #> 13654      2         4  40       62757   A4     6 #> 13655      2         4  40       62757   A5     4 #> 13656      2         4  40       62757   C1     5 #> 13657      2         4  40       62757   C2     5 #> 13658      2         4  40       62757   C3     5 #> 13659      2         4  40       62757   C4     2 #> 13660      2         4  40       62757   C5     2 #> 13661      2         4  40       62757   E1     4 #> 13662      2         4  40       62757   E2     2 #> 13663      2         4  40       62757   E3     3 #> 13664      2         4  40       62757   E4     5 #> 13665      2         4  40       62757   E5     5 #> 13666      2         4  40       62757   N1     4 #> 13667      2         4  40       62757   N2     4 #> 13668      2         4  40       62757   N3    NA #> 13669      2         4  40       62757   N4     4 #> 13670      2         4  40       62757   N5     2 #> 13671      2         4  40       62757   O1     5 #> 13672      2         4  40       62757   O2     5 #> 13673      2         4  40       62757   O3     3 #> 13674      2         4  40       62757   O4     5 #> 13675      2         4  40       62757   O5     2 #> 13676      1         3  21       62758   A1     2 #> 13677      1         3  21       62758   A2     5 #> 13678      1         3  21       62758   A3     4 #> 13679      1         3  21       62758   A4     4 #> 13680      1         3  21       62758   A5     4 #> 13681      1         3  21       62758   C1     2 #> 13682      1         3  21       62758   C2     2 #> 13683      1         3  21       62758   C3     4 #> 13684      1         3  21       62758   C4     2 #> 13685      1         3  21       62758   C5     5 #> 13686      1         3  21       62758   E1     1 #> 13687      1         3  21       62758   E2     1 #> 13688      1         3  21       62758   E3     5 #> 13689      1         3  21       62758   E4     6 #> 13690      1         3  21       62758   E5     5 #> 13691      1         3  21       62758   N1     3 #> 13692      1         3  21       62758   N2     3 #> 13693      1         3  21       62758   N3     1 #> 13694      1         3  21       62758   N4     1 #> 13695      1         3  21       62758   N5     1 #> 13696      1         3  21       62758   O1     5 #> 13697      1         3  21       62758   O2     1 #> 13698      1         3  21       62758   O3     4 #> 13699      1         3  21       62758   O4     4 #> 13700      1         3  21       62758   O5     1 #> 13701      2         4  21       62761   A1     1 #> 13702      2         4  21       62761   A2     6 #> 13703      2         4  21       62761   A3     5 #> 13704      2         4  21       62761   A4     4 #> 13705      2         4  21       62761   A5     5 #> 13706      2         4  21       62761   C1     6 #> 13707      2         4  21       62761   C2     5 #> 13708      2         4  21       62761   C3     6 #> 13709      2         4  21       62761   C4     1 #> 13710      2         4  21       62761   C5     2 #> 13711      2         4  21       62761   E1     1 #> 13712      2         4  21       62761   E2     1 #> 13713      2         4  21       62761   E3     5 #> 13714      2         4  21       62761   E4     5 #> 13715      2         4  21       62761   E5     5 #> 13716      2         4  21       62761   N1     1 #> 13717      2         4  21       62761   N2     1 #> 13718      2         4  21       62761   N3     2 #> 13719      2         4  21       62761   N4     2 #> 13720      2         4  21       62761   N5     3 #> 13721      2         4  21       62761   O1     5 #> 13722      2         4  21       62761   O2     2 #> 13723      2         4  21       62761   O3     5 #> 13724      2         4  21       62761   O4     6 #> 13725      2         4  21       62761   O5     1 #> 13726      2         1  19       62764   A1     4 #> 13727      2         1  19       62764   A2     3 #> 13728      2         1  19       62764   A3     5 #> 13729      2         1  19       62764   A4     6 #> 13730      2         1  19       62764   A5     5 #> 13731      2         1  19       62764   C1     4 #> 13732      2         1  19       62764   C2     5 #> 13733      2         1  19       62764   C3     5 #> 13734      2         1  19       62764   C4     1 #> 13735      2         1  19       62764   C5     2 #> 13736      2         1  19       62764   E1     3 #> 13737      2         1  19       62764   E2     4 #> 13738      2         1  19       62764   E3     4 #> 13739      2         1  19       62764   E4     6 #> 13740      2         1  19       62764   E5     5 #> 13741      2         1  19       62764   N1     5 #> 13742      2         1  19       62764   N2     6 #> 13743      2         1  19       62764   N3     6 #> 13744      2         1  19       62764   N4     3 #> 13745      2         1  19       62764   N5     4 #> 13746      2         1  19       62764   O1     6 #> 13747      2         1  19       62764   O2     3 #> 13748      2         1  19       62764   O3     4 #> 13749      2         1  19       62764   O4     5 #> 13750      2         1  19       62764   O5     3 #> 13751      2         3  36       62765   A1     1 #> 13752      2         3  36       62765   A2     6 #> 13753      2         3  36       62765   A3     6 #> 13754      2         3  36       62765   A4     6 #> 13755      2         3  36       62765   A5     1 #> 13756      2         3  36       62765   C1     1 #> 13757      2         3  36       62765   C2     4 #> 13758      2         3  36       62765   C3     6 #> 13759      2         3  36       62765   C4     1 #> 13760      2         3  36       62765   C5     1 #> 13761      2         3  36       62765   E1     4 #> 13762      2         3  36       62765   E2     1 #> 13763      2         3  36       62765   E3     3 #> 13764      2         3  36       62765   E4     3 #> 13765      2         3  36       62765   E5     2 #> 13766      2         3  36       62765   N1     1 #> 13767      2         3  36       62765   N2     1 #> 13768      2         3  36       62765   N3     1 #> 13769      2         3  36       62765   N4     1 #> 13770      2         3  36       62765   N5     1 #> 13771      2         3  36       62765   O1     1 #> 13772      2         3  36       62765   O2     1 #> 13773      2         3  36       62765   O3     2 #> 13774      2         3  36       62765   O4     5 #> 13775      2         3  36       62765   O5     1 #> 13776      1         4  42       62766   A1     4 #> 13777      1         4  42       62766   A2     5 #> 13778      1         4  42       62766   A3     4 #> 13779      1         4  42       62766   A4     2 #> 13780      1         4  42       62766   A5     2 #> 13781      1         4  42       62766   C1     5 #> 13782      1         4  42       62766   C2     3 #> 13783      1         4  42       62766   C3     3 #> 13784      1         4  42       62766   C4     3 #> 13785      1         4  42       62766   C5     5 #> 13786      1         4  42       62766   E1     5 #> 13787      1         4  42       62766   E2     6 #> 13788      1         4  42       62766   E3     3 #> 13789      1         4  42       62766   E4     5 #> 13790      1         4  42       62766   E5     3 #> 13791      1         4  42       62766   N1     6 #> 13792      1         4  42       62766   N2     6 #> 13793      1         4  42       62766   N3     5 #> 13794      1         4  42       62766   N4     6 #> 13795      1         4  42       62766   N5     3 #> 13796      1         4  42       62766   O1     6 #> 13797      1         4  42       62766   O2     6 #> 13798      1         4  42       62766   O3     5 #> 13799      1         4  42       62766   O4     6 #> 13800      1         4  42       62766   O5     4 #> 13801      1         2  24       62767   A1     4 #> 13802      1         2  24       62767   A2     5 #> 13803      1         2  24       62767   A3     4 #> 13804      1         2  24       62767   A4     6 #> 13805      1         2  24       62767   A5     4 #> 13806      1         2  24       62767   C1     1 #> 13807      1         2  24       62767   C2     1 #> 13808      1         2  24       62767   C3     2 #> 13809      1         2  24       62767   C4     4 #> 13810      1         2  24       62767   C5     5 #> 13811      1         2  24       62767   E1     1 #> 13812      1         2  24       62767   E2     1 #> 13813      1         2  24       62767   E3     3 #> 13814      1         2  24       62767   E4     4 #> 13815      1         2  24       62767   E5     6 #> 13816      1         2  24       62767   N1     6 #> 13817      1         2  24       62767   N2     4 #> 13818      1         2  24       62767   N3     1 #> 13819      1         2  24       62767   N4     1 #> 13820      1         2  24       62767   N5     1 #> 13821      1         2  24       62767   O1     6 #> 13822      1         2  24       62767   O2     5 #> 13823      1         2  24       62767   O3     1 #> 13824      1         2  24       62767   O4     6 #> 13825      1         2  24       62767   O5     6 #> 13826      2         3  44       62768   A1     1 #> 13827      2         3  44       62768   A2     6 #> 13828      2         3  44       62768   A3     6 #> 13829      2         3  44       62768   A4     6 #> 13830      2         3  44       62768   A5     6 #> 13831      2         3  44       62768   C1     6 #> 13832      2         3  44       62768   C2     6 #> 13833      2         3  44       62768   C3     5 #> 13834      2         3  44       62768   C4     1 #> 13835      2         3  44       62768   C5     2 #> 13836      2         3  44       62768   E1     1 #> 13837      2         3  44       62768   E2     1 #> 13838      2         3  44       62768   E3     6 #> 13839      2         3  44       62768   E4     5 #> 13840      2         3  44       62768   E5     5 #> 13841      2         3  44       62768   N1     1 #> 13842      2         3  44       62768   N2     2 #> 13843      2         3  44       62768   N3     1 #> 13844      2         3  44       62768   N4     1 #> 13845      2         3  44       62768   N5     4 #> 13846      2         3  44       62768   O1     5 #> 13847      2         3  44       62768   O2     4 #> 13848      2         3  44       62768   O3     4 #> 13849      2         3  44       62768   O4     5 #> 13850      2         3  44       62768   O5     4 #> 13851      2         3  30       62770   A1     3 #> 13852      2         3  30       62770   A2     6 #> 13853      2         3  30       62770   A3     6 #> 13854      2         3  30       62770   A4     6 #> 13855      2         3  30       62770   A5     5 #> 13856      2         3  30       62770   C1     2 #> 13857      2         3  30       62770   C2     5 #> 13858      2         3  30       62770   C3     5 #> 13859      2         3  30       62770   C4     2 #> 13860      2         3  30       62770   C5     2 #> 13861      2         3  30       62770   E1     3 #> 13862      2         3  30       62770   E2     4 #> 13863      2         3  30       62770   E3     4 #> 13864      2         3  30       62770   E4     5 #> 13865      2         3  30       62770   E5     4 #> 13866      2         3  30       62770   N1     3 #> 13867      2         3  30       62770   N2     4 #> 13868      2         3  30       62770   N3     4 #> 13869      2         3  30       62770   N4     5 #> 13870      2         3  30       62770   N5     6 #> 13871      2         3  30       62770   O1     2 #> 13872      2         3  30       62770   O2     5 #> 13873      2         3  30       62770   O3     4 #> 13874      2         3  30       62770   O4     5 #> 13875      2         3  30       62770   O5     4 #> 13876      2         3  23       62772   A1     1 #> 13877      2         3  23       62772   A2     5 #> 13878      2         3  23       62772   A3     5 #> 13879      2         3  23       62772   A4     6 #> 13880      2         3  23       62772   A5     5 #> 13881      2         3  23       62772   C1     4 #> 13882      2         3  23       62772   C2     5 #> 13883      2         3  23       62772   C3     4 #> 13884      2         3  23       62772   C4     4 #> 13885      2         3  23       62772   C5     4 #> 13886      2         3  23       62772   E1     2 #> 13887      2         3  23       62772   E2     1 #> 13888      2         3  23       62772   E3     5 #> 13889      2         3  23       62772   E4     6 #> 13890      2         3  23       62772   E5     5 #> 13891      2         3  23       62772   N1     2 #> 13892      2         3  23       62772   N2     2 #> 13893      2         3  23       62772   N3     4 #> 13894      2         3  23       62772   N4     4 #> 13895      2         3  23       62772   N5     3 #> 13896      2         3  23       62772   O1     5 #> 13897      2         3  23       62772   O2     5 #> 13898      2         3  23       62772   O3     4 #> 13899      2         3  23       62772   O4     6 #> 13900      2         3  23       62772   O5     2 #> 13901      2         2  26       62776   A1     2 #> 13902      2         2  26       62776   A2     5 #> 13903      2         2  26       62776   A3     3 #> 13904      2         2  26       62776   A4     2 #> 13905      2         2  26       62776   A5     4 #> 13906      2         2  26       62776   C1     2 #> 13907      2         2  26       62776   C2     2 #> 13908      2         2  26       62776   C3     4 #> 13909      2         2  26       62776   C4     4 #> 13910      2         2  26       62776   C5     4 #> 13911      2         2  26       62776   E1     3 #> 13912      2         2  26       62776   E2     3 #> 13913      2         2  26       62776   E3     6 #> 13914      2         2  26       62776   E4     5 #> 13915      2         2  26       62776   E5     4 #> 13916      2         2  26       62776   N1     5 #> 13917      2         2  26       62776   N2     6 #> 13918      2         2  26       62776   N3     4 #> 13919      2         2  26       62776   N4     5 #> 13920      2         2  26       62776   N5     4 #> 13921      2         2  26       62776   O1     6 #> 13922      2         2  26       62776   O2     6 #> 13923      2         2  26       62776   O3     5 #> 13924      2         2  26       62776   O4     6 #> 13925      2         2  26       62776   O5     1 #> 13926      1         3  32       62778   A1     1 #> 13927      1         3  32       62778   A2     6 #> 13928      1         3  32       62778   A3     6 #> 13929      1         3  32       62778   A4     5 #> 13930      1         3  32       62778   A5     6 #> 13931      1         3  32       62778   C1    NA #> 13932      1         3  32       62778   C2     5 #> 13933      1         3  32       62778   C3     5 #> 13934      1         3  32       62778   C4     1 #> 13935      1         3  32       62778   C5     1 #> 13936      1         3  32       62778   E1     2 #> 13937      1         3  32       62778   E2     1 #> 13938      1         3  32       62778   E3     6 #> 13939      1         3  32       62778   E4     5 #> 13940      1         3  32       62778   E5     6 #> 13941      1         3  32       62778   N1     1 #> 13942      1         3  32       62778   N2     5 #> 13943      1         3  32       62778   N3     1 #> 13944      1         3  32       62778   N4     1 #> 13945      1         3  32       62778   N5     3 #> 13946      1         3  32       62778   O1     6 #> 13947      1         3  32       62778   O2     3 #> 13948      1         3  32       62778   O3     5 #> 13949      1         3  32       62778   O4     2 #> 13950      1         3  32       62778   O5     1 #> 13951      1         3  29       62779   A1     1 #> 13952      1         3  29       62779   A2     4 #> 13953      1         3  29       62779   A3     5 #> 13954      1         3  29       62779   A4     4 #> 13955      1         3  29       62779   A5     5 #> 13956      1         3  29       62779   C1     3 #> 13957      1         3  29       62779   C2     5 #> 13958      1         3  29       62779   C3     4 #> 13959      1         3  29       62779   C4     2 #> 13960      1         3  29       62779   C5     4 #> 13961      1         3  29       62779   E1     4 #> 13962      1         3  29       62779   E2     3 #> 13963      1         3  29       62779   E3     4 #> 13964      1         3  29       62779   E4     4 #> 13965      1         3  29       62779   E5     4 #> 13966      1         3  29       62779   N1     2 #> 13967      1         3  29       62779   N2     3 #> 13968      1         3  29       62779   N3     1 #> 13969      1         3  29       62779   N4     4 #> 13970      1         3  29       62779   N5     3 #> 13971      1         3  29       62779   O1     4 #> 13972      1         3  29       62779   O2     4 #> 13973      1         3  29       62779   O3     4 #> 13974      1         3  29       62779   O4     5 #> 13975      1         3  29       62779   O5     3 #> 13976      2         3  47       62780   A1     1 #> 13977      2         3  47       62780   A2     6 #> 13978      2         3  47       62780   A3     6 #> 13979      2         3  47       62780   A4     6 #> 13980      2         3  47       62780   A5     5 #> 13981      2         3  47       62780   C1     6 #> 13982      2         3  47       62780   C2     6 #> 13983      2         3  47       62780   C3     6 #> 13984      2         3  47       62780   C4     1 #> 13985      2         3  47       62780   C5     2 #> 13986      2         3  47       62780   E1     3 #> 13987      2         3  47       62780   E2     3 #> 13988      2         3  47       62780   E3     5 #> 13989      2         3  47       62780   E4     6 #> 13990      2         3  47       62780   E5     6 #> 13991      2         3  47       62780   N1     1 #> 13992      2         3  47       62780   N2     3 #> 13993      2         3  47       62780   N3     4 #> 13994      2         3  47       62780   N4     2 #> 13995      2         3  47       62780   N5     3 #> 13996      2         3  47       62780   O1     6 #> 13997      2         3  47       62780   O2     1 #> 13998      2         3  47       62780   O3     6 #> 13999      2         3  47       62780   O4     6 #> 14000      2         3  47       62780   O5     3 #> 14001      1         3  44       62781   A1     2 #> 14002      1         3  44       62781   A2     5 #> 14003      1         3  44       62781   A3     4 #> 14004      1         3  44       62781   A4     6 #> 14005      1         3  44       62781   A5     4 #> 14006      1         3  44       62781   C1     6 #> 14007      1         3  44       62781   C2     5 #> 14008      1         3  44       62781   C3     4 #> 14009      1         3  44       62781   C4     2 #> 14010      1         3  44       62781   C5     1 #> 14011      1         3  44       62781   E1     3 #> 14012      1         3  44       62781   E2     2 #> 14013      1         3  44       62781   E3     3 #> 14014      1         3  44       62781   E4     4 #> 14015      1         3  44       62781   E5     5 #> 14016      1         3  44       62781   N1     2 #> 14017      1         3  44       62781   N2     3 #> 14018      1         3  44       62781   N3     1 #> 14019      1         3  44       62781   N4     1 #> 14020      1         3  44       62781   N5     1 #> 14021      1         3  44       62781   O1     5 #> 14022      1         3  44       62781   O2     5 #> 14023      1         3  44       62781   O3     4 #> 14024      1         3  44       62781   O4     4 #> 14025      1         3  44       62781   O5     2 #> 14026      1         3  25       62783   A1     5 #> 14027      1         3  25       62783   A2     5 #> 14028      1         3  25       62783   A3     5 #> 14029      1         3  25       62783   A4     5 #> 14030      1         3  25       62783   A5     5 #> 14031      1         3  25       62783   C1     5 #> 14032      1         3  25       62783   C2     5 #> 14033      1         3  25       62783   C3     5 #> 14034      1         3  25       62783   C4     5 #> 14035      1         3  25       62783   C5     5 #> 14036      1         3  25       62783   E1     5 #> 14037      1         3  25       62783   E2     5 #> 14038      1         3  25       62783   E3     5 #> 14039      1         3  25       62783   E4     5 #> 14040      1         3  25       62783   E5     5 #> 14041      1         3  25       62783   N1     5 #> 14042      1         3  25       62783   N2     5 #> 14043      1         3  25       62783   N3     5 #> 14044      1         3  25       62783   N4     5 #> 14045      1         3  25       62783   N5     5 #> 14046      1         3  25       62783   O1     5 #> 14047      1         3  25       62783   O2     5 #> 14048      1         3  25       62783   O3     5 #> 14049      1         3  25       62783   O4     5 #> 14050      1         3  25       62783   O5     5 #> 14051      2         3  28       62785   A1     3 #> 14052      2         3  28       62785   A2     6 #> 14053      2         3  28       62785   A3     5 #> 14054      2         3  28       62785   A4     6 #> 14055      2         3  28       62785   A5     5 #> 14056      2         3  28       62785   C1     5 #> 14057      2         3  28       62785   C2     6 #> 14058      2         3  28       62785   C3     5 #> 14059      2         3  28       62785   C4     1 #> 14060      2         3  28       62785   C5     4 #> 14061      2         3  28       62785   E1     2 #> 14062      2         3  28       62785   E2     2 #> 14063      2         3  28       62785   E3     4 #> 14064      2         3  28       62785   E4     6 #> 14065      2         3  28       62785   E5     5 #> 14066      2         3  28       62785   N1     4 #> 14067      2         3  28       62785   N2     4 #> 14068      2         3  28       62785   N3     4 #> 14069      2         3  28       62785   N4     3 #> 14070      2         3  28       62785   N5     3 #> 14071      2         3  28       62785   O1     6 #> 14072      2         3  28       62785   O2     1 #> 14073      2         3  28       62785   O3     5 #> 14074      2         3  28       62785   O4     4 #> 14075      2         3  28       62785   O5     4 #> 14076      2         4  32       62786   A1     1 #> 14077      2         4  32       62786   A2     5 #> 14078      2         4  32       62786   A3     6 #> 14079      2         4  32       62786   A4     6 #> 14080      2         4  32       62786   A5     6 #> 14081      2         4  32       62786   C1     6 #> 14082      2         4  32       62786   C2     6 #> 14083      2         4  32       62786   C3     5 #> 14084      2         4  32       62786   C4     1 #> 14085      2         4  32       62786   C5     4 #> 14086      2         4  32       62786   E1     3 #> 14087      2         4  32       62786   E2     2 #> 14088      2         4  32       62786   E3     6 #> 14089      2         4  32       62786   E4     5 #> 14090      2         4  32       62786   E5     6 #> 14091      2         4  32       62786   N1     4 #> 14092      2         4  32       62786   N2     4 #> 14093      2         4  32       62786   N3     5 #> 14094      2         4  32       62786   N4     3 #> 14095      2         4  32       62786   N5     1 #> 14096      2         4  32       62786   O1     6 #> 14097      2         4  32       62786   O2     1 #> 14098      2         4  32       62786   O3     6 #> 14099      2         4  32       62786   O4     5 #> 14100      2         4  32       62786   O5     2 #> 14101      2         3  37       62787   A1     1 #> 14102      2         3  37       62787   A2     1 #> 14103      2         3  37       62787   A3     5 #> 14104      2         3  37       62787   A4     6 #> 14105      2         3  37       62787   A5     3 #> 14106      2         3  37       62787   C1     5 #> 14107      2         3  37       62787   C2     3 #> 14108      2         3  37       62787   C3     1 #> 14109      2         3  37       62787   C4     2 #> 14110      2         3  37       62787   C5     5 #> 14111      2         3  37       62787   E1     4 #> 14112      2         3  37       62787   E2     3 #> 14113      2         3  37       62787   E3     1 #> 14114      2         3  37       62787   E4     5 #> 14115      2         3  37       62787   E5     4 #> 14116      2         3  37       62787   N1     3 #> 14117      2         3  37       62787   N2     3 #> 14118      2         3  37       62787   N3     3 #> 14119      2         3  37       62787   N4     3 #> 14120      2         3  37       62787   N5     2 #> 14121      2         3  37       62787   O1     5 #> 14122      2         3  37       62787   O2     5 #> 14123      2         3  37       62787   O3     2 #> 14124      2         3  37       62787   O4     6 #> 14125      2         3  37       62787   O5     6 #> 14126      2         3  39       62788   A1     1 #> 14127      2         3  39       62788   A2     6 #> 14128      2         3  39       62788   A3     6 #> 14129      2         3  39       62788   A4     6 #> 14130      2         3  39       62788   A5     6 #> 14131      2         3  39       62788   C1     6 #> 14132      2         3  39       62788   C2     6 #> 14133      2         3  39       62788   C3     5 #> 14134      2         3  39       62788   C4     2 #> 14135      2         3  39       62788   C5     3 #> 14136      2         3  39       62788   E1     1 #> 14137      2         3  39       62788   E2     2 #> 14138      2         3  39       62788   E3     6 #> 14139      2         3  39       62788   E4     5 #> 14140      2         3  39       62788   E5     5 #> 14141      2         3  39       62788   N1     2 #> 14142      2         3  39       62788   N2     5 #> 14143      2         3  39       62788   N3     5 #> 14144      2         3  39       62788   N4     5 #> 14145      2         3  39       62788   N5     4 #> 14146      2         3  39       62788   O1     5 #> 14147      2         3  39       62788   O2     3 #> 14148      2         3  39       62788   O3     4 #> 14149      2         3  39       62788   O4     6 #> 14150      2         3  39       62788   O5     2 #> 14151      1         2  40       62789   A1     1 #> 14152      1         2  40       62789   A2     6 #> 14153      1         2  40       62789   A3     4 #> 14154      1         2  40       62789   A4     6 #> 14155      1         2  40       62789   A5     6 #> 14156      1         2  40       62789   C1     4 #> 14157      1         2  40       62789   C2     4 #> 14158      1         2  40       62789   C3     3 #> 14159      1         2  40       62789   C4     5 #> 14160      1         2  40       62789   C5     2 #> 14161      1         2  40       62789   E1     1 #> 14162      1         2  40       62789   E2     1 #> 14163      1         2  40       62789   E3     3 #> 14164      1         2  40       62789   E4     6 #> 14165      1         2  40       62789   E5     4 #> 14166      1         2  40       62789   N1     1 #> 14167      1         2  40       62789   N2     2 #> 14168      1         2  40       62789   N3     2 #> 14169      1         2  40       62789   N4     2 #> 14170      1         2  40       62789   N5     1 #> 14171      1         2  40       62789   O1     5 #> 14172      1         2  40       62789   O2     1 #> 14173      1         2  40       62789   O3     5 #> 14174      1         2  40       62789   O4     5 #> 14175      1         2  40       62789   O5     2 #> 14176      2         4  26       62790   A1     1 #> 14177      2         4  26       62790   A2     5 #> 14178      2         4  26       62790   A3     5 #> 14179      2         4  26       62790   A4     6 #> 14180      2         4  26       62790   A5     6 #> 14181      2         4  26       62790   C1     6 #> 14182      2         4  26       62790   C2     5 #> 14183      2         4  26       62790   C3     6 #> 14184      2         4  26       62790   C4     1 #> 14185      2         4  26       62790   C5     1 #> 14186      2         4  26       62790   E1     3 #> 14187      2         4  26       62790   E2     1 #> 14188      2         4  26       62790   E3     6 #> 14189      2         4  26       62790   E4     6 #> 14190      2         4  26       62790   E5     5 #> 14191      2         4  26       62790   N1     4 #> 14192      2         4  26       62790   N2     4 #> 14193      2         4  26       62790   N3     4 #> 14194      2         4  26       62790   N4     3 #> 14195      2         4  26       62790   N5     1 #> 14196      2         4  26       62790   O1     6 #> 14197      2         4  26       62790   O2     1 #> 14198      2         4  26       62790   O3     5 #> 14199      2         4  26       62790   O4     1 #> 14200      2         4  26       62790   O5     4 #> 14201      1         3  27       62792   A1     5 #> 14202      1         3  27       62792   A2     4 #> 14203      1         3  27       62792   A3     4 #> 14204      1         3  27       62792   A4     5 #> 14205      1         3  27       62792   A5     5 #> 14206      1         3  27       62792   C1     4 #> 14207      1         3  27       62792   C2     3 #> 14208      1         3  27       62792   C3     4 #> 14209      1         3  27       62792   C4     4 #> 14210      1         3  27       62792   C5     3 #> 14211      1         3  27       62792   E1     3 #> 14212      1         3  27       62792   E2     1 #> 14213      1         3  27       62792   E3     2 #> 14214      1         3  27       62792   E4     3 #> 14215      1         3  27       62792   E5     5 #> 14216      1         3  27       62792   N1     5 #> 14217      1         3  27       62792   N2     5 #> 14218      1         3  27       62792   N3     3 #> 14219      1         3  27       62792   N4     1 #> 14220      1         3  27       62792   N5     1 #> 14221      1         3  27       62792   O1     4 #> 14222      1         3  27       62792   O2     6 #> 14223      1         3  27       62792   O3     4 #> 14224      1         3  27       62792   O4     5 #> 14225      1         3  27       62792   O5     6 #> 14226      2         3  41       62795   A1     2 #> 14227      2         3  41       62795   A2     5 #> 14228      2         3  41       62795   A3     2 #> 14229      2         3  41       62795   A4     5 #> 14230      2         3  41       62795   A5     4 #> 14231      2         3  41       62795   C1     5 #> 14232      2         3  41       62795   C2     4 #> 14233      2         3  41       62795   C3     1 #> 14234      2         3  41       62795   C4     5 #> 14235      2         3  41       62795   C5     5 #> 14236      2         3  41       62795   E1     2 #> 14237      2         3  41       62795   E2     5 #> 14238      2         3  41       62795   E3     1 #> 14239      2         3  41       62795   E4     6 #> 14240      2         3  41       62795   E5     5 #> 14241      2         3  41       62795   N1     4 #> 14242      2         3  41       62795   N2     4 #> 14243      2         3  41       62795   N3     2 #> 14244      2         3  41       62795   N4     4 #> 14245      2         3  41       62795   N5     5 #> 14246      2         3  41       62795   O1     2 #> 14247      2         3  41       62795   O2     6 #> 14248      2         3  41       62795   O3     1 #> 14249      2         3  41       62795   O4     6 #> 14250      2         3  41       62795   O5     6 #> 14251      2         3  34       62796   A1     1 #> 14252      2         3  34       62796   A2     5 #> 14253      2         3  34       62796   A3     5 #> 14254      2         3  34       62796   A4     5 #> 14255      2         3  34       62796   A5     5 #> 14256      2         3  34       62796   C1     2 #> 14257      2         3  34       62796   C2     4 #> 14258      2         3  34       62796   C3     4 #> 14259      2         3  34       62796   C4     6 #> 14260      2         3  34       62796   C5     5 #> 14261      2         3  34       62796   E1     4 #> 14262      2         3  34       62796   E2     6 #> 14263      2         3  34       62796   E3     3 #> 14264      2         3  34       62796   E4     5 #> 14265      2         3  34       62796   E5     1 #> 14266      2         3  34       62796   N1     4 #> 14267      2         3  34       62796   N2     5 #> 14268      2         3  34       62796   N3     4 #> 14269      2         3  34       62796   N4     4 #> 14270      2         3  34       62796   N5     4 #> 14271      2         3  34       62796   O1     5 #> 14272      2         3  34       62796   O2     6 #> 14273      2         3  34       62796   O3     2 #> 14274      2         3  34       62796   O4     5 #> 14275      2         3  34       62796   O5     4 #> 14276      2         3  23       62797   A1     1 #> 14277      2         3  23       62797   A2     6 #> 14278      2         3  23       62797   A3     6 #> 14279      2         3  23       62797   A4     6 #> 14280      2         3  23       62797   A5     6 #> 14281      2         3  23       62797   C1     3 #> 14282      2         3  23       62797   C2     4 #> 14283      2         3  23       62797   C3     1 #> 14284      2         3  23       62797   C4     1 #> 14285      2         3  23       62797   C5     4 #> 14286      2         3  23       62797   E1     1 #> 14287      2         3  23       62797   E2     1 #> 14288      2         3  23       62797   E3     6 #> 14289      2         3  23       62797   E4     6 #> 14290      2         3  23       62797   E5     6 #> 14291      2         3  23       62797   N1     1 #> 14292      2         3  23       62797   N2     1 #> 14293      2         3  23       62797   N3     2 #> 14294      2         3  23       62797   N4     1 #> 14295      2         3  23       62797   N5     1 #> 14296      2         3  23       62797   O1     5 #> 14297      2         3  23       62797   O2     6 #> 14298      2         3  23       62797   O3     3 #> 14299      2         3  23       62797   O4     4 #> 14300      2         3  23       62797   O5     3 #> 14301      1         3  31       62800   A1     3 #> 14302      1         3  31       62800   A2     5 #> 14303      1         3  31       62800   A3     6 #> 14304      1         3  31       62800   A4     6 #> 14305      1         3  31       62800   A5     4 #> 14306      1         3  31       62800   C1     3 #> 14307      1         3  31       62800   C2     5 #> 14308      1         3  31       62800   C3     3 #> 14309      1         3  31       62800   C4     1 #> 14310      1         3  31       62800   C5     1 #> 14311      1         3  31       62800   E1     1 #> 14312      1         3  31       62800   E2     1 #> 14313      1         3  31       62800   E3     5 #> 14314      1         3  31       62800   E4     5 #> 14315      1         3  31       62800   E5     6 #> 14316      1         3  31       62800   N1     4 #> 14317      1         3  31       62800   N2     6 #> 14318      1         3  31       62800   N3     6 #> 14319      1         3  31       62800   N4     5 #> 14320      1         3  31       62800   N5     3 #> 14321      1         3  31       62800   O1     6 #> 14322      1         3  31       62800   O2     1 #> 14323      1         3  31       62800   O3     5 #> 14324      1         3  31       62800   O4     6 #> 14325      1         3  31       62800   O5     2 #> 14326      2         3  38       62801   A1     6 #> 14327      2         3  38       62801   A2     6 #> 14328      2         3  38       62801   A3     6 #> 14329      2         3  38       62801   A4     6 #> 14330      2         3  38       62801   A5     6 #> 14331      2         3  38       62801   C1     5 #> 14332      2         3  38       62801   C2     6 #> 14333      2         3  38       62801   C3     5 #> 14334      2         3  38       62801   C4     1 #> 14335      2         3  38       62801   C5     1 #> 14336      2         3  38       62801   E1     5 #> 14337      2         3  38       62801   E2     5 #> 14338      2         3  38       62801   E3     4 #> 14339      2         3  38       62801   E4     2 #> 14340      2         3  38       62801   E5     5 #> 14341      2         3  38       62801   N1     2 #> 14342      2         3  38       62801   N2     3 #> 14343      2         3  38       62801   N3     1 #> 14344      2         3  38       62801   N4     3 #> 14345      2         3  38       62801   N5     5 #> 14346      2         3  38       62801   O1     5 #> 14347      2         3  38       62801   O2     5 #> 14348      2         3  38       62801   O3     4 #> 14349      2         3  38       62801   O4     5 #> 14350      2         3  38       62801   O5     3 #> 14351      2         3  44       62803   A1     1 #> 14352      2         3  44       62803   A2     6 #> 14353      2         3  44       62803   A3     5 #> 14354      2         3  44       62803   A4     5 #> 14355      2         3  44       62803   A5     5 #> 14356      2         3  44       62803   C1     4 #> 14357      2         3  44       62803   C2     4 #> 14358      2         3  44       62803   C3     4 #> 14359      2         3  44       62803   C4     4 #> 14360      2         3  44       62803   C5     2 #> 14361      2         3  44       62803   E1     2 #> 14362      2         3  44       62803   E2     3 #> 14363      2         3  44       62803   E3     3 #> 14364      2         3  44       62803   E4     5 #> 14365      2         3  44       62803   E5     2 #> 14366      2         3  44       62803   N1     1 #> 14367      2         3  44       62803   N2     1 #> 14368      2         3  44       62803   N3     2 #> 14369      2         3  44       62803   N4     2 #> 14370      2         3  44       62803   N5     5 #> 14371      2         3  44       62803   O1     3 #> 14372      2         3  44       62803   O2     2 #> 14373      2         3  44       62803   O3     4 #> 14374      2         3  44       62803   O4     5 #> 14375      2         3  44       62803   O5     4 #> 14376      2         3  31       62804   A1     3 #> 14377      2         3  31       62804   A2     6 #> 14378      2         3  31       62804   A3     1 #> 14379      2         3  31       62804   A4     5 #> 14380      2         3  31       62804   A5     6 #> 14381      2         3  31       62804   C1     6 #> 14382      2         3  31       62804   C2     3 #> 14383      2         3  31       62804   C3     3 #> 14384      2         3  31       62804   C4     2 #> 14385      2         3  31       62804   C5     4 #> 14386      2         3  31       62804   E1     1 #> 14387      2         3  31       62804   E2     1 #> 14388      2         3  31       62804   E3     5 #> 14389      2         3  31       62804   E4     6 #> 14390      2         3  31       62804   E5     6 #> 14391      2         3  31       62804   N1     3 #> 14392      2         3  31       62804   N2     5 #> 14393      2         3  31       62804   N3     2 #> 14394      2         3  31       62804   N4     3 #> 14395      2         3  31       62804   N5     2 #> 14396      2         3  31       62804   O1     6 #> 14397      2         3  31       62804   O2     3 #> 14398      2         3  31       62804   O3     6 #> 14399      2         3  31       62804   O4     6 #> 14400      2         3  31       62804   O5     1 #> 14401      1         5  33       62805   A1     1 #> 14402      1         5  33       62805   A2     6 #> 14403      1         5  33       62805   A3     5 #> 14404      1         5  33       62805   A4     6 #> 14405      1         5  33       62805   A5     5 #> 14406      1         5  33       62805   C1     4 #> 14407      1         5  33       62805   C2     5 #> 14408      1         5  33       62805   C3     5 #> 14409      1         5  33       62805   C4     2 #> 14410      1         5  33       62805   C5     4 #> 14411      1         5  33       62805   E1     2 #> 14412      1         5  33       62805   E2     3 #> 14413      1         5  33       62805   E3     5 #> 14414      1         5  33       62805   E4     5 #> 14415      1         5  33       62805   E5     4 #> 14416      1         5  33       62805   N1     4 #> 14417      1         5  33       62805   N2     5 #> 14418      1         5  33       62805   N3     5 #> 14419      1         5  33       62805   N4     4 #> 14420      1         5  33       62805   N5     3 #> 14421      1         5  33       62805   O1     5 #> 14422      1         5  33       62805   O2     1 #> 14423      1         5  33       62805   O3     5 #> 14424      1         5  33       62805   O4     6 #> 14425      1         5  33       62805   O5     2 #> 14426      2         3  28       62809   A1     3 #> 14427      2         3  28       62809   A2     2 #> 14428      2         3  28       62809   A3     3 #> 14429      2         3  28       62809   A4     4 #> 14430      2         3  28       62809   A5     2 #> 14431      2         3  28       62809   C1    NA #> 14432      2         3  28       62809   C2     3 #> 14433      2         3  28       62809   C3     2 #> 14434      2         3  28       62809   C4     2 #> 14435      2         3  28       62809   C5     3 #> 14436      2         3  28       62809   E1     6 #> 14437      2         3  28       62809   E2     6 #> 14438      2         3  28       62809   E3     2 #> 14439      2         3  28       62809   E4     5 #> 14440      2         3  28       62809   E5     3 #> 14441      2         3  28       62809   N1     6 #> 14442      2         3  28       62809   N2     5 #> 14443      2         3  28       62809   N3     5 #> 14444      2         3  28       62809   N4     2 #> 14445      2         3  28       62809   N5     5 #> 14446      2         3  28       62809   O1     3 #> 14447      2         3  28       62809   O2     6 #> 14448      2         3  28       62809   O3     1 #> 14449      2         3  28       62809   O4     1 #> 14450      2         3  28       62809   O5     3 #> 14451      1         3  46       62810   A1     1 #> 14452      1         3  46       62810   A2     5 #> 14453      1         3  46       62810   A3     5 #> 14454      1         3  46       62810   A4     5 #> 14455      1         3  46       62810   A5     6 #> 14456      1         3  46       62810   C1     6 #> 14457      1         3  46       62810   C2     6 #> 14458      1         3  46       62810   C3     6 #> 14459      1         3  46       62810   C4     1 #> 14460      1         3  46       62810   C5     1 #> 14461      1         3  46       62810   E1     1 #> 14462      1         3  46       62810   E2     2 #> 14463      1         3  46       62810   E3     5 #> 14464      1         3  46       62810   E4     6 #> 14465      1         3  46       62810   E5     6 #> 14466      1         3  46       62810   N1     1 #> 14467      1         3  46       62810   N2     1 #> 14468      1         3  46       62810   N3     1 #> 14469      1         3  46       62810   N4     1 #> 14470      1         3  46       62810   N5     1 #> 14471      1         3  46       62810   O1     6 #> 14472      1         3  46       62810   O2     6 #> 14473      1         3  46       62810   O3     5 #> 14474      1         3  46       62810   O4     4 #> 14475      1         3  46       62810   O5     2 #> 14476      2         3  36       62816   A1     2 #> 14477      2         3  36       62816   A2     6 #> 14478      2         3  36       62816   A3     6 #> 14479      2         3  36       62816   A4     5 #> 14480      2         3  36       62816   A5     3 #> 14481      2         3  36       62816   C1     6 #> 14482      2         3  36       62816   C2     5 #> 14483      2         3  36       62816   C3     6 #> 14484      2         3  36       62816   C4     1 #> 14485      2         3  36       62816   C5     6 #> 14486      2         3  36       62816   E1     1 #> 14487      2         3  36       62816   E2     5 #> 14488      2         3  36       62816   E3     4 #> 14489      2         3  36       62816   E4     4 #> 14490      2         3  36       62816   E5     6 #> 14491      2         3  36       62816   N1     6 #> 14492      2         3  36       62816   N2     6 #> 14493      2         3  36       62816   N3     6 #> 14494      2         3  36       62816   N4     6 #> 14495      2         3  36       62816   N5     6 #> 14496      2         3  36       62816   O1     2 #> 14497      2         3  36       62816   O2     1 #> 14498      2         3  36       62816   O3     3 #> 14499      2         3  36       62816   O4     6 #> 14500      2         3  36       62816   O5     2 #> 14501      2         3  28       62817   A1     4 #> 14502      2         3  28       62817   A2     5 #> 14503      2         3  28       62817   A3     5 #> 14504      2         3  28       62817   A4     6 #> 14505      2         3  28       62817   A5     6 #> 14506      2         3  28       62817   C1     3 #> 14507      2         3  28       62817   C2     5 #> 14508      2         3  28       62817   C3     6 #> 14509      2         3  28       62817   C4     5 #> 14510      2         3  28       62817   C5     3 #> 14511      2         3  28       62817   E1     6 #> 14512      2         3  28       62817   E2     6 #> 14513      2         3  28       62817   E3     5 #> 14514      2         3  28       62817   E4     6 #> 14515      2         3  28       62817   E5     5 #> 14516      2         3  28       62817   N1     5 #> 14517      2         3  28       62817   N2     3 #> 14518      2         3  28       62817   N3     2 #> 14519      2         3  28       62817   N4     1 #> 14520      2         3  28       62817   N5     5 #> 14521      2         3  28       62817   O1     5 #> 14522      2         3  28       62817   O2     6 #> 14523      2         3  28       62817   O3     4 #> 14524      2         3  28       62817   O4     1 #> 14525      2         3  28       62817   O5     5 #> 14526      2         3  28       62819   A1     1 #> 14527      2         3  28       62819   A2     4 #> 14528      2         3  28       62819   A3     6 #> 14529      2         3  28       62819   A4     6 #> 14530      2         3  28       62819   A5     6 #> 14531      2         3  28       62819   C1     3 #> 14532      2         3  28       62819   C2     4 #> 14533      2         3  28       62819   C3     2 #> 14534      2         3  28       62819   C4     2 #> 14535      2         3  28       62819   C5     5 #> 14536      2         3  28       62819   E1     4 #> 14537      2         3  28       62819   E2     3 #> 14538      2         3  28       62819   E3     4 #> 14539      2         3  28       62819   E4     4 #> 14540      2         3  28       62819   E5     4 #> 14541      2         3  28       62819   N1     2 #> 14542      2         3  28       62819   N2     2 #> 14543      2         3  28       62819   N3     2 #> 14544      2         3  28       62819   N4     3 #> 14545      2         3  28       62819   N5     1 #> 14546      2         3  28       62819   O1     4 #> 14547      2         3  28       62819   O2     3 #> 14548      2         3  28       62819   O3     5 #> 14549      2         3  28       62819   O4     6 #> 14550      2         3  28       62819   O5     6 #> 14551      2         3  42       62821   A1     3 #> 14552      2         3  42       62821   A2     6 #> 14553      2         3  42       62821   A3     6 #> 14554      2         3  42       62821   A4     6 #> 14555      2         3  42       62821   A5     5 #> 14556      2         3  42       62821   C1     6 #> 14557      2         3  42       62821   C2     5 #> 14558      2         3  42       62821   C3     6 #> 14559      2         3  42       62821   C4     1 #> 14560      2         3  42       62821   C5     2 #> 14561      2         3  42       62821   E1     3 #> 14562      2         3  42       62821   E2     1 #> 14563      2         3  42       62821   E3     5 #> 14564      2         3  42       62821   E4     6 #> 14565      2         3  42       62821   E5     5 #> 14566      2         3  42       62821   N1     1 #> 14567      2         3  42       62821   N2     3 #> 14568      2         3  42       62821   N3     2 #> 14569      2         3  42       62821   N4     1 #> 14570      2         3  42       62821   N5     1 #> 14571      2         3  42       62821   O1     5 #> 14572      2         3  42       62821   O2     1 #> 14573      2         3  42       62821   O3     5 #> 14574      2         3  42       62821   O4     6 #> 14575      2         3  42       62821   O5     1 #> 14576      2         3  32       62822   A1     1 #> 14577      2         3  32       62822   A2     5 #> 14578      2         3  32       62822   A3     5 #> 14579      2         3  32       62822   A4     6 #> 14580      2         3  32       62822   A5     4 #> 14581      2         3  32       62822   C1     4 #> 14582      2         3  32       62822   C2     4 #> 14583      2         3  32       62822   C3     4 #> 14584      2         3  32       62822   C4     3 #> 14585      2         3  32       62822   C5     2 #> 14586      2         3  32       62822   E1     1 #> 14587      2         3  32       62822   E2     2 #> 14588      2         3  32       62822   E3     4 #> 14589      2         3  32       62822   E4     5 #> 14590      2         3  32       62822   E5     4 #> 14591      2         3  32       62822   N1     3 #> 14592      2         3  32       62822   N2     3 #> 14593      2         3  32       62822   N3     3 #> 14594      2         3  32       62822   N4     1 #> 14595      2         3  32       62822   N5     2 #> 14596      2         3  32       62822   O1     4 #> 14597      2         3  32       62822   O2     2 #> 14598      2         3  32       62822   O3     4 #> 14599      2         3  32       62822   O4     4 #> 14600      2         3  32       62822   O5     3 #> 14601      2         3  19       62825   A1     2 #> 14602      2         3  19       62825   A2     5 #> 14603      2         3  19       62825   A3     2 #> 14604      2         3  19       62825   A4     1 #> 14605      2         3  19       62825   A5     3 #> 14606      2         3  19       62825   C1     4 #> 14607      2         3  19       62825   C2     2 #> 14608      2         3  19       62825   C3     4 #> 14609      2         3  19       62825   C4     4 #> 14610      2         3  19       62825   C5     5 #> 14611      2         3  19       62825   E1     4 #> 14612      2         3  19       62825   E2     4 #> 14613      2         3  19       62825   E3     2 #> 14614      2         3  19       62825   E4     4 #> 14615      2         3  19       62825   E5     3 #> 14616      2         3  19       62825   N1     3 #> 14617      2         3  19       62825   N2     5 #> 14618      2         3  19       62825   N3     5 #> 14619      2         3  19       62825   N4     4 #> 14620      2         3  19       62825   N5     1 #> 14621      2         3  19       62825   O1     5 #> 14622      2         3  19       62825   O2     2 #> 14623      2         3  19       62825   O3     3 #> 14624      2         3  19       62825   O4     3 #> 14625      2         3  19       62825   O5     2 #> 14626      2         3  25       62826   A1     2 #> 14627      2         3  25       62826   A2     5 #> 14628      2         3  25       62826   A3     4 #> 14629      2         3  25       62826   A4     6 #> 14630      2         3  25       62826   A5     5 #> 14631      2         3  25       62826   C1     4 #> 14632      2         3  25       62826   C2     5 #> 14633      2         3  25       62826   C3     4 #> 14634      2         3  25       62826   C4     3 #> 14635      2         3  25       62826   C5     4 #> 14636      2         3  25       62826   E1     1 #> 14637      2         3  25       62826   E2     3 #> 14638      2         3  25       62826   E3     4 #> 14639      2         3  25       62826   E4     5 #> 14640      2         3  25       62826   E5     5 #> 14641      2         3  25       62826   N1     5 #> 14642      2         3  25       62826   N2     5 #> 14643      2         3  25       62826   N3     4 #> 14644      2         3  25       62826   N4     3 #> 14645      2         3  25       62826   N5     3 #> 14646      2         3  25       62826   O1     4 #> 14647      2         3  25       62826   O2     4 #> 14648      2         3  25       62826   O3     4 #> 14649      2         3  25       62826   O4     4 #> 14650      2         3  25       62826   O5     4 #> 14651      2         5  37       62827   A1     4 #> 14652      2         5  37       62827   A2     5 #> 14653      2         5  37       62827   A3     5 #> 14654      2         5  37       62827   A4     6 #> 14655      2         5  37       62827   A5     5 #> 14656      2         5  37       62827   C1     4 #> 14657      2         5  37       62827   C2     6 #> 14658      2         5  37       62827   C3     6 #> 14659      2         5  37       62827   C4     4 #> 14660      2         5  37       62827   C5     4 #> 14661      2         5  37       62827   E1     1 #> 14662      2         5  37       62827   E2     5 #> 14663      2         5  37       62827   E3     4 #> 14664      2         5  37       62827   E4     4 #> 14665      2         5  37       62827   E5     5 #> 14666      2         5  37       62827   N1     3 #> 14667      2         5  37       62827   N2     5 #> 14668      2         5  37       62827   N3     4 #> 14669      2         5  37       62827   N4     4 #> 14670      2         5  37       62827   N5     4 #> 14671      2         5  37       62827   O1     5 #> 14672      2         5  37       62827   O2     4 #> 14673      2         5  37       62827   O3     5 #> 14674      2         5  37       62827   O4     4 #> 14675      2         5  37       62827   O5     5 #> 14676      2         3  29       62828   A1     2 #> 14677      2         3  29       62828   A2     6 #> 14678      2         3  29       62828   A3     5 #> 14679      2         3  29       62828   A4     6 #> 14680      2         3  29       62828   A5     6 #> 14681      2         3  29       62828   C1     6 #> 14682      2         3  29       62828   C2     6 #> 14683      2         3  29       62828   C3     5 #> 14684      2         3  29       62828   C4     4 #> 14685      2         3  29       62828   C5     2 #> 14686      2         3  29       62828   E1     1 #> 14687      2         3  29       62828   E2     2 #> 14688      2         3  29       62828   E3     5 #> 14689      2         3  29       62828   E4     6 #> 14690      2         3  29       62828   E5     5 #> 14691      2         3  29       62828   N1     3 #> 14692      2         3  29       62828   N2     5 #> 14693      2         3  29       62828   N3     1 #> 14694      2         3  29       62828   N4     4 #> 14695      2         3  29       62828   N5     3 #> 14696      2         3  29       62828   O1     6 #> 14697      2         3  29       62828   O2     4 #> 14698      2         3  29       62828   O3     5 #> 14699      2         3  29       62828   O4     5 #> 14700      2         3  29       62828   O5     3 #> 14701      2         3  32       62831   A1     6 #> 14702      2         3  32       62831   A2     2 #> 14703      2         3  32       62831   A3     5 #> 14704      2         3  32       62831   A4     6 #> 14705      2         3  32       62831   A5     2 #> 14706      2         3  32       62831   C1     6 #> 14707      2         3  32       62831   C2     5 #> 14708      2         3  32       62831   C3     3 #> 14709      2         3  32       62831   C4     5 #> 14710      2         3  32       62831   C5     3 #> 14711      2         3  32       62831   E1     5 #> 14712      2         3  32       62831   E2     2 #> 14713      2         3  32       62831   E3     4 #> 14714      2         3  32       62831   E4     5 #> 14715      2         3  32       62831   E5     6 #> 14716      2         3  32       62831   N1     6 #> 14717      2         3  32       62831   N2     6 #> 14718      2         3  32       62831   N3     6 #> 14719      2         3  32       62831   N4     5 #> 14720      2         3  32       62831   N5     6 #> 14721      2         3  32       62831   O1     5 #> 14722      2         3  32       62831   O2     5 #> 14723      2         3  32       62831   O3     4 #> 14724      2         3  32       62831   O4     4 #> 14725      2         3  32       62831   O5     6 #> 14726      1         3  27       62832   A1     1 #> 14727      1         3  27       62832   A2     5 #> 14728      1         3  27       62832   A3     5 #> 14729      1         3  27       62832   A4     6 #> 14730      1         3  27       62832   A5     5 #> 14731      1         3  27       62832   C1     5 #> 14732      1         3  27       62832   C2     5 #> 14733      1         3  27       62832   C3     5 #> 14734      1         3  27       62832   C4     3 #> 14735      1         3  27       62832   C5     3 #> 14736      1         3  27       62832   E1     2 #> 14737      1         3  27       62832   E2     1 #> 14738      1         3  27       62832   E3     5 #> 14739      1         3  27       62832   E4     6 #> 14740      1         3  27       62832   E5     6 #> 14741      1         3  27       62832   N1     3 #> 14742      1         3  27       62832   N2     3 #> 14743      1         3  27       62832   N3     1 #> 14744      1         3  27       62832   N4     2 #> 14745      1         3  27       62832   N5     1 #> 14746      1         3  27       62832   O1     6 #> 14747      1         3  27       62832   O2     5 #> 14748      1         3  27       62832   O3     2 #> 14749      1         3  27       62832   O4     6 #> 14750      1         3  27       62832   O5     4 #> 14751      2         3  27       62834   A1     1 #> 14752      2         3  27       62834   A2     6 #> 14753      2         3  27       62834   A3     4 #> 14754      2         3  27       62834   A4     4 #> 14755      2         3  27       62834   A5     4 #> 14756      2         3  27       62834   C1     4 #> 14757      2         3  27       62834   C2     4 #> 14758      2         3  27       62834   C3     6 #> 14759      2         3  27       62834   C4     2 #> 14760      2         3  27       62834   C5     2 #> 14761      2         3  27       62834   E1     6 #> 14762      2         3  27       62834   E2     6 #> 14763      2         3  27       62834   E3     5 #> 14764      2         3  27       62834   E4     5 #> 14765      2         3  27       62834   E5     6 #> 14766      2         3  27       62834   N1     2 #> 14767      2         3  27       62834   N2     4 #> 14768      2         3  27       62834   N3     4 #> 14769      2         3  27       62834   N4     1 #> 14770      2         3  27       62834   N5     2 #> 14771      2         3  27       62834   O1     6 #> 14772      2         3  27       62834   O2     4 #> 14773      2         3  27       62834   O3     5 #> 14774      2         3  27       62834   O4     6 #> 14775      2         3  27       62834   O5     2 #> 14776      1         3  39       62835   A1     1 #> 14777      1         3  39       62835   A2     5 #> 14778      1         3  39       62835   A3     4 #> 14779      1         3  39       62835   A4     5 #> 14780      1         3  39       62835   A5     5 #> 14781      1         3  39       62835   C1     4 #> 14782      1         3  39       62835   C2     4 #> 14783      1         3  39       62835   C3     3 #> 14784      1         3  39       62835   C4    NA #> 14785      1         3  39       62835   C5     4 #> 14786      1         3  39       62835   E1     3 #> 14787      1         3  39       62835   E2     2 #> 14788      1         3  39       62835   E3     4 #> 14789      1         3  39       62835   E4     5 #> 14790      1         3  39       62835   E5     4 #> 14791      1         3  39       62835   N1     4 #> 14792      1         3  39       62835   N2     5 #> 14793      1         3  39       62835   N3     5 #> 14794      1         3  39       62835   N4     6 #> 14795      1         3  39       62835   N5     5 #> 14796      1         3  39       62835   O1     5 #> 14797      1         3  39       62835   O2     6 #> 14798      1         3  39       62835   O3     3 #> 14799      1         3  39       62835   O4     6 #> 14800      1         3  39       62835   O5     2 #> 14801      1        NA  45       62837   A1     2 #> 14802      1        NA  45       62837   A2     5 #> 14803      1        NA  45       62837   A3     6 #> 14804      1        NA  45       62837   A4     5 #> 14805      1        NA  45       62837   A5     4 #> 14806      1        NA  45       62837   C1     1 #> 14807      1        NA  45       62837   C2     5 #> 14808      1        NA  45       62837   C3     6 #> 14809      1        NA  45       62837   C4     1 #> 14810      1        NA  45       62837   C5     4 #> 14811      1        NA  45       62837   E1     4 #> 14812      1        NA  45       62837   E2     1 #> 14813      1        NA  45       62837   E3     5 #> 14814      1        NA  45       62837   E4     4 #> 14815      1        NA  45       62837   E5     6 #> 14816      1        NA  45       62837   N1     4 #> 14817      1        NA  45       62837   N2     4 #> 14818      1        NA  45       62837   N3     4 #> 14819      1        NA  45       62837   N4     4 #> 14820      1        NA  45       62837   N5     1 #> 14821      1        NA  45       62837   O1     6 #> 14822      1        NA  45       62837   O2     1 #> 14823      1        NA  45       62837   O3     5 #> 14824      1        NA  45       62837   O4     6 #> 14825      1        NA  45       62837   O5     1 #> 14826      2         3  38       62839   A1     4 #> 14827      2         3  38       62839   A2     6 #> 14828      2         3  38       62839   A3     6 #> 14829      2         3  38       62839   A4     2 #> 14830      2         3  38       62839   A5     5 #> 14831      2         3  38       62839   C1     5 #> 14832      2         3  38       62839   C2     5 #> 14833      2         3  38       62839   C3     5 #> 14834      2         3  38       62839   C4     2 #> 14835      2         3  38       62839   C5     1 #> 14836      2         3  38       62839   E1     5 #> 14837      2         3  38       62839   E2     2 #> 14838      2         3  38       62839   E3     6 #> 14839      2         3  38       62839   E4     6 #> 14840      2         3  38       62839   E5     6 #> 14841      2         3  38       62839   N1     2 #> 14842      2         3  38       62839   N2     6 #> 14843      2         3  38       62839   N3     6 #> 14844      2         3  38       62839   N4     3 #> 14845      2         3  38       62839   N5     2 #> 14846      2         3  38       62839   O1     6 #> 14847      2         3  38       62839   O2     6 #> 14848      2         3  38       62839   O3     5 #> 14849      2         3  38       62839   O4     4 #> 14850      2         3  38       62839   O5     4 #> 14851      2         1  22       62840   A1     1 #> 14852      2         1  22       62840   A2     5 #> 14853      2         1  22       62840   A3     5 #> 14854      2         1  22       62840   A4     6 #> 14855      2         1  22       62840   A5     5 #> 14856      2         1  22       62840   C1     4 #> 14857      2         1  22       62840   C2     4 #> 14858      2         1  22       62840   C3     4 #> 14859      2         1  22       62840   C4     1 #> 14860      2         1  22       62840   C5     2 #> 14861      2         1  22       62840   E1     4 #> 14862      2         1  22       62840   E2     2 #> 14863      2         1  22       62840   E3     5 #> 14864      2         1  22       62840   E4     6 #> 14865      2         1  22       62840   E5     5 #> 14866      2         1  22       62840   N1     3 #> 14867      2         1  22       62840   N2     3 #> 14868      2         1  22       62840   N3     2 #> 14869      2         1  22       62840   N4     1 #> 14870      2         1  22       62840   N5     2 #> 14871      2         1  22       62840   O1     6 #> 14872      2         1  22       62840   O2     1 #> 14873      2         1  22       62840   O3     5 #> 14874      2         1  22       62840   O4     5 #> 14875      2         1  22       62840   O5     2 #> 14876      2         1  23       62844   A1     1 #> 14877      2         1  23       62844   A2     6 #> 14878      2         1  23       62844   A3     5 #> 14879      2         1  23       62844   A4     6 #> 14880      2         1  23       62844   A5     4 #> 14881      2         1  23       62844   C1     5 #> 14882      2         1  23       62844   C2     5 #> 14883      2         1  23       62844   C3     5 #> 14884      2         1  23       62844   C4     2 #> 14885      2         1  23       62844   C5     2 #> 14886      2         1  23       62844   E1     2 #> 14887      2         1  23       62844   E2     2 #> 14888      2         1  23       62844   E3     3 #> 14889      2         1  23       62844   E4     6 #> 14890      2         1  23       62844   E5     3 #> 14891      2         1  23       62844   N1     5 #> 14892      2         1  23       62844   N2     5 #> 14893      2         1  23       62844   N3     5 #> 14894      2         1  23       62844   N4     1 #> 14895      2         1  23       62844   N5     4 #> 14896      2         1  23       62844   O1     4 #> 14897      2         1  23       62844   O2     6 #> 14898      2         1  23       62844   O3     5 #> 14899      2         1  23       62844   O4     5 #> 14900      2         1  23       62844   O5     2 #> 14901      2         3  25       62846   A1     5 #> 14902      2         3  25       62846   A2     6 #> 14903      2         3  25       62846   A3     2 #> 14904      2         3  25       62846   A4     6 #> 14905      2         3  25       62846   A5     5 #> 14906      2         3  25       62846   C1     4 #> 14907      2         3  25       62846   C2     5 #> 14908      2         3  25       62846   C3     4 #> 14909      2         3  25       62846   C4     2 #> 14910      2         3  25       62846   C5     4 #> 14911      2         3  25       62846   E1     5 #> 14912      2         3  25       62846   E2     2 #> 14913      2         3  25       62846   E3     3 #> 14914      2         3  25       62846   E4     5 #> 14915      2         3  25       62846   E5     4 #> 14916      2         3  25       62846   N1     1 #> 14917      2         3  25       62846   N2     3 #> 14918      2         3  25       62846   N3     1 #> 14919      2         3  25       62846   N4     2 #> 14920      2         3  25       62846   N5     3 #> 14921      2         3  25       62846   O1     6 #> 14922      2         3  25       62846   O2     2 #> 14923      2         3  25       62846   O3     2 #> 14924      2         3  25       62846   O4     1 #> 14925      2         3  25       62846   O5     1 #> 14926      2         3  35       62847   A1    NA #> 14927      2         3  35       62847   A2     6 #> 14928      2         3  35       62847   A3     6 #> 14929      2         3  35       62847   A4    NA #> 14930      2         3  35       62847   A5     6 #> 14931      2         3  35       62847   C1     6 #> 14932      2         3  35       62847   C2     6 #> 14933      2         3  35       62847   C3     5 #> 14934      2         3  35       62847   C4     1 #> 14935      2         3  35       62847   C5     1 #> 14936      2         3  35       62847   E1     1 #> 14937      2         3  35       62847   E2     1 #> 14938      2         3  35       62847   E3     5 #> 14939      2         3  35       62847   E4     6 #> 14940      2         3  35       62847   E5     6 #> 14941      2         3  35       62847   N1     1 #> 14942      2         3  35       62847   N2     1 #> 14943      2         3  35       62847   N3     2 #> 14944      2         3  35       62847   N4     1 #> 14945      2         3  35       62847   N5     4 #> 14946      2         3  35       62847   O1     5 #> 14947      2         3  35       62847   O2     1 #> 14948      2         3  35       62847   O3     6 #> 14949      2         3  35       62847   O4     2 #> 14950      2         3  35       62847   O5     4 #> 14951      2         3  33       62849   A1     2 #> 14952      2         3  33       62849   A2     6 #> 14953      2         3  33       62849   A3     5 #> 14954      2         3  33       62849   A4     6 #> 14955      2         3  33       62849   A5     5 #> 14956      2         3  33       62849   C1     4 #> 14957      2         3  33       62849   C2     6 #> 14958      2         3  33       62849   C3     5 #> 14959      2         3  33       62849   C4     1 #> 14960      2         3  33       62849   C5     2 #> 14961      2         3  33       62849   E1     4 #> 14962      2         3  33       62849   E2     4 #> 14963      2         3  33       62849   E3     5 #> 14964      2         3  33       62849   E4     5 #> 14965      2         3  33       62849   E5     6 #> 14966      2         3  33       62849   N1     2 #> 14967      2         3  33       62849   N2     2 #> 14968      2         3  33       62849   N3     4 #> 14969      2         3  33       62849   N4     2 #> 14970      2         3  33       62849   N5     1 #> 14971      2         3  33       62849   O1     5 #> 14972      2         3  33       62849   O2     1 #> 14973      2         3  33       62849   O3     3 #> 14974      2         3  33       62849   O4     5 #> 14975      2         3  33       62849   O5     4 #> 14976      2         2  28       62851   A1     3 #> 14977      2         2  28       62851   A2     6 #> 14978      2         2  28       62851   A3     6 #> 14979      2         2  28       62851   A4     6 #> 14980      2         2  28       62851   A5     3 #> 14981      2         2  28       62851   C1     6 #> 14982      2         2  28       62851   C2     4 #> 14983      2         2  28       62851   C3     6 #> 14984      2         2  28       62851   C4     4 #> 14985      2         2  28       62851   C5     2 #> 14986      2         2  28       62851   E1     1 #> 14987      2         2  28       62851   E2     1 #> 14988      2         2  28       62851   E3     5 #> 14989      2         2  28       62851   E4     6 #> 14990      2         2  28       62851   E5     6 #> 14991      2         2  28       62851   N1     2 #> 14992      2         2  28       62851   N2     3 #> 14993      2         2  28       62851   N3     2 #> 14994      2         2  28       62851   N4     1 #> 14995      2         2  28       62851   N5     2 #> 14996      2         2  28       62851   O1     6 #> 14997      2         2  28       62851   O2     6 #> 14998      2         2  28       62851   O3     5 #> 14999      2         2  28       62851   O4     1 #> 15000      2         2  28       62851   O5     6 #> 15001      2         3  43       62853   A1     1 #> 15002      2         3  43       62853   A2     6 #> 15003      2         3  43       62853   A3     6 #> 15004      2         3  43       62853   A4     6 #> 15005      2         3  43       62853   A5     6 #> 15006      2         3  43       62853   C1     5 #> 15007      2         3  43       62853   C2     6 #> 15008      2         3  43       62853   C3     6 #> 15009      2         3  43       62853   C4     2 #> 15010      2         3  43       62853   C5     4 #> 15011      2         3  43       62853   E1     6 #> 15012      2         3  43       62853   E2     4 #> 15013      2         3  43       62853   E3     5 #> 15014      2         3  43       62853   E4     5 #> 15015      2         3  43       62853   E5     5 #> 15016      2         3  43       62853   N1     2 #> 15017      2         3  43       62853   N2     1 #> 15018      2         3  43       62853   N3     1 #> 15019      2         3  43       62853   N4     2 #> 15020      2         3  43       62853   N5     4 #> 15021      2         3  43       62853   O1     6 #> 15022      2         3  43       62853   O2     1 #> 15023      2         3  43       62853   O3     4 #> 15024      2         3  43       62853   O4     6 #> 15025      2         3  43       62853   O5     2 #> 15026      1         1  30       62856   A1     2 #> 15027      1         1  30       62856   A2     5 #> 15028      1         1  30       62856   A3     5 #> 15029      1         1  30       62856   A4     6 #> 15030      1         1  30       62856   A5     6 #> 15031      1         1  30       62856   C1     4 #> 15032      1         1  30       62856   C2     5 #> 15033      1         1  30       62856   C3     5 #> 15034      1         1  30       62856   C4     1 #> 15035      1         1  30       62856   C5     2 #> 15036      1         1  30       62856   E1     1 #> 15037      1         1  30       62856   E2     2 #> 15038      1         1  30       62856   E3     4 #> 15039      1         1  30       62856   E4     6 #> 15040      1         1  30       62856   E5     5 #> 15041      1         1  30       62856   N1     1 #> 15042      1         1  30       62856   N2     1 #> 15043      1         1  30       62856   N3     1 #> 15044      1         1  30       62856   N4     1 #> 15045      1         1  30       62856   N5     2 #> 15046      1         1  30       62856   O1     6 #> 15047      1         1  30       62856   O2     4 #> 15048      1         1  30       62856   O3     4 #> 15049      1         1  30       62856   O4     1 #> 15050      1         1  30       62856   O5     3 #> 15051      2         3  27       62857   A1     3 #> 15052      2         3  27       62857   A2     4 #> 15053      2         3  27       62857   A3     4 #> 15054      2         3  27       62857   A4     6 #> 15055      2         3  27       62857   A5     4 #> 15056      2         3  27       62857   C1     4 #> 15057      2         3  27       62857   C2     4 #> 15058      2         3  27       62857   C3     3 #> 15059      2         3  27       62857   C4     4 #> 15060      2         3  27       62857   C5     4 #> 15061      2         3  27       62857   E1     1 #> 15062      2         3  27       62857   E2     5 #> 15063      2         3  27       62857   E3     4 #> 15064      2         3  27       62857   E4     3 #> 15065      2         3  27       62857   E5     3 #> 15066      2         3  27       62857   N1     5 #> 15067      2         3  27       62857   N2     5 #> 15068      2         3  27       62857   N3     4 #> 15069      2         3  27       62857   N4     3 #> 15070      2         3  27       62857   N5     3 #> 15071      2         3  27       62857   O1     4 #> 15072      2         3  27       62857   O2     5 #> 15073      2         3  27       62857   O3     4 #> 15074      2         3  27       62857   O4     4 #> 15075      2         3  27       62857   O5     4 #> 15076      2         3  19       62858   A1     4 #> 15077      2         3  19       62858   A2     6 #> 15078      2         3  19       62858   A3     5 #> 15079      2         3  19       62858   A4     6 #> 15080      2         3  19       62858   A5     6 #> 15081      2         3  19       62858   C1     5 #> 15082      2         3  19       62858   C2     5 #> 15083      2         3  19       62858   C3     5 #> 15084      2         3  19       62858   C4     2 #> 15085      2         3  19       62858   C5     2 #> 15086      2         3  19       62858   E1     1 #> 15087      2         3  19       62858   E2     1 #> 15088      2         3  19       62858   E3     4 #> 15089      2         3  19       62858   E4     6 #> 15090      2         3  19       62858   E5     6 #> 15091      2         3  19       62858   N1     2 #> 15092      2         3  19       62858   N2     3 #> 15093      2         3  19       62858   N3     2 #> 15094      2         3  19       62858   N4     2 #> 15095      2         3  19       62858   N5     2 #> 15096      2         3  19       62858   O1     5 #> 15097      2         3  19       62858   O2     4 #> 15098      2         3  19       62858   O3     4 #> 15099      2         3  19       62858   O4     5 #> 15100      2         3  19       62858   O5     4 #> 15101      2         2  23       62861   A1     2 #> 15102      2         2  23       62861   A2     6 #> 15103      2         2  23       62861   A3     4 #> 15104      2         2  23       62861   A4     6 #> 15105      2         2  23       62861   A5     5 #> 15106      2         2  23       62861   C1     4 #> 15107      2         2  23       62861   C2     4 #> 15108      2         2  23       62861   C3     2 #> 15109      2         2  23       62861   C4     4 #> 15110      2         2  23       62861   C5     4 #> 15111      2         2  23       62861   E1     2 #> 15112      2         2  23       62861   E2     2 #> 15113      2         2  23       62861   E3     6 #> 15114      2         2  23       62861   E4     6 #> 15115      2         2  23       62861   E5     5 #> 15116      2         2  23       62861   N1     1 #> 15117      2         2  23       62861   N2     2 #> 15118      2         2  23       62861   N3     5 #> 15119      2         2  23       62861   N4     4 #> 15120      2         2  23       62861   N5     2 #> 15121      2         2  23       62861   O1     5 #> 15122      2         2  23       62861   O2     2 #> 15123      2         2  23       62861   O3     6 #> 15124      2         2  23       62861   O4     6 #> 15125      2         2  23       62861   O5     1 #> 15126      2         3  31       62863   A1     1 #> 15127      2         3  31       62863   A2     6 #> 15128      2         3  31       62863   A3     5 #> 15129      2         3  31       62863   A4     6 #> 15130      2         3  31       62863   A5     6 #> 15131      2         3  31       62863   C1     3 #> 15132      2         3  31       62863   C2     2 #> 15133      2         3  31       62863   C3     5 #> 15134      2         3  31       62863   C4     2 #> 15135      2         3  31       62863   C5    NA #> 15136      2         3  31       62863   E1     1 #> 15137      2         3  31       62863   E2     1 #> 15138      2         3  31       62863   E3     4 #> 15139      2         3  31       62863   E4     6 #> 15140      2         3  31       62863   E5     6 #> 15141      2         3  31       62863   N1     3 #> 15142      2         3  31       62863   N2     3 #> 15143      2         3  31       62863   N3     3 #> 15144      2         3  31       62863   N4     3 #> 15145      2         3  31       62863   N5     2 #> 15146      2         3  31       62863   O1     6 #> 15147      2         3  31       62863   O2     2 #> 15148      2         3  31       62863   O3     4 #> 15149      2         3  31       62863   O4     6 #> 15150      2         3  31       62863   O5     2 #> 15151      2         1  22       62864   A1     3 #> 15152      2         1  22       62864   A2     4 #> 15153      2         1  22       62864   A3     2 #> 15154      2         1  22       62864   A4     4 #> 15155      2         1  22       62864   A5     4 #> 15156      2         1  22       62864   C1     5 #> 15157      2         1  22       62864   C2     1 #> 15158      2         1  22       62864   C3     4 #> 15159      2         1  22       62864   C4     2 #> 15160      2         1  22       62864   C5     1 #> 15161      2         1  22       62864   E1     1 #> 15162      2         1  22       62864   E2     4 #> 15163      2         1  22       62864   E3     3 #> 15164      2         1  22       62864   E4     3 #> 15165      2         1  22       62864   E5     4 #> 15166      2         1  22       62864   N1     6 #> 15167      2         1  22       62864   N2     6 #> 15168      2         1  22       62864   N3     6 #> 15169      2         1  22       62864   N4     2 #> 15170      2         1  22       62864   N5     1 #> 15171      2         1  22       62864   O1     3 #> 15172      2         1  22       62864   O2     6 #> 15173      2         1  22       62864   O3     1 #> 15174      2         1  22       62864   O4     5 #> 15175      2         1  22       62864   O5     1 #> 15176      1         3  40       62867   A1     1 #> 15177      1         3  40       62867   A2     6 #> 15178      1         3  40       62867   A3     6 #> 15179      1         3  40       62867   A4     4 #> 15180      1         3  40       62867   A5     5 #> 15181      1         3  40       62867   C1     4 #> 15182      1         3  40       62867   C2     3 #> 15183      1         3  40       62867   C3     4 #> 15184      1         3  40       62867   C4     4 #> 15185      1         3  40       62867   C5     4 #> 15186      1         3  40       62867   E1     3 #> 15187      1         3  40       62867   E2     6 #> 15188      1         3  40       62867   E3     6 #> 15189      1         3  40       62867   E4     3 #> 15190      1         3  40       62867   E5     6 #> 15191      1         3  40       62867   N1     1 #> 15192      1         3  40       62867   N2     3 #> 15193      1         3  40       62867   N3     1 #> 15194      1         3  40       62867   N4     3 #> 15195      1         3  40       62867   N5     1 #> 15196      1         3  40       62867   O1     5 #> 15197      1         3  40       62867   O2     3 #> 15198      1         3  40       62867   O3     6 #> 15199      1         3  40       62867   O4     4 #> 15200      1         3  40       62867   O5     1 #> 15201      2         3  40       62869   A1     1 #> 15202      2         3  40       62869   A2     5 #> 15203      2         3  40       62869   A3     6 #> 15204      2         3  40       62869   A4     6 #> 15205      2         3  40       62869   A5     5 #> 15206      2         3  40       62869   C1     4 #> 15207      2         3  40       62869   C2     4 #> 15208      2         3  40       62869   C3     4 #> 15209      2         3  40       62869   C4     2 #> 15210      2         3  40       62869   C5     5 #> 15211      2         3  40       62869   E1     3 #> 15212      2         3  40       62869   E2     5 #> 15213      2         3  40       62869   E3     1 #> 15214      2         3  40       62869   E4     2 #> 15215      2         3  40       62869   E5     4 #> 15216      2         3  40       62869   N1     2 #> 15217      2         3  40       62869   N2     4 #> 15218      2         3  40       62869   N3     4 #> 15219      2         3  40       62869   N4     6 #> 15220      2         3  40       62869   N5     4 #> 15221      2         3  40       62869   O1     4 #> 15222      2         3  40       62869   O2     4 #> 15223      2         3  40       62869   O3     3 #> 15224      2         3  40       62869   O4     4 #> 15225      2         3  40       62869   O5     3 #> 15226      2         3  55       62870   A1     1 #> 15227      2         3  55       62870   A2     6 #> 15228      2         3  55       62870   A3     6 #> 15229      2         3  55       62870   A4     6 #> 15230      2         3  55       62870   A5     6 #> 15231      2         3  55       62870   C1     2 #> 15232      2         3  55       62870   C2    NA #> 15233      2         3  55       62870   C3     6 #> 15234      2         3  55       62870   C4     1 #> 15235      2         3  55       62870   C5    NA #> 15236      2         3  55       62870   E1     6 #> 15237      2         3  55       62870   E2     5 #> 15238      2         3  55       62870   E3     6 #> 15239      2         3  55       62870   E4     6 #> 15240      2         3  55       62870   E5     6 #> 15241      2         3  55       62870   N1     4 #> 15242      2         3  55       62870   N2     1 #> 15243      2         3  55       62870   N3     6 #> 15244      2         3  55       62870   N4     4 #> 15245      2         3  55       62870   N5     5 #> 15246      2         3  55       62870   O1     6 #> 15247      2         3  55       62870   O2     1 #> 15248      2         3  55       62870   O3     5 #> 15249      2         3  55       62870   O4     5 #> 15250      2         3  55       62870   O5     1 #> 15251      2         3  55       62872   A1     1 #> 15252      2         3  55       62872   A2     6 #> 15253      2         3  55       62872   A3     6 #> 15254      2         3  55       62872   A4     6 #> 15255      2         3  55       62872   A5     6 #> 15256      2         3  55       62872   C1     2 #> 15257      2         3  55       62872   C2     5 #> 15258      2         3  55       62872   C3     6 #> 15259      2         3  55       62872   C4     1 #> 15260      2         3  55       62872   C5    NA #> 15261      2         3  55       62872   E1     6 #> 15262      2         3  55       62872   E2     5 #> 15263      2         3  55       62872   E3     6 #> 15264      2         3  55       62872   E4     6 #> 15265      2         3  55       62872   E5     6 #> 15266      2         3  55       62872   N1     4 #> 15267      2         3  55       62872   N2     1 #> 15268      2         3  55       62872   N3     6 #> 15269      2         3  55       62872   N4     4 #> 15270      2         3  55       62872   N5     5 #> 15271      2         3  55       62872   O1     6 #> 15272      2         3  55       62872   O2     1 #> 15273      2         3  55       62872   O3     5 #> 15274      2         3  55       62872   O4     5 #> 15275      2         3  55       62872   O5     1 #> 15276      2         3  54       62874   A1     1 #> 15277      2         3  54       62874   A2     6 #> 15278      2         3  54       62874   A3     6 #> 15279      2         3  54       62874   A4     6 #> 15280      2         3  54       62874   A5     6 #> 15281      2         3  54       62874   C1     5 #> 15282      2         3  54       62874   C2     6 #> 15283      2         3  54       62874   C3     6 #> 15284      2         3  54       62874   C4     1 #> 15285      2         3  54       62874   C5     1 #> 15286      2         3  54       62874   E1     1 #> 15287      2         3  54       62874   E2     1 #> 15288      2         3  54       62874   E3     6 #> 15289      2         3  54       62874   E4     6 #> 15290      2         3  54       62874   E5     6 #> 15291      2         3  54       62874   N1     1 #> 15292      2         3  54       62874   N2     1 #> 15293      2         3  54       62874   N3     1 #> 15294      2         3  54       62874   N4     1 #> 15295      2         3  54       62874   N5     2 #> 15296      2         3  54       62874   O1     5 #> 15297      2         3  54       62874   O2     2 #> 15298      2         3  54       62874   O3     3 #> 15299      2         3  54       62874   O4     4 #> 15300      2         3  54       62874   O5     1 #> 15301      2         1  53       62876   A1     1 #> 15302      2         1  53       62876   A2     5 #> 15303      2         1  53       62876   A3     4 #> 15304      2         1  53       62876   A4     5 #> 15305      2         1  53       62876   A5     4 #> 15306      2         1  53       62876   C1     4 #> 15307      2         1  53       62876   C2     2 #> 15308      2         1  53       62876   C3     3 #> 15309      2         1  53       62876   C4    NA #> 15310      2         1  53       62876   C5     3 #> 15311      2         1  53       62876   E1     3 #> 15312      2         1  53       62876   E2     4 #> 15313      2         1  53       62876   E3     3 #> 15314      2         1  53       62876   E4     1 #> 15315      2         1  53       62876   E5     4 #> 15316      2         1  53       62876   N1     3 #> 15317      2         1  53       62876   N2     4 #> 15318      2         1  53       62876   N3     4 #> 15319      2         1  53       62876   N4     4 #> 15320      2         1  53       62876   N5     4 #> 15321      2         1  53       62876   O1     4 #> 15322      2         1  53       62876   O2     4 #> 15323      2         1  53       62876   O3    NA #> 15324      2         1  53       62876   O4     5 #> 15325      2         1  53       62876   O5     1 #> 15326      1         3  20       62877   A1     2 #> 15327      1         3  20       62877   A2     6 #> 15328      1         3  20       62877   A3     6 #> 15329      1         3  20       62877   A4     6 #> 15330      1         3  20       62877   A5     6 #> 15331      1         3  20       62877   C1     5 #> 15332      1         3  20       62877   C2     4 #> 15333      1         3  20       62877   C3     5 #> 15334      1         3  20       62877   C4     1 #> 15335      1         3  20       62877   C5     4 #> 15336      1         3  20       62877   E1     6 #> 15337      1         3  20       62877   E2     4 #> 15338      1         3  20       62877   E3     6 #> 15339      1         3  20       62877   E4     5 #> 15340      1         3  20       62877   E5     4 #> 15341      1         3  20       62877   N1     3 #> 15342      1         3  20       62877   N2     3 #> 15343      1         3  20       62877   N3     2 #> 15344      1         3  20       62877   N4     5 #> 15345      1         3  20       62877   N5     3 #> 15346      1         3  20       62877   O1    NA #> 15347      1         3  20       62877   O2     1 #> 15348      1         3  20       62877   O3     5 #> 15349      1         3  20       62877   O4     6 #> 15350      1         3  20       62877   O5     1 #> 15351      1         3  52       62878   A1     2 #> 15352      1         3  52       62878   A2     5 #> 15353      1         3  52       62878   A3     5 #> 15354      1         3  52       62878   A4     6 #> 15355      1         3  52       62878   A5     6 #> 15356      1         3  52       62878   C1     2 #> 15357      1         3  52       62878   C2     6 #> 15358      1         3  52       62878   C3     4 #> 15359      1         3  52       62878   C4     1 #> 15360      1         3  52       62878   C5     3 #> 15361      1         3  52       62878   E1     5 #> 15362      1         3  52       62878   E2     4 #> 15363      1         3  52       62878   E3     6 #> 15364      1         3  52       62878   E4     4 #> 15365      1         3  52       62878   E5     5 #> 15366      1         3  52       62878   N1     1 #> 15367      1         3  52       62878   N2     1 #> 15368      1         3  52       62878   N3     1 #> 15369      1         3  52       62878   N4     1 #> 15370      1         3  52       62878   N5     5 #> 15371      1         3  52       62878   O1     6 #> 15372      1         3  52       62878   O2     4 #> 15373      1         3  52       62878   O3     6 #> 15374      1         3  52       62878   O4     4 #> 15375      1         3  52       62878   O5     3 #> 15376      2         3  25       62879   A1     1 #> 15377      2         3  25       62879   A2     6 #> 15378      2         3  25       62879   A3     6 #> 15379      2         3  25       62879   A4     6 #> 15380      2         3  25       62879   A5     6 #> 15381      2         3  25       62879   C1     6 #> 15382      2         3  25       62879   C2     6 #> 15383      2         3  25       62879   C3     5 #> 15384      2         3  25       62879   C4     2 #> 15385      2         3  25       62879   C5     1 #> 15386      2         3  25       62879   E1     5 #> 15387      2         3  25       62879   E2     6 #> 15388      2         3  25       62879   E3     6 #> 15389      2         3  25       62879   E4     5 #> 15390      2         3  25       62879   E5     6 #> 15391      2         3  25       62879   N1     1 #> 15392      2         3  25       62879   N2     2 #> 15393      2         3  25       62879   N3     1 #> 15394      2         3  25       62879   N4     2 #> 15395      2         3  25       62879   N5     1 #> 15396      2         3  25       62879   O1     6 #> 15397      2         3  25       62879   O2     1 #> 15398      2         3  25       62879   O3     6 #> 15399      2         3  25       62879   O4     6 #> 15400      2         3  25       62879   O5     1 #> 15401      1         3  46       62881   A1     2 #> 15402      1         3  46       62881   A2     6 #> 15403      1         3  46       62881   A3     6 #> 15404      1         3  46       62881   A4     6 #> 15405      1         3  46       62881   A5     5 #> 15406      1         3  46       62881   C1     6 #> 15407      1         3  46       62881   C2     5 #> 15408      1         3  46       62881   C3     6 #> 15409      1         3  46       62881   C4     1 #> 15410      1         3  46       62881   C5     1 #> 15411      1         3  46       62881   E1     4 #> 15412      1         3  46       62881   E2     5 #> 15413      1         3  46       62881   E3     4 #> 15414      1         3  46       62881   E4     5 #> 15415      1         3  46       62881   E5     6 #> 15416      1         3  46       62881   N1     3 #> 15417      1         3  46       62881   N2     5 #> 15418      1         3  46       62881   N3     5 #> 15419      1         3  46       62881   N4     4 #> 15420      1         3  46       62881   N5     3 #> 15421      1         3  46       62881   O1     6 #> 15422      1         3  46       62881   O2     1 #> 15423      1         3  46       62881   O3     4 #> 15424      1         3  46       62881   O4     5 #> 15425      1         3  46       62881   O5     1 #> 15426      1         3  19       62883   A1     3 #> 15427      1         3  19       62883   A2     4 #> 15428      1         3  19       62883   A3     4 #> 15429      1         3  19       62883   A4     4 #> 15430      1         3  19       62883   A5     4 #> 15431      1         3  19       62883   C1     4 #> 15432      1         3  19       62883   C2     4 #> 15433      1         3  19       62883   C3     4 #> 15434      1         3  19       62883   C4     2 #> 15435      1         3  19       62883   C5     4 #> 15436      1         3  19       62883   E1     5 #> 15437      1         3  19       62883   E2     3 #> 15438      1         3  19       62883   E3     4 #> 15439      1         3  19       62883   E4     4 #> 15440      1         3  19       62883   E5     4 #> 15441      1         3  19       62883   N1     2 #> 15442      1         3  19       62883   N2     2 #> 15443      1         3  19       62883   N3     3 #> 15444      1         3  19       62883   N4     5 #> 15445      1         3  19       62883   N5     2 #> 15446      1         3  19       62883   O1     5 #> 15447      1         3  19       62883   O2     2 #> 15448      1         3  19       62883   O3     4 #> 15449      1         3  19       62883   O4     5 #> 15450      1         3  19       62883   O5     5 #> 15451      1         3  38       62887   A1     1 #> 15452      1         3  38       62887   A2     4 #> 15453      1         3  38       62887   A3     1 #> 15454      1         3  38       62887   A4     4 #> 15455      1         3  38       62887   A5     5 #> 15456      1         3  38       62887   C1     6 #> 15457      1         3  38       62887   C2     6 #> 15458      1         3  38       62887   C3     5 #> 15459      1         3  38       62887   C4     1 #> 15460      1         3  38       62887   C5     1 #> 15461      1         3  38       62887   E1     2 #> 15462      1         3  38       62887   E2     2 #> 15463      1         3  38       62887   E3     4 #> 15464      1         3  38       62887   E4     6 #> 15465      1         3  38       62887   E5     6 #> 15466      1         3  38       62887   N1    NA #> 15467      1         3  38       62887   N2     5 #> 15468      1         3  38       62887   N3    NA #> 15469      1         3  38       62887   N4     2 #> 15470      1         3  38       62887   N5     4 #> 15471      1         3  38       62887   O1     4 #> 15472      1         3  38       62887   O2     4 #> 15473      1         3  38       62887   O3     4 #> 15474      1         3  38       62887   O4     5 #> 15475      1         3  38       62887   O5     5 #> 15476      2         3  27       62889   A1     2 #> 15477      2         3  27       62889   A2     5 #> 15478      2         3  27       62889   A3     5 #> 15479      2         3  27       62889   A4     4 #> 15480      2         3  27       62889   A5     3 #> 15481      2         3  27       62889   C1     4 #> 15482      2         3  27       62889   C2     2 #> 15483      2         3  27       62889   C3     5 #> 15484      2         3  27       62889   C4     4 #> 15485      2         3  27       62889   C5     3 #> 15486      2         3  27       62889   E1     3 #> 15487      2         3  27       62889   E2     3 #> 15488      2         3  27       62889   E3     4 #> 15489      2         3  27       62889   E4     4 #> 15490      2         3  27       62889   E5     5 #> 15491      2         3  27       62889   N1     4 #> 15492      2         3  27       62889   N2     5 #> 15493      2         3  27       62889   N3     4 #> 15494      2         3  27       62889   N4     3 #> 15495      2         3  27       62889   N5     5 #> 15496      2         3  27       62889   O1     5 #> 15497      2         3  27       62889   O2     1 #> 15498      2         3  27       62889   O3     4 #> 15499      2         3  27       62889   O4     4 #> 15500      2         3  27       62889   O5     2 #> 15501      2         3  27       62890   A1     1 #> 15502      2         3  27       62890   A2     6 #> 15503      2         3  27       62890   A3     6 #> 15504      2         3  27       62890   A4     4 #> 15505      2         3  27       62890   A5     4 #> 15506      2         3  27       62890   C1     5 #> 15507      2         3  27       62890   C2     4 #> 15508      2         3  27       62890   C3     5 #> 15509      2         3  27       62890   C4     4 #> 15510      2         3  27       62890   C5     5 #> 15511      2         3  27       62890   E1     2 #> 15512      2         3  27       62890   E2     4 #> 15513      2         3  27       62890   E3     4 #> 15514      2         3  27       62890   E4     4 #> 15515      2         3  27       62890   E5     5 #> 15516      2         3  27       62890   N1     4 #> 15517      2         3  27       62890   N2     5 #> 15518      2         3  27       62890   N3     4 #> 15519      2         3  27       62890   N4     2 #> 15520      2         3  27       62890   N5     5 #> 15521      2         3  27       62890   O1     6 #> 15522      2         3  27       62890   O2     1 #> 15523      2         3  27       62890   O3     5 #> 15524      2         3  27       62890   O4     5 #> 15525      2         3  27       62890   O5     2 #> 15526      1         3  34       62891   A1     2 #> 15527      1         3  34       62891   A2     5 #> 15528      1         3  34       62891   A3     6 #> 15529      1         3  34       62891   A4     4 #> 15530      1         3  34       62891   A5     6 #> 15531      1         3  34       62891   C1     6 #> 15532      1         3  34       62891   C2     6 #> 15533      1         3  34       62891   C3     6 #> 15534      1         3  34       62891   C4     1 #> 15535      1         3  34       62891   C5     2 #> 15536      1         3  34       62891   E1     4 #> 15537      1         3  34       62891   E2     3 #> 15538      1         3  34       62891   E3     5 #> 15539      1         3  34       62891   E4     4 #> 15540      1         3  34       62891   E5     6 #> 15541      1         3  34       62891   N1     2 #> 15542      1         3  34       62891   N2     3 #> 15543      1         3  34       62891   N3     3 #> 15544      1         3  34       62891   N4     2 #> 15545      1         3  34       62891   N5     2 #> 15546      1         3  34       62891   O1     6 #> 15547      1         3  34       62891   O2     1 #> 15548      1         3  34       62891   O3     6 #> 15549      1         3  34       62891   O4     6 #> 15550      1         3  34       62891   O5     1 #> 15551      2         3  21       62897   A1     2 #> 15552      2         3  21       62897   A2     6 #> 15553      2         3  21       62897   A3     5 #> 15554      2         3  21       62897   A4     6 #> 15555      2         3  21       62897   A5     3 #> 15556      2         3  21       62897   C1     4 #> 15557      2         3  21       62897   C2     5 #> 15558      2         3  21       62897   C3     5 #> 15559      2         3  21       62897   C4     1 #> 15560      2         3  21       62897   C5     1 #> 15561      2         3  21       62897   E1     3 #> 15562      2         3  21       62897   E2     6 #> 15563      2         3  21       62897   E3     4 #> 15564      2         3  21       62897   E4     3 #> 15565      2         3  21       62897   E5     6 #> 15566      2         3  21       62897   N1     5 #> 15567      2         3  21       62897   N2     6 #> 15568      2         3  21       62897   N3     2 #> 15569      2         3  21       62897   N4     2 #> 15570      2         3  21       62897   N5     4 #> 15571      2         3  21       62897   O1     6 #> 15572      2         3  21       62897   O2     2 #> 15573      2         3  21       62897   O3     4 #> 15574      2         3  21       62897   O4     4 #> 15575      2         3  21       62897   O5     1 #> 15576      2         2  29       62898   A1     4 #> 15577      2         2  29       62898   A2     6 #> 15578      2         2  29       62898   A3     6 #> 15579      2         2  29       62898   A4     5 #> 15580      2         2  29       62898   A5     6 #> 15581      2         2  29       62898   C1     6 #> 15582      2         2  29       62898   C2     6 #> 15583      2         2  29       62898   C3     6 #> 15584      2         2  29       62898   C4     1 #> 15585      2         2  29       62898   C5     1 #> 15586      2         2  29       62898   E1     1 #> 15587      2         2  29       62898   E2     1 #> 15588      2         2  29       62898   E3     6 #> 15589      2         2  29       62898   E4     6 #> 15590      2         2  29       62898   E5     6 #> 15591      2         2  29       62898   N1     4 #> 15592      2         2  29       62898   N2     2 #> 15593      2         2  29       62898   N3     1 #> 15594      2         2  29       62898   N4     1 #> 15595      2         2  29       62898   N5     2 #> 15596      2         2  29       62898   O1     6 #> 15597      2         2  29       62898   O2     4 #> 15598      2         2  29       62898   O3     6 #> 15599      2         2  29       62898   O4     6 #> 15600      2         2  29       62898   O5     2 #> 15601      2         3  27       62899   A1     2 #> 15602      2         3  27       62899   A2     1 #> 15603      2         3  27       62899   A3     6 #> 15604      2         3  27       62899   A4     6 #> 15605      2         3  27       62899   A5     6 #> 15606      2         3  27       62899   C1     5 #> 15607      2         3  27       62899   C2     6 #> 15608      2         3  27       62899   C3     5 #> 15609      2         3  27       62899   C4     1 #> 15610      2         3  27       62899   C5     1 #> 15611      2         3  27       62899   E1     5 #> 15612      2         3  27       62899   E2     5 #> 15613      2         3  27       62899   E3     6 #> 15614      2         3  27       62899   E4     5 #> 15615      2         3  27       62899   E5     5 #> 15616      2         3  27       62899   N1     3 #> 15617      2         3  27       62899   N2     4 #> 15618      2         3  27       62899   N3     1 #> 15619      2         3  27       62899   N4     5 #> 15620      2         3  27       62899   N5     6 #> 15621      2         3  27       62899   O1     4 #> 15622      2         3  27       62899   O2     2 #> 15623      2         3  27       62899   O3    NA #> 15624      2         3  27       62899   O4     2 #> 15625      2         3  27       62899   O5     5 #> 15626      2         2  41       62901   A1     2 #> 15627      2         2  41       62901   A2     5 #> 15628      2         2  41       62901   A3     4 #> 15629      2         2  41       62901   A4     4 #> 15630      2         2  41       62901   A5     2 #> 15631      2         2  41       62901   C1     4 #> 15632      2         2  41       62901   C2     3 #> 15633      2         2  41       62901   C3     5 #> 15634      2         2  41       62901   C4     4 #> 15635      2         2  41       62901   C5     3 #> 15636      2         2  41       62901   E1     4 #> 15637      2         2  41       62901   E2     4 #> 15638      2         2  41       62901   E3     3 #> 15639      2         2  41       62901   E4     3 #> 15640      2         2  41       62901   E5     3 #> 15641      2         2  41       62901   N1     5 #> 15642      2         2  41       62901   N2     5 #> 15643      2         2  41       62901   N3     6 #> 15644      2         2  41       62901   N4     6 #> 15645      2         2  41       62901   N5     5 #> 15646      2         2  41       62901   O1     4 #> 15647      2         2  41       62901   O2     2 #> 15648      2         2  41       62901   O3     2 #> 15649      2         2  41       62901   O4     5 #> 15650      2         2  41       62901   O5     4 #> 15651      2         3  52       62903   A1     2 #> 15652      2         3  52       62903   A2     5 #> 15653      2         3  52       62903   A3     5 #> 15654      2         3  52       62903   A4     5 #> 15655      2         3  52       62903   A5     1 #> 15656      2         3  52       62903   C1     5 #> 15657      2         3  52       62903   C2     5 #> 15658      2         3  52       62903   C3     2 #> 15659      2         3  52       62903   C4     4 #> 15660      2         3  52       62903   C5     2 #> 15661      2         3  52       62903   E1     2 #> 15662      2         3  52       62903   E2     2 #> 15663      2         3  52       62903   E3    NA #> 15664      2         3  52       62903   E4     5 #> 15665      2         3  52       62903   E5     5 #> 15666      2         3  52       62903   N1     1 #> 15667      2         3  52       62903   N2     1 #> 15668      2         3  52       62903   N3     1 #> 15669      2         3  52       62903   N4     1 #> 15670      2         3  52       62903   N5     1 #> 15671      2         3  52       62903   O1     5 #> 15672      2         3  52       62903   O2     1 #> 15673      2         3  52       62903   O3     4 #> 15674      2         3  52       62903   O4     4 #> 15675      2         3  52       62903   O5     2 #> 15676      2         3  30       62908   A1     3 #> 15677      2         3  30       62908   A2     5 #> 15678      2         3  30       62908   A3     3 #> 15679      2         3  30       62908   A4     6 #> 15680      2         3  30       62908   A5     1 #> 15681      2         3  30       62908   C1     4 #> 15682      2         3  30       62908   C2     3 #> 15683      2         3  30       62908   C3     3 #> 15684      2         3  30       62908   C4     5 #> 15685      2         3  30       62908   C5     5 #> 15686      2         3  30       62908   E1     3 #> 15687      2         3  30       62908   E2     6 #> 15688      2         3  30       62908   E3     1 #> 15689      2         3  30       62908   E4     2 #> 15690      2         3  30       62908   E5     2 #> 15691      2         3  30       62908   N1     6 #> 15692      2         3  30       62908   N2     6 #> 15693      2         3  30       62908   N3     6 #> 15694      2         3  30       62908   N4     5 #> 15695      2         3  30       62908   N5     6 #> 15696      2         3  30       62908   O1     3 #> 15697      2         3  30       62908   O2     6 #> 15698      2         3  30       62908   O3     1 #> 15699      2         3  30       62908   O4     6 #> 15700      2         3  30       62908   O5     4 #> 15701      2         2  50       62910   A1     3 #> 15702      2         2  50       62910   A2     5 #> 15703      2         2  50       62910   A3     5 #> 15704      2         2  50       62910   A4     6 #> 15705      2         2  50       62910   A5     5 #> 15706      2         2  50       62910   C1     5 #> 15707      2         2  50       62910   C2     5 #> 15708      2         2  50       62910   C3     5 #> 15709      2         2  50       62910   C4     2 #> 15710      2         2  50       62910   C5     2 #> 15711      2         2  50       62910   E1     2 #> 15712      2         2  50       62910   E2     2 #> 15713      2         2  50       62910   E3     4 #> 15714      2         2  50       62910   E4     5 #> 15715      2         2  50       62910   E5     4 #> 15716      2         2  50       62910   N1     2 #> 15717      2         2  50       62910   N2     2 #> 15718      2         2  50       62910   N3     2 #> 15719      2         2  50       62910   N4     2 #> 15720      2         2  50       62910   N5     4 #> 15721      2         2  50       62910   O1     4 #> 15722      2         2  50       62910   O2     2 #> 15723      2         2  50       62910   O3     4 #> 15724      2         2  50       62910   O4     5 #> 15725      2         2  50       62910   O5     2 #> 15726      2         3  43       62911   A1     4 #> 15727      2         3  43       62911   A2     4 #> 15728      2         3  43       62911   A3     4 #> 15729      2         3  43       62911   A4     4 #> 15730      2         3  43       62911   A5     4 #> 15731      2         3  43       62911   C1     5 #> 15732      2         3  43       62911   C2     4 #> 15733      2         3  43       62911   C3     4 #> 15734      2         3  43       62911   C4     1 #> 15735      2         3  43       62911   C5     2 #> 15736      2         3  43       62911   E1     4 #> 15737      2         3  43       62911   E2     3 #> 15738      2         3  43       62911   E3     3 #> 15739      2         3  43       62911   E4     4 #> 15740      2         3  43       62911   E5     5 #> 15741      2         3  43       62911   N1     4 #> 15742      2         3  43       62911   N2     5 #> 15743      2         3  43       62911   N3     3 #> 15744      2         3  43       62911   N4     4 #> 15745      2         3  43       62911   N5     3 #> 15746      2         3  43       62911   O1     3 #> 15747      2         3  43       62911   O2     5 #> 15748      2         3  43       62911   O3     4 #> 15749      2         3  43       62911   O4     3 #> 15750      2         3  43       62911   O5     3 #> 15751      2         5  32       62916   A1     2 #> 15752      2         5  32       62916   A2     6 #> 15753      2         5  32       62916   A3     6 #> 15754      2         5  32       62916   A4     6 #> 15755      2         5  32       62916   A5     5 #> 15756      2         5  32       62916   C1     2 #> 15757      2         5  32       62916   C2     1 #> 15758      2         5  32       62916   C3     4 #> 15759      2         5  32       62916   C4     4 #> 15760      2         5  32       62916   C5     1 #> 15761      2         5  32       62916   E1     2 #> 15762      2         5  32       62916   E2     2 #> 15763      2         5  32       62916   E3     5 #> 15764      2         5  32       62916   E4     6 #> 15765      2         5  32       62916   E5     4 #> 15766      2         5  32       62916   N1     4 #> 15767      2         5  32       62916   N2     3 #> 15768      2         5  32       62916   N3     2 #> 15769      2         5  32       62916   N4     1 #> 15770      2         5  32       62916   N5     2 #> 15771      2         5  32       62916   O1     6 #> 15772      2         5  32       62916   O2     1 #> 15773      2         5  32       62916   O3     5 #> 15774      2         5  32       62916   O4     6 #> 15775      2         5  32       62916   O5     2 #> 15776      1         3  19       62918   A1     4 #> 15777      1         3  19       62918   A2     3 #> 15778      1         3  19       62918   A3     1 #> 15779      1         3  19       62918   A4     3 #> 15780      1         3  19       62918   A5     3 #> 15781      1         3  19       62918   C1     6 #> 15782      1         3  19       62918   C2     4 #> 15783      1         3  19       62918   C3     4 #> 15784      1         3  19       62918   C4     2 #> 15785      1         3  19       62918   C5     2 #> 15786      1         3  19       62918   E1     3 #> 15787      1         3  19       62918   E2     4 #> 15788      1         3  19       62918   E3     3 #> 15789      1         3  19       62918   E4     4 #> 15790      1         3  19       62918   E5     5 #> 15791      1         3  19       62918   N1     5 #> 15792      1         3  19       62918   N2     5 #> 15793      1         3  19       62918   N3     6 #> 15794      1         3  19       62918   N4     6 #> 15795      1         3  19       62918   N5     6 #> 15796      1         3  19       62918   O1     5 #> 15797      1         3  19       62918   O2     5 #> 15798      1         3  19       62918   O3     4 #> 15799      1         3  19       62918   O4     6 #> 15800      1         3  19       62918   O5     3 #> 15801      2         4  22       62920   A1     2 #> 15802      2         4  22       62920   A2     6 #> 15803      2         4  22       62920   A3     6 #> 15804      2         4  22       62920   A4     6 #> 15805      2         4  22       62920   A5     6 #> 15806      2         4  22       62920   C1     5 #> 15807      2         4  22       62920   C2     5 #> 15808      2         4  22       62920   C3     5 #> 15809      2         4  22       62920   C4     2 #> 15810      2         4  22       62920   C5     3 #> 15811      2         4  22       62920   E1     5 #> 15812      2         4  22       62920   E2     5 #> 15813      2         4  22       62920   E3     5 #> 15814      2         4  22       62920   E4     5 #> 15815      2         4  22       62920   E5     4 #> 15816      2         4  22       62920   N1     1 #> 15817      2         4  22       62920   N2     5 #> 15818      2         4  22       62920   N3     4 #> 15819      2         4  22       62920   N4     6 #> 15820      2         4  22       62920   N5     6 #> 15821      2         4  22       62920   O1     6 #> 15822      2         4  22       62920   O2     2 #> 15823      2         4  22       62920   O3     6 #> 15824      2         4  22       62920   O4     6 #> 15825      2         4  22       62920   O5     2 #> 15826      2         3  21       62922   A1     3 #> 15827      2         3  21       62922   A2     6 #> 15828      2         3  21       62922   A3     4 #> 15829      2         3  21       62922   A4     6 #> 15830      2         3  21       62922   A5     5 #> 15831      2         3  21       62922   C1     5 #> 15832      2         3  21       62922   C2     5 #> 15833      2         3  21       62922   C3     5 #> 15834      2         3  21       62922   C4     2 #> 15835      2         3  21       62922   C5     4 #> 15836      2         3  21       62922   E1     5 #> 15837      2         3  21       62922   E2     2 #> 15838      2         3  21       62922   E3     4 #> 15839      2         3  21       62922   E4     5 #> 15840      2         3  21       62922   E5     5 #> 15841      2         3  21       62922   N1     1 #> 15842      2         3  21       62922   N2     1 #> 15843      2         3  21       62922   N3     2 #> 15844      2         3  21       62922   N4     2 #> 15845      2         3  21       62922   N5     5 #> 15846      2         3  21       62922   O1     4 #> 15847      2         3  21       62922   O2     4 #> 15848      2         3  21       62922   O3     5 #> 15849      2         3  21       62922   O4     4 #> 15850      2         3  21       62922   O5     4 #> 15851      1         5  50       62926   A1     1 #> 15852      1         5  50       62926   A2     3 #> 15853      1         5  50       62926   A3     2 #> 15854      1         5  50       62926   A4     3 #> 15855      1         5  50       62926   A5     2 #> 15856      1         5  50       62926   C1     5 #> 15857      1         5  50       62926   C2     5 #> 15858      1         5  50       62926   C3     2 #> 15859      1         5  50       62926   C4     4 #> 15860      1         5  50       62926   C5     5 #> 15861      1         5  50       62926   E1     5 #> 15862      1         5  50       62926   E2     5 #> 15863      1         5  50       62926   E3     3 #> 15864      1         5  50       62926   E4     2 #> 15865      1         5  50       62926   E5     3 #> 15866      1         5  50       62926   N1     4 #> 15867      1         5  50       62926   N2     4 #> 15868      1         5  50       62926   N3     5 #> 15869      1         5  50       62926   N4     6 #> 15870      1         5  50       62926   N5     2 #> 15871      1         5  50       62926   O1     6 #> 15872      1         5  50       62926   O2     2 #> 15873      1         5  50       62926   O3     5 #> 15874      1         5  50       62926   O4     6 #> 15875      1         5  50       62926   O5     2 #> 15876      2         4  27       62931   A1     1 #> 15877      2         4  27       62931   A2     6 #> 15878      2         4  27       62931   A3     6 #> 15879      2         4  27       62931   A4     4 #> 15880      2         4  27       62931   A5     6 #> 15881      2         4  27       62931   C1     5 #> 15882      2         4  27       62931   C2     5 #> 15883      2         4  27       62931   C3     6 #> 15884      2         4  27       62931   C4     1 #> 15885      2         4  27       62931   C5     2 #> 15886      2         4  27       62931   E1     5 #> 15887      2         4  27       62931   E2     2 #> 15888      2         4  27       62931   E3     4 #> 15889      2         4  27       62931   E4     5 #> 15890      2         4  27       62931   E5     3 #> 15891      2         4  27       62931   N1     4 #> 15892      2         4  27       62931   N2     3 #> 15893      2         4  27       62931   N3     5 #> 15894      2         4  27       62931   N4     4 #> 15895      2         4  27       62931   N5     6 #> 15896      2         4  27       62931   O1     5 #> 15897      2         4  27       62931   O2     1 #> 15898      2         4  27       62931   O3     3 #> 15899      2         4  27       62931   O4     5 #> 15900      2         4  27       62931   O5     6 #> 15901      2         3  48       62933   A1     1 #> 15902      2         3  48       62933   A2     5 #> 15903      2         3  48       62933   A3     4 #> 15904      2         3  48       62933   A4     5 #> 15905      2         3  48       62933   A5     5 #> 15906      2         3  48       62933   C1     1 #> 15907      2         3  48       62933   C2     6 #> 15908      2         3  48       62933   C3     4 #> 15909      2         3  48       62933   C4     1 #> 15910      2         3  48       62933   C5     1 #> 15911      2         3  48       62933   E1     4 #> 15912      2         3  48       62933   E2     4 #> 15913      2         3  48       62933   E3     1 #> 15914      2         3  48       62933   E4     5 #> 15915      2         3  48       62933   E5     5 #> 15916      2         3  48       62933   N1     1 #> 15917      2         3  48       62933   N2     2 #> 15918      2         3  48       62933   N3     1 #> 15919      2         3  48       62933   N4     3 #> 15920      2         3  48       62933   N5     4 #> 15921      2         3  48       62933   O1     3 #> 15922      2         3  48       62933   O2     3 #> 15923      2         3  48       62933   O3     3 #> 15924      2         3  48       62933   O4     1 #> 15925      2         3  48       62933   O5     2 #> 15926      2        NA  12       62934   A1     4 #> 15927      2        NA  12       62934   A2     4 #> 15928      2        NA  12       62934   A3     2 #> 15929      2        NA  12       62934   A4     4 #> 15930      2        NA  12       62934   A5     4 #> 15931      2        NA  12       62934   C1     4 #> 15932      2        NA  12       62934   C2     4 #> 15933      2        NA  12       62934   C3     4 #> 15934      2        NA  12       62934   C4     3 #> 15935      2        NA  12       62934   C5     4 #> 15936      2        NA  12       62934   E1     3 #> 15937      2        NA  12       62934   E2     5 #> 15938      2        NA  12       62934   E3     2 #> 15939      2        NA  12       62934   E4     2 #> 15940      2        NA  12       62934   E5     4 #> 15941      2        NA  12       62934   N1     2 #> 15942      2        NA  12       62934   N2     3 #> 15943      2        NA  12       62934   N3     3 #> 15944      2        NA  12       62934   N4     4 #> 15945      2        NA  12       62934   N5     4 #> 15946      2        NA  12       62934   O1     2 #> 15947      2        NA  12       62934   O2     2 #> 15948      2        NA  12       62934   O3     4 #> 15949      2        NA  12       62934   O4     4 #> 15950      2        NA  12       62934   O5     2 #> 15951      2         3  43       62936   A1     1 #> 15952      2         3  43       62936   A2     4 #> 15953      2         3  43       62936   A3     4 #> 15954      2         3  43       62936   A4     5 #> 15955      2         3  43       62936   A5     5 #> 15956      2         3  43       62936   C1     3 #> 15957      2         3  43       62936   C2     1 #> 15958      2         3  43       62936   C3     2 #> 15959      2         3  43       62936   C4     2 #> 15960      2         3  43       62936   C5     2 #> 15961      2         3  43       62936   E1     4 #> 15962      2         3  43       62936   E2     5 #> 15963      2         3  43       62936   E3     2 #> 15964      2         3  43       62936   E4     3 #> 15965      2         3  43       62936   E5     5 #> 15966      2         3  43       62936   N1     2 #> 15967      2         3  43       62936   N2    NA #> 15968      2         3  43       62936   N3     1 #> 15969      2         3  43       62936   N4     4 #> 15970      2         3  43       62936   N5     1 #> 15971      2         3  43       62936   O1     2 #> 15972      2         3  43       62936   O2     4 #> 15973      2         3  43       62936   O3     4 #> 15974      2         3  43       62936   O4     5 #> 15975      2         3  43       62936   O5     2 #> 15976      1         3  37       62938   A1     2 #> 15977      1         3  37       62938   A2     5 #> 15978      1         3  37       62938   A3     5 #> 15979      1         3  37       62938   A4     6 #> 15980      1         3  37       62938   A5     5 #> 15981      1         3  37       62938   C1     6 #> 15982      1         3  37       62938   C2     5 #> 15983      1         3  37       62938   C3     4 #> 15984      1         3  37       62938   C4     2 #> 15985      1         3  37       62938   C5     2 #> 15986      1         3  37       62938   E1     1 #> 15987      1         3  37       62938   E2     1 #> 15988      1         3  37       62938   E3     6 #> 15989      1         3  37       62938   E4     6 #> 15990      1         3  37       62938   E5     6 #> 15991      1         3  37       62938   N1     2 #> 15992      1         3  37       62938   N2     2 #> 15993      1         3  37       62938   N3     2 #> 15994      1         3  37       62938   N4     2 #> 15995      1         3  37       62938   N5     1 #> 15996      1         3  37       62938   O1     6 #> 15997      1         3  37       62938   O2     2 #> 15998      1         3  37       62938   O3     6 #> 15999      1         3  37       62938   O4     4 #> 16000      1         3  37       62938   O5     2 #> 16001      2         3  18       62939   A1     3 #> 16002      2         3  18       62939   A2     5 #> 16003      2         3  18       62939   A3     5 #> 16004      2         3  18       62939   A4     5 #> 16005      2         3  18       62939   A5     4 #> 16006      2         3  18       62939   C1     4 #> 16007      2         3  18       62939   C2     4 #> 16008      2         3  18       62939   C3     4 #> 16009      2         3  18       62939   C4     3 #> 16010      2         3  18       62939   C5     3 #> 16011      2         3  18       62939   E1     4 #> 16012      2         3  18       62939   E2     2 #> 16013      2         3  18       62939   E3     4 #> 16014      2         3  18       62939   E4     4 #> 16015      2         3  18       62939   E5     5 #> 16016      2         3  18       62939   N1     3 #> 16017      2         3  18       62939   N2     3 #> 16018      2         3  18       62939   N3     3 #> 16019      2         3  18       62939   N4     2 #> 16020      2         3  18       62939   N5     2 #> 16021      2         3  18       62939   O1     5 #> 16022      2         3  18       62939   O2     2 #> 16023      2         3  18       62939   O3     5 #> 16024      2         3  18       62939   O4     5 #> 16025      2         3  18       62939   O5     1 #> 16026      2         3  18       62941   A1     2 #> 16027      2         3  18       62941   A2     5 #> 16028      2         3  18       62941   A3     4 #> 16029      2         3  18       62941   A4     5 #> 16030      2         3  18       62941   A5     5 #> 16031      2         3  18       62941   C1     4 #> 16032      2         3  18       62941   C2     4 #> 16033      2         3  18       62941   C3     4 #> 16034      2         3  18       62941   C4     3 #> 16035      2         3  18       62941   C5     3 #> 16036      2         3  18       62941   E1     4 #> 16037      2         3  18       62941   E2     3 #> 16038      2         3  18       62941   E3     3 #> 16039      2         3  18       62941   E4     4 #> 16040      2         3  18       62941   E5     5 #> 16041      2         3  18       62941   N1     3 #> 16042      2         3  18       62941   N2     4 #> 16043      2         3  18       62941   N3     2 #> 16044      2         3  18       62941   N4     3 #> 16045      2         3  18       62941   N5     3 #> 16046      2         3  18       62941   O1     5 #> 16047      2         3  18       62941   O2     2 #> 16048      2         3  18       62941   O3     5 #> 16049      2         3  18       62941   O4     5 #> 16050      2         3  18       62941   O5     2 #> 16051      2         3  24       62942   A1     1 #> 16052      2         3  24       62942   A2     5 #> 16053      2         3  24       62942   A3     5 #> 16054      2         3  24       62942   A4     6 #> 16055      2         3  24       62942   A5     6 #> 16056      2         3  24       62942   C1     5 #> 16057      2         3  24       62942   C2     6 #> 16058      2         3  24       62942   C3     4 #> 16059      2         3  24       62942   C4     2 #> 16060      2         3  24       62942   C5     2 #> 16061      2         3  24       62942   E1     5 #> 16062      2         3  24       62942   E2     4 #> 16063      2         3  24       62942   E3     5 #> 16064      2         3  24       62942   E4     5 #> 16065      2         3  24       62942   E5     5 #> 16066      2         3  24       62942   N1     4 #> 16067      2         3  24       62942   N2     4 #> 16068      2         3  24       62942   N3     2 #> 16069      2         3  24       62942   N4     5 #> 16070      2         3  24       62942   N5     4 #> 16071      2         3  24       62942   O1     4 #> 16072      2         3  24       62942   O2     1 #> 16073      2         3  24       62942   O3     3 #> 16074      2         3  24       62942   O4     4 #> 16075      2         3  24       62942   O5     2 #> 16076      2         3  23       62948   A1     4 #> 16077      2         3  23       62948   A2     6 #> 16078      2         3  23       62948   A3     6 #> 16079      2         3  23       62948   A4     6 #> 16080      2         3  23       62948   A5     6 #> 16081      2         3  23       62948   C1     4 #> 16082      2         3  23       62948   C2     3 #> 16083      2         3  23       62948   C3     1 #> 16084      2         3  23       62948   C4     3 #> 16085      2         3  23       62948   C5     2 #> 16086      2         3  23       62948   E1     1 #> 16087      2         3  23       62948   E2     4 #> 16088      2         3  23       62948   E3     4 #> 16089      2         3  23       62948   E4     6 #> 16090      2         3  23       62948   E5     5 #> 16091      2         3  23       62948   N1     3 #> 16092      2         3  23       62948   N2     6 #> 16093      2         3  23       62948   N3     6 #> 16094      2         3  23       62948   N4     2 #> 16095      2         3  23       62948   N5     1 #> 16096      2         3  23       62948   O1     5 #> 16097      2         3  23       62948   O2     4 #> 16098      2         3  23       62948   O3     3 #> 16099      2         3  23       62948   O4     5 #> 16100      2         3  23       62948   O5     1 #> 16101      2         3  20       62949   A1     3 #> 16102      2         3  20       62949   A2     4 #> 16103      2         3  20       62949   A3     6 #> 16104      2         3  20       62949   A4     3 #> 16105      2         3  20       62949   A5     3 #> 16106      2         3  20       62949   C1     6 #> 16107      2         3  20       62949   C2     4 #> 16108      2         3  20       62949   C3     4 #> 16109      2         3  20       62949   C4     2 #> 16110      2         3  20       62949   C5     3 #> 16111      2         3  20       62949   E1     1 #> 16112      2         3  20       62949   E2     2 #> 16113      2         3  20       62949   E3     6 #> 16114      2         3  20       62949   E4     6 #> 16115      2         3  20       62949   E5     6 #> 16116      2         3  20       62949   N1    NA #> 16117      2         3  20       62949   N2     6 #> 16118      2         3  20       62949   N3     4 #> 16119      2         3  20       62949   N4     2 #> 16120      2         3  20       62949   N5     3 #> 16121      2         3  20       62949   O1     6 #> 16122      2         3  20       62949   O2     3 #> 16123      2         3  20       62949   O3     6 #> 16124      2         3  20       62949   O4     4 #> 16125      2         3  20       62949   O5    NA #> 16126      2         2  32       62950   A1     1 #> 16127      2         2  32       62950   A2     6 #> 16128      2         2  32       62950   A3     6 #> 16129      2         2  32       62950   A4     6 #> 16130      2         2  32       62950   A5     6 #> 16131      2         2  32       62950   C1     6 #> 16132      2         2  32       62950   C2     5 #> 16133      2         2  32       62950   C3     4 #> 16134      2         2  32       62950   C4     1 #> 16135      2         2  32       62950   C5     1 #> 16136      2         2  32       62950   E1     5 #> 16137      2         2  32       62950   E2     3 #> 16138      2         2  32       62950   E3     4 #> 16139      2         2  32       62950   E4     5 #> 16140      2         2  32       62950   E5     6 #> 16141      2         2  32       62950   N1     3 #> 16142      2         2  32       62950   N2     5 #> 16143      2         2  32       62950   N3     2 #> 16144      2         2  32       62950   N4     2 #> 16145      2         2  32       62950   N5     2 #> 16146      2         2  32       62950   O1     1 #> 16147      2         2  32       62950   O2     2 #> 16148      2         2  32       62950   O3     5 #> 16149      2         2  32       62950   O4     3 #> 16150      2         2  32       62950   O5     2 #> 16151      1         4  37       62951   A1     1 #> 16152      1         4  37       62951   A2     4 #> 16153      1         4  37       62951   A3     5 #> 16154      1         4  37       62951   A4     5 #> 16155      1         4  37       62951   A5     5 #> 16156      1         4  37       62951   C1     4 #> 16157      1         4  37       62951   C2     2 #> 16158      1         4  37       62951   C3     4 #> 16159      1         4  37       62951   C4     2 #> 16160      1         4  37       62951   C5     4 #> 16161      1         4  37       62951   E1     4 #> 16162      1         4  37       62951   E2     2 #> 16163      1         4  37       62951   E3     2 #> 16164      1         4  37       62951   E4     5 #> 16165      1         4  37       62951   E5     3 #> 16166      1         4  37       62951   N1     2 #> 16167      1         4  37       62951   N2     2 #> 16168      1         4  37       62951   N3     2 #> 16169      1         4  37       62951   N4     2 #> 16170      1         4  37       62951   N5     1 #> 16171      1         4  37       62951   O1     5 #> 16172      1         4  37       62951   O2     1 #> 16173      1         4  37       62951   O3     5 #> 16174      1         4  37       62951   O4     6 #> 16175      1         4  37       62951   O5     2 #> 16176      1         3  19       62953   A1     1 #> 16177      1         3  19       62953   A2     6 #> 16178      1         3  19       62953   A3     6 #> 16179      1         3  19       62953   A4     6 #> 16180      1         3  19       62953   A5     5 #> 16181      1         3  19       62953   C1     6 #> 16182      1         3  19       62953   C2     5 #> 16183      1         3  19       62953   C3     5 #> 16184      1         3  19       62953   C4     1 #> 16185      1         3  19       62953   C5     2 #> 16186      1         3  19       62953   E1     5 #> 16187      1         3  19       62953   E2     5 #> 16188      1         3  19       62953   E3     4 #> 16189      1         3  19       62953   E4     5 #> 16190      1         3  19       62953   E5     2 #> 16191      1         3  19       62953   N1     2 #> 16192      1         3  19       62953   N2     3 #> 16193      1         3  19       62953   N3     5 #> 16194      1         3  19       62953   N4     2 #> 16195      1         3  19       62953   N5     2 #> 16196      1         3  19       62953   O1     6 #> 16197      1         3  19       62953   O2     2 #> 16198      1         3  19       62953   O3     5 #> 16199      1         3  19       62953   O4     4 #> 16200      1         3  19       62953   O5     2 #> 16201      2         3  29       62954   A1     2 #> 16202      2         3  29       62954   A2     4 #> 16203      2         3  29       62954   A3     4 #> 16204      2         3  29       62954   A4     5 #> 16205      2         3  29       62954   A5     2 #> 16206      2         3  29       62954   C1     5 #> 16207      2         3  29       62954   C2     5 #> 16208      2         3  29       62954   C3     1 #> 16209      2         3  29       62954   C4     1 #> 16210      2         3  29       62954   C5     6 #> 16211      2         3  29       62954   E1     3 #> 16212      2         3  29       62954   E2     4 #> 16213      2         3  29       62954   E3     3 #> 16214      2         3  29       62954   E4     2 #> 16215      2         3  29       62954   E5     1 #> 16216      2         3  29       62954   N1     2 #> 16217      2         3  29       62954   N2     2 #> 16218      2         3  29       62954   N3     4 #> 16219      2         3  29       62954   N4     6 #> 16220      2         3  29       62954   N5     4 #> 16221      2         3  29       62954   O1     2 #> 16222      2         3  29       62954   O2     2 #> 16223      2         3  29       62954   O3     6 #> 16224      2         3  29       62954   O4     5 #> 16225      2         3  29       62954   O5     1 #> 16226      2         3  20       62957   A1     2 #> 16227      2         3  20       62957   A2     5 #> 16228      2         3  20       62957   A3     2 #> 16229      2         3  20       62957   A4     6 #> 16230      2         3  20       62957   A5     4 #> 16231      2         3  20       62957   C1     6 #> 16232      2         3  20       62957   C2     5 #> 16233      2         3  20       62957   C3     4 #> 16234      2         3  20       62957   C4     1 #> 16235      2         3  20       62957   C5     2 #> 16236      2         3  20       62957   E1     2 #> 16237      2         3  20       62957   E2     2 #> 16238      2         3  20       62957   E3     5 #> 16239      2         3  20       62957   E4     5 #> 16240      2         3  20       62957   E5     5 #> 16241      2         3  20       62957   N1     1 #> 16242      2         3  20       62957   N2     1 #> 16243      2         3  20       62957   N3     1 #> 16244      2         3  20       62957   N4     1 #> 16245      2         3  20       62957   N5     1 #> 16246      2         3  20       62957   O1     5 #> 16247      2         3  20       62957   O2     1 #> 16248      2         3  20       62957   O3     3 #> 16249      2         3  20       62957   O4     5 #> 16250      2         3  20       62957   O5     2 #> 16251      2         4  26       62962   A1     1 #> 16252      2         4  26       62962   A2     5 #> 16253      2         4  26       62962   A3     6 #> 16254      2         4  26       62962   A4     6 #> 16255      2         4  26       62962   A5     5 #> 16256      2         4  26       62962   C1     4 #> 16257      2         4  26       62962   C2     2 #> 16258      2         4  26       62962   C3     4 #> 16259      2         4  26       62962   C4     1 #> 16260      2         4  26       62962   C5     1 #> 16261      2         4  26       62962   E1     2 #> 16262      2         4  26       62962   E2     2 #> 16263      2         4  26       62962   E3     5 #> 16264      2         4  26       62962   E4     6 #> 16265      2         4  26       62962   E5     5 #> 16266      2         4  26       62962   N1     1 #> 16267      2         4  26       62962   N2     2 #> 16268      2         4  26       62962   N3     1 #> 16269      2         4  26       62962   N4     1 #> 16270      2         4  26       62962   N5     2 #> 16271      2         4  26       62962   O1     4 #> 16272      2         4  26       62962   O2     4 #> 16273      2         4  26       62962   O3     6 #> 16274      2         4  26       62962   O4     4 #> 16275      2         4  26       62962   O5     1 #> 16276      2         4  33       62965   A1     2 #> 16277      2         4  33       62965   A2     4 #> 16278      2         4  33       62965   A3     2 #> 16279      2         4  33       62965   A4     5 #> 16280      2         4  33       62965   A5     4 #> 16281      2         4  33       62965   C1     5 #> 16282      2         4  33       62965   C2     5 #> 16283      2         4  33       62965   C3     4 #> 16284      2         4  33       62965   C4     4 #> 16285      2         4  33       62965   C5     6 #> 16286      2         4  33       62965   E1     5 #> 16287      2         4  33       62965   E2     5 #> 16288      2         4  33       62965   E3     2 #> 16289      2         4  33       62965   E4     2 #> 16290      2         4  33       62965   E5     4 #> 16291      2         4  33       62965   N1     4 #> 16292      2         4  33       62965   N2     5 #> 16293      2         4  33       62965   N3     5 #> 16294      2         4  33       62965   N4     6 #> 16295      2         4  33       62965   N5     4 #> 16296      2         4  33       62965   O1     6 #> 16297      2         4  33       62965   O2     1 #> 16298      2         4  33       62965   O3     6 #> 16299      2         4  33       62965   O4     6 #> 16300      2         4  33       62965   O5     1 #> 16301      2         5  38       62968   A1     4 #> 16302      2         5  38       62968   A2     4 #> 16303      2         5  38       62968   A3     4 #> 16304      2         5  38       62968   A4     6 #> 16305      2         5  38       62968   A5     2 #> 16306      2         5  38       62968   C1     2 #> 16307      2         5  38       62968   C2     4 #> 16308      2         5  38       62968   C3     4 #> 16309      2         5  38       62968   C4     3 #> 16310      2         5  38       62968   C5     4 #> 16311      2         5  38       62968   E1     5 #> 16312      2         5  38       62968   E2     4 #> 16313      2         5  38       62968   E3     2 #> 16314      2         5  38       62968   E4     4 #> 16315      2         5  38       62968   E5     4 #> 16316      2         5  38       62968   N1     2 #> 16317      2         5  38       62968   N2     3 #> 16318      2         5  38       62968   N3     4 #> 16319      2         5  38       62968   N4     5 #> 16320      2         5  38       62968   N5     3 #> 16321      2         5  38       62968   O1     4 #> 16322      2         5  38       62968   O2     2 #> 16323      2         5  38       62968   O3     5 #> 16324      2         5  38       62968   O4     5 #> 16325      2         5  38       62968   O5     4 #> 16326      1         2  26       62969   A1     1 #> 16327      1         2  26       62969   A2     5 #> 16328      1         2  26       62969   A3     6 #> 16329      1         2  26       62969   A4     6 #> 16330      1         2  26       62969   A5     6 #> 16331      1         2  26       62969   C1     6 #> 16332      1         2  26       62969   C2     5 #> 16333      1         2  26       62969   C3     5 #> 16334      1         2  26       62969   C4     1 #> 16335      1         2  26       62969   C5     5 #> 16336      1         2  26       62969   E1     1 #> 16337      1         2  26       62969   E2     6 #> 16338      1         2  26       62969   E3     6 #> 16339      1         2  26       62969   E4     6 #> 16340      1         2  26       62969   E5     5 #> 16341      1         2  26       62969   N1     4 #> 16342      1         2  26       62969   N2     5 #> 16343      1         2  26       62969   N3     5 #> 16344      1         2  26       62969   N4     1 #> 16345      1         2  26       62969   N5     1 #> 16346      1         2  26       62969   O1     4 #> 16347      1         2  26       62969   O2     1 #> 16348      1         2  26       62969   O3     6 #> 16349      1         2  26       62969   O4     3 #> 16350      1         2  26       62969   O5     3 #> 16351      2         3  30       62971   A1     3 #> 16352      2         3  30       62971   A2     5 #> 16353      2         3  30       62971   A3     5 #> 16354      2         3  30       62971   A4     6 #> 16355      2         3  30       62971   A5     6 #> 16356      2         3  30       62971   C1     5 #> 16357      2         3  30       62971   C2     5 #> 16358      2         3  30       62971   C3     5 #> 16359      2         3  30       62971   C4     2 #> 16360      2         3  30       62971   C5     2 #> 16361      2         3  30       62971   E1     1 #> 16362      2         3  30       62971   E2     2 #> 16363      2         3  30       62971   E3     5 #> 16364      2         3  30       62971   E4     6 #> 16365      2         3  30       62971   E5     6 #> 16366      2         3  30       62971   N1     6 #> 16367      2         3  30       62971   N2     6 #> 16368      2         3  30       62971   N3     5 #> 16369      2         3  30       62971   N4     5 #> 16370      2         3  30       62971   N5     5 #> 16371      2         3  30       62971   O1     6 #> 16372      2         3  30       62971   O2     6 #> 16373      2         3  30       62971   O3    NA #> 16374      2         3  30       62971   O4     6 #> 16375      2         3  30       62971   O5     5 #> 16376      1         3  36       62974   A1     5 #> 16377      1         3  36       62974   A2     6 #> 16378      1         3  36       62974   A3     6 #> 16379      1         3  36       62974   A4     6 #> 16380      1         3  36       62974   A5     6 #> 16381      1         3  36       62974   C1     5 #> 16382      1         3  36       62974   C2     5 #> 16383      1         3  36       62974   C3     4 #> 16384      1         3  36       62974   C4     1 #> 16385      1         3  36       62974   C5     1 #> 16386      1         3  36       62974   E1     5 #> 16387      1         3  36       62974   E2     1 #> 16388      1         3  36       62974   E3     5 #> 16389      1         3  36       62974   E4     6 #> 16390      1         3  36       62974   E5     3 #> 16391      1         3  36       62974   N1     1 #> 16392      1         3  36       62974   N2     1 #> 16393      1         3  36       62974   N3     3 #> 16394      1         3  36       62974   N4     1 #> 16395      1         3  36       62974   N5     1 #> 16396      1         3  36       62974   O1     5 #> 16397      1         3  36       62974   O2     1 #> 16398      1         3  36       62974   O3     6 #> 16399      1         3  36       62974   O4     4 #> 16400      1         3  36       62974   O5     4 #> 16401      2         3  41       62976   A1     1 #> 16402      2         3  41       62976   A2     6 #> 16403      2         3  41       62976   A3     6 #> 16404      2         3  41       62976   A4     6 #> 16405      2         3  41       62976   A5     6 #> 16406      2         3  41       62976   C1     6 #> 16407      2         3  41       62976   C2     5 #> 16408      2         3  41       62976   C3     5 #> 16409      2         3  41       62976   C4     3 #> 16410      2         3  41       62976   C5     1 #> 16411      2         3  41       62976   E1     5 #> 16412      2         3  41       62976   E2     4 #> 16413      2         3  41       62976   E3     4 #> 16414      2         3  41       62976   E4     4 #> 16415      2         3  41       62976   E5     5 #> 16416      2         3  41       62976   N1     1 #> 16417      2         3  41       62976   N2     1 #> 16418      2         3  41       62976   N3     2 #> 16419      2         3  41       62976   N4     4 #> 16420      2         3  41       62976   N5     2 #> 16421      2         3  41       62976   O1     6 #> 16422      2         3  41       62976   O2     2 #> 16423      2         3  41       62976   O3     4 #> 16424      2         3  41       62976   O4     6 #> 16425      2         3  41       62976   O5     1 #> 16426      2         5  34       62983   A1     1 #> 16427      2         5  34       62983   A2     5 #> 16428      2         5  34       62983   A3     5 #> 16429      2         5  34       62983   A4     6 #> 16430      2         5  34       62983   A5     6 #> 16431      2         5  34       62983   C1     5 #> 16432      2         5  34       62983   C2     3 #> 16433      2         5  34       62983   C3     4 #> 16434      2         5  34       62983   C4     2 #> 16435      2         5  34       62983   C5     3 #> 16436      2         5  34       62983   E1     3 #> 16437      2         5  34       62983   E2     5 #> 16438      2         5  34       62983   E3     4 #> 16439      2         5  34       62983   E4     5 #> 16440      2         5  34       62983   E5     4 #> 16441      2         5  34       62983   N1     4 #> 16442      2         5  34       62983   N2     5 #> 16443      2         5  34       62983   N3     3 #> 16444      2         5  34       62983   N4     2 #> 16445      2         5  34       62983   N5     5 #> 16446      2         5  34       62983   O1     4 #> 16447      2         5  34       62983   O2     2 #> 16448      2         5  34       62983   O3     5 #> 16449      2         5  34       62983   O4     6 #> 16450      2         5  34       62983   O5     2 #> 16451      2         3  35       62984   A1     1 #> 16452      2         3  35       62984   A2     6 #> 16453      2         3  35       62984   A3     5 #> 16454      2         3  35       62984   A4     6 #> 16455      2         3  35       62984   A5     4 #> 16456      2         3  35       62984   C1     4 #> 16457      2         3  35       62984   C2     2 #> 16458      2         3  35       62984   C3     4 #> 16459      2         3  35       62984   C4     5 #> 16460      2         3  35       62984   C5     3 #> 16461      2         3  35       62984   E1     1 #> 16462      2         3  35       62984   E2     6 #> 16463      2         3  35       62984   E3     4 #> 16464      2         3  35       62984   E4     4 #> 16465      2         3  35       62984   E5     1 #> 16466      2         3  35       62984   N1     4 #> 16467      2         3  35       62984   N2     5 #> 16468      2         3  35       62984   N3     4 #> 16469      2         3  35       62984   N4     4 #> 16470      2         3  35       62984   N5     1 #> 16471      2         3  35       62984   O1     1 #> 16472      2         3  35       62984   O2     1 #> 16473      2         3  35       62984   O3     2 #> 16474      2         3  35       62984   O4     5 #> 16475      2         3  35       62984   O5     1 #> 16476      2         3  40       62989   A1     3 #> 16477      2         3  40       62989   A2     6 #> 16478      2         3  40       62989   A3     5 #> 16479      2         3  40       62989   A4     6 #> 16480      2         3  40       62989   A5     5 #> 16481      2         3  40       62989   C1     3 #> 16482      2         3  40       62989   C2     5 #> 16483      2         3  40       62989   C3     5 #> 16484      2         3  40       62989   C4     1 #> 16485      2         3  40       62989   C5     1 #> 16486      2         3  40       62989   E1     3 #> 16487      2         3  40       62989   E2     1 #> 16488      2         3  40       62989   E3     3 #> 16489      2         3  40       62989   E4     4 #> 16490      2         3  40       62989   E5     4 #> 16491      2         3  40       62989   N1     3 #> 16492      2         3  40       62989   N2     3 #> 16493      2         3  40       62989   N3     2 #> 16494      2         3  40       62989   N4     3 #> 16495      2         3  40       62989   N5     4 #> 16496      2         3  40       62989   O1     4 #> 16497      2         3  40       62989   O2     4 #> 16498      2         3  40       62989   O3     4 #> 16499      2         3  40       62989   O4     4 #> 16500      2         3  40       62989   O5     2 #> 16501      2         3  33       62990   A1     6 #> 16502      2         3  33       62990   A2     1 #> 16503      2         3  33       62990   A3     1 #> 16504      2         3  33       62990   A4     1 #> 16505      2         3  33       62990   A5     1 #> 16506      2         3  33       62990   C1     6 #> 16507      2         3  33       62990   C2     6 #> 16508      2         3  33       62990   C3     6 #> 16509      2         3  33       62990   C4     1 #> 16510      2         3  33       62990   C5     1 #> 16511      2         3  33       62990   E1     6 #> 16512      2         3  33       62990   E2     6 #> 16513      2         3  33       62990   E3     1 #> 16514      2         3  33       62990   E4     1 #> 16515      2         3  33       62990   E5     6 #> 16516      2         3  33       62990   N1     6 #> 16517      2         3  33       62990   N2     6 #> 16518      2         3  33       62990   N3     6 #> 16519      2         3  33       62990   N4     6 #> 16520      2         3  33       62990   N5     1 #> 16521      2         3  33       62990   O1     6 #> 16522      2         3  33       62990   O2     1 #> 16523      2         3  33       62990   O3     6 #> 16524      2         3  33       62990   O4     6 #> 16525      2         3  33       62990   O5     1 #> 16526      2         3  28       62991   A1     5 #> 16527      2         3  28       62991   A2     5 #> 16528      2         3  28       62991   A3     4 #> 16529      2         3  28       62991   A4     5 #> 16530      2         3  28       62991   A5     5 #> 16531      2         3  28       62991   C1     5 #> 16532      2         3  28       62991   C2     5 #> 16533      2         3  28       62991   C3     5 #> 16534      2         3  28       62991   C4     3 #> 16535      2         3  28       62991   C5     1 #> 16536      2         3  28       62991   E1     4 #> 16537      2         3  28       62991   E2     5 #> 16538      2         3  28       62991   E3     4 #> 16539      2         3  28       62991   E4     4 #> 16540      2         3  28       62991   E5     5 #> 16541      2         3  28       62991   N1     4 #> 16542      2         3  28       62991   N2     4 #> 16543      2         3  28       62991   N3     4 #> 16544      2         3  28       62991   N4     4 #> 16545      2         3  28       62991   N5     4 #> 16546      2         3  28       62991   O1     4 #> 16547      2         3  28       62991   O2     5 #> 16548      2         3  28       62991   O3     4 #> 16549      2         3  28       62991   O4     4 #> 16550      2         3  28       62991   O5     4 #> 16551      2         3  29       62994   A1     1 #> 16552      2         3  29       62994   A2     6 #> 16553      2         3  29       62994   A3     5 #> 16554      2         3  29       62994   A4     2 #> 16555      2         3  29       62994   A5     5 #> 16556      2         3  29       62994   C1     5 #> 16557      2         3  29       62994   C2     5 #> 16558      2         3  29       62994   C3     3 #> 16559      2         3  29       62994   C4     3 #> 16560      2         3  29       62994   C5     5 #> 16561      2         3  29       62994   E1     1 #> 16562      2         3  29       62994   E2     1 #> 16563      2         3  29       62994   E3     4 #> 16564      2         3  29       62994   E4     6 #> 16565      2         3  29       62994   E5     4 #> 16566      2         3  29       62994   N1     5 #> 16567      2         3  29       62994   N2     5 #> 16568      2         3  29       62994   N3     6 #> 16569      2         3  29       62994   N4     5 #> 16570      2         3  29       62994   N5     5 #> 16571      2         3  29       62994   O1     4 #> 16572      2         3  29       62994   O2     2 #> 16573      2         3  29       62994   O3     4 #> 16574      2         3  29       62994   O4     3 #> 16575      2         3  29       62994   O5     2 #> 16576      2        NA  17       62995   A1     6 #> 16577      2        NA  17       62995   A2     1 #> 16578      2        NA  17       62995   A3     1 #> 16579      2        NA  17       62995   A4     2 #> 16580      2        NA  17       62995   A5     3 #> 16581      2        NA  17       62995   C1     2 #> 16582      2        NA  17       62995   C2     4 #> 16583      2        NA  17       62995   C3     2 #> 16584      2        NA  17       62995   C4     4 #> 16585      2        NA  17       62995   C5     4 #> 16586      2        NA  17       62995   E1     3 #> 16587      2        NA  17       62995   E2     5 #> 16588      2        NA  17       62995   E3     4 #> 16589      2        NA  17       62995   E4     3 #> 16590      2        NA  17       62995   E5     4 #> 16591      2        NA  17       62995   N1     5 #> 16592      2        NA  17       62995   N2     6 #> 16593      2        NA  17       62995   N3     4 #> 16594      2        NA  17       62995   N4     4 #> 16595      2        NA  17       62995   N5     4 #> 16596      2        NA  17       62995   O1     5 #> 16597      2        NA  17       62995   O2     1 #> 16598      2        NA  17       62995   O3     4 #> 16599      2        NA  17       62995   O4     4 #> 16600      2        NA  17       62995   O5     2 #> 16601      2         3  50       62996   A1     2 #> 16602      2         3  50       62996   A2     5 #> 16603      2         3  50       62996   A3     5 #> 16604      2         3  50       62996   A4     5 #> 16605      2         3  50       62996   A5     4 #> 16606      2         3  50       62996   C1     4 #> 16607      2         3  50       62996   C2     5 #> 16608      2         3  50       62996   C3     5 #> 16609      2         3  50       62996   C4     2 #> 16610      2         3  50       62996   C5     1 #> 16611      2         3  50       62996   E1     1 #> 16612      2         3  50       62996   E2     1 #> 16613      2         3  50       62996   E3     3 #> 16614      2         3  50       62996   E4     5 #> 16615      2         3  50       62996   E5     4 #> 16616      2         3  50       62996   N1     1 #> 16617      2         3  50       62996   N2     2 #> 16618      2         3  50       62996   N3     2 #> 16619      2         3  50       62996   N4     4 #> 16620      2         3  50       62996   N5     1 #> 16621      2         3  50       62996   O1     3 #> 16622      2         3  50       62996   O2     2 #> 16623      2         3  50       62996   O3     4 #> 16624      2         3  50       62996   O4     5 #> 16625      2         3  50       62996   O5     2 #> 16626      1         3  20       62997   A1     2 #> 16627      1         3  20       62997   A2    NA #> 16628      1         3  20       62997   A3     5 #> 16629      1         3  20       62997   A4     3 #> 16630      1         3  20       62997   A5     6 #> 16631      1         3  20       62997   C1     2 #> 16632      1         3  20       62997   C2     1 #> 16633      1         3  20       62997   C3     5 #> 16634      1         3  20       62997   C4     2 #> 16635      1         3  20       62997   C5     6 #> 16636      1         3  20       62997   E1     1 #> 16637      1         3  20       62997   E2     1 #> 16638      1         3  20       62997   E3     5 #> 16639      1         3  20       62997   E4     6 #> 16640      1         3  20       62997   E5     6 #> 16641      1         3  20       62997   N1     6 #> 16642      1         3  20       62997   N2     6 #> 16643      1         3  20       62997   N3     5 #> 16644      1         3  20       62997   N4     5 #> 16645      1         3  20       62997   N5     3 #> 16646      1         3  20       62997   O1     4 #> 16647      1         3  20       62997   O2     6 #> 16648      1         3  20       62997   O3     3 #> 16649      1         3  20       62997   O4     3 #> 16650      1         3  20       62997   O5     3 #> 16651      2         3  19       63004   A1     5 #> 16652      2         3  19       63004   A2     5 #> 16653      2         3  19       63004   A3     5 #> 16654      2         3  19       63004   A4     6 #> 16655      2         3  19       63004   A5     3 #> 16656      2         3  19       63004   C1     4 #> 16657      2         3  19       63004   C2     5 #> 16658      2         3  19       63004   C3     5 #> 16659      2         3  19       63004   C4     2 #> 16660      2         3  19       63004   C5     2 #> 16661      2         3  19       63004   E1     4 #> 16662      2         3  19       63004   E2     6 #> 16663      2         3  19       63004   E3     4 #> 16664      2         3  19       63004   E4     5 #> 16665      2         3  19       63004   E5     4 #> 16666      2         3  19       63004   N1     5 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 53334 rows ]  data_to_long(   tidyr::who,   select = new_sp_m014:newrel_f65,   names_to = c(\"diagnosis\", \"gender\", \"age\"),   names_pattern = \"new_?(.*)_(.)(.*)\",   values_to = \"count\" ) #> # A tibble: 405,440 × 8 #>    country     iso2  iso3   year diagnosis gender age   count #>    <chr>       <chr> <chr> <dbl> <chr>     <chr>  <chr> <dbl> #>  1 Afghanistan AF    AFG    1980 sp        m      014      NA #>  2 Afghanistan AF    AFG    1980 sp        m      1524     NA #>  3 Afghanistan AF    AFG    1980 sp        m      2534     NA #>  4 Afghanistan AF    AFG    1980 sp        m      3544     NA #>  5 Afghanistan AF    AFG    1980 sp        m      4554     NA #>  6 Afghanistan AF    AFG    1980 sp        m      5564     NA #>  7 Afghanistan AF    AFG    1980 sp        m      65       NA #>  8 Afghanistan AF    AFG    1980 sp        f      014      NA #>  9 Afghanistan AF    AFG    1980 sp        f      1524     NA #> 10 Afghanistan AF    AFG    1980 sp        f      2534     NA #> # ℹ 405,430 more rows"},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape (pivot) data from long to wide — data_to_wide","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"function \"widens\" data, increasing number columns decreasing number rows. dependency-free base-R equivalent tidyr::pivot_wider().","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"","code":"data_to_wide(   data,   id_cols = NULL,   values_from = \"Value\",   names_from = \"Name\",   names_sep = \"_\",   names_prefix = \"\",   names_glue = NULL,   values_fill = NULL,   verbose = TRUE,   ...,   colnames_from,   rows_from,   sep )  reshape_wider(   data,   id_cols = NULL,   values_from = \"Value\",   names_from = \"Name\",   names_sep = \"_\",   names_prefix = \"\",   names_glue = NULL,   values_fill = NULL,   verbose = TRUE,   ...,   colnames_from,   rows_from,   sep )"},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"data data frame pivot. id_cols name column identifies rows. NULL, use unique rows. values_from name column contains values used future variable values. names_from name column contains levels used future column names. names_sep names_from values_from contains multiple variables, used join values together single string use column name. names_prefix String added start every variable name. particularly useful names_from numeric vector want create syntactic variable names. names_glue Instead names_sep names_prefix, can supply glue specification uses names_from columns create custom column names. Note delimiters supported names_glue curly brackets, { }. values_fill Optionally, (scalar) value used replace missing values new columns created. verbose Toggle warnings. ... used now. colnames_from Deprecated. Use names_from instead. rows_from Deprecated. Use id_cols instead. sep Deprecated. Use names_sep instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"tibble provided input, reshape_wider() also returns tibble. Otherwise, returns data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"","code":"data_long <- read.table(header = TRUE, text = \"  subject sex condition measurement        1   M   control         7.9        1   M     cond1        12.3        1   M     cond2        10.7        2   F   control         6.3        2   F     cond1        10.6        2   F     cond2        11.1        3   F   control         9.5        3   F     cond1        13.1        3   F     cond2        13.8        4   M   control        11.5        4   M     cond1        13.4        4   M     cond2        12.9\")   data_to_wide(   data_long,   id_cols = \"subject\",   names_from = \"condition\",   values_from = \"measurement\" ) #>   subject control cond1 cond2 #> 1       1     7.9  12.3  10.7 #> 2       2     6.3  10.6  11.1 #> 3       3     9.5  13.1  13.8 #> 4       4    11.5  13.4  12.9  data_to_wide(   data_long,   id_cols = \"subject\",   names_from = \"condition\",   values_from = \"measurement\",   names_prefix = \"Var.\",   names_sep = \".\" ) #>   subject Var.control Var.cond1 Var.cond2 #> 1       1         7.9      12.3      10.7 #> 2       2         6.3      10.6      11.1 #> 3       3         9.5      13.1      13.8 #> 4       4        11.5      13.4      12.9  production <- expand.grid(   product = c(\"A\", \"B\"),   country = c(\"AI\", \"EI\"),   year = 2000:2014 ) production <- data_filter(production, (product == \"A\" & country == \"AI\") | product == \"B\")  production$production <- rnorm(nrow(production))  data_to_wide(   production,   names_from = c(\"product\", \"country\"),   values_from = \"production\",   names_glue = \"prod_{product}_{country}\" ) #>    year  prod_A_AI    prod_B_AI  prod_B_EI #> 1  2000 -0.8408539  1.430252916  0.3920247 #> 2  2001 -0.4726417 -0.996105337 -0.1950098 #> 3  2002  1.3394131 -0.711765324 -0.9245581 #> 4  2003  0.8737440 -1.043327370  1.0166035 #> 5  2004 -2.2241873  1.878421273 -0.5218175 #> 6  2005 -0.6546695  0.993425211 -0.2819180 #> 7  2006 -1.0952392  1.164258300  0.2246749 #> 8  2007 -1.1649528  0.748724154 -1.3051249 #> 9  2008 -0.3766038  0.004485138  1.5616184 #> 10 2009 -0.7426178 -0.331893557  0.1463996 #> 11 2010  0.4176823  0.036978385 -1.7524488 #> 12 2011  0.1575659  0.411082845 -0.9077312 #> 13 2012  1.4151629 -0.205867410 -0.8926030 #> 14 2013  0.5674379  0.764974595 -1.9997762 #> 15 2014 -1.5185747  0.560874533 -0.8971569"},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":null,"dir":"Reference","previous_headings":"","what":"Keep only one row from all with duplicated IDs — data_unique","title":"Keep only one row from all with duplicated IDs — data_unique","text":"rows least one duplicated ID, keep one. Methods selecting duplicated row either first duplicate, last duplicate, \"best\" duplicate (default), based duplicate smallest number NA. case ties, picks first duplicate, one likely valid authentic, given practice effects. Contrarily dplyr::distinct(), data_unique() keeps columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keep only one row from all with duplicated IDs — data_unique","text":"","code":"data_unique(   data,   select = NULL,   keep = \"best\",   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keep only one row from all with duplicated IDs — data_unique","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". keep method used duplicate selection, either \"best\" (default), \"first\", \"last\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keep only one row from all with duplicated IDs — data_unique","text":"data frame, containing chosen duplicates.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keep only one row from all with duplicated IDs — data_unique","text":"","code":"df1 <- data.frame(   id = c(1, 2, 3, 1, 3),   item1 = c(NA, 1, 1, 2, 3),   item2 = c(NA, 1, 1, 2, 3),   item3 = c(NA, 1, 1, 2, 3) )  data_unique(df1, select = \"id\") #> (2 duplicates removed, with method 'best') #>   id item1 item2 item3 #> 1  1     2     2     2 #> 2  2     1     1     1 #> 3  3     1     1     1"},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":null,"dir":"Reference","previous_headings":"","what":"Unite (","title":"Unite (","text":"Merge values multiple variables per observation one new variable.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unite (","text":"","code":"data_unite(   data,   new_column = NULL,   select = NULL,   exclude = NULL,   separator = \"_\",   append = FALSE,   remove_na = FALSE,   ignore_case = FALSE,   verbose = TRUE,   regex = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unite (","text":"data data frame. new_column name new column, string. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. separator character use values. append Logical, FALSE (default), removes original columns united. TRUE, columns preserved new column appended data frame. remove_na Logical, TRUE, missing values (NA) included united values. FALSE, missing values represented \"NA\" united values. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. verbose Toggle warnings. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unite (","text":"data, newly created variable.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unite (","text":"","code":"d <- data.frame(   x = 1:3,   y = letters[1:3],   z = 6:8 ) d #>   x y z #> 1 1 a 6 #> 2 2 b 7 #> 3 3 c 8 data_unite(d, new_column = \"xyz\") #>     xyz #> 1 1_a_6 #> 2 2_b_7 #> 3 3_c_8 data_unite(d, new_column = \"xyz\", remove = FALSE) #>     xyz #> 1 1_a_6 #> 2 2_b_7 #> 3 3_c_8 data_unite(d, new_column = \"xyz\", select = c(\"x\", \"z\")) #>   y xyz #> 1 a 1_6 #> 2 b 2_7 #> 3 c 3_8 data_unite(d, new_column = \"xyz\", select = c(\"x\", \"z\"), append = TRUE) #>   x y z xyz #> 1 1 a 6 1_6 #> 2 2 b 7 2_7 #> 3 3 c 8 3_8"},{"path":"https://easystats.github.io/datawizard/reference/datawizard-package.html","id":null,"dir":"Reference","previous_headings":"","what":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","title":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","text":"lightweight package assist key steps involved data analysis workflow: wrangling raw data get needed form, applying preprocessing steps statistical transformations, compute statistical summaries data properties distributions. also data wrangling backend packages 'easystats' ecosystem. References: Patil et al. (2022) doi:10.21105/joss.04684 .","code":""},{"path":"https://easystats.github.io/datawizard/reference/datawizard-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","text":"datawizard","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/datawizard-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","text":"Maintainer: Etienne Bacher etienne.bacher@protonmail.com (ORCID) Authors: Indrajeet Patil patilindrajeet.science@gmail.com (ORCID) (@patilindrajeets) Dominique Makowski dom.makowski@gmail.com (ORCID) (@Dom_Makowski) Daniel Lüdecke d.luedecke@uke.de (ORCID) (@strengejacke) Mattan S. Ben-Shachar matanshm@post.bgu.ac.il (ORCID) Brenton M. Wiernik brenton@wiernik.org (ORCID) (@bmwiernik) contributors: Rémi Thériault remi.theriault@mail.mcgill.ca (ORCID) (@rempsyc) [contributor] Thomas J. Faulkenberry faulkenberry@tarleton.edu [reviewer] Robert Garrett rcg4@illinois.edu [reviewer]","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute group-meaned and de-meaned variables — demean","title":"Compute group-meaned and de-meaned variables — demean","text":"demean() computes group- de-meaned versions variable can used regression analysis model - within-subject effect. degroup() generic terms centering-operation. demean() always uses mean-centering, degroup() can also use mode median centering.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute group-meaned and de-meaned variables — demean","text":"","code":"demean(   x,   select,   group,   suffix_demean = \"_within\",   suffix_groupmean = \"_between\",   add_attributes = TRUE,   verbose = TRUE )  degroup(   x,   select,   group,   center = \"mean\",   suffix_demean = \"_within\",   suffix_groupmean = \"_between\",   add_attributes = TRUE,   verbose = TRUE )  detrend(   x,   select,   group,   center = \"mean\",   suffix_demean = \"_within\",   suffix_groupmean = \"_between\",   add_attributes = TRUE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute group-meaned and de-meaned variables — demean","text":"x data frame. select Character vector (formula) names variables select group- de-meaned. group Character vector (formula) name variable indicates group- cluster-ID. suffix_demean, suffix_groupmean String value, appended names group-meaned de-meaned variables x. default, de-meaned variables suffixed \"_within\" grouped-meaned variables \"_between\". add_attributes Logical, TRUE, returned variables gain attributes indicate within- -effects. relevant printing model_parameters() - cases, within- -effects printed separated blocks. verbose Toggle warnings messages. center Method centering. demean() always performs mean-centering, degroup() can use center = \"median\" center = \"mode\" median- mode-centering, also \"min\" \"max\".","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute group-meaned and de-meaned variables — demean","text":"data frame group-/de-meaned variables, get suffix \"_between\" (group-meaned variable) \"_within\" (de-meaned variable) default.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"heterogeneity-bias","dir":"Reference","previous_headings":"","what":"Heterogeneity Bias","title":"Compute group-meaned and de-meaned variables — demean","text":"Mixed models include different levels sources variability, .e. error terms level. macro-indicators (level-2 predictors, higher-level units, general: group-level predictors vary within across groups) included fixed effects (.e. treated covariate level-1), variance left unaccounted covariate absorbed error terms level-1 level-2 (Bafumi Gelman 2006; Gelman Hill 2007, Chapter 12.6.): “covariates contain two parts: one specific higher-level entity vary occasions, one represents difference occasions, within higher-level entities” (Bell et al. 2015). Hence, error terms correlated covariate, violates one assumptions mixed models (iid, independent identically distributed error terms). bias also called heterogeneity bias (Bell et al. 2015). resolve problem, level-2 predictors used (level-1) covariates separated \"within\" \"\" effects \"de-meaning\" \"group-meaning\": demeaning time-varying predictors, “higher level, mean term longer constrained Level 1 effects, free account higher-level variance associated variable” (Bell et al. 2015).","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"panel-data-and-correlating-fixed-and-group-effects","dir":"Reference","previous_headings":"","what":"Panel data and correlating fixed and group effects","title":"Compute group-meaned and de-meaned variables — demean","text":"demean() intended create group- de-meaned variables panel regression models (fixed effects models), complex random-effect-within-models (see Bell et al. 2015, 2018), group-effects (random effects) fixed effects correlate (see Bafumi Gelman 2006). can happen, instance, analyzing panel data, can lead Heterogeneity Bias. control correlating predictors group effects, recommended include group-meaned de-meaned version time-varying covariates (group-meaned version time-invariant covariates higher level, e.g. level-2 predictors) model. , one can fit complex multilevel models panel data, including time-varying predictors, time-invariant predictors random effects.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"why-mixed-models-are-preferred-over-fixed-effects-models","dir":"Reference","previous_headings":"","what":"Why mixed models are preferred over fixed effects models","title":"Compute group-meaned and de-meaned variables — demean","text":"mixed models approach can model causes endogeneity explicitly including (separated) within- -effects time-varying fixed effects including time-constant fixed effects. Furthermore, mixed models also include random effects, thus mixed models approach superior classic fixed-effects models, lack information variation group-effects -subject effects. Furthermore, fixed effects regression include random slopes, means fixed effects regressions neglecting “cross-cluster differences effects lower-level controls () reduces precision estimated context effects, resulting unnecessarily wide confidence intervals low statistical power” (Heisig et al. 2017).","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"terminology","dir":"Reference","previous_headings":"","what":"Terminology","title":"Compute group-meaned and de-meaned variables — demean","text":"group-meaned variable simply mean independent variable within group (id-level cluster) represented group. represents cluster-mean independent variable. regression coefficient group-meaned variable -subject-effect. de-meaned variable centered version group-meaned variable. De-meaning sometimes also called person-mean centering centering within clusters. regression coefficient de-meaned variable represents within-subject-effect.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-with-continuous-predictors","dir":"Reference","previous_headings":"","what":"De-meaning with continuous predictors","title":"Compute group-meaned and de-meaned variables — demean","text":"continuous time-varying predictors, recommendation include de-meaned group-meaned versions fixed effects, raw (untransformed) time-varying predictors . de-meaned predictor also included random effect (random slope). regression models, coefficient de-meaned predictors indicates within-subject effect, coefficient group-meaned predictor indicates -subject effect.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-with-binary-predictors","dir":"Reference","previous_headings":"","what":"De-meaning with binary predictors","title":"Compute group-meaned and de-meaned variables — demean","text":"binary time-varying predictors, two recommendations. First include raw (untransformed) binary predictor fixed effect de-meaned variable random effect (random slope). alternative add de-meaned version(s) binary time-varying covariates additional fixed effect well (instead adding random slope). Centering time-varying binary variables obtain within-effects (level 1) necessary. sensible interpretation left typical 0/1 format (Hoffmann 2015, chapter 8-2.). demean() thus coerce categorical time-varying predictors numeric compute de- group-meaned versions variables, raw (untransformed) binary predictor de-meaned version added model.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-of-factors-with-more-than-levels","dir":"Reference","previous_headings":"","what":"De-meaning of factors with more than 2 levels","title":"Compute group-meaned and de-meaned variables — demean","text":"Factors two levels demeaned two ways: first, also converted numeric de-meaned; second, dummy variables created (binary, 0/1 coding level) binary dummy-variables de-meaned way (described ). Packages like panelr internally convert factors dummies demeaning, behaviour can mimicked .","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-interaction-terms","dir":"Reference","previous_headings":"","what":"De-meaning interaction terms","title":"Compute group-meaned and de-meaned variables — demean","text":"multiple ways deal interaction terms within- -effects. classical approach simply use product term de-meaned variables (.e. introducing de-meaned variables interaction term model formula, e.g. y ~ x_within * time_within). approach, however, might subject bias (see Giesselmann & Schmidt-Catran 2020).  Another option first calculate product term apply de-meaning . approach produces estimator “reflects unit-level differences interacted variables whose moderators vary within units”, desirable within interaction two time-dependent variables required.  third option, interaction result genuine within estimator, \"double de-mean\" interaction terms (Giesselmann & Schmidt-Catran 2018), however, currently supported demean(). required, wmb() function panelr package used.  de-mean interaction terms within-models, simply specify term interaction select-argument, e.g. select = \"*b\" (see 'Examples').","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"analysing-panel-data-with-mixed-models-using-lme-","dir":"Reference","previous_headings":"","what":"Analysing panel data with mixed models using lme4","title":"Compute group-meaned and de-meaned variables — demean","text":"description translate formulas described Bell et al. 2018 R using lmer() lme4 can found vignette.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute group-meaned and de-meaned variables — demean","text":"Bafumi J, Gelman . 2006. Fitting Multilevel Models Predictors Group Effects Correlate. . Philadelphia, PA: Annual meeting American Political Science Association. Bell , Fairbrother M, Jones K. 2019. Fixed Random Effects Models: Making Informed Choice. Quality & Quantity (53); 1051-1074 Bell , Jones K. 2015. Explaining Fixed Effects: Random Effects Modeling Time-Series Cross-Sectional Panel Data. Political Science Research Methods, 3(1), 133–153. Gelman , Hill J. 2007. Data Analysis Using Regression Multilevel/Hierarchical Models. Analytical Methods Social Research. Cambridge, New York: Cambridge University Press Giesselmann M, Schmidt-Catran, AW. 2020. Interactions fixed effects regression models. Sociological Methods & Research, 1–28. https://doi.org/10.1177/0049124120914934 Heisig JP, Schaeffer M, Giesecke J. 2017. Costs Simplicity: Multilevel Models May Benefit Accounting Cross-Cluster Differences Effects Controls. American Sociological Review 82 (4): 796–827. Hoffman L. 2015. Longitudinal analysis: modeling within-person fluctuation change. New York: Routledge","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute group-meaned and de-meaned variables — demean","text":"","code":"data(iris) iris$ID <- sample(1:4, nrow(iris), replace = TRUE) # fake-ID iris$binary <- as.factor(rbinom(150, 1, .35)) # binary variable  x <- demean(iris, select = c(\"Sepal.Length\", \"Petal.Length\"), group = \"ID\") head(x) #>   Sepal.Length_between Petal.Length_between Sepal.Length_within #> 1             5.809375             3.687500          -0.7093750 #> 2             5.692500             3.385000          -0.7925000 #> 3             5.809375             3.687500          -1.1093750 #> 4             5.692500             3.385000          -1.0925000 #> 5             5.895238             3.811905          -0.8952381 #> 6             5.980556             4.172222          -0.5805556 #>   Petal.Length_within #> 1           -2.287500 #> 2           -1.985000 #> 3           -2.387500 #> 4           -1.885000 #> 5           -2.411905 #> 6           -2.472222  x <- demean(iris, select = c(\"Sepal.Length\", \"binary\", \"Species\"), group = \"ID\") #> Categorical predictors (Species, binary) have been coerced to numeric #>   values to compute de- and group-meaned variables. head(x) #>   Sepal.Length_between Species_between binary_between Species_setosa_between #> 1             5.809375        0.968750      0.3125000              0.3437500 #> 2             5.692500        0.875000      0.2500000              0.4250000 #> 3             5.809375        0.968750      0.3125000              0.3437500 #> 4             5.692500        0.875000      0.2500000              0.4250000 #> 5             5.895238        1.047619      0.3333333              0.3571429 #> 6             5.980556        1.111111      0.4166667              0.1944444 #>   Species_versicolor_between Species_virginica_between Sepal.Length_within #> 1                  0.3437500                 0.3125000          -0.7093750 #> 2                  0.2750000                 0.3000000          -0.7925000 #> 3                  0.3437500                 0.3125000          -1.1093750 #> 4                  0.2750000                 0.3000000          -1.0925000 #> 5                  0.2380952                 0.4047619          -0.8952381 #> 6                  0.5000000                 0.3055556          -0.5805556 #>   Species_within binary_within Species_setosa_within Species_versicolor_within #> 1      -0.968750    -0.3125000             0.6562500                -0.3437500 #> 2      -0.875000    -0.2500000             0.5750000                -0.2750000 #> 3      -0.968750    -0.3125000             0.6562500                -0.3437500 #> 4      -0.875000     0.7500000             0.5750000                -0.2750000 #> 5      -1.047619     0.6666667             0.6428571                -0.2380952 #> 6      -1.111111    -0.4166667             0.8055556                -0.5000000 #>   Species_virginica_within #> 1               -0.3125000 #> 2               -0.3000000 #> 3               -0.3125000 #> 4               -0.3000000 #> 5               -0.4047619 #> 6               -0.3055556   # demean interaction term x*y dat <- data.frame(   a = c(1, 2, 3, 4, 1, 2, 3, 4),   x = c(4, 3, 3, 4, 1, 2, 1, 2),   y = c(1, 2, 1, 2, 4, 3, 2, 1),   ID = c(1, 2, 3, 1, 2, 3, 1, 2) ) demean(dat, select = c(\"a\", \"x*y\"), group = \"ID\") #>   a_between x_y_between   a_within x_y_within #> 1  2.666667    4.666667 -1.6666667 -0.6666667 #> 2  2.333333    4.000000 -0.3333333  2.0000000 #> 3  2.500000    4.500000  0.5000000 -1.5000000 #> 4  2.666667    4.666667  1.3333333  3.3333333 #> 5  2.333333    4.000000 -1.3333333  0.0000000 #> 6  2.500000    4.500000 -0.5000000  1.5000000 #> 7  2.666667    4.666667  0.3333333 -2.6666667 #> 8  2.333333    4.000000  1.6666667 -2.0000000  # or in formula-notation demean(dat, select = ~ a + x * y, group = ~ID) #>   a_between x_y_between   a_within x_y_within #> 1  2.666667    4.666667 -1.6666667 -0.6666667 #> 2  2.333333    4.000000 -0.3333333  2.0000000 #> 3  2.500000    4.500000  0.5000000 -1.5000000 #> 4  2.666667    4.666667  1.3333333  3.3333333 #> 5  2.333333    4.000000 -1.3333333  0.0000000 #> 6  2.500000    4.500000 -0.5000000  1.5000000 #> 7  2.666667    4.666667  0.3333333 -2.6666667 #> 8  2.333333    4.000000  1.6666667 -2.0000000"},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"Describe a distribution — describe_distribution","title":"Describe a distribution — describe_distribution","text":"function describes distribution set indices (e.g., measures centrality, dispersion, range, skewness, kurtosis).","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Describe a distribution — describe_distribution","text":"","code":"describe_distribution(x, ...)  # S3 method for numeric describe_distribution(   x,   centrality = \"mean\",   dispersion = TRUE,   iqr = TRUE,   range = TRUE,   quartiles = FALSE,   ci = NULL,   iterations = 100,   threshold = 0.1,   verbose = TRUE,   ... )  # S3 method for factor describe_distribution(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...)  # S3 method for data.frame describe_distribution(   x,   select = NULL,   exclude = NULL,   centrality = \"mean\",   dispersion = TRUE,   iqr = TRUE,   range = TRUE,   quartiles = FALSE,   include_factors = FALSE,   ci = NULL,   iterations = 100,   threshold = 0.1,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Describe a distribution — describe_distribution","text":"x numeric vector, character vector, data frame, list. See Details. ... Additional arguments passed methods. centrality point-estimates (centrality indices) compute. Character (vector) list one options: \"median\", \"mean\", \"MAP\" (see map_estimate()), \"trimmed\" (just mean(x, trim = threshold)), \"mode\" \"\". dispersion Logical, TRUE, computes indices dispersion related estimate(s) (SD MAD mean median, respectively). Dispersion available \"MAP\" \"mode\" centrality indices. iqr Logical, TRUE, interquartile range calculated (based stats::IQR(), using type = 6). range Return range (min max). quartiles Return first third quartiles (25th 75pth percentiles). ci Confidence Interval (CI) level. Default NULL, .e. confidence intervals computed. NULL, confidence intervals based bootstrap replicates (see iterations). centrality = \"\", bootstrapped confidence interval refers first centrality index (typically median). iterations number bootstrap replicates computing confidence intervals. applies ci NULL. threshold centrality = \"trimmed\" (.e. trimmed mean), indicates fraction (0 0.5) observations trimmed end vector mean computed. verbose Toggle warnings messages. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. include_factors Logical, TRUE, factors included output, however, columns range (first last factor levels) well n missing contain information. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Describe a distribution — describe_distribution","text":"data frame columns describe properties variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Describe a distribution — describe_distribution","text":"x data frame, numeric variables kept displayed summary. x list, behavior different whether x stored list. x stored (example, describe_distribution(mylist) mylist created ), artificial variable names used summary (Var_1, Var_2, etc.). x unstored list (example, describe_distribution(list(mtcars$mpg))), \"mtcars$mpg\" used variable name.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Describe a distribution — describe_distribution","text":"also plot()-method implemented see-package.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Describe a distribution — describe_distribution","text":"","code":"describe_distribution(rnorm(100)) #>  Mean |   SD |  IQR |         Range | Skewness | Kurtosis |   n | n_Missing #> --------------------------------------------------------------------------- #> -0.11 | 1.06 | 1.43 | [-3.51, 2.50] |    -0.17 |     0.50 | 100 |         0  data(iris) describe_distribution(iris) #> Variable     | Mean |   SD |  IQR |        Range | Skewness | Kurtosis |   n | n_Missing #> ---------------------------------------------------------------------------------------- #> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] |     0.31 |    -0.55 | 150 |         0 #> Sepal.Width  | 3.06 | 0.44 | 0.52 | [2.00, 4.40] |     0.32 |     0.23 | 150 |         0 #> Petal.Length | 3.76 | 1.77 | 3.52 | [1.00, 6.90] |    -0.27 |    -1.40 | 150 |         0 #> Petal.Width  | 1.20 | 0.76 | 1.50 | [0.10, 2.50] |    -0.10 |    -1.34 | 150 |         0 describe_distribution(iris, include_factors = TRUE, quartiles = TRUE) #> Variable     | Mean |   SD |  IQR |               Range |  Quartiles | Skewness | Kurtosis |   n | n_Missing #> ------------------------------------------------------------------------------------------------------------ #> Sepal.Length | 5.84 | 0.83 | 1.30 |          [4.3, 7.9] | 5.10, 6.40 |     0.31 |    -0.55 | 150 |         0 #> Sepal.Width  | 3.06 | 0.44 | 0.52 |            [2, 4.4] | 2.80, 3.30 |     0.32 |     0.23 | 150 |         0 #> Petal.Length | 3.76 | 1.77 | 3.52 |            [1, 6.9] | 1.60, 5.10 |    -0.27 |    -1.40 | 150 |         0 #> Petal.Width  | 1.20 | 0.76 | 1.50 |          [0.1, 2.5] | 0.30, 1.80 |    -0.10 |    -1.34 | 150 |         0 #> Species      |      |      |      | [setosa, virginica] |            |     0.00 |    -1.51 | 150 |         0 describe_distribution(list(mtcars$mpg, mtcars$cyl)) #> Variable   |  Mean |   SD |  IQR |          Range | Skewness | Kurtosis |  n | n_Missing #> ---------------------------------------------------------------------------------------- #> mtcars$mpg | 20.09 | 6.03 | 7.53 | [10.40, 33.90] |     0.67 |    -0.02 | 32 |         0 #> mtcars$cyl |  6.19 | 1.79 | 4.00 |   [4.00, 8.00] |    -0.19 |    -1.76 | 32 |         0"},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute mode for a statistical distribution — distribution_mode","title":"Compute mode for a statistical distribution — distribution_mode","text":"Compute mode statistical distribution","code":""},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute mode for a statistical distribution — distribution_mode","text":"","code":"distribution_mode(x)"},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute mode for a statistical distribution — distribution_mode","text":"x atomic vector, list, data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute mode for a statistical distribution — distribution_mode","text":"value appears frequently provided data. returned data structure entered one.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute mode for a statistical distribution — distribution_mode","text":"","code":"distribution_mode(c(1, 2, 3, 3, 4, 5)) #> [1] 3 distribution_mode(c(1.5, 2.3, 3.7, 3.7, 4.0, 5)) #> [1] 3.7"},{"path":"https://easystats.github.io/datawizard/reference/dot-is_deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","title":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","text":"Print message saying argument deprecated user use replacement instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/dot-is_deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","text":"","code":".is_deprecated(arg, replacement)"},{"path":"https://easystats.github.io/datawizard/reference/dot-is_deprecated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","text":"arg Argument deprecated replacement Argument replaces deprecated argument","code":""},{"path":"https://easystats.github.io/datawizard/reference/efc.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample dataset from the EFC Survey — efc","title":"Sample dataset from the EFC Survey — efc","text":"Selected variables EUROFAMCARE survey. Useful testing \"real-life\" data sets, including random missing values. data set also value variable label attributes.","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":null,"dir":"Reference","previous_headings":"","what":"Find or get columns in a data frame based on search patterns — find_columns","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"find_columns() returns column names data set match certain search pattern, get_columns() returns found data. data_select() alias get_columns(), data_find() alias find_columns().","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"","code":"find_columns(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_find(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  get_columns(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_select(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings. ... Arguments passed functions. Mostly used yet.","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"find_columns() returns character vector column names matched pattern select exclude, NULL matching column name found. get_columns() returns data frame matching columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"Note possible either pass entire select helper pattern inside select helper function argument:   means also possible use loop values arguments patterns:   However, behavior limited \"single-level function\". work nested functions, like :   case, better pass whole select helper argument outer():","code":"foo <- function(data, pattern) {   find_columns(data, select = starts_with(pattern)) } foo(iris, pattern = \"Sep\")  foo2 <- function(data, pattern) {   find_columns(data, select = pattern) } foo2(iris, pattern = starts_with(\"Sep\")) for (i in c(\"Sepal\", \"Sp\")) {   head(iris) |>     find_columns(select = starts_with(i)) |>     print() } inner <- function(data, arg) {   find_columns(data, select = arg) } outer <- function(data, arg) {   inner(data, starts_with(arg)) } outer(iris, \"Sep\") outer <- function(data, arg) {   inner(data, arg) } outer(iris, starts_with(\"Sep\"))"},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"","code":"# Find columns names by pattern find_columns(iris, starts_with(\"Sepal\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  find_columns(iris, ends_with(\"Width\")) #> [1] \"Sepal.Width\" \"Petal.Width\" find_columns(iris, regex(\"\\\\.\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  \"Petal.Length\" \"Petal.Width\"  find_columns(iris, c(\"Petal.Width\", \"Sepal.Length\")) #> [1] \"Petal.Width\"  \"Sepal.Length\"  # starts with \"Sepal\", but not allowed to end with \"width\" find_columns(iris, starts_with(\"Sepal\"), exclude = contains(\"Width\")) #> [1] \"Sepal.Length\"  # find numeric with mean > 3.5 numeric_mean_35 <- function(x) is.numeric(x) && mean(x, na.rm = TRUE) > 3.5 find_columns(iris, numeric_mean_35) #> [1] \"Sepal.Length\" \"Petal.Length\""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert value labels into factor levels — labels_to_levels","title":"Convert value labels into factor levels — labels_to_levels","text":"Convert value labels factor levels","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert value labels into factor levels — labels_to_levels","text":"","code":"labels_to_levels(x, ...)  # S3 method for factor labels_to_levels(x, verbose = TRUE, ...)  # S3 method for data.frame labels_to_levels(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   append = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert value labels into factor levels — labels_to_levels","text":"x data frame factor. variable types (e.g. numerics) allowed. ... Currently used. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert value labels into factor levels — labels_to_levels","text":"x, factors former levels replaced value labels.","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert value labels into factor levels — labels_to_levels","text":"labels_to_levels() allows use value labels factors levels.","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert value labels into factor levels — labels_to_levels","text":"","code":"data(efc) # create factor x <- as.factor(efc$c172code) # add value labels - these are not factor levels yet x <- assign_labels(x, values = c(`1` = \"low\", `2` = \"mid\", `3` = \"high\")) levels(x) #> [1] \"1\" \"2\" \"3\" data_tabulate(x) #> x <categorical> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  8 |  8.00 |    8.89 |         8.89 #> 2     | 66 | 66.00 |   73.33 |        82.22 #> 3     | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>  x <- labels_to_levels(x) levels(x) #> [1] \"low\"  \"mid\"  \"high\" data_tabulate(x) #> x <categorical> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> low   |  8 |  8.00 |    8.89 |         8.89 #> mid   | 66 | 66.00 |   73.33 |        82.22 #> high  | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>"},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"function allows use () datawizard's transformers inside model formula. See examples .  Currently, center(), standardize(), normalize(), & rescale() supported.","code":""},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"","code":"# S3 method for dw_transformer makepredictcall(var, call)"},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"var variable. call term formula, call.","code":""},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"replacement call predvars attribute   terms.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"","code":"data(\"mtcars\") train <- mtcars[1:30, ] test <- mtcars[31:32, ]  m1 <- lm(mpg ~ center(hp), data = train) predict(m1, newdata = test) # Data is \"centered\" before the prediction is made, #> Maserati Bora    Volvo 142E  #>      4.269496     22.911189  # according to the center of the old data  m2 <- lm(mpg ~ standardize(hp), data = train) m3 <- lm(mpg ~ scale(hp), data = train) # same as above predict(m2, newdata = test) # Data is \"standardized\" before the prediction is made. #> Maserati Bora    Volvo 142E  #>      4.269496     22.911189  predict(m3, newdata = test) # Data is \"standardized\" before the prediction is made. #> Maserati Bora    Volvo 142E  #>      4.269496     22.911189    m4 <- lm(mpg ~ normalize(hp), data = mtcars) m5 <- lm(mpg ~ rescale(hp, to = c(-3, 3)), data = mtcars)  (newdata <- data.frame(hp = c(range(mtcars$hp), 400))) # 400 is outside original range! #>    hp #> 1  52 #> 2 335 #> 3 400  model.frame(delete.response(terms(m4)), data = newdata) #>   normalize(hp) #> 1      0.000000 #> 2      1.000000 #> 3      1.229682 model.frame(delete.response(terms(m5)), data = newdata) #>   rescale(hp, to = c(-3, 3)) #> 1                  -3.000000 #> 2                   3.000000 #> 3                   4.378092"},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Helpers — mean_sd","title":"Summary Helpers — mean_sd","text":"Summary Helpers","code":""},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Helpers — mean_sd","text":"","code":"mean_sd(x, times = 1L, remove_na = TRUE, named = TRUE, na.rm = TRUE, ...)  median_mad(   x,   times = 1L,   remove_na = TRUE,   constant = 1.4826,   named = TRUE,   na.rm = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Helpers — mean_sd","text":"x numeric vector (one can coerced one via .numeric()) summarized. times many SDs Mean (MADs around Median) remove_na Logical. NA values removed computing (TRUE) (FALSE, default)? named vector named? (E.g., c(\"-SD\" = -1, Mean = 1, \"+SD\" = 2).) na.rm Deprecated. Please use remove_na instead. ... used. constant scale factor.","code":""},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Helpers — mean_sd","text":"(possibly named) numeric vector length 2*times + 1 SDs mean, mean, SDs mean (median MAD).","code":""},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Helpers — mean_sd","text":"","code":"mean_sd(mtcars$mpg) #>      -SD     Mean      +SD  #> 14.06368 20.09062 26.11757   mean_sd(mtcars$mpg, times = 2L) #>     -2 SD     -1 SD      Mean     +1 SD     +2 SD  #>  8.036729 14.063677 20.090625 26.117573 32.144521   median_mad(mtcars$mpg) #>     -MAD   Median     +MAD  #> 13.78851 19.20000 24.61149"},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of mean values by group — means_by_group","title":"Summary of mean values by group — means_by_group","text":"Computes summary table means groups.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of mean values by group — means_by_group","text":"","code":"means_by_group(x, ...)  # S3 method for numeric means_by_group(x, group = NULL, ci = 0.95, weights = NULL, digits = NULL, ...)  # S3 method for data.frame means_by_group(   x,   select = NULL,   group = NULL,   ci = 0.95,   weights = NULL,   digits = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of mean values by group — means_by_group","text":"x vector data frame. ... Currently used group x numeric vector, group factor indicates group-classifying categories. x data frame, group character string, naming variable x used grouping. Numeric vectors coerced factors. group refer single variable. ci Level confidence interval mean estimates. Default 0.95. Use ci = NA suppress confidence intervals. weights x numeric vector, weights vector weights applied weight observations. x data frame, weights can also character string indicating name variable x used weighting. Default NULL, weights used. digits Optional scalar, indicating amount digits decimal point rounding estimates values. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of mean values by group — means_by_group","text":"data frame information mean summary statistics sub-group.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summary of mean values by group — means_by_group","text":"function comparable aggregate(x, group, mean), provides information, including summary statistics One-Way-ANOVA using x dependent group independent variable. emmeans::contrast() used get p-values sub-group. P-values indicate whether group-mean significantly different total mean.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of mean values by group — means_by_group","text":"","code":"data(efc) means_by_group(efc, \"c12hour\", \"e42dep\") #> # Mean of average number of hours of care per week by elder's dependency #>  #> Category             |   Mean |  N |    SD |           95% CI |      p #> ---------------------------------------------------------------------- #> independent          |  17.00 |  2 | 11.31 | [-68.46, 102.46] | 0.573  #> slightly dependent   |  34.25 |  4 | 29.97 | [-26.18,  94.68] | 0.626  #> moderately dependent |  52.75 | 28 | 51.83 | [ 29.91,  75.59] | > .999 #> severely dependent   | 106.97 | 63 | 65.88 | [ 91.74, 122.19] | 0.001  #> Total                |  86.46 | 97 | 66.40 |                  |        #>  #> Anova: R2=0.186; adj.R2=0.160; F=7.098; p<.001  data(iris) means_by_group(iris, \"Sepal.Width\", \"Species\") #> # Mean of Sepal.Width by Species #>  #> Category   | Mean |   N |   SD |       95% CI |      p #> ------------------------------------------------------ #> setosa     | 3.43 |  50 | 0.38 | [3.33, 3.52] | < .001 #> versicolor | 2.77 |  50 | 0.31 | [2.68, 2.86] | < .001 #> virginica  | 2.97 |  50 | 0.32 | [2.88, 3.07] | 0.035  #> Total      | 3.06 | 150 | 0.44 |              |        #>  #> Anova: R2=0.401; adj.R2=0.393; F=49.160; p<.001  # weighting efc$weight <- abs(rnorm(n = nrow(efc), mean = 1, sd = .5)) means_by_group(efc, \"c12hour\", \"e42dep\", weights = \"weight\") #> # Mean of average number of hours of care per week by elder's dependency #>  #> Category             |   Mean |  N |    SD |           95% CI |     p #> --------------------------------------------------------------------- #> independent          |  19.00 |  1 | 11.31 | [-84.29, 122.30] | 0.685 #> slightly dependent   |  32.41 |  3 | 29.36 | [-34.71,  99.53] | 0.685 #> moderately dependent |  53.32 | 30 | 51.24 | [ 30.70,  75.93] | 0.907 #> severely dependent   | 100.17 | 65 | 66.62 | [ 84.84, 115.50] | 0.018 #> Total                |  82.66 | 97 | 65.34 |                  |       #>  #> Anova: R2=0.143; adj.R2=0.115; F=5.163; p=0.002"},{"path":"https://easystats.github.io/datawizard/reference/nhanes_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample dataset from the National Health and Nutrition Examination Survey — nhanes_sample","title":"Sample dataset from the National Health and Nutrition Examination Survey — nhanes_sample","text":"Selected variables National Health Nutrition Examination Survey used example Lumley (2010), Appendix E.","code":""},{"path":"https://easystats.github.io/datawizard/reference/nhanes_sample.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample dataset from the National Health and Nutrition Examination Survey — nhanes_sample","text":"Lumley T (2010). Complex Surveys: guide analysis using R. Wiley","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize numeric variable to 0-1 range — normalize","title":"Normalize numeric variable to 0-1 range — normalize","text":"Performs normalization data, .e., scales variables range 0 - 1. special case rescale(). unnormalize() counterpart, works variables normalized normalize().","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize numeric variable to 0-1 range — normalize","text":"","code":"normalize(x, ...)  # S3 method for numeric normalize(x, include_bounds = TRUE, verbose = TRUE, ...)  # S3 method for data.frame normalize(   x,   select = NULL,   exclude = NULL,   include_bounds = TRUE,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  unnormalize(x, ...)  # S3 method for numeric unnormalize(x, verbose = TRUE, ...)  # S3 method for data.frame unnormalize(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  # S3 method for grouped_df unnormalize(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize numeric variable to 0-1 range — normalize","text":"x numeric vector, (grouped) data frame, matrix. See 'Details'. ... Arguments passed methods. include_bounds Numeric logical. Using can useful case beta-regression, response variable allowed include zeros ones. TRUE, input normalized range includes zero one. FALSE, return value compressed, using Smithson Verkuilen's (2006) formula (x * (n - 1) + 0.5) / n, avoid zeros ones normalized variables. Else, numeric (e.g., 0.001), include_bounds defines \"distance\" lower upper bound, .e. normalized vectors rescaled range 0 + include_bounds 1 - include_bounds. verbose Toggle warnings messages . select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, standardized variables get new column names (suffix \"_z\") appended (column bind) x, thus returning original standardized variables. FALSE, original variables x overwritten standardized versions. character value, standardized variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize numeric variable to 0-1 range — normalize","text":"normalized object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Normalize numeric variable to 0-1 range — normalize","text":"x matrix, normalization performed across values (column- row-wise). column-wise normalization, convert matrix data.frame. x grouped data frame (grouped_df), normalization performed separately group.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Normalize numeric variable to 0-1 range — normalize","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Normalize numeric variable to 0-1 range — normalize","text":"Smithson M, Verkuilen J (2006). Better Lemon Squeezer? Maximum-Likelihood Regression Beta-Distributed Dependent Variables. Psychological Methods, 11(1), 54–71.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalize numeric variable to 0-1 range — normalize","text":"","code":"normalize(c(0, 1, 5, -5, -2)) #> [1] 0.5 0.6 1.0 0.0 0.3 #> (original range = -5 to 5) #>  normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE) #> [1] 0.50 0.58 0.90 0.10 0.34 #> (original range = -5 to 5) #>  # use a value defining the bounds normalize(c(0, 1, 5, -5, -2), include_bounds = .001) #> [1] 0.5000 0.5998 0.9990 0.0010 0.3004 #> (original range = -5 to 5) #>   head(normalize(trees)) #>        Girth     Height      Volume #> 1 0.00000000 0.29166667 0.001497006 #> 2 0.02439024 0.08333333 0.001497006 #> 3 0.04065041 0.00000000 0.000000000 #> 4 0.17886179 0.37500000 0.092814371 #> 5 0.19512195 0.75000000 0.128742515 #> 6 0.20325203 0.83333333 0.142215569"},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":null,"dir":"Reference","previous_headings":"","what":"(Signed) rank transformation — ranktransform","title":"(Signed) rank transformation — ranktransform","text":"Transform numeric values integers rank (.e., 1st smallest, 2nd smallest, 3rd smallest, etc.). Setting sign argument TRUE give signed ranks, ranking done according absolute size sign preserved (.e., 2, 1, -3, 4).","code":""},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Signed) rank transformation — ranktransform","text":"","code":"ranktransform(x, ...)  # S3 method for numeric ranktransform(x, sign = FALSE, method = \"average\", verbose = TRUE, ...)  # S3 method for data.frame ranktransform(   x,   select = NULL,   exclude = NULL,   sign = FALSE,   method = \"average\",   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Signed) rank transformation — ranktransform","text":"x Object. ... Arguments passed methods. sign Logical, TRUE, return signed ranks. method Treatment ties. Can one \"average\" (default), \"first\", \"last\", \"random\", \"max\" \"min\". See rank() details. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"(Signed) rank transformation — ranktransform","text":"rank-transformed object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"(Signed) rank transformation — ranktransform","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Signed) rank transformation — ranktransform","text":"","code":"ranktransform(c(0, 1, 5, -5, -2)) #> [1] 3 4 5 1 2  # Won't work # ranktransform(c(0, 1, 5, -5, -2), sign = TRUE)  head(ranktransform(trees)) #>   Girth Height Volume #> 1     1    6.0    2.5 #> 2     2    3.0    2.5 #> 3     3    1.0    1.0 #> 4     4    8.5    5.0 #> 5     5   25.5    7.0 #> 6     6   28.0    9.0"},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode values from one or more variables into a new variable — recode_into","title":"Recode values from one or more variables into a new variable — recode_into","text":"functions recodes values one variables new variable. convenient function avoid nested ifelse() statements, similar dplyr::case_when().","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode values from one or more variables into a new variable — recode_into","text":"","code":"recode_into(   ...,   data = NULL,   default = NA,   overwrite = TRUE,   preserve_na = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode values from one or more variables into a new variable — recode_into","text":"... sequence two-sided formulas, left hand side (LHS) logical matching condition determines values match case. LHS formula also called \"recode pattern\" (e.g., messages). right hand side (RHS) indicates replacement value. data Optional, name data frame. can used avoid writing data name multiple times .... See 'Examples'. default Indicates default value chosen match formulas ... found. provided, NA used default value. overwrite Logical, TRUE (default) one recode pattern apply case, already recoded values overwritten subsequent recode patterns. FALSE, former recoded cases altered later recode patterns apply cases . warning message printed alert situations avoid unintentional recodings. preserve_na Logical, TRUE default NA, missing values original variable set back NA recoded variable (unless overwritten recode patterns). FALSE, missing values original variable recoded default. Setting preserve_na = TRUE prevents unintentional overwriting missing values default, means find valid values original data missing values. See 'Examples'. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode values from one or more variables into a new variable — recode_into","text":"vector recoded values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode values from one or more variables into a new variable — recode_into","text":"","code":"x <- 1:30 recode_into(   x > 15 ~ \"a\",   x > 10 & x <= 15 ~ \"b\",   default = \"c\" ) #>  [1] \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"b\" \"b\" \"b\" \"b\" \"b\" \"a\" \"a\" \"a\" \"a\" #> [20] \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\"  x <- 1:10 # default behaviour: second recode pattern \"x > 5\" overwrites # some of the formerly recoded cases from pattern \"x >= 3 & x <= 7\" recode_into(   x >= 3 & x <= 7 ~ 1,   x > 5 ~ 2,   default = 0,   verbose = FALSE ) #>  [1] 0 0 1 1 1 2 2 2 2 2  # setting \"overwrite = FALSE\" will not alter formerly recoded cases recode_into(   x >= 3 & x <= 7 ~ 1,   x > 5 ~ 2,   default = 0,   overwrite = FALSE,   verbose = FALSE ) #>  [1] 0 0 1 1 1 1 1 2 2 2  set.seed(123) d <- data.frame(   x = sample(1:5, 30, TRUE),   y = sample(letters[1:5], 30, TRUE),   stringsAsFactors = FALSE )  # from different variables into new vector recode_into(   d$x %in% 1:3 & d$y %in% c(\"a\", \"b\") ~ 1,   d$x > 3 ~ 2,   default = 0 ) #>  [1] 1 1 1 0 0 2 2 0 1 1 2 0 0 0 2 1 1 2 1 0 1 1 0 2 0 1 2 2 1 2  # no need to write name of data frame each time recode_into(   x %in% 1:3 & y %in% c(\"a\", \"b\") ~ 1,   x > 3 ~ 2,   data = d,   default = 0 ) #>  [1] 1 1 1 0 0 2 2 0 1 1 2 0 0 0 2 1 1 2 1 0 1 1 0 2 0 1 2 2 1 2  # handling of missing values d <- data.frame(   x = c(1, NA, 2, NA, 3, 4),   y = c(1, 11, 3, NA, 5, 6) ) # first NA in x is overwritten by valid value from y # we have no known value for second NA in x and y, # thus we get one NA in the result recode_into(   x <= 3 ~ 1,   y > 5 ~ 2,   data = d,   default = 0,   preserve_na = TRUE ) #> [1]  1  2  1 NA  1  2 # first NA in x is overwritten by valid value from y # default value is used for second NA recode_into(   x <= 3 ~ 1,   y > 5 ~ 2,   data = d,   default = 0,   preserve_na = FALSE ) #> Missing values in original variable are overwritten by default value. If #>   you want to preserve missing values, set `preserve_na = TRUE`. #> [1] 1 2 1 0 1 2"},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode old values of variables into new values — recode_values","title":"Recode old values of variables into new values — recode_values","text":"functions recodes old values new values can used recode numeric character vectors, factors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode old values of variables into new values — recode_values","text":"","code":"recode_values(x, ...)  # S3 method for numeric recode_values(   x,   recode = NULL,   default = NULL,   preserve_na = TRUE,   verbose = TRUE,   ... )  # S3 method for data.frame recode_values(   x,   select = NULL,   exclude = NULL,   recode = NULL,   default = NULL,   preserve_na = TRUE,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  change_code(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode old values of variables into new values — recode_values","text":"x data frame, numeric character vector, factor. ... used. recode list named vectors, indicate recode pairs. names list-elements (.e. left-hand side) represent new values, values list-elements indicate original (old) values replaced. recoding numeric vectors, element names surrounded backticks. example, recode=list(`0`=1) recode 1 0 numeric vector. See also 'Examples' 'Details'. default Defines default value values match recode-pairs. Note , preserve_na=FALSE, missing values (NA) also captured default argument, thus also recoded specified value. See 'Examples' 'Details'. preserve_na Logical, TRUE, NA (missing values) preserved. overrides arguments, including default. Hence, preserve_na=TRUE, default longer convert NA specified default value. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode old values of variables into new values — recode_values","text":"x, old values replaced new values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Recode old values of variables into new values — recode_values","text":"section describes pattern recode arguments, also provides shortcuts, particular recoding numeric values. Single values Single values either need wrapped backticks (case numeric values) \"\" (character factor levels). Example: recode=list(`0`=1,`1`=2) recode 1 0, 2 1. factors character vectors, example : recode=list(x=\"\",y=\"b\") (recode \"\" \"x\" \"b\" \"y\"). Multiple values Multiple values recoded new value can separated comma. Example: recode=list(`1`=c(1,4),`2`=c(2,3)) recode values 1 4 1, 2 3 2. also possible define  old values character string, like:  recode=list(`1`=\"1,4\",`2`=\"2,3\") factors character vectors, example : recode=list(x=c(\"\",\"b\"),y=c(\"c\",\"d\")). Value range Numeric value ranges can defined using :. Example: recode=list(`1`=1:3,`2`=4:6) recode values 1 3 1, 4 6 2. min max placeholder use minimum maximum value (numeric) variable. Useful, e.g., recoding ranges values. Example: recode=list(`1`=\"min:10\",`2`=\"11:max\"). default values default argument defines default value values match recode-pairs. example, recode=list(`1`=c(1,2),`2`=c(3,4)), default=9 recode values 1 2 1, 3 4 2, values 9. preserve_na set FALSE, NA (missing values) also recoded specified default value. Reversing rescaling See reverse() rescale().","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Recode old values of variables into new values — recode_values","text":"can use options(data_recode_pattern = \"old=new\") switch behaviour recode-argument, .e. recode-pairs now following pattern old values = new values, e.g. getOption(\"data_recode_pattern\") set \"old=new\", recode(`1`=0) recode 1 0. default recode(`1`=0) recode 0 1.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Recode old values of variables into new values — recode_values","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode old values of variables into new values — recode_values","text":"","code":"# numeric ---------- set.seed(123) x <- sample(c(1:4, NA), 15, TRUE) table(x, useNA = \"always\") #> x #>    1    2    3    4 <NA>  #>    2    3    6    2    2   out <- recode_values(x, list(`0` = 1, `1` = 2:3, `2` = 4)) out #>  [1]  1  1  1  1  1 NA  2  0  1  1 NA  1  1  0  2 table(out, useNA = \"always\") #> out #>    0    1    2 <NA>  #>    2    9    2    2   # to recode NA values, set preserve_na to FALSE out <- recode_values(   x,   list(`0` = 1, `1` = 2:3, `2` = 4, `9` = NA),   preserve_na = FALSE ) out #>  [1] 1 1 1 1 1 9 2 0 1 1 9 1 1 0 2 table(out, useNA = \"always\") #> out #>    0    1    2    9 <NA>  #>    2    9    2    2    0   # preserve na ---------- out <- recode_values(x, list(`0` = 1, `1` = 2:3), default = 77) out #>  [1]  1  1  1  1  1 NA 77  0  1  1 NA  1  1  0 77 table(out, useNA = \"always\") #> out #>    0    1   77 <NA>  #>    2    9    2    2   # recode na into default ---------- out <- recode_values(   x,   list(`0` = 1, `1` = 2:3),   default = 77,   preserve_na = FALSE ) out #>  [1]  1  1  1  1  1 77 77  0  1  1 77  1  1  0 77 table(out, useNA = \"always\") #> out #>    0    1   77 <NA>  #>    2    9    4    0    # factors (character vectors are similar) ---------- set.seed(123) x <- as.factor(sample(c(\"a\", \"b\", \"c\"), 15, TRUE)) table(x) #> x #> a b c  #> 2 7 6   out <- recode_values(x, list(x = \"a\", y = c(\"b\", \"c\"))) out #>  [1] y y y y y y y y y x y y x y y #> Levels: x y table(out) #> out #>  x  y  #>  2 13   out <- recode_values(x, list(x = \"a\", y = \"b\", z = \"c\")) out #>  [1] z z z y z y y y z x y y x y z #> Levels: x y z table(out) #> out #> x y z  #> 2 7 6   out <- recode_values(x, list(y = \"b,c\"), default = 77) # same as # recode_values(x, list(y = c(\"b\", \"c\")), default = 77) out #>  [1] y  y  y  y  y  y  y  y  y  77 y  y  77 y  y  #> Levels: 77 y table(out) #> out #> 77  y  #>  2 13    # data frames ---------- set.seed(123) d <- data.frame(   x = sample(c(1:4, NA), 12, TRUE),   y = as.factor(sample(c(\"a\", \"b\", \"c\"), 12, TRUE)),   stringsAsFactors = FALSE )  recode_values(   d,   recode = list(`0` = 1, `1` = 2:3, `2` = 4, x = \"a\", y = c(\"b\", \"c\")),   append = TRUE ) #>     x y x_r y_r #> 1   3 c   1   y #> 2   3 a   1   x #> 3   2 a   1   x #> 4   2 a   1   x #> 5   3 a   1   x #> 6  NA c  NA   y #> 7   4 b   2   y #> 8   1 c   0   y #> 9   2 b   1   y #> 10  3 a   1   x #> 11 NA b  NA   y #> 12  3 c   1   y   # switch recode pattern to \"old=new\" ---------- options(data_recode_pattern = \"old=new\")  # numeric set.seed(123) x <- sample(c(1:4, NA), 15, TRUE) table(x, useNA = \"always\") #> x #>    1    2    3    4 <NA>  #>    2    3    6    2    2   out <- recode_values(x, list(`1` = 0, `2:3` = 1, `4` = 2)) table(out, useNA = \"always\") #> out #>    0    1    2 <NA>  #>    2    9    2    2   # factors (character vectors are similar) set.seed(123) x <- as.factor(sample(c(\"a\", \"b\", \"c\"), 15, TRUE)) table(x) #> x #> a b c  #> 2 7 6   out <- recode_values(x, list(a = \"x\", `b, c` = \"y\")) table(out) #> out #>  x  y  #>  2 13   # reset options options(data_recode_pattern = NULL)"},{"path":"https://easystats.github.io/datawizard/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. insight print_html, print_md","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Return or remove variables or observations that are completely missing — remove_empty","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"functions check rows columns data frame completely contain missing values, .e. observations variables completely missing values, either (1) returns indices; (2) removes data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"","code":"empty_columns(x)  empty_rows(x)  remove_empty_columns(x)  remove_empty_rows(x)  remove_empty(x)"},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"x data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"empty_columns() empty_rows(), numeric (named) vector row column indices variables completely missing values. remove_empty_columns() remove_empty_rows(), data frame \"empty\" columns rows removed, respectively. remove_empty(), empty rows columns removed.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"character vectors, empty string values (.e. \"\") also considered missing value. Thus, character vector contains NA \"\"``, considered empty variable removed. applies observations (rows) contain NAor\"\"`.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"","code":"tmp <- data.frame(   a = c(1, 2, 3, NA, 5),   b = c(1, NA, 3, NA, 5),   c = c(NA, NA, NA, NA, NA),   d = c(1, NA, 3, NA, 5) )  tmp #>    a  b  c  d #> 1  1  1 NA  1 #> 2  2 NA NA NA #> 3  3  3 NA  3 #> 4 NA NA NA NA #> 5  5  5 NA  5  # indices of empty columns or rows empty_columns(tmp) #> c  #> 3  empty_rows(tmp) #> [1] 4  # remove empty columns or rows remove_empty_columns(tmp) #>    a  b  d #> 1  1  1  1 #> 2  2 NA NA #> 3  3  3  3 #> 4 NA NA NA #> 5  5  5  5 remove_empty_rows(tmp) #>   a  b  c  d #> 1 1  1 NA  1 #> 2 2 NA NA NA #> 3 3  3 NA  3 #> 5 5  5 NA  5  # remove empty columns and rows remove_empty(tmp) #>   a  b  d #> 1 1  1  1 #> 2 2 NA NA #> 3 3  3  3 #> 5 5  5  5  # also remove \"empty\" character vectors tmp <- data.frame(   a = c(1, 2, 3, NA, 5),   b = c(1, NA, 3, NA, 5),   c = c(\"\", \"\", \"\", \"\", \"\"),   stringsAsFactors = FALSE ) empty_columns(tmp) #> c  #> 3"},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert infinite or NaN values into NA — replace_nan_inf","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"Replaces infinite (Inf -Inf) NaN values NA.","code":""},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"","code":"replace_nan_inf(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"x vector dataframe ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"Data Inf, -Inf, NaN converted NA.","code":""},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"","code":"# a vector x <- c(1, 2, NA, 3, NaN, 4, NA, 5, Inf, -Inf, 6, 7) replace_nan_inf(x) #>  [1]  1  2 NA  3 NA  4 NA  5 NA NA  6  7  # a data frame df <- data.frame(   x = c(1, NA, 5, Inf, 2, NA),   y = c(3, NaN, 4, -Inf, 6, 7),   stringsAsFactors = FALSE ) replace_nan_inf(df) #>    x  y #> 1  1  3 #> 2 NA NA #> 3  5  4 #> 4 NA NA #> 5  2  6 #> 6 NA  7"},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale Variables to a New Range — rescale","title":"Rescale Variables to a New Range — rescale","text":"Rescale variables new range. Can also used reverse-score variables (change keying/scoring direction).","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale Variables to a New Range — rescale","text":"","code":"rescale(x, ...)  change_scale(x, ...)  # S3 method for numeric rescale(x, to = c(0, 100), range = NULL, verbose = TRUE, ...)  # S3 method for data.frame rescale(   x,   select = NULL,   exclude = NULL,   to = c(0, 100),   range = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale Variables to a New Range — rescale","text":"x (grouped) data frame, numeric vector factor. ... Arguments passed methods. Numeric vector length 2 giving new range variable rescaling. reverse-score variable, range given maximum value first. See examples. range Initial (old) range values. NULL, take range input vector (range(x)). verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale Variables to a New Range — rescale","text":"rescaled object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Rescale Variables to a New Range — rescale","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale Variables to a New Range — rescale","text":"","code":"rescale(c(0, 1, 5, -5, -2)) #> [1]  50  60 100   0  30 #> (original range = -5 to 5) #>  rescale(c(0, 1, 5, -5, -2), to = c(-5, 5)) #> [1]  0  1  5 -5 -2 #> (original range = -5 to 5) #>  rescale(c(1, 2, 3, 4, 5), to = c(-2, 2)) #> [1] -2 -1  0  1  2 #> (original range = 1 to 5) #>   # Specify the \"theoretical\" range of the input vector rescale(c(1, 3, 4), to = c(0, 40), range = c(0, 4)) #> [1] 10 30 40 #> (original range = 0 to 4) #>   # Reverse-score a variable rescale(c(1, 2, 3, 4, 5), to = c(5, 1)) #> [1] 5 4 3 2 1 #> (original range = 1 to 5) #>  rescale(c(1, 2, 3, 4, 5), to = c(2, -2)) #> [1]  2  1  0 -1 -2 #> (original range = 1 to 5) #>   # Data frames head(rescale(iris, to = c(0, 1))) #> Variables of class `factor` can't be rescaled and remain unchanged. #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   0.22222222   0.6250000   0.06779661  0.04166667  setosa #> 2   0.16666667   0.4166667   0.06779661  0.04166667  setosa #> 3   0.11111111   0.5000000   0.05084746  0.04166667  setosa #> 4   0.08333333   0.4583333   0.08474576  0.04166667  setosa #> 5   0.19444444   0.6666667   0.06779661  0.04166667  setosa #> 6   0.30555556   0.7916667   0.11864407  0.12500000  setosa head(rescale(iris, to = c(0, 1), select = \"Sepal.Length\")) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   0.22222222         3.5          1.4         0.2  setosa #> 2   0.16666667         3.0          1.4         0.2  setosa #> 3   0.11111111         3.2          1.3         0.2  setosa #> 4   0.08333333         3.1          1.5         0.2  setosa #> 5   0.19444444         3.6          1.4         0.2  setosa #> 6   0.30555556         3.9          1.7         0.4  setosa  # One can specify a list of ranges head(rescale(iris, to = list(   \"Sepal.Length\" = c(0, 1),   \"Petal.Length\" = c(-1, 0) ))) #> Variables of class `factor` can't be rescaled and remain unchanged. #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   0.22222222         3.5   -0.9322034         0.2  setosa #> 2   0.16666667         3.0   -0.9322034         0.2  setosa #> 3   0.11111111         3.2   -0.9491525         0.2  setosa #> 4   0.08333333         3.1   -0.9152542         0.2  setosa #> 5   0.19444444         3.6   -0.9322034         0.2  setosa #> 6   0.30555556         3.9   -0.8813559         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale design weights for multilevel analysis — rescale_weights","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"functions fit multilevel mixed effects models allow specify frequency weights, design (.e. sampling probability) weights, used analyzing complex samples survey data. rescale_weights() implements algorithm proposed Asparouhov (2006) Carle (2009) rescale design weights survey data account grouping structure multilevel models, can used multilevel modelling.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"","code":"rescale_weights(data, group, probability_weights, nest = FALSE)"},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"data data frame. group Variable names (character vector, formula), indicating grouping structure (strata) survey data (level-2-cluster variable). also possible create weights multiple group variables; cases, created weighting variable suffixed name group variable. probability_weights Variable indicating probability (design sampling) weights survey data (level-1-weight). nest Logical, TRUE group indicates least two group variables, groups \"nested\", .e. groups now combination group level variables group.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"data, including new weighting variables: pweights_a pweights_b, represent rescaled design weights use multilevel models (use variables weights argument).","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"Rescaling based two methods: pweights_a, sample weights probability_weights adjusted factor represents proportion group size divided sum sampling weights within group. adjustment factor pweights_b sum sample weights within group divided sum squared sample weights within group (see Carle (2009), Appendix B). words, pweights_a \"scales weights new weights sum cluster sample size\" pweights_b \"scales weights new weights sum effective cluster size\". Regarding choice scaling methods B, Carle suggests \"analysts wish discuss point estimates report results based weighting method . analysts interested residual -group variance, method B may generally provide least biased estimates\". general, recommended fit non-weighted model weighted models scaling methods comparing models, see whether \"inferential decisions converge\", gain confidence results. Though bias scaled weights decreases increasing group size, method preferred insufficient low group size concern. group ID probably PSU may used random effects (e.g. nested design, group PSU varying intercepts), depending survey design mimicked.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"Carle .C. (2009). Fitting multilevel models complex survey data design weights: Recommendations. BMC Medical Research Methodology 9(49): 1-13 Asparouhov T. (2006). General Multi-Level Modeling Sampling Weights. Communications Statistics - Theory Methods 35: 439-460","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"","code":"if (require(\"lme4\")) {   data(nhanes_sample)   head(rescale_weights(nhanes_sample, \"SDMVSTRA\", \"WTINT2YR\"))    # also works with multiple group-variables   head(rescale_weights(nhanes_sample, c(\"SDMVSTRA\", \"SDMVPSU\"), \"WTINT2YR\"))    # or nested structures.   x <- rescale_weights(     data = nhanes_sample,     group = c(\"SDMVSTRA\", \"SDMVPSU\"),     probability_weights = \"WTINT2YR\",     nest = TRUE   )   head(x)    nhanes_sample <- rescale_weights(nhanes_sample, \"SDMVSTRA\", \"WTINT2YR\")    glmer(     total ~ factor(RIAGENDR) * (log(age) + factor(RIDRETH1)) + (1 | SDMVPSU),     family = poisson(),     data = nhanes_sample,     weights = pweights_a   ) } #> Generalized linear mixed model fit by maximum likelihood (Laplace #>   Approximation) [glmerMod] #>  Family: poisson  ( log ) #> Formula: total ~ factor(RIAGENDR) * (log(age) + factor(RIDRETH1)) + (1 |   #>     SDMVPSU) #>    Data: nhanes_sample #> Weights: pweights_a #>       AIC       BIC    logLik  deviance  df.resid  #>  78844.27  78920.47 -39409.14  78818.27      2582  #> Random effects: #>  Groups  Name        Std.Dev. #>  SDMVPSU (Intercept) 0.1018   #> Number of obs: 2595, groups:  SDMVPSU, 2 #> Fixed Effects: #>                         (Intercept)                    factor(RIAGENDR)2   #>                            2.491801                            -1.021308   #>                            log(age)                    factor(RIDRETH1)2   #>                            0.838726                            -0.088627   #>                   factor(RIDRETH1)3                    factor(RIDRETH1)4   #>                           -0.013333                             0.722511   #>                   factor(RIDRETH1)5           factor(RIAGENDR)2:log(age)   #>                           -0.106521                            -1.012695   #> factor(RIAGENDR)2:factor(RIDRETH1)2  factor(RIAGENDR)2:factor(RIDRETH1)3   #>                           -0.009086                             0.732985   #> factor(RIAGENDR)2:factor(RIDRETH1)4  factor(RIAGENDR)2:factor(RIDRETH1)5   #>                            0.275967                             0.542074"},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape CI between wide/long formats — reshape_ci","title":"Reshape CI between wide/long formats — reshape_ci","text":"Reshape CI wide/long formats.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape CI between wide/long formats — reshape_ci","text":"","code":"reshape_ci(x, ci_type = \"CI\")"},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape CI between wide/long formats — reshape_ci","text":"x data frame containing columns named CI_low CI_high (similar, see ci_type). ci_type String indicating \"type\" (.e. prefix) interval columns. Per easystats convention, confidence credible intervals named CI_low CI_high, related ci_type \"CI\". column names intervals differ, ci_type can used indicate name, e.g. ci_type = \"SI\" can used support intervals, column names data frame SI_low SI_high.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reshape CI between wide/long formats — reshape_ci","text":"data frame columns corresponding confidence intervals reshaped either wide long format.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape CI between wide/long formats — reshape_ci","text":"","code":"x <- data.frame(   Parameter = c(\"Term 1\", \"Term 2\", \"Term 1\", \"Term 2\"),   CI = c(.8, .8, .9, .9),   CI_low = c(.2, .3, .1, .15),   CI_high = c(.5, .6, .8, .85),   stringsAsFactors = FALSE )  reshape_ci(x) #>   Parameter CI_low_0.8 CI_high_0.8 CI_low_0.9 CI_high_0.9 #> 1    Term 1        0.2         0.5       0.10        0.80 #> 2    Term 2        0.3         0.6       0.15        0.85 reshape_ci(reshape_ci(x)) #>   Parameter  CI CI_low CI_high #> 1    Term 1 0.8   0.20    0.50 #> 2    Term 1 0.9   0.10    0.80 #> 3    Term 2 0.8   0.30    0.60 #> 4    Term 2 0.9   0.15    0.85"},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse-Score Variables — reverse","title":"Reverse-Score Variables — reverse","text":"Reverse-score variables (change keying/scoring direction).","code":""},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse-Score Variables — reverse","text":"","code":"reverse(x, ...)  reverse_scale(x, ...)  # S3 method for numeric reverse(x, range = NULL, verbose = TRUE, ...)  # S3 method for data.frame reverse(   x,   select = NULL,   exclude = NULL,   range = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse-Score Variables — reverse","text":"x (grouped) data frame, numeric vector factor. ... Arguments passed methods. range Range values used reference reversing scale. numeric variables, can NULL numeric vector length two, indicating lowest highest value reference range. NULL, take range input vector (range(x)). factors, range can NULL, numeric vector length two, (numeric) vector least length factor levels (.e. must equal larger nlevels(x)). Note providing range factors usually makes sense factor levels numeric, characters. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse-Score Variables — reverse","text":"reverse-scored object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Reverse-Score Variables — reverse","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse-Score Variables — reverse","text":"","code":"reverse(c(1, 2, 3, 4, 5)) #> [1] 5 4 3 2 1 reverse(c(-2, -1, 0, 2, 1)) #> [1]  2  1  0 -2 -1  # Specify the \"theoretical\" range of the input vector reverse(c(1, 3, 4), range = c(0, 4)) #> [1] 3 1 0  # Factor variables reverse(factor(c(1, 2, 3, 4, 5))) #> [1] 5 4 3 2 1 #> Levels: 1 2 3 4 5 reverse(factor(c(1, 2, 3, 4, 5)), range = 0:10) #> [1] 9 8 7 6 5 #> Levels: 0 1 2 3 4 5 6 7 8 9 10  # Data frames head(reverse(iris)) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species #> 1          7.1         2.9          6.5         2.4 virginica #> 2          7.3         3.4          6.5         2.4 virginica #> 3          7.5         3.2          6.6         2.4 virginica #> 4          7.6         3.3          6.4         2.4 virginica #> 5          7.2         2.8          6.5         2.4 virginica #> 6          6.8         2.5          6.2         2.2 virginica head(reverse(iris, select = \"Sepal.Length\")) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          7.1         3.5          1.4         0.2  setosa #> 2          7.3         3.0          1.4         0.2  setosa #> 3          7.5         3.2          1.3         0.2  setosa #> 4          7.6         3.1          1.5         0.2  setosa #> 5          7.2         3.6          1.4         0.2  setosa #> 6          6.8         3.9          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":null,"dir":"Reference","previous_headings":"","what":"Row means (optionally with minimum amount of valid values) — row_means","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"function similar SPSS MEAN.n function computes row means data frame matrix least min_valid values row valid (NA).","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"","code":"row_means(   data,   select = NULL,   exclude = NULL,   min_valid = NULL,   digits = NULL,   ignore_case = FALSE,   regex = FALSE,   remove_na = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"data data frame least two columns, row means applied. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. min_valid Optional, numeric value length 1. May either numeric value indicates amount valid values per row calculate row mean; value 0 1, indicating proportion valid values per row calculate row mean (see 'Details'). NULL (default), cases considered. row's sum valid values less min_valid, NA returned. digits Numeric value indicating number decimal places used rounding mean values. Negative values allowed (see 'Details'). default, digits = NULL rounding used. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. remove_na Logical, TRUE (default), removes missing (NA) values calculating row means. applies min_valuid specified. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"vector row means rows least n valid values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"Rounding negative number digits means rounding power ten, example row_means(df, 3, digits = -2) rounds nearest hundred. min_valid, NULL, min_valid must numeric value 0 ncol(data). row data frame least min_valid non-missing values, row mean returned. min_valid non-integer value 0 1, min_valid considered indicate proportion required non-missing values per row. E.g., min_valid = 0.75, row must least ncol(data) * min_valid non-missing values row mean calculated. See 'Examples'.","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"","code":"dat <- data.frame(   c1 = c(1, 2, NA, 4),   c2 = c(NA, 2, NA, 5),   c3 = c(NA, 4, NA, NA),   c4 = c(2, 3, 7, 8) )  # default, all means are shown, if no NA values are present row_means(dat) #> [1]   NA 2.75   NA   NA  # remove all NA before computing row means row_means(dat, remove_na = TRUE) #> [1] 1.500000 2.750000 7.000000 5.666667  # needs at least 4 non-missing values per row row_means(dat, min_valid = 4) # 1 valid return value #> [1]   NA 2.75   NA   NA  # needs at least 3 non-missing values per row row_means(dat, min_valid = 3) # 2 valid return values #> [1]       NA 2.750000       NA 5.666667  # needs at least 2 non-missing values per row row_means(dat, min_valid = 2) #> [1] 1.500000 2.750000       NA 5.666667  # needs at least 1 non-missing value per row, for two selected variables row_means(dat, select = c(\"c1\", \"c3\"), min_valid = 1) #> [1]  1  3 NA  4  # needs at least 50% of non-missing values per row row_means(dat, min_valid = 0.5) # 3 valid return values #> [1] 1.500000 2.750000       NA 5.666667  # needs at least 75% of non-missing values per row row_means(dat, min_valid = 0.75) # 2 valid return values #> [1]       NA 2.750000       NA 5.666667"},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for working with row names or row ids — rownames_as_column","title":"Tools for working with row names or row ids — rownames_as_column","text":"Tools working row names row ids","code":""},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for working with row names or row ids — rownames_as_column","text":"","code":"rownames_as_column(x, var = \"rowname\")  column_as_rownames(x, var = \"rowname\")  rowid_as_column(x, var = \"rowid\")"},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for working with row names or row ids — rownames_as_column","text":"x data frame. var Name column use row names/ids. column_as_rownames(), argument can variable name column number. rownames_as_column() rowid_as_column(), column name must already exist data.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tools for working with row names or row ids — rownames_as_column","text":"data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tools for working with row names or row ids — rownames_as_column","text":"similar tibble's functions column_to_rownames(), rownames_to_column() rowid_to_column(). Note behavior rowid_as_column() different grouped dataframe: instead making rowid unique across full dataframe, creates rowid per group. Therefore, can several rows rowid belong different groups. familiar dplyr, similar following:","code":"data |>   group_by(grp) |>   mutate(id = row_number()) |>   ungroup()"},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for working with row names or row ids — rownames_as_column","text":"","code":"# Convert between row names and column -------------------------------- test <- rownames_as_column(mtcars, var = \"car\") test #>                    car  mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> 1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> 2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> 3           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> 4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> 5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> 6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> 7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> 8            Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> 9             Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> 10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> 11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> 12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> 13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> 14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> 15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> 16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> 17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> 18            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> 19         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> 20      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> 21       Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1 #> 22    Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> 23         AMC Javelin 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> 24          Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> 25    Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> 26           Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> 27       Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> 28        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> 29      Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> 30        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> 31       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> 32          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2 head(column_as_rownames(test, var = \"car\")) #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  test_data <- head(iris)  rowid_as_column(test_data) #>   rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1     1          5.1         3.5          1.4         0.2  setosa #> 2     2          4.9         3.0          1.4         0.2  setosa #> 3     3          4.7         3.2          1.3         0.2  setosa #> 4     4          4.6         3.1          1.5         0.2  setosa #> 5     5          5.0         3.6          1.4         0.2  setosa #> 6     6          5.4         3.9          1.7         0.4  setosa rowid_as_column(test_data, var = \"my_id\") #>   my_id Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1     1          5.1         3.5          1.4         0.2  setosa #> 2     2          4.9         3.0          1.4         0.2  setosa #> 3     3          4.7         3.2          1.3         0.2  setosa #> 4     4          4.6         3.1          1.5         0.2  setosa #> 5     5          5.0         3.6          1.4         0.2  setosa #> 6     6          5.4         3.9          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Skewness and (Excess) Kurtosis — skewness","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"Compute Skewness (Excess) Kurtosis","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"","code":"skewness(x, ...)  # S3 method for numeric skewness(   x,   remove_na = TRUE,   type = \"2\",   iterations = NULL,   verbose = TRUE,   na.rm = TRUE,   ... )  kurtosis(x, ...)  # S3 method for numeric kurtosis(   x,   remove_na = TRUE,   type = \"2\",   iterations = NULL,   verbose = TRUE,   na.rm = TRUE,   ... )  # S3 method for parameters_kurtosis print(x, digits = 3, test = FALSE, ...)  # S3 method for parameters_skewness print(x, digits = 3, test = FALSE, ...)  # S3 method for parameters_skewness summary(object, test = FALSE, ...)  # S3 method for parameters_kurtosis summary(object, test = FALSE, ...)"},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"x numeric vector data.frame. ... Arguments passed methods. remove_na Logical. NA values removed computing (TRUE) (FALSE, default)? type Type algorithm computing skewness. May one 1 (\"1\", \"\" \"classic\"), 2 (\"2\", \"II\" \"SPSS\" \"SAS\") 3 ( \"3\", \"III\" \"Minitab\"). See 'Details'. iterations number bootstrap replicates computing standard errors. NULL (default), parametric standard errors computed. verbose Toggle warnings messages. na.rm Deprecated. Please use remove_na instead. digits Number decimal places. test Logical, TRUE, tests skewness kurtosis significantly different zero. object object returned skewness() kurtosis().","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"Values skewness kurtosis.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"skewness","dir":"Reference","previous_headings":"","what":"Skewness","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"Symmetric distributions skewness around zero, negative skewness values indicates \"left-skewed\" distribution, positive skewness values indicates \"right-skewed\" distribution. Examples relationship skewness distributions : Normal distribution (symmetric distribution) skewness 0 Half-normal distribution skewness just 1 Exponential distribution skewness 2 Lognormal distribution can skewness positive value, depending parameters (https://en.wikipedia.org/wiki/Skewness)","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"types-of-skewness","dir":"Reference","previous_headings":"","what":"Types of Skewness","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"skewness() supports three different methods estimating skewness, discussed Joanes Gill (1988): Type \"1\" \"classical\" method, g1 = (sum((x - mean(x))^3) / n) / (sum((x - mean(x))^2) / n)^1.5 Type \"2\" first calculates type-1 skewness, adjusts result: G1 = g1 * sqrt(n * (n - 1)) / (n - 2). SAS SPSS usually return. Type \"3\" first calculates type-1 skewness, adjusts result: b1 = g1 * ((1 - 1 / n))^1.5. Minitab usually returns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"kurtosis","dir":"Reference","previous_headings":"","what":"Kurtosis","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"kurtosis measure \"tailedness\" distribution. distribution kurtosis values zero called \"mesokurtic\". kurtosis value larger zero indicates \"leptokurtic\" distribution fatter tails. kurtosis value zero indicates \"platykurtic\" distribution thinner tails (https://en.wikipedia.org/wiki/Kurtosis).","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"types-of-kurtosis","dir":"Reference","previous_headings":"","what":"Types of Kurtosis","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"kurtosis() supports three different methods estimating kurtosis, discussed Joanes Gill (1988): Type \"1\" \"classical\" method, g2 = n * sum((x - mean(x))^4) / (sum((x - mean(x))^2)^2) - 3. Type \"2\" first calculates type-1 kurtosis, adjusts result: G2 = ((n + 1) * g2 + 6) * (n - 1)/((n - 2) * (n - 3)). SAS SPSS usually return Type \"3\" first calculates type-1 kurtosis, adjusts result: b2 = (g2 + 3) * (1 - 1 / n)^2 - 3. Minitab usually returns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"standard-errors","dir":"Reference","previous_headings":"","what":"Standard Errors","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"recommended compute empirical (bootstrapped) standard errors (via iterations argument) relying analytic standard errors (Wright & Herrington, 2011).","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"D. N. Joanes C. . Gill (1998). Comparing measures sample skewness kurtosis. Statistician, 47, 183–189. Wright, D. B., & Herrington, J. . (2011). Problematic standard errors confidence intervals skewness kurtosis. Behavior research methods, 43(1), 8-17.","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"","code":"skewness(rnorm(1000)) #> Skewness |    SE #> ---------------- #>    0.063 | 0.077 kurtosis(rnorm(1000)) #> Kurtosis |    SE #> ---------------- #>   -0.071 | 0.154"},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":null,"dir":"Reference","previous_headings":"","what":"Shift numeric value range — slide","title":"Shift numeric value range — slide","text":"functions shifts value range numeric variable, new range starts given value.","code":""},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shift numeric value range — slide","text":"","code":"slide(x, ...)  # S3 method for numeric slide(x, lowest = 0, ...)  # S3 method for data.frame slide(   x,   select = NULL,   exclude = NULL,   lowest = 0,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shift numeric value range — slide","text":"x data frame numeric vector. ... used. lowest Numeric, indicating lowest (minimum) value converting factors character vectors numeric values. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shift numeric value range — slide","text":"x, range numeric variables starts new value.","code":""},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Shift numeric value range — slide","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shift numeric value range — slide","text":"","code":"# numeric head(mtcars$gear) #> [1] 4 4 4 3 3 3 head(slide(mtcars$gear)) #> [1] 1 1 1 0 0 0 head(slide(mtcars$gear, lowest = 10)) #> [1] 11 11 11 10 10 10  # data frame sapply(slide(mtcars, lowest = 1), min) #>  mpg  cyl disp   hp drat   wt qsec   vs   am gear carb  #>    1    1    1    1    1    1    1    1    1    1    1  sapply(mtcars, min) #>    mpg    cyl   disp     hp   drat     wt   qsec     vs     am   gear   carb  #> 10.400  4.000 71.100 52.000  2.760  1.513 14.500  0.000  0.000  3.000  1.000"},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantify the smoothness of a vector — smoothness","title":"Quantify the smoothness of a vector — smoothness","text":"Quantify smoothness vector","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantify the smoothness of a vector — smoothness","text":"","code":"smoothness(x, method = \"cor\", lag = 1, iterations = NULL, ...)"},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantify the smoothness of a vector — smoothness","text":"x Numeric vector (similar time series). method Can \"diff\" (standard deviation standardized differences) \"cor\" (default, lag-one autocorrelation). lag integer indicating lag use. less 1, interpreted expressed percentage length vector. iterations number bootstrap replicates computing standard errors. NULL (default), parametric standard errors computed. ... Arguments passed methods.","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantify the smoothness of a vector — smoothness","text":"Value smoothness.","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantify the smoothness of a vector — smoothness","text":"https://stats.stackexchange.com/questions/24607/--measure-smoothness---time-series--r","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantify the smoothness of a vector — smoothness","text":"","code":"x <- (-10:10)^3 + rnorm(21, 0, 100) plot(x)  smoothness(x, method = \"cor\") #> [1] 0.9291692 #> attr(,\"class\") #> [1] \"parameters_smoothness\" \"numeric\"               smoothness(x, method = \"diff\") #> [1] 1.584401 #> attr(,\"class\") #> [1] \"parameters_smoothness\" \"numeric\""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-fit a model with standardized data — standardize.default","title":"Re-fit a model with standardized data — standardize.default","text":"Performs standardization data (z-scoring) using standardize() re-fits model standardized data.  Standardization done completely refitting model standardized data. Hence, approach equal standardizing variables fitting model return new model object. method particularly recommended complex models include interactions transformations (e.g., polynomial spline terms). robust (default FALSE) argument enables robust standardization data, based median MAD instead mean SD.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Re-fit a model with standardized data — standardize.default","text":"","code":"# S3 method for default standardize(   x,   robust = FALSE,   two_sd = FALSE,   weights = TRUE,   verbose = TRUE,   include_response = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Re-fit a model with standardized data — standardize.default","text":"x statistical model. robust Logical, TRUE, centering done subtracting median variables dividing median absolute deviation (MAD). FALSE, variables standardized subtracting mean dividing standard deviation (SD). two_sd TRUE, variables scaled two times deviation (SD MAD depending robust). method can useful obtain model coefficients continuous parameters comparable coefficients related binary predictors, applied predictors (outcome) (Gelman, 2008). weights TRUE (default), weighted-standardization carried . verbose Toggle warnings messages . include_response TRUE (default), response value also standardized. FALSE, predictors standardized. Note GLMs models non-linear link functions, response value standardized, make re-fitting model work. model contains stats::offset(), offset variable(s) standardized response standardized. two_sd = TRUE, offsets standardized one-sd (similar response). (mediate models, include_response refers outcome y model; m model's response always standardized possible). ... Arguments passed methods.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Re-fit a model with standardized data — standardize.default","text":"statistical model fitted standardized data","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"generalized-linear-models","dir":"Reference","previous_headings":"","what":"Generalized Linear Models","title":"Re-fit a model with standardized data — standardize.default","text":"Standardization generalized linear models (GLM, GLMM, etc) done respect predictors (outcome remains -, unstandardized) - maintaining interpretability coefficients (e.g., binomial model: exponent standardized parameter change 1 SD predictor, etc.)","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"dealing-with-factors","dir":"Reference","previous_headings":"","what":"Dealing with Factors","title":"Re-fit a model with standardized data — standardize.default","text":"standardize(model) standardize_parameters(model, method = \"refit\") standardize categorical predictors (.e. factors) / dummy-variables, may different behaviour compared R packages (lm.beta) software packages (like SPSS). mimic behaviours, either use standardize_parameters(model, method = \"basic\") obtain post-hoc standardized parameters, standardize data standardize(data, force = TRUE) fitting model.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"transformed-variables","dir":"Reference","previous_headings":"","what":"Transformed Variables","title":"Re-fit a model with standardized data — standardize.default","text":"model's formula contains transformations (e.g. y ~ exp(X)) transformation effectively takes place standardization (e.g., exp(scale(X))). Since transformations undefined none positive values, log() sqrt(), relevel variables shifted (post standardization) Z - min(Z) + 1 Z - min(Z) (respectively).","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Re-fit a model with standardized data — standardize.default","text":"","code":"model <- lm(Infant.Mortality ~ Education * Fertility, data = swiss) coef(standardize(model)) #>         (Intercept)           Education           Fertility Education:Fertility  #>          0.06386069          0.47482848          0.63270919          0.09829777"},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardization (Z-scoring) — standardize","title":"Standardization (Z-scoring) — standardize","text":"Performs standardization data (z-scoring), .e., centering scaling, data expressed terms standard deviation (.e., mean = 0, SD = 1) Median Absolute Deviance (median = 0, MAD = 1). applied statistical model, function extracts dataset, standardizes , refits model standardized version dataset. normalize() function can also used scale numeric variables within 0 - 1 range.  model standardization, see standardize.default().","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardization (Z-scoring) — standardize","text":"","code":"standardize(x, ...)  standardise(x, ...)  # S3 method for numeric standardize(   x,   robust = FALSE,   two_sd = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   scale = NULL,   verbose = TRUE,   ... )  # S3 method for factor standardize(   x,   robust = FALSE,   two_sd = FALSE,   weights = NULL,   force = FALSE,   verbose = TRUE,   ... )  # S3 method for data.frame standardize(   x,   select = NULL,   exclude = NULL,   robust = FALSE,   two_sd = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   scale = NULL,   remove_na = c(\"none\", \"selected\", \"all\"),   force = FALSE,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  unstandardize(x, ...)  unstandardise(x, ...)  # S3 method for numeric unstandardize(   x,   center = NULL,   scale = NULL,   reference = NULL,   robust = FALSE,   two_sd = FALSE,   ... )  # S3 method for data.frame unstandardize(   x,   center = NULL,   scale = NULL,   reference = NULL,   robust = FALSE,   two_sd = FALSE,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardization (Z-scoring) — standardize","text":"x (grouped) data frame, vector statistical model (unstandardize() model). ... Arguments passed methods. robust Logical, TRUE, centering done subtracting median variables dividing median absolute deviation (MAD). FALSE, variables standardized subtracting mean dividing standard deviation (SD). two_sd TRUE, variables scaled two times deviation (SD MAD depending robust). method can useful obtain model coefficients continuous parameters comparable coefficients related binary predictors, applied predictors (outcome) (Gelman, 2008). weights Can NULL (weighting), : model: TRUE (default), weighted-standardization carried . data.frames: numeric vector weights, character name column data.frame contains weights. numeric vectors: numeric vector weights. reference data frame variable centrality deviation computed instead input variable. Useful standardizing subset new data according another data frame. center, scale standardize():  Numeric values, can used alternative reference define reference centrality deviation. scale center length 1, recycled match length selected variables standardization. Else, center scale must length number selected variables. Values center scale matched selected variables provided order, unless named vector given. case, names matched names selected variables. unstandardize(): center scale correspond center (mean / median) scale (SD / MAD) original non-standardized data (data frames, named, column order correspond numeric column). However, one can also directly provide original data reference, center scale computed (according robust two_sd). Alternatively, input contains attributes center scale (output standardize()), take rest arguments absent. verbose Toggle warnings messages . force Logical, TRUE, forces recoding factors character vectors well. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. remove_na missing values (NA) treated: \"none\" (default): column's standardization done separately, ignoring NAs. Else, rows NA columns selected select / exclude (\"selected\") columns (\"\") dropped standardization, resulting data frame include cases. append Logical string. TRUE, standardized variables get new column names (suffix \"_z\") appended (column bind) x, thus returning original standardized variables. FALSE, original variables x overwritten standardized versions. character value, standardized variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardization (Z-scoring) — standardize","text":"standardized object (either standardize data frame statistical model fitted standardized data).","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Standardization (Z-scoring) — standardize","text":"x vector data frame remove_na = \"none\"), missing values preserved, return value length / number rows original input.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Standardization (Z-scoring) — standardize","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Standardization (Z-scoring) — standardize","text":"","code":"d <- iris[1:4, ]  # vectors standardise(d$Petal.Length) #> [1]  0.000000  0.000000 -1.224745  1.224745 #> (center: 1.4, scale = 0.082) #>   # Data frames # overwrite standardise(d, select = c(\"Sepal.Length\", \"Sepal.Width\")) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1    1.2402159   1.3887301          1.4         0.2  setosa #> 2    0.3382407  -0.9258201          1.4         0.2  setosa #> 3   -0.5637345   0.0000000          1.3         0.2  setosa #> 4   -1.0147221  -0.4629100          1.5         0.2  setosa  # append standardise(d, select = c(\"Sepal.Length\", \"Sepal.Width\"), append = TRUE) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_z #> 1          5.1         3.5          1.4         0.2  setosa      1.2402159 #> 2          4.9         3.0          1.4         0.2  setosa      0.3382407 #> 3          4.7         3.2          1.3         0.2  setosa     -0.5637345 #> 4          4.6         3.1          1.5         0.2  setosa     -1.0147221 #>   Sepal.Width_z #> 1     1.3887301 #> 2    -0.9258201 #> 3     0.0000000 #> 4    -0.4629100  # append, suffix standardise(d, select = c(\"Sepal.Length\", \"Sepal.Width\"), append = \"_std\") #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_std #> 1          5.1         3.5          1.4         0.2  setosa        1.2402159 #> 2          4.9         3.0          1.4         0.2  setosa        0.3382407 #> 3          4.7         3.2          1.3         0.2  setosa       -0.5637345 #> 4          4.6         3.1          1.5         0.2  setosa       -1.0147221 #>   Sepal.Width_std #> 1       1.3887301 #> 2      -0.9258201 #> 3       0.0000000 #> 4      -0.4629100  # standardizing with reference center and scale d <- data.frame(   a = c(-2, -1, 0, 1, 2),   b = c(3, 4, 5, 6, 7) )  # default standardization, based on mean and sd of each variable standardize(d) # means are 0 and 5, sd ~ 1.581139 #>            a          b #> 1 -1.2649111 -1.2649111 #> 2 -0.6324555 -0.6324555 #> 3  0.0000000  0.0000000 #> 4  0.6324555  0.6324555 #> 5  1.2649111  1.2649111  # standardization, based on mean and sd set to the same values standardize(d, center = c(0, 5), scale = c(1.581, 1.581)) #>            a          b #> 1 -1.2650221 -1.2650221 #> 2 -0.6325111 -0.6325111 #> 3  0.0000000  0.0000000 #> 4  0.6325111  0.6325111 #> 5  1.2650221  1.2650221  # standardization, mean and sd for each variable newly defined standardize(d, center = c(3, 4), scale = c(2, 4)) #>      a     b #> 1 -2.5 -0.25 #> 2 -2.0  0.00 #> 3 -1.5  0.25 #> 4 -1.0  0.50 #> 5 -0.5  0.75  # standardization, taking same mean and sd for each variable standardize(d, center = 1, scale = 3) #>            a         b #> 1 -1.0000000 0.6666667 #> 2 -0.6666667 1.0000000 #> 3 -0.3333333 1.3333333 #> 4  0.0000000 1.6666667 #> 5  0.3333333 2.0000000"},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Convenient text formatting functionalities — text_format","title":"Convenient text formatting functionalities — text_format","text":"Convenience functions manipulate format text.","code":""},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convenient text formatting functionalities — text_format","text":"","code":"text_format(   text,   sep = \", \",   last = \" and \",   width = NULL,   enclose = NULL,   ... )  format_text(   text,   sep = \", \",   last = \" and \",   width = NULL,   enclose = NULL,   ... )  text_fullstop(text)  text_lastchar(text, n = 1)  text_concatenate(text, sep = \", \", last = \" and \", enclose = NULL)  text_paste(text, text2 = NULL, sep = \", \", enclose = NULL, ...)  text_remove(text, pattern = \"\", ...)  text_wrap(text, width = NULL, ...)"},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convenient text formatting functionalities — text_format","text":"text, text2 character string. sep Separator. last Last separator. width Positive integer giving target column width wrapping lines output. Can \"auto\", case select 90\\ default width. enclose Character used wrap elements text, can , e.g., enclosed quotes backticks. NULL (default), text elements enclosed. ... arguments passed functions. n number characters find. pattern Character vector. data_rename(), indicates columns selected renaming. Can NULL (case columns selected). data_addprefix() data_addsuffix(), character string, added prefix suffix column names.","code":""},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convenient text formatting functionalities — text_format","text":"character string.","code":""},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convenient text formatting functionalities — text_format","text":"","code":"# Add full stop if missing text_fullstop(c(\"something\", \"something else.\")) #> [1] \"something.\"      \"something else.\"  # Find last characters text_lastchar(c(\"ABC\", \"DEF\"), n = 2) #>  ABC  DEF  #> \"BC\" \"EF\"   # Smart concatenation text_concatenate(c(\"First\", \"Second\", \"Last\")) #> [1] \"First, Second and Last\" text_concatenate(c(\"First\", \"Second\", \"Last\"), last = \" or \", enclose = \"`\") #> [1] \"`First`, `Second` or `Last`\"  # Remove parts of string text_remove(c(\"one!\", \"two\", \"three!\"), \"!\") #> [1] \"one\"   \"two\"   \"three\"  # Wrap text long_text <- paste(rep(\"abc \", 100), collapse = \"\") cat(text_wrap(long_text, width = 50)) #>  abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc  # Paste with optional separator text_paste(c(\"A\", \"\", \"B\"), c(\"42\", \"42\", \"42\")) #> [1] \"A, 42\" \"42\"    \"B, 42\""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data to factors — to_factor","title":"Convert data to factors — to_factor","text":"Convert data factors","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data to factors — to_factor","text":"","code":"to_factor(x, ...)  # S3 method for numeric to_factor(x, labels_to_levels = TRUE, verbose = TRUE, ...)  # S3 method for data.frame to_factor(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   append = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data to factors — to_factor","text":"x data frame vector. ... Arguments passed methods. labels_to_levels Logical, TRUE, value labels used factor levels x converted factor. Else, factor levels based values x (.e. using .factor()). verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data to factors — to_factor","text":"factor, data frame factors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert data to factors — to_factor","text":"Convert variables data factors. data labelled, value labels used factor levels. counterpart convert variables numeric to_numeric().","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Convert data to factors — to_factor","text":"Factors ignored returned . want use value labels levels factors, use labels_to_levels() instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Convert data to factors — to_factor","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data to factors — to_factor","text":"","code":"str(to_factor(iris)) #> 'data.frame':\t150 obs. of  5 variables: #>  $ Sepal.Length: Factor w/ 35 levels \"4.3\",\"4.4\",\"4.5\",..: 9 7 5 4 8 12 4 8 2 7 ... #>  $ Sepal.Width : Factor w/ 23 levels \"2\",\"2.2\",\"2.3\",..: 15 10 12 11 16 19 14 14 9 11 ... #>  $ Petal.Length: Factor w/ 43 levels \"1\",\"1.1\",\"1.2\",..: 5 5 4 6 5 8 5 6 5 6 ... #>  $ Petal.Width : Factor w/ 22 levels \"0.1\",\"0.2\",\"0.3\",..: 2 2 2 2 2 4 3 2 2 1 ... #>  $ Species     : Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ...  # use labels as levels data(efc) str(efc$c172code) #>  num [1:100] 2 2 1 2 2 2 2 2 NA 2 ... #>  - attr(*, \"label\")= chr \"carer's level of education\" #>  - attr(*, \"labels\")= Named num [1:3] 1 2 3 #>   ..- attr(*, \"names\")= chr [1:3] \"low level of education\" \"intermediate level of education\" \"high level of education\" head(to_factor(efc$c172code)) #> [1] intermediate level of education intermediate level of education #> [3] low level of education          intermediate level of education #> [5] intermediate level of education intermediate level of education #> 3 Levels: low level of education ... high level of education"},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data to numeric — to_numeric","title":"Convert data to numeric — to_numeric","text":"Convert data numeric converting characters factors factors either numeric levels dummy variables. \"counterpart\" convert variables factors to_factor().","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data to numeric — to_numeric","text":"","code":"to_numeric(x, ...)  # S3 method for data.frame to_numeric(   x,   select = NULL,   exclude = NULL,   dummy_factors = TRUE,   preserve_levels = FALSE,   lowest = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data to numeric — to_numeric","text":"x data frame, factor vector. ... Arguments passed methods. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. dummy_factors Transform factors dummy factors (factor levels different columns filled binary 0-1 value). preserve_levels Logical, applies x factor. TRUE, x numeric factor levels, converted related numeric values. possible, converted numeric values start 1 number levels. lowest Numeric, indicating lowest (minimum) value converting factors character vectors numeric values. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data to numeric — to_numeric","text":"data frame numeric variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"selection-of-variables-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - select argument","title":"Convert data to numeric — to_numeric","text":"functions select argument complete input data frame returned, even select selects range variables. However, to_numeric(), factors might converted dummies, thus, number variables returned data frame longer match input data frame. Hence, select used, variables (dummies) specified select returned. Use append=TRUE also include original variables returned data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data to numeric — to_numeric","text":"","code":"to_numeric(head(ToothGrowth)) #>    len supp.OJ supp.VC dose #> 1  4.2       0       1  0.5 #> 2 11.5       0       1  0.5 #> 3  7.3       0       1  0.5 #> 4  5.8       0       1  0.5 #> 5  6.4       0       1  0.5 #> 6 10.0       0       1  0.5 to_numeric(head(ToothGrowth), dummy_factors = FALSE) #>    len supp dose #> 1  4.2    2  0.5 #> 2 11.5    2  0.5 #> 3  7.3    2  0.5 #> 4  5.8    2  0.5 #> 5  6.4    2  0.5 #> 6 10.0    2  0.5  # factors x <- as.factor(mtcars$gear) to_numeric(x, dummy_factors = FALSE) #>  [1] 2 2 2 1 1 1 1 2 2 2 2 1 1 1 1 1 1 2 2 2 1 1 1 1 1 2 3 3 3 3 3 2 to_numeric(x, dummy_factors = FALSE, preserve_levels = TRUE) #>  [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4"},{"path":"https://easystats.github.io/datawizard/reference/visualisation_recipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare objects for visualisation — visualisation_recipe","title":"Prepare objects for visualisation — visualisation_recipe","text":"function prepares objects visualisation returning list layers data geoms can easily plotted using instance ggplot2. see package installed, call visualization_recipe() can replaced plot(), internally call former plot using ggplot. resulting plot can customized ad-hoc (adding ggplot's geoms, theme specifications), via arguments visualisation_recipe() control aesthetic parameters. See specific documentation page object's class: modelbased: https://easystats.github.io/modelbased/reference/visualisation_recipe.estimate_predicted.html correlation: https://easystats.github.io/correlation/reference/visualisation_recipe.easycormatrix.html","code":""},{"path":"https://easystats.github.io/datawizard/reference/visualisation_recipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare objects for visualisation — visualisation_recipe","text":"","code":"visualisation_recipe(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/visualisation_recipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare objects for visualisation — visualisation_recipe","text":"x easystats object. ... arguments passed functions.","code":""},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted Mean, Median, SD, and MAD — weighted_mean","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"Weighted Mean, Median, SD, MAD","code":""},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"","code":"weighted_mean(x, weights = NULL, remove_na = TRUE, verbose = TRUE, ...)  weighted_median(x, weights = NULL, remove_na = TRUE, verbose = TRUE, ...)  weighted_sd(x, weights = NULL, remove_na = TRUE, verbose = TRUE, ...)  weighted_mad(   x,   weights = NULL,   constant = 1.4826,   remove_na = TRUE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"x object containing values whose weighted mean     computed. weights numerical vector weights length x giving weights use elements x. weights = NULL, x passed non-weighted function. remove_na Logical, TRUE (default), removes missing (NA) infinite values x weights. verbose Show warning weights negative? ... arguments passed methods. constant scale factor.","code":""},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"","code":"## GPA from Siegel 1994 x <- c(3.7, 3.3, 3.5, 2.8) wt <- c(5, 5, 4, 1) / 15  weighted_mean(x, wt) #> [1] 3.453333 weighted_median(x, wt) #> [1] 3.5  weighted_sd(x, wt) #> [1] 0.2852935 weighted_mad(x, wt) #> [1] 0.29652"},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Winsorize data — winsorize","title":"Winsorize data — winsorize","text":"Winsorize data","code":""},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Winsorize data — winsorize","text":"","code":"winsorize(data, ...)  # S3 method for numeric winsorize(   data,   threshold = 0.2,   method = \"percentile\",   robust = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Winsorize data — winsorize","text":"data data frame vector. ... Currently used. threshold amount winsorization, depends value method: method = \"percentile\": amount winsorize tail. value threshold must 0 0.5 length 1. method = \"zscore\": number SD/MAD-deviations mean/median (see robust). value threshold must greater 0 length 1. method = \"raw\": vector length 2 lower upper bound winsorization. method One \"percentile\" (default), \"zscore\", \"raw\". robust Logical, TRUE, winsorizing \"zscore\" method done via median median absolute deviation (MAD); FALSE, via mean standard deviation. verbose used anymore since datawizard 0.6.6.","code":""},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Winsorize data — winsorize","text":"data frame winsorized columns winsorized vector.","code":""},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Winsorize data — winsorize","text":"Winsorizing winsorization transformation statistics limiting extreme values statistical data reduce effect possibly spurious outliers. distribution many statistics can heavily influenced outliers. typical strategy set outliers (values beyond certain threshold) specified percentile data; example, 90% winsorization see data 5th percentile set 5th percentile, data 95th percentile set 95th percentile. Winsorized estimators usually robust outliers standard forms.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Winsorize data — winsorize","text":"","code":"hist(iris$Sepal.Length, main = \"Original data\")   hist(winsorize(iris$Sepal.Length, threshold = 0.2),   xlim = c(4, 8), main = \"Percentile Winsorization\" )   hist(winsorize(iris$Sepal.Length, threshold = 1.5, method = \"zscore\"),   xlim = c(4, 8), main = \"Mean (+/- SD) Winsorization\" )   hist(winsorize(iris$Sepal.Length, threshold = 1.5, method = \"zscore\", robust = TRUE),   xlim = c(4, 8), main = \"Median (+/- MAD) Winsorization\" )   hist(winsorize(iris$Sepal.Length, threshold = c(5, 7.5), method = \"raw\"),   xlim = c(4, 8), main = \"Raw Thresholds\" )   # Also works on a data frame: winsorize(iris, threshold = 0.2) #>     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species #> 1            5.1         3.4          1.5         0.2     setosa #> 2            5.0         3.0          1.5         0.2     setosa #> 3            5.0         3.2          1.5         0.2     setosa #> 4            5.0         3.1          1.5         0.2     setosa #> 5            5.0         3.4          1.5         0.2     setosa #> 6            5.4         3.4          1.7         0.4     setosa #> 7            5.0         3.4          1.5         0.3     setosa #> 8            5.0         3.4          1.5         0.2     setosa #> 9            5.0         2.9          1.5         0.2     setosa #> 10           5.0         3.1          1.5         0.2     setosa #> 11           5.4         3.4          1.5         0.2     setosa #> 12           5.0         3.4          1.6         0.2     setosa #> 13           5.0         3.0          1.5         0.2     setosa #> 14           5.0         3.0          1.5         0.2     setosa #> 15           5.8         3.4          1.5         0.2     setosa #> 16           5.7         3.4          1.5         0.4     setosa #> 17           5.4         3.4          1.5         0.4     setosa #> 18           5.1         3.4          1.5         0.3     setosa #> 19           5.7         3.4          1.7         0.3     setosa #> 20           5.1         3.4          1.5         0.3     setosa #> 21           5.4         3.4          1.7         0.2     setosa #> 22           5.1         3.4          1.5         0.4     setosa #> 23           5.0         3.4          1.5         0.2     setosa #> 24           5.1         3.3          1.7         0.5     setosa #> 25           5.0         3.4          1.9         0.2     setosa #> 26           5.0         3.0          1.6         0.2     setosa #> 27           5.0         3.4          1.6         0.4     setosa #> 28           5.2         3.4          1.5         0.2     setosa #> 29           5.2         3.4          1.5         0.2     setosa #> 30           5.0         3.2          1.6         0.2     setosa #> 31           5.0         3.1          1.6         0.2     setosa #> 32           5.4         3.4          1.5         0.4     setosa #> 33           5.2         3.4          1.5         0.2     setosa #> 34           5.5         3.4          1.5         0.2     setosa #> 35           5.0         3.1          1.5         0.2     setosa #> 36           5.0         3.2          1.5         0.2     setosa #> 37           5.5         3.4          1.5         0.2     setosa #> 38           5.0         3.4          1.5         0.2     setosa #> 39           5.0         3.0          1.5         0.2     setosa #> 40           5.1         3.4          1.5         0.2     setosa #> 41           5.0         3.4          1.5         0.3     setosa #> 42           5.0         2.7          1.5         0.3     setosa #> 43           5.0         3.2          1.5         0.2     setosa #> 44           5.0         3.4          1.6         0.6     setosa #> 45           5.1         3.4          1.9         0.4     setosa #> 46           5.0         3.0          1.5         0.3     setosa #> 47           5.1         3.4          1.6         0.2     setosa #> 48           5.0         3.2          1.5         0.2     setosa #> 49           5.3         3.4          1.5         0.2     setosa #> 50           5.0         3.3          1.5         0.2     setosa #> 51           6.5         3.2          4.7         1.4 versicolor #> 52           6.4         3.2          4.5         1.5 versicolor #> 53           6.5         3.1          4.9         1.5 versicolor #> 54           5.5         2.7          4.0         1.3 versicolor #> 55           6.5         2.8          4.6         1.5 versicolor #> 56           5.7         2.8          4.5         1.3 versicolor #> 57           6.3         3.3          4.7         1.6 versicolor #> 58           5.0         2.7          3.3         1.0 versicolor #> 59           6.5         2.9          4.6         1.3 versicolor #> 60           5.2         2.7          3.9         1.4 versicolor #> 61           5.0         2.7          3.5         1.0 versicolor #> 62           5.9         3.0          4.2         1.5 versicolor #> 63           6.0         2.7          4.0         1.0 versicolor #> 64           6.1         2.9          4.7         1.4 versicolor #> 65           5.6         2.9          3.6         1.3 versicolor #> 66           6.5         3.1          4.4         1.4 versicolor #> 67           5.6         3.0          4.5         1.5 versicolor #> 68           5.8         2.7          4.1         1.0 versicolor #> 69           6.2         2.7          4.5         1.5 versicolor #> 70           5.6         2.7          3.9         1.1 versicolor #> 71           5.9         3.2          4.8         1.8 versicolor #> 72           6.1         2.8          4.0         1.3 versicolor #> 73           6.3         2.7          4.9         1.5 versicolor #> 74           6.1         2.8          4.7         1.2 versicolor #> 75           6.4         2.9          4.3         1.3 versicolor #> 76           6.5         3.0          4.4         1.4 versicolor #> 77           6.5         2.8          4.8         1.4 versicolor #> 78           6.5         3.0          5.0         1.7 versicolor #> 79           6.0         2.9          4.5         1.5 versicolor #> 80           5.7         2.7          3.5         1.0 versicolor #> 81           5.5         2.7          3.8         1.1 versicolor #> 82           5.5         2.7          3.7         1.0 versicolor #> 83           5.8         2.7          3.9         1.2 versicolor #> 84           6.0         2.7          5.1         1.6 versicolor #> 85           5.4         3.0          4.5         1.5 versicolor #> 86           6.0         3.4          4.5         1.6 versicolor #> 87           6.5         3.1          4.7         1.5 versicolor #> 88           6.3         2.7          4.4         1.3 versicolor #> 89           5.6         3.0          4.1         1.3 versicolor #> 90           5.5         2.7          4.0         1.3 versicolor #> 91           5.5         2.7          4.4         1.2 versicolor #> 92           6.1         3.0          4.6         1.4 versicolor #> 93           5.8         2.7          4.0         1.2 versicolor #> 94           5.0         2.7          3.3         1.0 versicolor #> 95           5.6         2.7          4.2         1.3 versicolor #> 96           5.7         3.0          4.2         1.2 versicolor #> 97           5.7         2.9          4.2         1.3 versicolor #> 98           6.2         2.9          4.3         1.3 versicolor #> 99           5.1         2.7          3.0         1.1 versicolor #> 100          5.7         2.8          4.1         1.3 versicolor #> 101          6.3         3.3          5.3         1.9  virginica #> 102          5.8         2.7          5.1         1.9  virginica #> 103          6.5         3.0          5.3         1.9  virginica #> 104          6.3         2.9          5.3         1.8  virginica #> 105          6.5         3.0          5.3         1.9  virginica #> 106          6.5         3.0          5.3         1.9  virginica #> 107          5.0         2.7          4.5         1.7  virginica #> 108          6.5         2.9          5.3         1.8  virginica #> 109          6.5         2.7          5.3         1.8  virginica #> 110          6.5         3.4          5.3         1.9  virginica #> 111          6.5         3.2          5.1         1.9  virginica #> 112          6.4         2.7          5.3         1.9  virginica #> 113          6.5         3.0          5.3         1.9  virginica #> 114          5.7         2.7          5.0         1.9  virginica #> 115          5.8         2.8          5.1         1.9  virginica #> 116          6.4         3.2          5.3         1.9  virginica #> 117          6.5         3.0          5.3         1.8  virginica #> 118          6.5         3.4          5.3         1.9  virginica #> 119          6.5         2.7          5.3         1.9  virginica #> 120          6.0         2.7          5.0         1.5  virginica #> 121          6.5         3.2          5.3         1.9  virginica #> 122          5.6         2.8          4.9         1.9  virginica #> 123          6.5         2.8          5.3         1.9  virginica #> 124          6.3         2.7          4.9         1.8  virginica #> 125          6.5         3.3          5.3         1.9  virginica #> 126          6.5         3.2          5.3         1.8  virginica #> 127          6.2         2.8          4.8         1.8  virginica #> 128          6.1         3.0          4.9         1.8  virginica #> 129          6.4         2.8          5.3         1.9  virginica #> 130          6.5         3.0          5.3         1.6  virginica #> 131          6.5         2.8          5.3         1.9  virginica #> 132          6.5         3.4          5.3         1.9  virginica #> 133          6.4         2.8          5.3         1.9  virginica #> 134          6.3         2.8          5.1         1.5  virginica #> 135          6.1         2.7          5.3         1.4  virginica #> 136          6.5         3.0          5.3         1.9  virginica #> 137          6.3         3.4          5.3         1.9  virginica #> 138          6.4         3.1          5.3         1.8  virginica #> 139          6.0         3.0          4.8         1.8  virginica #> 140          6.5         3.1          5.3         1.9  virginica #> 141          6.5         3.1          5.3         1.9  virginica #> 142          6.5         3.1          5.1         1.9  virginica #> 143          5.8         2.7          5.1         1.9  virginica #> 144          6.5         3.2          5.3         1.9  virginica #> 145          6.5         3.3          5.3         1.9  virginica #> 146          6.5         3.0          5.2         1.9  virginica #> 147          6.3         2.7          5.0         1.9  virginica #> 148          6.5         3.0          5.2         1.9  virginica #> 149          6.2         3.4          5.3         1.9  virginica #> 150          5.9         3.0          5.1         1.8  virginica"},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-080","dir":"Changelog","previous_headings":"","what":"datawizard 0.8.0","title":"datawizard 0.8.0","text":"CRAN release: 2023-06-16 BREAKING CHANGES following re-exported functions insight now removed: object_has_names(), object_has_rownames(), is_empty_object(), compact_list(), compact_character(). Argument na.rm renamed remove_na throughout datawizard functions. na.rm kept backward compatibility, deprecated later removed future updates. way expressions defined data_filter() revised. filter argument replaced ..., allowing separate multiple expression comma (combined &). Furthermore, expressions can now also defined strings, provided character vectors, allow string-friendly programming. CHANGES Weighted-functions (weighted_sd(), weighted_mean(), …) gain remove_na argument, remove keep missing infinite values. default, remove_na = TRUE, .e. missing infinite values removed default. reverse_scale(), normalize() rescale() gain append argument (similar data frame methods transformation functions), append recoded variables input data frame instead overwriting existing variables. NEW FUNCTIONS rowid_as_column() complement rownames_as_column() (mimic tibble::rowid_to_column()). Note behavior different tibble::rowid_to_column() grouped data. See Details section docs. data_unite(), merge values multiple variables one new variable. data_separate(), counterpart data_unite(), separate single variable multiple new variables. data_modify(), create new variables, modify remove existing variables data frame. MINOR CHANGES to_numeric() variables type Date, POSIXct POSIXlt now includes class name warning message. Added print() method center(), standardize(), normalize() rescale(). BUG FIXES standardize_parameters() now works package namespace model formula (#401). data_merge() longer yields warning tibbles join = \"bind\". center() standardize() work grouped data frames (class grouped_df) force = TRUE. data.frame method describe_distribution() returns NULL instead error valid variable passed (example factor variable include_factors = FALSE) (#421).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-071","dir":"Changelog","previous_headings":"","what":"datawizard 0.7.1","title":"datawizard 0.7.1","text":"CRAN release: 2023-04-03 BREAKING CHANGES add_labs() renamed assign_labels(). Since add_labs() existed days, alias backwards compatibility. NEW FUNCTIONS labels_to_levels(), use value labels factors levels. MINOR CHANGES data_read() now checks imported object actually data frame (coercible data frame), , longer errors, gives informative warning type object imported. BUG FIXES Fix test CRAN check Mac OS arm64","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-070","dir":"Changelog","previous_headings":"","what":"datawizard 0.7.0","title":"datawizard 0.7.0","text":"CRAN release: 2023-03-22 BREAKING CHANGES selection patterns, expressions like -var1:var3 exclude variables var1 var3 longer accepted. correct expression -(var1:var3). 2 reasons: consistent behavior numerics (-1:2 accepted -(1:2) ); consistent dplyr::select(), throws warning uses first variable first expression. NEW FUNCTIONS recode_into(), similar dplyr::case_when(), recode values one variables new variable. mean_sd() median_mad() summarizing vectors mean (median) range one SD (MAD) . data_write() counterpart data_read(), write data frames CSV, SPSS, SAS, Stata files many file types. One advantage existing functions write data packages labelled (numeric) data can converted factors (values labels used factor levels) even text formats like CSV similar. allows exporting “labelled” data file formats, . add_labs(), manually add value variable labels attributes variables. attributes stored \"label\" \"labels\" attributes, similar labelled class haven package. MINOR CHANGES data_rename() gets verbose argument. winsorize() now errors threshold incorrect (previously, provided warning returned unchanged data). argument verbose now useless kept backward compatibility. documentation now contains details valid values threshold (#357). functions arguments select /exclude, now one warning per misspelled variable. previous behavior one warning. Fixed inconsistent behaviour standardize() one arguments center scale provided (#365). unstandardize() replace_nan_inf() now work select helpers (#376). Added informative warning error messages reverse(). Furthermore, docs now describe range argument clearly (#380). unnormalize() errors unexpected inputs (#383). BUG FIXES empty_columns() (therefore remove_empty_columns()) now correctly detects columns containing NA_character_ (#349). Select helpers now work custom functions argument called select (#356). Fix unexpected warning convert_na_to() select list (#352). Fixed issue correct labelling numeric variables nine unique values associated value labels.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-065","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.5","title":"datawizard 0.6.5","text":"CRAN release: 2022-12-14 MAJOR CHANGES Etienne Bacher new maintainer. MINOR CHANGES standardize(), center(), normalize() rescale() can used model formulas, similar base::scale(). data_codebook() now includes proportion category/value, addition counts. Furthermore, data contains tagged NA values, included frequency table. BUG FIXES center(x) now works correctly x single value either reference center specified (#324). Fixed issue data_codebook(), failed labelled vectors values labels sorted order.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-064","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.4","title":"datawizard 0.6.4","text":"CRAN release: 2022-11-19 NEW FUNCTIONS data_codebook(): generate codebooks data frames. New functions deal duplicates: data_duplicated() (keep duplicates, including first occurrence) data_unique() (returns data, excluding duplicates except one instance , based selected method). MINOR CHANGES .data.frame methods now preserve custom attributes. include_bounds argument normalize() can now also numeric value, defining limit upper lower bound (.e. distance 1 0). data_filter() now works grouped data. BUG FIXES data_read() longer prints message empty columns data actually empty columns. data_to_wide() now drops columns id_cols (specified), names_from, values_from. behaviour observed tidyr::pivot_wider().","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-063","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.3","title":"datawizard 0.6.3","text":"CRAN release: 2022-10-22 MAJOR CHANGES new publication datawizard package: https://joss.theoj.org/papers/10.21105/joss.04684 Fixes failing tests due changes R-devel. data_to_long() data_to_wide() significant performance improvements, sometimes high ten-fold speedup. MINOR CHANGES column names misspelled, functions now suggest existing columns possibly meant. Miscellaneous performance gains. convert_to_na() now requires argument na class ‘Date’ convert specific dates NA. example, convert_to_na(x, na = \"2022-10-17\") must changed convert_to_na(x, na = .Date(\"2022-10-17\")). BUG FIXES data_to_long() data_to_wide() now correctly keep date format.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-062","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.2","title":"datawizard 0.6.2","text":"CRAN release: 2022-10-04 BREAKING CHANGES Methods grouped data frames (.grouped_df) longer support dplyr::group_by() dplyr version 0.8.0. empty_columns() remove_empty_columns() now also remove columns contain empty characters. Likewise, empty_rows() remove_empty_rows() remove observations completely missing empty character values. MINOR CHANGES data_read() gains convert_factors argument, turn automatic conversion numeric variables factors. BUG FIXES data_arrange() now works data frames grouped using data_group() (#274).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-061","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.1","title":"datawizard 0.6.1","text":"CRAN release: 2022-09-25 Updates tests upcoming changes tidyselect package (#267).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-060","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.0","title":"datawizard 0.6.0","text":"CRAN release: 2022-09-15 BREAKING CHANGES minimum needed R version bumped 3.6. Following deprecated functions removed: data_cut(), data_recode(), data_shift(), data_reverse(), data_rescale(), data_to_factor(), data_to_numeric() New text_format() alias introduced format_text(), latter removed next release. New recode_values() alias introduced change_code(), latter removed next release. data_merge() now errors columns specified datasets. Using negative values arguments select exclude now removes columns selection/exclusion. previous behavior start selection/exclusion end dataset, inconsistent use “-” selecting possibilities. NEW FUNCTIONS data_peek(): peek values type variables data frame. coef_var(): compute coefficient variation. CHANGES data_filter() give informative messages malformed syntax filter argument. now possible use curly brackets pass variable names data_filter(), like following example. See examples section documentation data_filter(). regex argument added functions use select-helpers already argument. Select helpers starts_with(), ends_with(), contains() now accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). Arguments select exclude present functions improved work loops custom functions. example, following code now works: now vignette summarizing various ways select exclude variables datawizard functions.","code":"foo <- function(data) {   i <- \"Sep\"   find_columns(data, select = starts_with(i)) } foo(iris)  for (i in c(\"Sepal\", \"Sp\")) {   head(iris) |>     find_columns(select = starts_with(i)) |>     print() }"},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-051","dir":"Changelog","previous_headings":"","what":"datawizard 0.5.1","title":"datawizard 0.5.1","text":"CRAN release: 2022-08-17 Fixes failing tests due poorman update.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-050","dir":"Changelog","previous_headings":"","what":"datawizard 0.5.0","title":"datawizard 0.5.0","text":"CRAN release: 2022-08-07 MAJOR CHANGES Following statistical transformation functions renamed data_*() prefix, since work exclusively data frames, typically first used vectors, therefore misleading names: data_cut() -> categorize() data_recode() -> change_code() data_shift() -> slide() data_reverse() -> reverse() data_rescale() -> rescale() data_to_factor() -> to_factor() data_to_numeric() -> to_numeric() Note functions also .data.frame() methods still work data frames well. Former function names still available aliases, deprecated removed future release. Bumps needed minimum R version 3.5. Removed deprecated function data_findcols(). Please use replacement, data_find(). Removed alias extract() data_extract() function since collided tidyr::extract(). Argument training_proportion data_partition() deprecated. Please use proportion now. Given continued significant contributions package, Etienne Bacher (@etiennebacher) now included author. unstandardise() now works center(x) unnormalize() now works change_scale(x) reshape_wider() now follows consistently tidyr::pivot_wider() syntax. Arguments colnames_from, sep, rows_from deprecated replaced names_from, names_sep, id_cols respectively. reshape_wider() also gains argument names_glue (#182, #198). Similarly, reshape_longer() now follows consistently tidyr::pivot_longer() syntax. Argument colnames_to deprecated replaced names_to. reshape_longer() also gains new arguments: names_prefix, names_sep, names_pattern, values_drop_na (#189). CHANGES text formatting helpers (like text_concatenate()) gain enclose argument, wrap text elements surrounding characters. winsorize now accepts “raw” “zscore” methods (addition “percentile”). Additionally, robust set TRUE together method = \"zscore\", winsorizes via median median absolute deviation (MAD); else via mean standard deviation. (@rempsyc, #177, #49, #47). convert_na_to now accepts numeric replacements character vectors single replacement multiple vector classes. (@rempsyc, #214). data_partition() now allows create multiple partitions data, returning multiple training remaining test set. Functions like center(), normalize() standardize() longer fail data contains infinite values (Inf). NEW FUNCTIONS row_to_colnames() colnames_to_row() move row column names, column names row (@etiennebacher, #169). data_arrange() sort rows dataframe according values selected columns. BUG FIXES Fixed wrong column names data_to_wide() (#173).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-041","dir":"Changelog","previous_headings":"","what":"datawizard 0.4.1","title":"datawizard 0.4.1","text":"CRAN release: 2022-05-16 BREAKING Added standardize.default() method (moved package effectsize), consistent default-method now package generic. standardize.default() behaves exactly like effectsize particularly works regression model objects. effectsize now re-exports standardize() datawizard. NEW FUNCTIONS data_shift() shift value range numeric variables. data_recode() recode old new values. data_to_factor() counterpart data_to_numeric(). data_tabulate() create frequency tables variables. data_read() read (import) data files (text, foreign statistical packages). unnormalize() counterpart normalize(). function works variables normalized normalize(). data_group() data_ungroup() create grouped data frames, remove grouping information grouped data frames. CHANGES data_find() added alias find_colums(), consistent name patterns datawizard functions. data_findcols() removed future update usage discouraged. select argument (thus, also exclude argument) now also accepts functions testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3). Arguments select exclude now allow negation select-helpers, like -ends_with(\"\"), -.numeric -Sepal.Width:Petal.Length. Many functions now get .default method, capture unsupported classes. now yields message returns original input, hence, .data.frame methods won’t stop due error. filter argument data_filter() can also numeric vector, indicate row indices rows returned. convert_to_na() gets methods variables class logical Date. convert_to_na() factors (data frames) gains drop_levels argument, drop unused levels replaced NA. data_to_numeric() gains two arguments, preserve_levels lowest, give better control conversion factors. BUG FIXES logicals passed center() standardize() force = TRUE, properly converted numeric variables.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-040","dir":"Changelog","previous_headings":"","what":"datawizard 0.4.0","title":"datawizard 0.4.0","text":"CRAN release: 2022-03-30 MAJOR CHANGES data_match() now returns filtered data default. Old behavior (returning rows indices) can set setting return_indices = TRUE. following functions now re-exported insight package: object_has_names(), object_has_rownames(), is_empty_object(), compact_list(), compact_character() data_findcols() become deprecated future updates. Please use new replacements find_columns() get_columns(). vignette Analysing Longitudinal Panel Data now moved parameters package. NEW FUNCTIONS convert rownames column, vice versa: rownames_as_column() column_as_rownames() (@etiennebacher, #80). find_columns() get_columns() find column names retrieve subsets data frames, based various select-methods (including select-helpers). function supersede data_findcols() future. data_filter() complement data_match(), works logical expressions filtering rows data frames. computing weighted centrality measures dispersion: weighted_mean(), weighted_median(), weighted_sd() weighted_mad(). replace NA vectors dataframes: convert_na_to() (@etiennebacher, #111). MINOR CHANGES select argument several functions (like data_remove(), reshape_longer(), data_extract()) now allows use select-helpers selecting variables based specific patterns. data_extract() gains new arguments allow type-safe return values, .e. always return vector data frame. Thus, data_extract() can now used select multiple variables pull single variable data frames. data_match() gains match argument, indicate logical operation matching results combined. Improved support labelled data many functions, .e. returned data frame preserve value variable label attributes, possible applicable. describe_distribution() now works lists (@etiennebacher, #105). data_rename() doesn’t use pattern anymore rename columns replacement provided (@etiennebacher, #103). data_rename() now adds suffix duplicated names replacement (@etiennebacher, #103). BUG FIXES data_to_numeric() produced wrong results factors dummy_factors =   TRUE factor contained missing values. data_match() produced wrong results data contained missing values. Fixed CRAN check issues data_extract() one variable extracted data frame.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-030","dir":"Changelog","previous_headings":"","what":"datawizard 0.3.0","title":"datawizard 0.3.0","text":"CRAN release: 2022-03-02 NEW FUNCTIONS find remove empty rows columns data frame: empty_rows(), empty_columns(), remove_empty_rows(), remove_empty_columns(), remove_empty. check names: object_has_names() object_has_rownames(). rotate data frames: data_rotate(). reverse score variables: data_reverse(). merge/join multiple data frames: data_merge() (alias data_join()). cut (recode) data groups: data_cut(). replace specific values NAs: convert_to_na(). replace Inf NaN values NAs: replace_nan_inf(). Arguments cols, data_relocate() can now also numeric values, indicating position destination column.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-023","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.3","title":"datawizard 0.2.3","text":"CRAN release: 2022-01-26 New functions: work lists: is_empty_object() compact_list() work strings: compact_character()","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-022","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.2","title":"datawizard 0.2.2","text":"CRAN release: 2022-01-04 New function data_extract() (alias extract()) pull single variables data frame, possibly naming value row names data frame. reshape_ci() gains ci_type argument, reshape data frames CI-columns prefixes \"CI\". standardize() center() gain arguments center scale, define references centrality deviation used centering standardizing variables. center() gains arguments force reference, similar standardize(). functionality append argument center() standardize() revised. made suffix argument redundant, thus removed. Fixed issue standardize(). Fixed issue data_findcols().","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-021","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.1","title":"datawizard 0.2.1","text":"CRAN release: 2021-10-04 Exports plot method visualisation_recipe() objects see package. centre(), standardise(), unstandardise() exported aliases center(), standardize(), unstandardize(), respectively.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-0201","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.0.1","title":"datawizard 0.2.0.1","text":"CRAN release: 2021-09-02 mainly maintenance release addresses issues conflicting namespaces.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-020","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.0","title":"datawizard 0.2.0","text":"CRAN release: 2021-08-17 New function: visualisation_recipe(). following function now moved performance package: check_multimodal(). Minor updates documentation, including new vignette demean().","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-010","dir":"Changelog","previous_headings":"","what":"datawizard 0.1.0","title":"datawizard 0.1.0","text":"CRAN release: 2021-06-18 First release.","code":""}]
+[{"path":[]},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement patilindrajeet.science@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to datawizard","title":"Contributing to datawizard","text":"outlines propose change datawizard.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to datawizard","text":"Small typos grammatical errors documentation may edited directly using GitHub web interface, long changes made source file. want fix typos documentation, please edit related .R file R/ folder. edit .Rd file man/.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"filing-an-issue","dir":"","previous_headings":"","what":"Filing an issue","title":"Contributing to datawizard","text":"easiest way propose change new feature file issue. ’ve found bug, may also create associated issue. possible, try illustrate proposal bug minimal reproducible example.","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"pull-requests","dir":"","previous_headings":"","what":"Pull requests","title":"Contributing to datawizard","text":"Please create Git branch pull request (PR). contributed code roughly follow R style guide, particular easystats convention code-style. datawizard uses roxygen2, Markdown syntax, documentation. datawizard uses testthat. Adding tests PR makes easier merge PR code base. PR user-visible change, may add bullet top NEWS.md describing changes made. may optionally add GitHub username, links relevant issue(s)/PR(s).","code":""},{"path":"https://easystats.github.io/datawizard/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to datawizard","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://easystats.github.io/datawizard/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 datawizard authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://easystats.github.io/datawizard/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with {datawizard}","title":"Getting help with {datawizard}","text":"Thanks using datawizard. filing issue, places explore pieces put together make process smooth possible. Start making minimal reproducible example using reprex package. haven’t heard used reprex , ’re treat! Seriously, reprex make R-question-asking endeavors easier (pretty insane ROI five ten minutes ’ll take learn ’s ). additional reprex pointers, check Get help! resource used tidyverse team. Armed reprex, next step figure ask: ’s question: start StackOverflow. people answer questions. ’s bug: ’re right place, file issue. ’re sure: let community help figure ! problem bug feature request, can easily return report . opening new issue, sure search issues pull requests make sure bug hasn’t reported /already fixed development version. default, search pre-populated :issue :open. can edit qualifiers (e.g. :pr, :closed) needed. example, ’d simply remove :open search issues repo, open closed. Thanks help!","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"quoted-names","dir":"Articles","previous_headings":"Selecting variables","what":"Quoted names","title":"A quick summary of selection syntax in `{datawizard}`","text":"simple way select one several variables. Just use character vector containing variables names, like base R.","code":"data_select(iris, c(\"Sepal.Length\", \"Petal.Width\")) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"unquoted-names","dir":"Articles","previous_headings":"Selecting variables","what":"Unquoted names","title":"A quick summary of selection syntax in `{datawizard}`","text":"also possible use unquoted names. useful use tidyverse want consistent way variable names passed.","code":"iris %>%   group_by(Species) %>%   standardise(Petal.Length) %>%   ungroup() #> # A tibble: 10 × 5 #>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species    #>           <dbl>       <dbl>        <dbl>       <dbl> <fct>      #>  1          4.3         3         -1.09          0.1 setosa     #>  2          5           3.3        0.873         0.2 setosa     #>  3          7.7         3.8        1.50          2.2 virginica  #>  4          4.4         3.2        0.218         0.2 setosa     #>  5          5.9         3         -0.542         1.8 virginica  #>  6          6.5         3         -0.414         2   virginica  #>  7          5.5         2.5       -1.09          1.3 versicolor #>  8          5.5         2.6        0.218         1.2 versicolor #>  9          5.8         2.7       -0.542         1.9 virginica  #> 10          6.1         3          0.873         1.4 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"positions","dir":"Articles","previous_headings":"Selecting variables","what":"Positions","title":"A quick summary of selection syntax in `{datawizard}`","text":"addition variable names, select can also take indices variables select dataframe.","code":"data_select(iris, c(1, 2, 5)) #>    Sepal.Length Sepal.Width    Species #> 1           4.3         3.0     setosa #> 2           5.0         3.3     setosa #> 3           7.7         3.8  virginica #> 4           4.4         3.2     setosa #> 5           5.9         3.0  virginica #> 6           6.5         3.0  virginica #> 7           5.5         2.5 versicolor #> 8           5.5         2.6 versicolor #> 9           5.8         2.7  virginica #> 10          6.1         3.0 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"functions","dir":"Articles","previous_headings":"Selecting variables","what":"Functions","title":"A quick summary of selection syntax in `{datawizard}`","text":"can also pass function select argument. function applied columns return TRUE FALSE. example, want keep numeric columns, can use .numeric. Note can provide custom function select, provided returns TRUE FALSE applied column.","code":"data_select(iris, is.numeric) #>    Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1           4.3         3.0          1.1         0.1 #> 2           5.0         3.3          1.4         0.2 #> 3           7.7         3.8          6.7         2.2 #> 4           4.4         3.2          1.3         0.2 #> 5           5.9         3.0          5.1         1.8 #> 6           6.5         3.0          5.2         2.0 #> 7           5.5         2.5          4.0         1.3 #> 8           5.5         2.6          4.4         1.2 #> 9           5.8         2.7          5.1         1.9 #> 10          6.1         3.0          4.6         1.4 my_function <- function(i) {   is.numeric(i) && mean(i, na.rm = TRUE) > 3.5 }  data_select(iris, my_function) #>    Sepal.Length Petal.Length #> 1           4.3          1.1 #> 2           5.0          1.4 #> 3           7.7          6.7 #> 4           4.4          1.3 #> 5           5.9          5.1 #> 6           6.5          5.2 #> 7           5.5          4.0 #> 8           5.5          4.4 #> 9           5.8          5.1 #> 10          6.1          4.6"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"patterns","dir":"Articles","previous_headings":"Selecting variables","what":"Patterns","title":"A quick summary of selection syntax in `{datawizard}`","text":"larger datasets, tedious write names variables select, fragile rely variable positions may change later. end, can use four select helpers: starts_with(), ends_with(), contains(), regex(). first three can take several patterns, regex() takes single regular expression.","code":"data_select(iris, starts_with(\"Sep\", \"Peta\")) #>    Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1           4.3         3.0          1.1         0.1 #> 2           5.0         3.3          1.4         0.2 #> 3           7.7         3.8          6.7         2.2 #> 4           4.4         3.2          1.3         0.2 #> 5           5.9         3.0          5.1         1.8 #> 6           6.5         3.0          5.2         2.0 #> 7           5.5         2.5          4.0         1.3 #> 8           5.5         2.6          4.4         1.2 #> 9           5.8         2.7          5.1         1.9 #> 10          6.1         3.0          4.6         1.4  data_select(iris, ends_with(\"dth\", \"ies\")) #>    Sepal.Width Petal.Width    Species #> 1          3.0         0.1     setosa #> 2          3.3         0.2     setosa #> 3          3.8         2.2  virginica #> 4          3.2         0.2     setosa #> 5          3.0         1.8  virginica #> 6          3.0         2.0  virginica #> 7          2.5         1.3 versicolor #> 8          2.6         1.2 versicolor #> 9          2.7         1.9  virginica #> 10         3.0         1.4 versicolor  data_select(iris, contains(\"pal\", \"ec\")) #>    Sepal.Length Sepal.Width    Species #> 1           4.3         3.0     setosa #> 2           5.0         3.3     setosa #> 3           7.7         3.8  virginica #> 4           4.4         3.2     setosa #> 5           5.9         3.0  virginica #> 6           6.5         3.0  virginica #> 7           5.5         2.5 versicolor #> 8           5.5         2.6 versicolor #> 9           5.8         2.7  virginica #> 10          6.1         3.0 versicolor  data_select(iris, regex(\"^Sep|ies\")) #>    Sepal.Length Sepal.Width    Species #> 1           4.3         3.0     setosa #> 2           5.0         3.3     setosa #> 3           7.7         3.8  virginica #> 4           4.4         3.2     setosa #> 5           5.9         3.0  virginica #> 6           6.5         3.0  virginica #> 7           5.5         2.5 versicolor #> 8           5.5         2.6 versicolor #> 9           5.8         2.7  virginica #> 10          6.1         3.0 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"excluding-variables","dir":"Articles","previous_headings":"","what":"Excluding variables","title":"A quick summary of selection syntax in `{datawizard}`","text":"want keep variables except ones? two ways can invert selection. first way put minus sign \"-\" front select argument. Note use numeric indices, can’t mix negative positive values. means use select = -(1:2) want exclude first two columns; select = -1:2 work: thing variable names: second way use argument exclude. argument possibilities select. Although may required contexts, wanted , use select exclude arguments time.","code":"data_select(iris, -c(\"Sepal.Length\", \"Petal.Width\")) #>    Sepal.Width Petal.Length    Species #> 1          3.0          1.1     setosa #> 2          3.3          1.4     setosa #> 3          3.8          6.7  virginica #> 4          3.2          1.3     setosa #> 5          3.0          5.1  virginica #> 6          3.0          5.2  virginica #> 7          2.5          4.0 versicolor #> 8          2.6          4.4 versicolor #> 9          2.7          5.1  virginica #> 10         3.0          4.6 versicolor  data_select(iris, -starts_with(\"Sep\", \"Peta\")) #>       Species #> 1      setosa #> 2      setosa #> 3   virginica #> 4      setosa #> 5   virginica #> 6   virginica #> 7  versicolor #> 8  versicolor #> 9   virginica #> 10 versicolor  data_select(iris, -is.numeric) #>       Species #> 1      setosa #> 2      setosa #> 3   virginica #> 4      setosa #> 5   virginica #> 6   virginica #> 7  versicolor #> 8  versicolor #> 9   virginica #> 10 versicolor data_select(iris, -(1:2)) #>    Petal.Length Petal.Width    Species #> 1           1.1         0.1     setosa #> 2           1.4         0.2     setosa #> 3           6.7         2.2  virginica #> 4           1.3         0.2     setosa #> 5           5.1         1.8  virginica #> 6           5.2         2.0  virginica #> 7           4.0         1.3 versicolor #> 8           4.4         1.2 versicolor #> 9           5.1         1.9  virginica #> 10          4.6         1.4 versicolor data_select(iris, -(Petal.Length:Species)) #>    Sepal.Length Sepal.Width #> 1           4.3         3.0 #> 2           5.0         3.3 #> 3           7.7         3.8 #> 4           4.4         3.2 #> 5           5.9         3.0 #> 6           6.5         3.0 #> 7           5.5         2.5 #> 8           5.5         2.6 #> 9           5.8         2.7 #> 10          6.1         3.0 data_select(iris, exclude = c(\"Sepal.Length\", \"Petal.Width\")) #>    Sepal.Width Petal.Length    Species #> 1          3.0          1.1     setosa #> 2          3.3          1.4     setosa #> 3          3.8          6.7  virginica #> 4          3.2          1.3     setosa #> 5          3.0          5.1  virginica #> 6          3.0          5.2  virginica #> 7          2.5          4.0 versicolor #> 8          2.6          4.4 versicolor #> 9          2.7          5.1  virginica #> 10         3.0          4.6 versicolor  data_select(iris, exclude = starts_with(\"Sep\", \"Peta\")) #>       Species #> 1      setosa #> 2      setosa #> 3   virginica #> 4      setosa #> 5   virginica #> 6   virginica #> 7  versicolor #> 8  versicolor #> 9   virginica #> 10 versicolor"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"programming-with-selections","dir":"Articles","previous_headings":"","what":"Programming with selections","title":"A quick summary of selection syntax in `{datawizard}`","text":"Since datawizard 0.6.0, possible pass function arguments loop indices select exclude arguments. makes easier program datawizard. example, want let user decide selection want use: also possible pass values loops, example list patterns want relocate columns based patterns, one one: loop , columns starting \"Sep\" moved end data frame, thing made columns starting \"Pet\".","code":"my_function <- function(data, selection) {   find_columns(data, select = selection) } my_function(iris, \"Sepal.Length\") #> [1] \"Sepal.Length\" my_function(iris, starts_with(\"Sep\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  my_function_2 <- function(data, pattern) {   find_columns(data, select = starts_with(pattern)) } my_function_2(iris, \"Sep\") #> [1] \"Sepal.Length\" \"Sepal.Width\" new_iris <- iris for (i in c(\"Sep\", \"Pet\")) {   new_iris <- new_iris %>%     data_relocate(select = starts_with(i), after = -1) } new_iris #>       Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1      setosa          4.3         3.0          1.1         0.1 #> 2      setosa          5.0         3.3          1.4         0.2 #> 3   virginica          7.7         3.8          6.7         2.2 #> 4      setosa          4.4         3.2          1.3         0.2 #> 5   virginica          5.9         3.0          5.1         1.8 #> 6   virginica          6.5         3.0          5.2         2.0 #> 7  versicolor          5.5         2.5          4.0         1.3 #> 8  versicolor          5.5         2.6          4.4         1.2 #> 9   virginica          5.8         2.7          5.1         1.9 #> 10 versicolor          6.1         3.0          4.6         1.4"},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"ignore-the-case","dir":"Articles","previous_headings":"Useful to know","what":"Ignore the case","title":"A quick summary of selection syntax in `{datawizard}`","text":"every selection uses variable names, can ignore case selection applying ignore_case = TRUE.","code":"data_select(iris, c(\"sepal.length\", \"petal.width\"), ignore_case = TRUE) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4  data_select(iris, ~ Sepal.length + petal.Width, ignore_case = TRUE) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4  data_select(iris, starts_with(\"sep\", \"peta\"), ignore_case = TRUE) #>    Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1           4.3         3.0          1.1         0.1 #> 2           5.0         3.3          1.4         0.2 #> 3           7.7         3.8          6.7         2.2 #> 4           4.4         3.2          1.3         0.2 #> 5           5.9         3.0          5.1         1.8 #> 6           6.5         3.0          5.2         2.0 #> 7           5.5         2.5          4.0         1.3 #> 8           5.5         2.6          4.4         1.2 #> 9           5.8         2.7          5.1         1.9 #> 10          6.1         3.0          4.6         1.4"},{"path":"https://easystats.github.io/datawizard/articles/selection_syntax.html","id":"formulas","dir":"Articles","previous_headings":"Useful to know","what":"Formulas","title":"A quick summary of selection syntax in `{datawizard}`","text":"also possible use formulas select variables: made easier use selection custom functions datawizard 0.6.0, kept available backward compatibility.","code":"data_select(iris, ~ Sepal.Length + Petal.Width) #>    Sepal.Length Petal.Width #> 1           4.3         0.1 #> 2           5.0         0.2 #> 3           7.7         2.2 #> 4           4.4         0.2 #> 5           5.9         1.8 #> 6           6.5         2.0 #> 7           5.5         1.3 #> 8           5.5         1.2 #> 9           5.8         1.9 #> 10          6.1         1.4"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data Standardization","text":"make sense data effects, scientists might want standardize (Z-score) variables. makes data unitless, expressed terms deviation index centrality (e.g., mean median). However, aside benefits, standardization also comes challenges issues, scientist aware .","code":""},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"methods-of-standardization","dir":"Articles","previous_headings":"Introduction","what":"Methods of Standardization","title":"Data Standardization","text":"datawizard package offers two methods standardization via standardize() function: Normal standardization: center around mean, SD units (default). Robust standardization: center around median, MAD (median absolute deviation) units (robust = TRUE). Let’s look following example: can see different methods give different central variation values: standardize() can also used standardize full data frame - numeric variable standardized separately: Weighted standardization also supported via weights argument, factors can also standardized (’re kind thing) setting force = TRUE, converts factors treatment-coded dummy variables standardizing.","code":"library(datawizard) library(effectsize) # for data  # let's have a look at what the data look like data(\"hardlyworking\", package = \"effectsize\") head(hardlyworking) #>     salary xtra_hours n_comps age seniority is_senior #> 1 19744.65       4.16       1  32         3     FALSE #> 2 11301.95       1.62       0  34         3     FALSE #> 3 20635.62       1.19       3  33         5      TRUE #> 4 23047.16       7.19       1  35         3     FALSE #> 5 27342.15      11.26       0  33         4     FALSE #> 6 25656.63       3.63       2  30         5      TRUE # let's use both methods of standardization hardlyworking$xtra_hours_z <- standardize(hardlyworking$xtra_hours) hardlyworking$xtra_hours_zr <- standardize(hardlyworking$xtra_hours, robust = TRUE) library(dplyr)  hardlyworking %>%   select(starts_with(\"xtra_hours\")) %>%   data_to_long() %>%   group_by(Name) %>%   summarise(     mean = mean(Value),     sd = sd(Value),     median = median(Value),     mad = mad(Value)   ) hardlyworking_z <- standardize(hardlyworking) hardlyworking_z %>%   select(-xtra_hours_z, -xtra_hours_zr) %>%   data_to_long() %>%   group_by(Name) %>%   summarise(     mean = mean(Value),     sd = sd(Value),     median = median(Value),     mad = mad(Value)   )"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"variable-wise-vs--participant-wise","dir":"Articles","previous_headings":"Introduction","what":"Variable-wise vs. Participant-wise","title":"Data Standardization","text":"Standardization important step extra caution required repeated-measures designs, three ways standardizing data: Variable-wise: common method. simple scaling column. Participant-wise: Variables standardized “within” participant, .e., participant, participant’s mean SD. Full: Participant-wise first re-standardizing variable-wise. Unfortunately, method used often explicitly stated. issue methods can generate important discrepancies (can turn contribute reproducibility crisis). Let’s investigate 3 methods.","code":""},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"the-data","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"The Data","title":"Data Standardization","text":"take emotion dataset participants exposed negative pictures rate emotions (valence) amount memories associated picture (autobiographical link). One make hypothesis young participants context war violence, negative pictures (mutilations) less related memories less negative pictures (involving example car crashes sick people). words, expect positive relationship valence (high values corresponding less negativity) autobiographical link. Let’s look data, averaged participants: can see means SDs, lot variability participants means individual within-participant SD.","code":"# Download the 'emotion' dataset load(url(\"https://raw.githubusercontent.com/neuropsychology/psycho.R/master/data/emotion.rda\"))  # Discard neutral pictures (keep only negative) emotion <- emotion %>% filter(Emotion_Condition == \"Negative\")  # Summary emotion %>%   drop_na(Subjective_Valence, Autobiographical_Link) %>%   group_by(Participant_ID) %>%   summarise(     n_Trials = n(),     Valence_Mean = mean(Subjective_Valence),     Valence_SD = sd(Subjective_Valence)   ) #> # A tibble: 19 × 4 #> # Groups:   Participant_ID [19] #>    Participant_ID n_Trials Valence_Mean Valence_SD #>    <fct>             <int>        <dbl>      <dbl> #>  1 10S                  24       -58.1       42.6  #>  2 11S                  24       -73.2       37.0  #>  3 12S                  24       -57.5       26.6  #>  4 13S                  24       -63.2       23.7  #>  5 14S                  24       -56.6       26.5  #>  6 15S                  24       -60.6       33.7  #>  7 16S                  24       -46.1       24.9  #>  8 17S                  24        -1.54       4.98 #>  9 18S                  24       -67.2       35.0  #> 10 19S                  24       -59.6       33.2  #> 11 1S                   24       -53.0       42.9  #> 12 2S                   23       -43.0       39.2  #> 13 3S                   24       -64.3       34.4  #> 14 4S                   24       -81.6       27.6  #> 15 5S                   24       -58.1       25.3  #> 16 6S                   24       -74.7       29.2  #> 17 7S                   24       -62.3       39.7  #> 18 8S                   24       -56.9       32.7  #> 19 9S                   24       -31.5       52.7"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"effect-of-standardization","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Effect of Standardization","title":"Data Standardization","text":"create three data frames standardized three techniques. Let’s see three standardization techniques affected Valence variable.","code":"Z_VariableWise <- emotion %>%   standardize()  Z_ParticipantWise <- emotion %>%   group_by(Participant_ID) %>%   standardize()  Z_Full <- emotion %>%   group_by(Participant_ID) %>%   standardize() %>%   ungroup() %>%   standardize()"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"across-participants","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Across Participants","title":"Data Standardization","text":"can calculate mean SD Valence across participants: means SD appear fairly similar (0 1)…  marginal distributions…","code":"# Create a convenient function to print summarise_Subjective_Valence <- function(data) {   df_name <- deparse(substitute(data))   data %>%     ungroup() %>%     summarise(       DF = df_name,       Mean = mean(Subjective_Valence),       SD = sd(Subjective_Valence)     ) } # Check the results rbind(   summarise_Subjective_Valence(Z_VariableWise),   summarise_Subjective_Valence(Z_ParticipantWise),   summarise_Subjective_Valence(Z_Full) ) library(see) library(ggplot2)  ggplot() +   geom_density(aes(Z_VariableWise$Subjective_Valence,     color = \"Z_VariableWise\"   ), linewidth = 1) +   geom_density(aes(Z_ParticipantWise$Subjective_Valence,     color = \"Z_ParticipantWise\"   ), linewidth = 1) +   geom_density(aes(Z_Full$Subjective_Valence,     color = \"Z_Full\"   ), linewidth = 1) +   see::theme_modern() +   labs(color = \"\")"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"at-the-participant-level","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"At the Participant Level","title":"Data Standardization","text":"However, can also look happens participant level. Let’s look first 5 participants: Seems like full participant-wise standardization give similar results, different ones variable-wise standardization.","code":"# Create convenient function print_participants <- function(data) {   df_name <- deparse(substitute(data))   data %>%     group_by(Participant_ID) %>%     summarise(       DF = df_name,       Mean = mean(Subjective_Valence),       SD = sd(Subjective_Valence)     ) %>%     head(5) %>%     select(DF, everything()) }  # Check the results rbind(   print_participants(Z_VariableWise),   print_participants(Z_ParticipantWise),   print_participants(Z_Full) )"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"compare","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Compare","title":"Data Standardization","text":"Let’s correlation variable-wise participant-wise methods.  three standardization methods roughly present characteristics general level (mean 0 SD 1) similar distribution, values exactly ! Let’s now answer original question investigating linear relationship valence autobiographical link. can running mixed-effects model participants entered random effects. can extract parameters interest model, find: can see, variable-wise standardization affects coefficient (expected, changes unit), test statistic statistical significance. However, using participant-wise standardization affect coefficient significance. method better justified, choice depends specific case, context, data goal.","code":"r <- cor.test(   Z_VariableWise$Subjective_Valence,   Z_ParticipantWise$Subjective_Valence )  data.frame(   Original = emotion$Subjective_Valence,   VariableWise = Z_VariableWise$Subjective_Valence,   ParticipantWise = Z_ParticipantWise$Subjective_Valence ) %>%   ggplot(aes(x = VariableWise, y = ParticipantWise, colour = Original)) +   geom_point(alpha = 0.75, shape = 16) +   geom_smooth(method = \"lm\", color = \"black\") +   scale_color_distiller(palette = 1) +   ggtitle(paste0(\"r = \", round(r$estimate, 2))) +   see::theme_modern() library(lme4) m_raw <- lmer(   formula = Subjective_Valence ~ Autobiographical_Link + (1 | Participant_ID),   data = emotion ) m_VariableWise <- update(m_raw, data = Z_VariableWise) m_ParticipantWise <- update(m_raw, data = Z_ParticipantWise) m_Full <- update(m_raw, data = Z_Full) # Convenient function get_par <- function(model) {   mod_name <- deparse(substitute(model))   parameters::model_parameters(model) %>%     mutate(Model = mod_name) %>%     select(-Parameter) %>%     select(Model, everything()) %>%     .[-1, ] }  # Run the model on all datasets rbind(   get_par(m_raw),   get_par(m_VariableWise),   get_par(m_ParticipantWise),   get_par(m_Full) ) #> # Fixed Effects #>  #> Model             | Coefficient |   SE |        95% CI | t(451) |     p #> ----------------------------------------------------------------------- #> m_raw             |        0.09 | 0.07 | [-0.04, 0.22] |   1.36 | 0.174 #> m_VariableWise    |        0.07 | 0.05 | [-0.03, 0.17] |   1.36 | 0.174 #> m_ParticipantWise |        0.08 | 0.05 | [-0.01, 0.17] |   1.75 | 0.080 #> m_Full            |        0.08 | 0.05 | [-0.01, 0.17] |   1.75 | 0.080 #>  #> # Random Effects: Participant_ID #>  #> Model             | Coefficient |   SE |         95% CI #> ------------------------------------------------------- #> m_raw             |       16.49 | 3.24 | [11.22, 24.22] #> m_VariableWise    |        0.45 | 0.09 | [ 0.30,  0.65] #> m_ParticipantWise |        0.00 |      |                #> m_Full            |        0.00 |      |                #>  #> # Random Effects: Residual #>  #> Model             | Coefficient |   SE |         95% CI #> ------------------------------------------------------- #> m_raw             |       33.56 | 1.14 | [31.40, 35.86] #> m_VariableWise    |        0.91 | 0.03 | [ 0.85,  0.97] #> m_ParticipantWise |        0.98 |      |                #> m_Full            |        1.00 |      |"},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"conclusion","dir":"Articles","previous_headings":"Introduction > Variable-wise vs. Participant-wise","what":"Conclusion","title":"Data Standardization","text":"Standardization can useful cases justified. Variable Participant-wise standardization methods appear produce similar data. Variable Participant-wise standardization can lead different results. chosen method can strongly influence results therefore explicitly stated justified enhance reproducibility results. showed yet another way sneakily tweaking data can change results. prevent use bad practice, can highlight importance open data, open analysis/scripts, preregistration.","code":""},{"path":"https://easystats.github.io/datawizard/articles/standardize_data.html","id":"see-also","dir":"Articles","previous_headings":"","what":"See also","title":"Data Standardization","text":"datawizard::demean(): https://easystats.github.io/datawizard/reference/demean.html standardize_parameters(method = \"pseudo\") mixed-effects models https://easystats.github.io/parameters/articles/standardize_parameters_effsize.html","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Coming from 'tidyverse'","text":"datawizard package aims make basic data wrangling easier base R. data wrangling workflow supports similar one supported tidyverse package combination dplyr tidyr. However, one main features dependencies: {stats} {utils} (included base R) insight, core package easystats ecosystem. package grew organically simultaneously satisfy “0 non-base hard dependency” principle easystats data wrangling needs constituent packages ecosystem. One drawback genesis features tidyverse packages supported since features necessary easystats ecosystem implemented. missing features (summarize pipe operator %>%) made available dependency-free packages, {poorman}. also important note datawizard designed avoid namespace collisions tidyverse packages. article, see go basic data wrangling steps datawizard. also compare tidyverse syntax achieving . way, decide make switch, can easily find translations . vignette largely inspired dplyr’s Getting started vignette.","code":"library(dplyr) library(tidyr) library(datawizard)  data(efc) efc <- head(efc)"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"workhorses","dir":"Articles","previous_headings":"","what":"Workhorses","title":"Coming from 'tidyverse'","text":"look tidyverse equivalents, can first look datawizard’s key functions data wrangling: Note functions datawizard strict equivalent dplyr tidyr (e.g data_rotate()), won’t discuss next section.","code":""},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"equivalence-with-dplyr-tidyr","dir":"Articles","previous_headings":"","what":"Equivalence with {dplyr} / {tidyr}","title":"Coming from 'tidyverse'","text":"look individually, let’s first look summary table equivalence.","code":""},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"filtering","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Filtering","title":"Coming from 'tidyverse'","text":"data_filter() wrapper around subset(). However, want several filtering conditions, can either use & (subset()) , (dplyr::filter()).","code":"# ---------- datawizard ----------- starwars %>%   data_filter(     skin_color == \"light\",     eye_color == \"brown\"   )  # or starwars %>%   data_filter(     skin_color == \"light\" &       eye_color == \"brown\"   ) # ---------- tidyverse ----------- starwars %>%   filter(     skin_color == \"light\",     eye_color == \"brown\"   ) ## # A tibble: 7 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Leia Org…    150    49 brown      light      brown             19 fema… femin… ## 2 Biggs Da…    183    84 black      light      brown             24 male  mascu… ## 3 Cordé        157    NA brown      light      brown             NA fema… femin… ## 4 Dormé        165    NA brown      light      brown             NA fema… femin… ## 5 Raymus A…    188    79 brown      light      brown             NA male  mascu… ## 6 Poe Dame…     NA    NA brown      light      brown             NA male  mascu… ## 7 Padmé Am…    165    45 brown      light      brown             46 fema… femin… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> ## # A tibble: 7 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Leia Org…    150    49 brown      light      brown             19 fema… femin… ## 2 Biggs Da…    183    84 black      light      brown             24 male  mascu… ## 3 Cordé        157    NA brown      light      brown             NA fema… femin… ## 4 Dormé        165    NA brown      light      brown             NA fema… femin… ## 5 Raymus A…    188    79 brown      light      brown             NA male  mascu… ## 6 Poe Dame…     NA    NA brown      light      brown             NA male  mascu… ## 7 Padmé Am…    165    45 brown      light      brown             46 fema… femin… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list>"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"selecting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Selecting","title":"Coming from 'tidyverse'","text":"data_select() equivalent dplyr::select(). main difference two functions data_select() uses two arguments (select exclude) requires quoted column names want select several variables, dplyr::select() accepts unquoted column names. can find list select helpers ?data_select.","code":"# ---------- datawizard ----------- starwars %>%   data_select(select = c(\"hair_color\", \"skin_color\", \"eye_color\")) # ---------- tidyverse ----------- starwars %>%   select(hair_color, skin_color, eye_color) ## # A tibble: 6 × 3 ##   hair_color  skin_color  eye_color ##   <chr>       <chr>       <chr>     ## 1 blond       fair        blue      ## 2 NA          gold        yellow    ## 3 NA          white, blue red       ## 4 none        white       yellow    ## 5 brown       light       brown     ## 6 brown, grey light       blue # ---------- datawizard ----------- starwars %>%   data_select(select = -ends_with(\"color\")) # ---------- tidyverse ----------- starwars %>%   select(-ends_with(\"color\")) ## # A tibble: 6 × 11 ##   name     height  mass birth_year sex   gender homeworld species films vehicles ##   <chr>     <int> <dbl>      <dbl> <chr> <chr>  <chr>     <chr>   <lis> <list>   ## 1 Luke Sk…    172    77       19   male  mascu… Tatooine  Human   <chr> <chr>    ## 2 C-3PO       167    75      112   none  mascu… Tatooine  Droid   <chr> <chr>    ## 3 R2-D2        96    32       33   none  mascu… Naboo     Droid   <chr> <chr>    ## 4 Darth V…    202   136       41.9 male  mascu… Tatooine  Human   <chr> <chr>    ## 5 Leia Or…    150    49       19   fema… femin… Alderaan  Human   <chr> <chr>    ## 6 Owen La…    178   120       52   male  mascu… Tatooine  Human   <chr> <chr>    ## # ℹ 1 more variable: starships <list> # ---------- datawizard ----------- starwars %>%   data_select(select = -(hair_color:eye_color)) # ---------- tidyverse ----------- starwars %>%   select(!(hair_color:eye_color)) ## # A tibble: 6 × 11 ##   name     height  mass birth_year sex   gender homeworld species films vehicles ##   <chr>     <int> <dbl>      <dbl> <chr> <chr>  <chr>     <chr>   <lis> <list>   ## 1 Luke Sk…    172    77       19   male  mascu… Tatooine  Human   <chr> <chr>    ## 2 C-3PO       167    75      112   none  mascu… Tatooine  Droid   <chr> <chr>    ## 3 R2-D2        96    32       33   none  mascu… Naboo     Droid   <chr> <chr>    ## 4 Darth V…    202   136       41.9 male  mascu… Tatooine  Human   <chr> <chr>    ## 5 Leia Or…    150    49       19   fema… femin… Alderaan  Human   <chr> <chr>    ## 6 Owen La…    178   120       52   male  mascu… Tatooine  Human   <chr> <chr>    ## # ℹ 1 more variable: starships <list> # ---------- datawizard ----------- starwars %>%   data_select(exclude = regex(\"color$\")) # ---------- tidyverse ----------- starwars %>%   select(-contains(\"color$\")) ## # A tibble: 6 × 11 ##   name     height  mass birth_year sex   gender homeworld species films vehicles ##   <chr>     <int> <dbl>      <dbl> <chr> <chr>  <chr>     <chr>   <lis> <list>   ## 1 Luke Sk…    172    77       19   male  mascu… Tatooine  Human   <chr> <chr>    ## 2 C-3PO       167    75      112   none  mascu… Tatooine  Droid   <chr> <chr>    ## 3 R2-D2        96    32       33   none  mascu… Naboo     Droid   <chr> <chr>    ## 4 Darth V…    202   136       41.9 male  mascu… Tatooine  Human   <chr> <chr>    ## 5 Leia Or…    150    49       19   fema… femin… Alderaan  Human   <chr> <chr>    ## 6 Owen La…    178   120       52   male  mascu… Tatooine  Human   <chr> <chr>    ## # ℹ 1 more variable: starships <list> # ---------- datawizard ----------- starwars %>%   data_select(select = is.numeric) # ---------- tidyverse ----------- starwars %>%   select(where(is.numeric)) ## # A tibble: 6 × 3 ##   height  mass birth_year ##    <int> <dbl>      <dbl> ## 1    172    77       19   ## 2    167    75      112   ## 3     96    32       33   ## 4    202   136       41.9 ## 5    150    49       19   ## 6    178   120       52"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"modifying","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Modifying","title":"Coming from 'tidyverse'","text":"data_modify() wrapper around base::transform() several additional benefits: allows us use newly created variables following expressions; works grouped data; preserves variable attributes labels; accepts expressions character vectors easy program last point also main difference data_modify() dplyr::mutate(). data_modify() accepts expressions strings: makes easy use custom functions:","code":"# ---------- datawizard ----------- efc %>%   data_modify(     c12hour_c = center(c12hour),     c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),     c12hour_z2 = standardize(c12hour)   ) # ---------- tidyverse ----------- efc %>%   mutate(     c12hour_c = center(c12hour),     c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),     c12hour_z2 = standardize(c12hour)   ) ##   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z c12hour_z2 ## 1      16      2      3        2      12     -67.6 -0.9420928 -0.9420928 ## 2     148      2      3        2      20      64.4  0.8974967  0.8974967 ## 3      70      2      3        1      11     -13.6 -0.1895335 -0.1895335 ## 4      NA      2   <NA>        2      10        NA         NA         NA ## 5     168      2      4        2      12      84.4  1.1762224  1.1762224 ## 6      16      2      4        2      19     -67.6 -0.9420928 -0.9420928 new_exp <- c(   \"c12hour_c = center(c12hour)\",   \"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)\" ) data_modify(efc, new_exp) ##   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z ## 1      16      2      3        2      12     -67.6 -0.9420928 ## 2     148      2      3        2      20      64.4  0.8974967 ## 3      70      2      3        1      11     -13.6 -0.1895335 ## 4      NA      2   <NA>        2      10        NA         NA ## 5     168      2      4        2      12      84.4  1.1762224 ## 6      16      2      4        2      19     -67.6 -0.9420928 miles_to_km <- function(data, var) {   data_modify(     data,     paste0(\"km = \", var, \"* 1.609344\")   ) }  distance <- data.frame(miles = c(1, 8, 233, 88, 9)) distance ##   miles ## 1     1 ## 2     8 ## 3   233 ## 4    88 ## 5     9 miles_to_km(distance, \"miles\") ##   miles         km ## 1     1   1.609344 ## 2     8  12.874752 ## 3   233 374.977152 ## 4    88 141.622272 ## 5     9  14.484096"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"sorting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Sorting","title":"Coming from 'tidyverse'","text":"data_arrange() equivalent dplyr::arrange(). takes two arguments: data frame, vector column names used sort rows. Note contrary functions datawizard, possible use select helpers starts_with() data_arrange(). can also sort variables descending order putting \"-\" front name, like :","code":"# ---------- datawizard ----------- starwars %>%   data_arrange(c(\"hair_color\", \"height\")) # ---------- tidyverse ----------- starwars %>%   arrange(hair_color, height) ## # A tibble: 6 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu… ## 2 Leia Org…    150    49 brown      light      brown           19   fema… femin… ## 3 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu… ## 4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu… ## 5 R2-D2         96    32 NA         white, bl… red             33   none  mascu… ## 6 C-3PO        167    75 NA         gold       yellow         112   none  mascu… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> # ---------- datawizard ----------- starwars %>%   data_arrange(c(\"-hair_color\", \"-height\")) # ---------- tidyverse ----------- starwars %>%   arrange(desc(hair_color), -height) ## # A tibble: 6 × 14 ##   name      height  mass hair_color skin_color eye_color birth_year sex   gender ##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Darth Va…    202   136 none       white      yellow          41.9 male  mascu… ## 2 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu… ## 3 Leia Org…    150    49 brown      light      brown           19   fema… femin… ## 4 Luke Sky…    172    77 blond      fair       blue            19   male  mascu… ## 5 C-3PO        167    75 NA         gold       yellow         112   none  mascu… ## 6 R2-D2         96    32 NA         white, bl… red             33   none  mascu… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list>"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"extracting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Extracting","title":"Coming from 'tidyverse'","text":"Although mostly work data frames, sometimes useful extract single column vector. can done data_extract(), reproduces behavior dplyr::pull(): can also specify several variables select. case, data_extract() equivalent data_select():","code":"# ---------- datawizard ----------- starwars %>%   data_extract(gender) # ---------- tidyverse ----------- starwars %>%   pull(gender) ## [1] \"masculine\" \"masculine\" \"masculine\" \"masculine\" \"feminine\"  \"masculine\" starwars %>%   data_extract(select = contains(\"color\")) ## # A tibble: 6 × 3 ##   hair_color  skin_color  eye_color ##   <chr>       <chr>       <chr>     ## 1 blond       fair        blue      ## 2 NA          gold        yellow    ## 3 NA          white, blue red       ## 4 none        white       yellow    ## 5 brown       light       brown     ## 6 brown, grey light       blue"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"renaming","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Renaming","title":"Coming from 'tidyverse'","text":"data_rename() equivalent dplyr::rename() syntax two different. dplyr::rename() takes new-old pairs column names, data_rename() requires vector column names rename, vector new names columns must length. way data_rename() designed makes easy apply modifications vector column names. example, can remove underscores use TitleCase following code: also possible add prefix suffix subset variables data_addprefix() data_addsuffix(). argument select accepts select helpers saw data_select():","code":"# ---------- datawizard ----------- starwars %>%   data_rename(     pattern = c(\"sex\", \"hair_color\"),     replacement = c(\"Sex\", \"Hair Color\")   ) # ---------- tidyverse ----------- starwars %>%   rename(     Sex = sex,     \"Hair Color\" = hair_color   ) ## # A tibble: 6 × 14 ##   name    height  mass `Hair Color` skin_color eye_color birth_year Sex   gender ##   <chr>    <int> <dbl> <chr>        <chr>      <chr>          <dbl> <chr> <chr>  ## 1 Luke S…    172    77 blond        fair       blue            19   male  mascu… ## 2 C-3PO      167    75 NA           gold       yellow         112   none  mascu… ## 3 R2-D2       96    32 NA           white, bl… red             33   none  mascu… ## 4 Darth …    202   136 none         white      yellow          41.9 male  mascu… ## 5 Leia O…    150    49 brown        light      brown           19   fema… femin… ## 6 Owen L…    178   120 brown, grey  light      blue            52   male  mascu… ## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> to_rename <- names(starwars)  starwars %>%   data_rename(     pattern = to_rename,     replacement = tools::toTitleCase(gsub(\"_\", \" \", to_rename, fixed = TRUE))   ) ## # A tibble: 6 × 14 ##   Name     Height  Mass `Hair Color` `Skin Color` `Eye Color` `Birth Year` Sex   ##   <chr>     <int> <dbl> <chr>        <chr>        <chr>              <dbl> <chr> ## 1 Luke Sk…    172    77 blond        fair         blue                19   male  ## 2 C-3PO       167    75 NA           gold         yellow             112   none  ## 3 R2-D2        96    32 NA           white, blue  red                 33   none  ## 4 Darth V…    202   136 none         white        yellow              41.9 male  ## 5 Leia Or…    150    49 brown        light        brown               19   fema… ## 6 Owen La…    178   120 brown, grey  light        blue                52   male  ## # ℹ 6 more variables: Gender <chr>, Homeworld <chr>, Species <chr>, ## #   Films <list>, Vehicles <list>, Starships <list> starwars %>%   data_addprefix(     pattern = \"OLD.\",     select = contains(\"color\")   ) %>%   data_addsuffix(     pattern = \".NEW\",     select = -contains(\"color\")   ) ## # A tibble: 6 × 14 ##   name.NEW       height.NEW mass.NEW OLD.hair_color OLD.skin_color OLD.eye_color ##   <chr>               <int>    <dbl> <chr>          <chr>          <chr>         ## 1 Luke Skywalker        172       77 blond          fair           blue          ## 2 C-3PO                 167       75 NA             gold           yellow        ## 3 R2-D2                  96       32 NA             white, blue    red           ## 4 Darth Vader           202      136 none           white          yellow        ## 5 Leia Organa           150       49 brown          light          brown         ## 6 Owen Lars             178      120 brown, grey    light          blue          ## # ℹ 8 more variables: birth_year.NEW <dbl>, sex.NEW <chr>, gender.NEW <chr>, ## #   homeworld.NEW <chr>, species.NEW <chr>, films.NEW <list>, ## #   vehicles.NEW <list>, starships.NEW <list>"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"relocating","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Relocating","title":"Coming from 'tidyverse'","text":"Sometimes, want relocate one small subset columns dataset. Rather typing many names data_select(), can use data_relocate(), equivalent dplyr::relocate(). Just like data_select(), can specify list variables want relocate select exclude. , arguments after1 specify selected columns relocated: addition column names, accept column indices. Finally, one can use = -1 relocate selected columns just last column, = -1 relocate last column.","code":"# ---------- datawizard ----------- starwars %>%   data_relocate(sex:homeworld, before = \"height\") # ---------- tidyverse ----------- starwars %>%   relocate(sex:homeworld, .before = height) ## # A tibble: 6 × 14 ##   name       sex   gender homeworld height  mass hair_color skin_color eye_color ##   <chr>      <chr> <chr>  <chr>      <int> <dbl> <chr>      <chr>      <chr>     ## 1 Luke Skyw… male  mascu… Tatooine     172    77 blond      fair       blue      ## 2 C-3PO      none  mascu… Tatooine     167    75 NA         gold       yellow    ## 3 R2-D2      none  mascu… Naboo         96    32 NA         white, bl… red       ## 4 Darth Vad… male  mascu… Tatooine     202   136 none       white      yellow    ## 5 Leia Orga… fema… femin… Alderaan     150    49 brown      light      brown     ## 6 Owen Lars  male  mascu… Tatooine     178   120 brown, gr… light      blue      ## # ℹ 5 more variables: birth_year <dbl>, species <chr>, films <list>, ## #   vehicles <list>, starships <list> # ---------- datawizard ----------- starwars %>%   data_relocate(sex:homeworld, after = -1) ## # A tibble: 6 × 14 ##   name     height  mass hair_color skin_color eye_color birth_year species films ##   <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>   <lis> ## 1 Luke Sk…    172    77 blond      fair       blue            19   Human   <chr> ## 2 C-3PO       167    75 NA         gold       yellow         112   Droid   <chr> ## 3 R2-D2        96    32 NA         white, bl… red             33   Droid   <chr> ## 4 Darth V…    202   136 none       white      yellow          41.9 Human   <chr> ## 5 Leia Or…    150    49 brown      light      brown           19   Human   <chr> ## 6 Owen La…    178   120 brown, gr… light      blue            52   Human   <chr> ## # ℹ 5 more variables: vehicles <list>, starships <list>, sex <chr>, ## #   gender <chr>, homeworld <chr>"},{"path":[]},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"longer","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Reshaping","what":"Longer","title":"Coming from 'tidyverse'","text":"Reshaping data wide long long wide format can done data_to_long() data_to_wide(). functions designed match tidyr::pivot_longer() tidyr::pivot_wider() arguments, thing change function name. However, tidyr::pivot_longer() tidyr::pivot_wider() features available yet. use relig_income dataset, {tidyr} vignette. like reshape dataset 3 columns: religion, count, income. column “religion” doesn’t need change, exclude -religion. , remaining column corresponds income category. Therefore, want move column names single column called “income”. Finally, values corresponding columns reshaped single new column, called “count”. explore bit arguments data_to_long(), use another dataset: billboard dataset.","code":"relig_income ## # A tibble: 18 × 11 ##    religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` `$75-100k` ##    <chr>      <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>      <dbl> ##  1 Agnostic      27        34        60        81        76       137        122 ##  2 Atheist       12        27        37        52        35        70         73 ##  3 Buddhist      27        21        30        34        33        58         62 ##  4 Catholic     418       617       732       670       638      1116        949 ##  5 Don’t k…      15        14        15        11        10        35         21 ##  6 Evangel…     575       869      1064       982       881      1486        949 ##  7 Hindu          1         9         7         9        11        34         47 ##  8 Histori…     228       244       236       238       197       223        131 ##  9 Jehovah…      20        27        24        24        21        30         15 ## 10 Jewish        19        19        25        25        30        95         69 ## 11 Mainlin…     289       495       619       655       651      1107        939 ## 12 Mormon        29        40        48        51        56       112         85 ## 13 Muslim         6         7         9        10         9        23         16 ## 14 Orthodox      13        17        23        32        32        47         38 ## 15 Other C…       9         7        11        13        13        14         18 ## 16 Other F…      20        33        40        46        49        63         46 ## 17 Other W…       5         2         3         4         2         7          3 ## 18 Unaffil…     217       299       374       365       341       528        407 ## # ℹ 3 more variables: `$100-150k` <dbl>, `>150k` <dbl>, ## #   `Don't know/refused` <dbl> # ---------- datawizard ----------- relig_income %>%   data_to_long(     -religion,     names_to = \"income\",     values_to = \"count\"   ) # ---------- tidyverse ----------- relig_income %>%   pivot_longer(     !religion,     names_to = \"income\",     values_to = \"count\"   ) ## # A tibble: 180 × 3 ##    religion income             count ##    <chr>    <chr>              <dbl> ##  1 Agnostic <$10k                 27 ##  2 Agnostic $10-20k               34 ##  3 Agnostic $20-30k               60 ##  4 Agnostic $30-40k               81 ##  5 Agnostic $40-50k               76 ##  6 Agnostic $50-75k              137 ##  7 Agnostic $75-100k             122 ##  8 Agnostic $100-150k            109 ##  9 Agnostic >150k                 84 ## 10 Agnostic Don't know/refused    96 ## # ℹ 170 more rows billboard ## # A tibble: 317 × 79 ##    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7   wk8 ##    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ##  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99    NA ##  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA    NA ##  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54    53 ##  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55    59 ##  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36    49 ##  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2     2 ##  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA    NA ##  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35    38 ##  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16    14 ## 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61    58 ## # ℹ 307 more rows ## # ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>, ## #   wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>, ## #   wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, ## #   wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, ## #   wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, ## #   wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, … # ---------- datawizard ----------- billboard %>%   data_to_long(     cols = starts_with(\"wk\"),     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) # ---------- tidyverse ----------- billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) ## # A tibble: 5,307 × 5 ##    artist  track                   date.entered week   rank ##    <chr>   <chr>                   <date>       <chr> <dbl> ##  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87 ##  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82 ##  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72 ##  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77 ##  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87 ##  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94 ##  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99 ##  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91 ##  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87 ## 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92 ## # ℹ 5,297 more rows"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"wider","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Reshaping","what":"Wider","title":"Coming from 'tidyverse'","text":", use example tidyr vignette show close data_to_wide() pivot_wider() :","code":"fish_encounters ## # A tibble: 114 × 3 ##    fish  station  seen ##    <fct> <fct>   <int> ##  1 4842  Release     1 ##  2 4842  I80_1       1 ##  3 4842  Lisbon      1 ##  4 4842  Rstr        1 ##  5 4842  Base_TD     1 ##  6 4842  BCE         1 ##  7 4842  BCW         1 ##  8 4842  BCE2        1 ##  9 4842  BCW2        1 ## 10 4842  MAE         1 ## # ℹ 104 more rows # ---------- datawizard ----------- fish_encounters %>%   data_to_wide(     names_from = \"station\",     values_from = \"seen\",     values_fill = 0   ) # ---------- tidyverse ----------- fish_encounters %>%   pivot_wider(     names_from = station,     values_from = seen,     values_fill = 0   ) ## # A tibble: 19 × 12 ##    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE   MAW ##    <fct>   <dbl> <dbl>  <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ##  1 4842        1     1      1     1       1     1     1     1     1     1     1 ##  2 4843        1     1      1     1       1     1     1     1     1     1     1 ##  3 4844        1     1      1     1       1     1     1     1     1     1     1 ##  4 4845        1     1      1     1       1     0     0     0     0     0     0 ##  5 4847        1     1      1     0       0     0     0     0     0     0     0 ##  6 4848        1     1      1     1       0     0     0     0     0     0     0 ##  7 4849        1     1      0     0       0     0     0     0     0     0     0 ##  8 4850        1     1      0     1       1     1     1     0     0     0     0 ##  9 4851        1     1      0     0       0     0     0     0     0     0     0 ## 10 4854        1     1      0     0       0     0     0     0     0     0     0 ## 11 4855        1     1      1     1       1     0     0     0     0     0     0 ## 12 4857        1     1      1     1       1     1     1     1     1     0     0 ## 13 4858        1     1      1     1       1     1     1     1     1     1     1 ## 14 4859        1     1      1     1       1     0     0     0     0     0     0 ## 15 4861        1     1      1     1       1     1     1     1     1     1     1 ## 16 4862        1     1      1     1       1     1     1     1     1     0     0 ## 17 4863        1     1      0     0       0     0     0     0     0     0     0 ## 18 4864        1     1      0     0       0     0     0     0     0     0     0 ## 19 4865        1     1      1     0       0     0     0     0     0     0     0"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"joining","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Joining","title":"Coming from 'tidyverse'","text":"datawizard, joining datasets done data_join() (alias data_merge()). Contrary dplyr, unique function takes care types join, specified inside function argument join (default, join = \"left\"). , show perform four common joins: full, left, right inner. use datasets band_membersand band_instruments provided dplyr:","code":"band_members ## # A tibble: 3 × 2 ##   name  band    ##   <chr> <chr>   ## 1 Mick  Stones  ## 2 John  Beatles ## 3 Paul  Beatles band_instruments ## # A tibble: 3 × 2 ##   name  plays  ##   <chr> <chr>  ## 1 John  guitar ## 2 Paul  bass   ## 3 Keith guitar"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"full-join","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Joining","what":"Full join","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"full\") # ---------- tidyverse ----------- band_members %>%   full_join(band_instruments) ## # A tibble: 4 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 Mick  Stones  NA     ## 2 John  Beatles guitar ## 3 Paul  Beatles bass   ## 4 Keith NA      guitar"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"left-and-right-joins","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Joining","what":"Left and right joins","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"left\") # ---------- tidyverse ----------- band_members %>%   left_join(band_instruments) ## # A tibble: 3 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 Mick  Stones  NA     ## 2 John  Beatles guitar ## 3 Paul  Beatles bass # ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"right\") # ---------- tidyverse ----------- band_members %>%   right_join(band_instruments) ## # A tibble: 3 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 John  Beatles guitar ## 2 Paul  Beatles bass   ## 3 Keith NA      guitar"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"inner-join","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr} > Joining","what":"Inner join","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- band_members %>%   data_join(band_instruments, join = \"inner\") # ---------- tidyverse ----------- band_members %>%   inner_join(band_instruments) ## # A tibble: 2 × 3 ##   name  band    plays  ## * <chr> <chr>   <chr>  ## 1 John  Beatles guitar ## 2 Paul  Beatles bass"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"uniting","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Uniting","title":"Coming from 'tidyverse'","text":"Uniting variables useful e.g create unique indices combining several variables gather years, months, days single date. data_unite() offers interface close tidyr::unite():","code":"test <- data.frame(   year = 2002:2004,   month = c(\"02\", \"03\", \"09\"),   day = c(\"11\", \"22\", \"28\"),   stringsAsFactors = FALSE ) test ##   year month day ## 1 2002    02  11 ## 2 2003    03  22 ## 3 2004    09  28 # ---------- datawizard ----------- test %>%   data_unite(     new_column = \"date\",     select = c(\"year\", \"month\", \"day\"),     separator = \"-\"   ) # ---------- tidyverse ----------- test %>%   unite(     col = \"date\",     year, month, day,     sep = \"-\"   ) ##         date ## 1 2002-02-11 ## 2 2003-03-22 ## 3 2004-09-28 # ---------- datawizard ----------- test %>%   data_unite(     new_column = \"date\",     select = c(\"year\", \"month\", \"day\"),     separator = \"-\",     append = TRUE   ) # ---------- tidyverse ----------- test %>%   unite(     col = \"date\",     year, month, day,     sep = \"-\",     remove = FALSE   ) ##   year month day       date ## 1 2002    02  11 2002-02-11 ## 2 2003    03  22 2003-03-22 ## 3 2004    09  28 2004-09-28"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"separating","dir":"Articles","previous_headings":"Equivalence with {dplyr} / {tidyr}","what":"Separating","title":"Coming from 'tidyverse'","text":"Separating variables counterpart uniting variables useful split values multiple columns, e.g. splitting date values years, months days. data_separate() offers interface close tidyr::separate(): Unlike tidyr::separate(), can separate multiple columns one step data_separate().","code":"test <- data.frame(   date_arrival = c(\"2002-02-11\", \"2003-03-22\", \"2004-09-28\"),   date_departure = c(\"2002-03-15\", \"2003-03-28\", \"2004-09-30\"),   stringsAsFactors = FALSE ) test ##   date_arrival date_departure ## 1   2002-02-11     2002-03-15 ## 2   2003-03-22     2003-03-28 ## 3   2004-09-28     2004-09-30 # ---------- datawizard ----------- test %>%   data_separate(     select = \"date_arrival\",     new_columns = c(\"Year\", \"Month\", \"Day\")   ) # ---------- tidyverse ----------- test %>%   separate(     date_arrival,     into = c(\"Year\", \"Month\", \"Day\")   ) ##   date_departure Year Month Day ## 1     2002-03-15 2002    02  11 ## 2     2003-03-28 2003    03  22 ## 3     2004-09-30 2004    09  28 test %>%   data_separate(     new_columns = list(       date_arrival = c(\"Arr_Year\", \"Arr_Month\", \"Arr_Day\"),       date_departure = c(\"Dep_Year\", \"Dep_Month\", \"Dep_Day\")     )   ) ##   Arr_Year Arr_Month Arr_Day Dep_Year Dep_Month Dep_Day ## 1     2002        02      11     2002        03      15 ## 2     2003        03      22     2003        03      28 ## 3     2004        09      28     2004        09      30"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"other-useful-functions","dir":"Articles","previous_headings":"","what":"Other useful functions","title":"Coming from 'tidyverse'","text":"datawizard contains functions necessarily included dplyr tidyr directly modify data. inspired package janitor.","code":""},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"work-with-rownames","dir":"Articles","previous_headings":"Other useful functions","what":"Work with rownames","title":"Coming from 'tidyverse'","text":"can convert column rownames move rownames new column rownames_as_column() column_as_rownames():","code":"mtcars <- head(mtcars) mtcars ##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb ## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 ## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 ## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 ## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 ## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 ## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 mtcars2 <- mtcars %>%   rownames_as_column(var = \"model\")  mtcars2 ##               model  mpg cyl disp  hp drat    wt  qsec vs am gear carb ## 1         Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 ## 2     Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 ## 3        Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 ## 4    Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 ## 5 Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 ## 6           Valiant 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 mtcars2 %>%   column_as_rownames(var = \"model\") ##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb ## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 ## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 ## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 ## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 ## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 ## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"work-with-row-ids","dir":"Articles","previous_headings":"Other useful functions","what":"Work with row ids","title":"Coming from 'tidyverse'","text":"rowid_as_column() close identical tibble::rowid_to_column(). main difference use grouped data. tibble::rowid_to_column() uses one distinct rowid every row dataset, rowid_as_column() creates one id every row group. Therefore, two rows different groups can row id. means rowid_as_column() closer using n() mutate(), like following:","code":"test <- data.frame(   group = c(\"A\", \"A\", \"B\", \"B\"),   value = c(3, 5, 8, 1),   stringsAsFactors = FALSE ) test ##   group value ## 1     A     3 ## 2     A     5 ## 3     B     8 ## 4     B     1 test %>%   data_group(group) %>%   tibble::rowid_to_column() ##   rowid group value ## 1     1     A     3 ## 2     2     A     5 ## 3     3     B     8 ## 4     4     B     1 test %>%   data_group(group) %>%   rowid_as_column() ## # A tibble: 4 × 3 ## # Groups:   group [2] ##   rowid group value ##   <int> <chr> <dbl> ## 1     1 A         3 ## 2     2 A         5 ## 3     1 B         8 ## 4     2 B         1 test %>%   data_group(group) %>%   mutate(id = seq_len(n())) ## # A tibble: 4 × 3 ## # Groups:   group [2] ##   group value    id ##   <chr> <dbl> <int> ## 1 A         3     1 ## 2 A         5     2 ## 3 B         8     1 ## 4 B         1     2"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"work-with-column-names","dir":"Articles","previous_headings":"Other useful functions","what":"Work with column names","title":"Coming from 'tidyverse'","text":"dealing messy data, sometimes useful use row column names, vice versa. can done row_to_colnames() colnames_to_row().","code":"x <- data.frame(   X_1 = c(NA, \"Title\", 1:3),   X_2 = c(NA, \"Title2\", 4:6) ) x ##     X_1    X_2 ## 1  <NA>   <NA> ## 2 Title Title2 ## 3     1      4 ## 4     2      5 ## 5     3      6 x2 <- x %>%   row_to_colnames(row = 2) x2 ##   Title Title2 ## 1  <NA>   <NA> ## 3     1      4 ## 4     2      5 ## 5     3      6 x2 %>%   colnames_to_row() ##       x1     x2 ## 1  Title Title2 ## 11  <NA>   <NA> ## 3      1      4 ## 4      2      5 ## 5      3      6"},{"path":"https://easystats.github.io/datawizard/articles/tidyverse_translation.html","id":"take-a-quick-look-at-the-data","dir":"Articles","previous_headings":"Other useful functions","what":"Take a quick look at the data","title":"Coming from 'tidyverse'","text":"","code":"# ---------- datawizard ----------- data_peek(iris) # ---------- tidyverse ----------- glimpse(iris) ## Data frame with 150 rows and 5 variables ##  ## Variable     | Type    | Values                                         ## ----------------------------------------------------------------------- ## Sepal.Length | numeric | 5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5, 4.4, ...   ## Sepal.Width  | numeric | 3.5, 3, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, ... ## Petal.Length | numeric | 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, ...    ## Petal.Width  | numeric | 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, ...    ## Species      | factor  | setosa, setosa, setosa, setosa, setosa, ..."},{"path":"https://easystats.github.io/datawizard/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Indrajeet Patil. Author.            @patilindrajeets Etienne Bacher. Author, maintainer. Dominique Makowski. Author.            @Dom_Makowski Daniel Lüdecke. Author.            @strengejacke Mattan S. Ben-Shachar. Author. Brenton M. Wiernik. Author.            @bmwiernik Rémi Thériault. Contributor.            @rempsyc Thomas J. Faulkenberry. Reviewer. Robert Garrett. Reviewer.","code":""},{"path":"https://easystats.github.io/datawizard/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Patil et al., (2022). datawizard: R Package Easy Data Preparation Statistical Transformations. Journal Open Source Software, 7(78), 4684, https://doi.org/10.21105/joss.04684","code":"@Article{,   title = {{datawizard}: An {R} Package for Easy Data Preparation and Statistical Transformations},   author = {Indrajeet Patil and Dominique Makowski and Mattan S. Ben-Shachar and Brenton M. Wiernik and Etienne Bacher and Daniel Lüdecke},   journal = {Journal of Open Source Software},   year = {2022},   volume = {7},   number = {78},   pages = {4684},   doi = {10.21105/joss.04684}, }"},{"path":"https://easystats.github.io/datawizard/index.html","id":"datawizard-easy-data-wrangling-and-statistical-transformations-","dir":"","previous_headings":"","what":"Easy Data Wrangling and Statistical Transformations","title":"Easy Data Wrangling and Statistical Transformations","text":"datawizard lightweight package easily manipulate, clean, transform, prepare data analysis. part easystats ecosystem, suite R packages deal entire statistical analysis, cleaning data reporting results. covers two aspects data preparation: Data manipulation: datawizard offers similar set functions tidyverse packages, dplyr tidyr, select, filter reshape data, key differences. 1) data manipulation functions start prefix data_* (makes easy identify). 2) Although functions can used exactly tidyverse equivalents, also string-friendly (makes easy program use inside functions). Finally, datawizard super lightweight (dependencies, similar poorman), makes awesome developers use packages. Statistical transformations: datawizard also powerful functions easily apply common data transformations, including standardization, normalization, rescaling, rank-transformation, scale reversing, recoding, binning, etc.","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Easy Data Wrangling and Statistical Transformations","text":"Tip Instead library(datawizard), use library(easystats). make features easystats-ecosystem available. stay updated, use easystats::install_latest().","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Easy Data Wrangling and Statistical Transformations","text":"cite package, run following command:","code":"citation(\"datawizard\") To cite package 'datawizard' in publications use:    Patil et al., (2022). datawizard: An R Package for Easy Data   Preparation and Statistical Transformations. Journal of Open Source   Software, 7(78), 4684, https://doi.org/10.21105/joss.04684  A BibTeX entry for LaTeX users is    @Article{,     title = {{datawizard}: An {R} Package for Easy Data Preparation and Statistical Transformations},     author = {Indrajeet Patil and Dominique Makowski and Mattan S. Ben-Shachar and Brenton M. Wiernik and Etienne Bacher and Daniel Lüdecke},     journal = {Journal of Open Source Software},     year = {2022},     volume = {7},     number = {78},     pages = {4684},     doi = {10.21105/joss.04684},   }"},{"path":"https://easystats.github.io/datawizard/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"Easy Data Wrangling and Statistical Transformations","text":"courses tutorials statistical modeling assume working clean tidy dataset. practice, however, major part statistical modeling preparing data–cleaning values, creating new columns, reshaping dataset, transforming variables. datawizard provides easy use tools perform common, critical, sometimes tedious data preparation tasks.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/index.html","id":"select-filter-and-remove-variables","dir":"","previous_headings":"Data wrangling","what":"Select, filter and remove variables","title":"Easy Data Wrangling and Statistical Transformations","text":"package provides helpers filter rows meeting certain conditions… … logical expressions: Finding columns data frame, retrieving data selected columns, can achieved using find_columns() get_columns(): also possible extract one variables: Due consistent API, removing variables just simple:","code":"data_match(mtcars, data.frame(vs = 0, am = 1)) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 data_filter(mtcars, vs == 0 & am == 1) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 # find column names matching a pattern find_columns(iris, starts_with(\"Sepal\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  # return data columns matching a pattern get_columns(iris, starts_with(\"Sepal\")) |> head() #>   Sepal.Length Sepal.Width #> 1          5.1         3.5 #> 2          4.9         3.0 #> 3          4.7         3.2 #> 4          4.6         3.1 #> 5          5.0         3.6 #> 6          5.4         3.9 # single variable data_extract(mtcars, \"gear\") #>  [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4  # more variables head(data_extract(iris, ends_with(\"Width\"))) #>   Sepal.Width Petal.Width #> 1         3.5         0.2 #> 2         3.0         0.2 #> 3         3.2         0.2 #> 4         3.1         0.2 #> 5         3.6         0.2 #> 6         3.9         0.4 head(data_remove(iris, starts_with(\"Sepal\"))) #>   Petal.Length Petal.Width Species #> 1          1.4         0.2  setosa #> 2          1.4         0.2  setosa #> 3          1.3         0.2  setosa #> 4          1.5         0.2  setosa #> 5          1.4         0.2  setosa #> 6          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/index.html","id":"reorder-or-rename","dir":"","previous_headings":"Data wrangling","what":"Reorder or rename","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"head(data_relocate(iris, select = \"Species\", before = \"Sepal.Length\")) #>   Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1  setosa          5.1         3.5          1.4         0.2 #> 2  setosa          4.9         3.0          1.4         0.2 #> 3  setosa          4.7         3.2          1.3         0.2 #> 4  setosa          4.6         3.1          1.5         0.2 #> 5  setosa          5.0         3.6          1.4         0.2 #> 6  setosa          5.4         3.9          1.7         0.4 head(data_rename(iris, c(\"Sepal.Length\", \"Sepal.Width\"), c(\"length\", \"width\"))) #>   length width Petal.Length Petal.Width Species #> 1    5.1   3.5          1.4         0.2  setosa #> 2    4.9   3.0          1.4         0.2  setosa #> 3    4.7   3.2          1.3         0.2  setosa #> 4    4.6   3.1          1.5         0.2  setosa #> 5    5.0   3.6          1.4         0.2  setosa #> 6    5.4   3.9          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/index.html","id":"merge","dir":"","previous_headings":"Data wrangling","what":"Merge","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"x <- data.frame(a = 1:3, b = c(\"a\", \"b\", \"c\"), c = 5:7, id = 1:3) y <- data.frame(c = 6:8, d = c(\"f\", \"g\", \"h\"), e = 100:102, id = 2:4)  x #>   a b c id #> 1 1 a 5  1 #> 2 2 b 6  2 #> 3 3 c 7  3 y #>   c d   e id #> 1 6 f 100  2 #> 2 7 g 101  3 #> 3 8 h 102  4  data_merge(x, y, join = \"full\") #>    a    b c id    d   e #> 3  1    a 5  1 <NA>  NA #> 1  2    b 6  2    f 100 #> 2  3    c 7  3    g 101 #> 4 NA <NA> 8  4    h 102  data_merge(x, y, join = \"left\") #>   a b c id    d   e #> 3 1 a 5  1 <NA>  NA #> 1 2 b 6  2    f 100 #> 2 3 c 7  3    g 101  data_merge(x, y, join = \"right\") #>    a    b c id d   e #> 1  2    b 6  2 f 100 #> 2  3    c 7  3 g 101 #> 3 NA <NA> 8  4 h 102  data_merge(x, y, join = \"semi\", by = \"c\") #>   a b c id #> 2 2 b 6  2 #> 3 3 c 7  3  data_merge(x, y, join = \"anti\", by = \"c\") #>   a b c id #> 1 1 a 5  1  data_merge(x, y, join = \"inner\") #>   a b c id d   e #> 1 2 b 6  2 f 100 #> 2 3 c 7  3 g 101  data_merge(x, y, join = \"bind\") #>    a    b c id    d   e #> 1  1    a 5  1 <NA>  NA #> 2  2    b 6  2 <NA>  NA #> 3  3    c 7  3 <NA>  NA #> 4 NA <NA> 6  2    f 100 #> 5 NA <NA> 7  3    g 101 #> 6 NA <NA> 8  4    h 102"},{"path":"https://easystats.github.io/datawizard/index.html","id":"reshape","dir":"","previous_headings":"Data wrangling","what":"Reshape","title":"Easy Data Wrangling and Statistical Transformations","text":"common data wrangling task reshape data. Either go wide/Cartesian long/tidy format way","code":"wide_data <- data.frame(replicate(5, rnorm(10)))  head(data_to_long(wide_data)) #>   name       value #> 1   X1 -0.08281164 #> 2   X2 -1.12490028 #> 3   X3 -0.70632036 #> 4   X4 -0.70278946 #> 5   X5  0.07633326 #> 6   X1  1.93468099 long_data <- data_to_long(wide_data, rows_to = \"Row_ID\") # Save row number  data_to_wide(long_data,   names_from = \"name\",   values_from = \"value\",   id_cols = \"Row_ID\" ) #>    Row_ID          X1          X2          X3         X4          X5 #> 1       1 -0.08281164 -1.12490028 -0.70632036 -0.7027895  0.07633326 #> 2       2  1.93468099 -0.87430362  0.96687656  0.2998642 -0.23035595 #> 3       3 -2.05128979  0.04386162 -0.71016648  1.1494697  0.31746484 #> 4       4  0.27773897 -0.58397514 -0.05917365 -0.3016415 -1.59268440 #> 5       5 -1.52596060 -0.82329858 -0.23094342 -0.5473394 -0.18194062 #> 6       6 -0.26916362  0.11059280  0.69200045 -0.3854041  1.75614174 #> 7       7  1.23305388  0.36472778  1.35682290  0.2763720  0.11394932 #> 8       8  0.63360774  0.05370100  1.78872284  0.1518608 -0.29216508 #> 9       9  0.35271746  1.36867235  0.41071582 -0.4313808  1.75409316 #> 10     10 -0.56048248 -0.38045724 -2.18785470 -1.8705001  1.80958455"},{"path":"https://easystats.github.io/datawizard/index.html","id":"empty-rows-and-columns","dir":"","previous_headings":"Data wrangling","what":"Empty rows and columns","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"tmp <- data.frame(   a = c(1, 2, 3, NA, 5),   b = c(1, NA, 3, NA, 5),   c = c(NA, NA, NA, NA, NA),   d = c(1, NA, 3, NA, 5) )  tmp #>    a  b  c  d #> 1  1  1 NA  1 #> 2  2 NA NA NA #> 3  3  3 NA  3 #> 4 NA NA NA NA #> 5  5  5 NA  5  # indices of empty columns or rows empty_columns(tmp) #> c  #> 3 empty_rows(tmp) #> [1] 4  # remove empty columns or rows remove_empty_columns(tmp) #>    a  b  d #> 1  1  1  1 #> 2  2 NA NA #> 3  3  3  3 #> 4 NA NA NA #> 5  5  5  5 remove_empty_rows(tmp) #>   a  b  c  d #> 1 1  1 NA  1 #> 2 2 NA NA NA #> 3 3  3 NA  3 #> 5 5  5 NA  5  # remove empty columns and rows remove_empty(tmp) #>   a  b  d #> 1 1  1  1 #> 2 2 NA NA #> 3 3  3  3 #> 5 5  5  5"},{"path":"https://easystats.github.io/datawizard/index.html","id":"recode-or-cut-dataframe","dir":"","previous_headings":"Data wrangling","what":"Recode or cut dataframe","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"set.seed(123) x <- sample(1:10, size = 50, replace = TRUE)  table(x) #> x #>  1  2  3  4  5  6  7  8  9 10  #>  2  3  5  3  7  5  5  2 11  7  # cut into 3 groups, based on distribution (quantiles) table(categorize(x, split = \"quantile\", n_groups = 3)) #>  #>  1  2  3  #> 13 19 18"},{"path":"https://easystats.github.io/datawizard/index.html","id":"data-transformations","dir":"","previous_headings":"","what":"Data Transformations","title":"Easy Data Wrangling and Statistical Transformations","text":"packages also contains multiple functions help transform data.","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"standardize","dir":"","previous_headings":"Data Transformations","what":"Standardize","title":"Easy Data Wrangling and Statistical Transformations","text":"example, standardize (z-score) data:","code":"# before summary(swiss) #>    Fertility      Agriculture     Examination      Education     #>  Min.   :35.00   Min.   : 1.20   Min.   : 3.00   Min.   : 1.00   #>  1st Qu.:64.70   1st Qu.:35.90   1st Qu.:12.00   1st Qu.: 6.00   #>  Median :70.40   Median :54.10   Median :16.00   Median : 8.00   #>  Mean   :70.14   Mean   :50.66   Mean   :16.49   Mean   :10.98   #>  3rd Qu.:78.45   3rd Qu.:67.65   3rd Qu.:22.00   3rd Qu.:12.00   #>  Max.   :92.50   Max.   :89.70   Max.   :37.00   Max.   :53.00   #>     Catholic       Infant.Mortality #>  Min.   :  2.150   Min.   :10.80    #>  1st Qu.:  5.195   1st Qu.:18.15    #>  Median : 15.140   Median :20.00    #>  Mean   : 41.144   Mean   :19.94    #>  3rd Qu.: 93.125   3rd Qu.:21.70    #>  Max.   :100.000   Max.   :26.60  # after summary(standardize(swiss)) #>    Fertility         Agriculture       Examination         Education       #>  Min.   :-2.81327   Min.   :-2.1778   Min.   :-1.69084   Min.   :-1.0378   #>  1st Qu.:-0.43569   1st Qu.:-0.6499   1st Qu.:-0.56273   1st Qu.:-0.5178   #>  Median : 0.02061   Median : 0.1515   Median :-0.06134   Median :-0.3098   #>  Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.0000   #>  3rd Qu.: 0.66504   3rd Qu.: 0.7481   3rd Qu.: 0.69074   3rd Qu.: 0.1062   #>  Max.   : 1.78978   Max.   : 1.7190   Max.   : 2.57094   Max.   : 4.3702   #>     Catholic       Infant.Mortality   #>  Min.   :-0.9350   Min.   :-3.13886   #>  1st Qu.:-0.8620   1st Qu.:-0.61543   #>  Median :-0.6235   Median : 0.01972   #>  Mean   : 0.0000   Mean   : 0.00000   #>  3rd Qu.: 1.2464   3rd Qu.: 0.60337   #>  Max.   : 1.4113   Max.   : 2.28566"},{"path":"https://easystats.github.io/datawizard/index.html","id":"winsorize","dir":"","previous_headings":"Data Transformations","what":"Winsorize","title":"Easy Data Wrangling and Statistical Transformations","text":"winsorize data:","code":"# before anscombe #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1  10 10 10  8  8.04 9.14  7.46  6.58 #> 2   8  8  8  8  6.95 8.14  6.77  5.76 #> 3  13 13 13  8  7.58 8.74 12.74  7.71 #> 4   9  9  9  8  8.81 8.77  7.11  8.84 #> 5  11 11 11  8  8.33 9.26  7.81  8.47 #> 6  14 14 14  8  9.96 8.10  8.84  7.04 #> 7   6  6  6  8  7.24 6.13  6.08  5.25 #> 8   4  4  4 19  4.26 3.10  5.39 12.50 #> 9  12 12 12  8 10.84 9.13  8.15  5.56 #> 10  7  7  7  8  4.82 7.26  6.42  7.91 #> 11  5  5  5  8  5.68 4.74  5.73  6.89  # after winsorize(anscombe) #>    x1 x2 x3 x4   y1   y2   y3   y4 #> 1  10 10 10  8 8.04 9.13 7.46 6.58 #> 2   8  8  8  8 6.95 8.14 6.77 5.76 #> 3  12 12 12  8 7.58 8.74 8.15 7.71 #> 4   9  9  9  8 8.81 8.77 7.11 8.47 #> 5  11 11 11  8 8.33 9.13 7.81 8.47 #> 6  12 12 12  8 8.81 8.10 8.15 7.04 #> 7   6  6  6  8 7.24 6.13 6.08 5.76 #> 8   6  6  6  8 5.68 6.13 6.08 8.47 #> 9  12 12 12  8 8.81 9.13 8.15 5.76 #> 10  7  7  7  8 5.68 7.26 6.42 7.91 #> 11  6  6  6  8 5.68 6.13 6.08 6.89"},{"path":"https://easystats.github.io/datawizard/index.html","id":"center","dir":"","previous_headings":"Data Transformations","what":"Center","title":"Easy Data Wrangling and Statistical Transformations","text":"grand-mean center data","code":"center(anscombe) #>    x1 x2 x3 x4          y1         y2    y3         y4 #> 1   1  1  1 -1  0.53909091  1.6390909 -0.04 -0.9209091 #> 2  -1 -1 -1 -1 -0.55090909  0.6390909 -0.73 -1.7409091 #> 3   4  4  4 -1  0.07909091  1.2390909  5.24  0.2090909 #> 4   0  0  0 -1  1.30909091  1.2690909 -0.39  1.3390909 #> 5   2  2  2 -1  0.82909091  1.7590909  0.31  0.9690909 #> 6   5  5  5 -1  2.45909091  0.5990909  1.34 -0.4609091 #> 7  -3 -3 -3 -1 -0.26090909 -1.3709091 -1.42 -2.2509091 #> 8  -5 -5 -5 10 -3.24090909 -4.4009091 -2.11  4.9990909 #> 9   3  3  3 -1  3.33909091  1.6290909  0.65 -1.9409091 #> 10 -2 -2 -2 -1 -2.68090909 -0.2409091 -1.08  0.4090909 #> 11 -4 -4 -4 -1 -1.82090909 -2.7609091 -1.77 -0.6109091"},{"path":"https://easystats.github.io/datawizard/index.html","id":"ranktransform","dir":"","previous_headings":"Data Transformations","what":"Ranktransform","title":"Easy Data Wrangling and Statistical Transformations","text":"rank-transform data:","code":"# before head(trees) #>   Girth Height Volume #> 1   8.3     70   10.3 #> 2   8.6     65   10.3 #> 3   8.8     63   10.2 #> 4  10.5     72   16.4 #> 5  10.7     81   18.8 #> 6  10.8     83   19.7  # after head(ranktransform(trees)) #>   Girth Height Volume #> 1     1    6.0    2.5 #> 2     2    3.0    2.5 #> 3     3    1.0    1.0 #> 4     4    8.5    5.0 #> 5     5   25.5    7.0 #> 6     6   28.0    9.0"},{"path":"https://easystats.github.io/datawizard/index.html","id":"rescale","dir":"","previous_headings":"Data Transformations","what":"Rescale","title":"Easy Data Wrangling and Statistical Transformations","text":"rescale numeric variable new range:","code":"change_scale(c(0, 1, 5, -5, -2)) #> [1]  50  60 100   0  30 #> attr(,\"min_value\") #> [1] -5 #> attr(,\"max_value\") #> [1] 5 #> attr(,\"new_min\") #> [1] 0 #> attr(,\"new_max\") #> [1] 100 #> attr(,\"range_difference\") #> [1] 10 #> attr(,\"to_range\") #> [1]   0 100 #> attr(,\"class\") #> [1] \"dw_transformer\" \"numeric\""},{"path":"https://easystats.github.io/datawizard/index.html","id":"rotate-or-transpose","dir":"","previous_headings":"Data Transformations","what":"Rotate or transpose","title":"Easy Data Wrangling and Statistical Transformations","text":"","code":"x <- mtcars[1:3, 1:4]  x #>                mpg cyl disp  hp #> Mazda RX4     21.0   6  160 110 #> Mazda RX4 Wag 21.0   6  160 110 #> Datsun 710    22.8   4  108  93  data_rotate(x) #>      Mazda RX4 Mazda RX4 Wag Datsun 710 #> mpg         21            21       22.8 #> cyl          6             6        4.0 #> disp       160           160      108.0 #> hp         110           110       93.0"},{"path":"https://easystats.github.io/datawizard/index.html","id":"data-properties","dir":"","previous_headings":"","what":"Data properties","title":"Easy Data Wrangling and Statistical Transformations","text":"datawizard provides way provide comprehensive descriptive summary variables dataframe: even just variable also additional data properties can computed using package.","code":"data(iris) describe_distribution(iris) #> Variable     | Mean |   SD |  IQR |        Range | Skewness | Kurtosis |   n | n_Missing #> ---------------------------------------------------------------------------------------- #> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] |     0.31 |    -0.55 | 150 |         0 #> Sepal.Width  | 3.06 | 0.44 | 0.52 | [2.00, 4.40] |     0.32 |     0.23 | 150 |         0 #> Petal.Length | 3.76 | 1.77 | 3.52 | [1.00, 6.90] |    -0.27 |    -1.40 | 150 |         0 #> Petal.Width  | 1.20 | 0.76 | 1.50 | [0.10, 2.50] |    -0.10 |    -1.34 | 150 |         0 describe_distribution(mtcars$wt) #> Mean |   SD |  IQR |        Range | Skewness | Kurtosis |  n | n_Missing #> ------------------------------------------------------------------------ #> 3.22 | 0.98 | 1.19 | [1.51, 5.42] |     0.47 |     0.42 | 32 |         0 x <- (-10:10)^3 + rnorm(21, 0, 100) smoothness(x, method = \"diff\") #> [1] 1.791243 #> attr(,\"class\") #> [1] \"parameters_smoothness\" \"numeric\""},{"path":"https://easystats.github.io/datawizard/index.html","id":"function-design-and-pipe-workflow","dir":"","previous_headings":"","what":"Function design and pipe-workflow","title":"Easy Data Wrangling and Statistical Transformations","text":"design datawizard functions follows design principle makes easy user understand remember functions work: first argument data methods work data frames, two arguments following select exclude variables following arguments arguments related specific tasks functions important, functions accept data frames usually first argument, also return (modified) data frame . Thus, datawizard integrates smoothly “pipe-workflow”.","code":"iris |>   # all rows where Species is \"versicolor\" or \"virginica\"   data_filter(Species %in% c(\"versicolor\", \"virginica\")) |>   # select only columns with \".\" in names (i.e. drop Species)   get_columns(contains(\"\\\\.\")) |>   # move columns that ends with \"Length\" to start of data frame   data_relocate(ends_with(\"Length\")) |>   # remove fourth column   data_remove(4) |>   head() #>    Sepal.Length Petal.Length Sepal.Width #> 51          7.0          4.7         3.2 #> 52          6.4          4.5         3.2 #> 53          6.9          4.9         3.1 #> 54          5.5          4.0         2.3 #> 55          6.5          4.6         2.8 #> 56          5.7          4.5         2.8"},{"path":"https://easystats.github.io/datawizard/index.html","id":"contributing-and-support","dir":"","previous_headings":"","what":"Contributing and Support","title":"Easy Data Wrangling and Statistical Transformations","text":"case want file issue contribute another way package, please follow guide. questions functionality, may either contact us via email also file issue.","code":""},{"path":"https://easystats.github.io/datawizard/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Easy Data Wrangling and Statistical Transformations","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust data for the effect of other variable(s) — adjust","title":"Adjust data for the effect of other variable(s) — adjust","text":"function can used adjust data effect variables present dataset. based underlying fitting regressions models, allowing quite flexibility, including factors random effects mixed models (multilevel partialization), continuous variables smooth terms general additive models (non-linear partialization) /fitting models Bayesian framework. values returned function residuals regression models. Note regular correlation two \"adjusted\" variables equivalent partial correlation .","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust data for the effect of other variable(s) — adjust","text":"","code":"adjust(   data,   effect = NULL,   select = is.numeric,   exclude = NULL,   multilevel = FALSE,   additive = FALSE,   bayesian = FALSE,   keep_intercept = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE )  data_adjust(   data,   effect = NULL,   select = is.numeric,   exclude = NULL,   multilevel = FALSE,   additive = FALSE,   bayesian = FALSE,   keep_intercept = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE )"},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust data for the effect of other variable(s) — adjust","text":"data data frame. effect Character vector column names adjusted (regressed ). NULL (default), variables selected. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. multilevel TRUE, factors included random factors. Else, FALSE (default), included fixed effects simple regression model. additive TRUE, continuous variables included smooth terms additive models. goal regress-potential non-linear effects. bayesian TRUE, models fitted Bayesian framework using rstanarm. keep_intercept FALSE (default), intercept model re-added. avoids centering around 0 happens default regressing another variable (see examples visual representation ). ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust data for the effect of other variable(s) — adjust","text":"data frame comparable data, adjusted variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/adjust.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust data for the effect of other variable(s) — adjust","text":"","code":"adjusted_all <- adjust(attitude) head(adjusted_all) #>        rating complaints privileges    learning     raises   critical #> 1  -8.1102953  5.5583770 -15.848949 -2.75102306  0.5742664  15.605502 #> 2   1.6472337  0.0646564  -1.422592 -3.06207012 -1.5567655  -2.315781 #> 3   1.0605589 -7.5116953  11.174609  5.59808033  4.8603132   8.061801 #> 4  -0.2268416  3.8345277  -4.567441  0.03866933 -7.1185324  13.002574 #> 5   6.5462010 -1.2420122  -3.051098  0.87312095 -2.7131349   6.500353 #> 6 -10.9418499  5.2030745   2.664156 -1.24552098  4.1370346 -21.678382 #>      advance #> 1  2.8684130 #> 2  5.3937097 #> 3 -6.4236221 #> 4 -0.3951046 #> 5  2.1988621 #> 6 -3.1912418 adjusted_one <- adjust(attitude, effect = \"complaints\", select = \"rating\") head(adjusted_one) #>        rating complaints privileges learning raises critical advance #> 1  -9.8614202         51         30       39     61       92      45 #> 2   0.3286522         64         51       54     63       73      47 #> 3   3.8009933         70         68       69     76       86      48 #> 4  -0.9167380         63         45       47     54       84      35 #> 5   7.7641147         78         56       66     71       83      47 #> 6 -12.8798594         55         49       44     54       49      34 # \\donttest{ adjust(attitude, effect = \"complaints\", select = \"rating\", bayesian = TRUE) #>          rating complaints privileges learning raises critical advance #> 1   -9.89339760         51         30       39     61       92      45 #> 2    0.30196226         64         51       54     63       73      47 #> 3    3.77674373         70         68       69     76       86      48 #> 4   -0.94383466         63         45       47     54       84      35 #> 5    7.74311903         78         56       66     71       83      47 #> 6  -12.91020995         55         49       44     54       49      34 #> 7   -6.96064701         67         42       56     66       68      35 #> 8    0.00572829         75         50       55     70       66      41 #> 9   -4.27369332         82         72       67     71       83      31 #> 10   6.56457152         61         45       47     62       80      41 #> 11   9.59819622         53         53       58     58       67      34 #> 12   7.31877461         60         47       39     59       74      41 #> 13   7.81036843         62         57       42     55       63      25 #> 14  -9.02789641         83         83       45     59       77      35 #> 15   4.49732211         77         54       72     79       77      46 #> 16  -1.30731803         90         50       72     60       54      36 #> 17  -4.53630259         85         64       69     79       79      63 #> 18   5.31877461         60         65       75     55       80      60 #> 19  -2.22325627         70         46       57     75       85      46 #> 20  -8.17281922         58         68       54     64       78      52 #> 21   5.40283637         40         33       34     43       64      33 #> 22   3.56457152         61         52       62     66       80      41 #> 23 -11.20644392         66         52       50     63       80      37 #> 24  -2.33455437         37         42       58     50       57      49 #> 25   7.84399314         54         42       48     66       75      33 #> 26  -6.50267789         77         66       63     88       76      72 #> 27   7.00572829         75         58       74     80       78      49 #> 28  -9.41861613         57         44       45     51       83      38 #> 29   6.46369741         85         71       71     77       74      55 #> 30   5.72630668         82         39       59     64       78      39 adjust(attitude, effect = \"complaints\", select = \"rating\", additive = TRUE) #>          rating complaints privileges learning raises critical advance #> 1   -9.86142016         51         30       39     61       92      45 #> 2    0.32865220         64         51       54     63       73      47 #> 3    3.80099328         70         68       69     76       86      48 #> 4   -0.91673799         63         45       47     54       84      35 #> 5    7.76411473         78         56       66     71       83      47 #> 6  -12.87985944         55         49       44     54       49      34 #> 7   -6.93517726         67         42       56     66       68      35 #> 8    0.02794419         75         50       55     70       66      41 #> 9   -4.25432454         82         72       67     71       83      31 #> 10   6.59248165         61         45       47     62       80      41 #> 11   9.62936020         53         53       58     58       67      34 #> 12   7.34709147         60         47       39     59       74      41 #> 13   7.83787183         62         57       42     55       63      25 #> 14  -9.00893436         83         83       45     59       77      35 #> 15   4.51872455         77         54       72     79       77      46 #> 16  -1.29120309         90         50       72     60       54      36 #> 17  -4.51815400         85         64       69     79       79      63 #> 18   5.34709147         60         65       75     55       80      60 #> 19  -2.19900672         70         46       57     75       85      46 #> 20  -8.14368889         58         68       54     64       78      52 #> 21   5.43928784         40         33       34     43       64      33 #> 22   3.59248165         61         52       62     66       80      41 #> 23 -11.18056744         66         52       50     63       80      37 #> 24  -2.29688270         37         42       58     50       57      49 #> 25   7.87475038         54         42       48     66       75      33 #> 26  -6.48127545         77         66       63     88       76      72 #> 27   7.02794419         75         58       74     80       78      49 #> 28  -9.38907907         57         44       45     51       83      38 #> 29   6.48184600         85         71       71     77       74      55 #> 30   5.74567546         82         39       59     64       78      39 attitude$complaints_LMH <- cut(attitude$complaints, 3) adjust(attitude, effect = \"complaints_LMH\", select = \"rating\", multilevel = TRUE) #>         rating complaints privileges learning raises critical advance #> 1   -9.9809282         51         30       39     61       92      45 #> 2    2.6250549         64         51       54     63       73      47 #> 3   10.6250549         70         68       69     76       86      48 #> 4    0.6250549         63         45       47     54       84      35 #> 5    5.6503521         78         56       66     71       83      47 #> 6  -17.3749451         55         49       44     54       49      34 #> 7   -2.3749451         67         42       56     66       68      35 #> 8   -4.3496479         75         50       55     70       66      41 #> 9   -3.3496479         82         72       67     71       83      31 #> 10   6.6250549         61         45       47     62       80      41 #> 11  11.0190718         53         53       58     58       67      34 #> 12   6.6250549         60         47       39     59       74      41 #> 13   8.6250549         62         57       42     55       63      25 #> 14  -7.3496479         83         83       45     59       77      35 #> 15   1.6503521         77         54       72     79       77      46 #> 16   5.6503521         90         50       72     60       54      36 #> 17  -1.3496479         85         64       69     79       79      63 #> 18   4.6250549         60         65       75     55       80      60 #> 19   4.6250549         70         46       57     75       85      46 #> 20 -10.3749451         58         68       54     64       78      52 #> 21  -2.9809282         40         33       34     43       64      33 #> 22   3.6250549         61         52       62     66       80      41 #> 23  -7.3749451         66         52       50     63       80      37 #> 24 -12.9809282         37         42       58     50       57      49 #> 25  10.0190718         54         42       48     66       75      33 #> 26  -9.3496479         77         66       63     88       76      72 #> 27   2.6503521         75         58       74     80       78      49 #> 28 -12.3749451         57         44       45     51       83      38 #> 29   9.6503521         85         71       71     77       74      55 #> 30   6.6503521         82         39       59     64       78      39 #>    complaints_LMH #> 1     (36.9,54.7] #> 2     (54.7,72.3] #> 3     (54.7,72.3] #> 4     (54.7,72.3] #> 5     (72.3,90.1] #> 6     (54.7,72.3] #> 7     (54.7,72.3] #> 8     (72.3,90.1] #> 9     (72.3,90.1] #> 10    (54.7,72.3] #> 11    (36.9,54.7] #> 12    (54.7,72.3] #> 13    (54.7,72.3] #> 14    (72.3,90.1] #> 15    (72.3,90.1] #> 16    (72.3,90.1] #> 17    (72.3,90.1] #> 18    (54.7,72.3] #> 19    (54.7,72.3] #> 20    (54.7,72.3] #> 21    (36.9,54.7] #> 22    (54.7,72.3] #> 23    (54.7,72.3] #> 24    (36.9,54.7] #> 25    (36.9,54.7] #> 26    (72.3,90.1] #> 27    (72.3,90.1] #> 28    (54.7,72.3] #> 29    (72.3,90.1] #> 30    (72.3,90.1] # }  # Generate data data <- simulate_correlation(n = 100, r = 0.7) data$V2 <- (5 * data$V2) + 20 # Add intercept  # Adjust adjusted <- adjust(data, effect = \"V1\", select = \"V2\") adjusted_icpt <- adjust(data, effect = \"V1\", select = \"V2\", keep_intercept = TRUE)  # Visualize plot(data$V1, data$V2,   pch = 19, col = \"blue\",   ylim = c(min(adjusted$V2), max(data$V2)),   main = \"Original (blue), adjusted (green), and adjusted - intercept kept (red) data\" ) abline(lm(V2 ~ V1, data = data), col = \"blue\") points(adjusted$V1, adjusted$V2, pch = 19, col = \"green\") abline(lm(V2 ~ V1, data = adjusted), col = \"green\") points(adjusted_icpt$V1, adjusted_icpt$V2, pch = 19, col = \"red\") abline(lm(V2 ~ V1, data = adjusted_icpt), col = \"red\")"},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign variable and value labels — assign_labels","title":"Assign variable and value labels — assign_labels","text":"Assign variable values labels variable variables data frame. Labels stored attributes (\"label\" variable labels \"labels\") value labels.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign variable and value labels — assign_labels","text":"","code":"assign_labels(x, ...)  # S3 method for numeric assign_labels(x, variable = NULL, values = NULL, ...)  # S3 method for data.frame assign_labels(   x,   select = NULL,   exclude = NULL,   values = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assign variable and value labels — assign_labels","text":"x data frame, factor vector. ... Currently used. variable variable label string. values value labels (named) character vector. values named vector, length labels must equal length unique values. named vector, left-hand side (LHS) value x, right-hand side (RHS) associated value label. Non-matching labels omitted. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign variable and value labels — assign_labels","text":"labelled variable, data frame labelled variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Assign variable and value labels — assign_labels","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/assign_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assign variable and value labels — assign_labels","text":"","code":"x <- 1:3 # labelling by providing required number of labels assign_labels(   x,   variable = \"My x\",   values = c(\"one\", \"two\", \"three\") ) #> [1] 1 2 3 #> attr(,\"label\") #> [1] \"My x\" #> attr(,\"labels\") #>   one   two three  #>     1     2     3   # labelling using named vectors data(iris) out <- assign_labels(   iris$Species,   variable = \"Labelled Species\",   values = c(`setosa` = \"Spec1\", `versicolor` = \"Spec2\", `virginica` = \"Spec3\") ) str(out) #>  Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  - attr(*, \"label\")= chr \"Labelled Species\" #>  - attr(*, \"labels\")= Named chr [1:3] \"setosa\" \"versicolor\" \"virginica\" #>   ..- attr(*, \"names\")= chr [1:3] \"Spec1\" \"Spec2\" \"Spec3\"  # data frame example out <- assign_labels(   iris,   select = \"Species\",   variable = \"Labelled Species\",   values = c(`setosa` = \"Spec1\", `versicolor` = \"Spec2\", `virginica` = \"Spec3\") ) str(out$Species) #>  Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ... #>  - attr(*, \"label\")= chr \"Labelled Species\" #>  - attr(*, \"labels\")= Named chr [1:3] \"setosa\" \"versicolor\" \"virginica\" #>   ..- attr(*, \"names\")= chr [1:3] \"Spec1\" \"Spec2\" \"Spec3\"  # Partial labelling x <- 1:5 assign_labels(   x,   variable = \"My x\",   values = c(`1` = \"lowest\", `5` = \"highest\") ) #> [1] 1 2 3 4 5 #> attr(,\"label\") #> [1] \"My x\" #> attr(,\"labels\") #>  lowest highest  #>       1       5"},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode (or ","title":"Recode (or ","text":"functions divides range variables intervals recodes values inside intervals according related interval. basically wrapper around base R's cut(), providing simplified accessible way define interval breaks (cut-values).","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode (or ","text":"","code":"categorize(x, ...)  # S3 method for numeric categorize(   x,   split = \"median\",   n_groups = NULL,   range = NULL,   lowest = 1,   labels = NULL,   verbose = TRUE,   ... )  # S3 method for data.frame categorize(   x,   select = NULL,   exclude = NULL,   split = \"median\",   n_groups = NULL,   range = NULL,   lowest = 1,   labels = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode (or ","text":"x (grouped) data frame, numeric vector factor. ... used. split Character vector, indicating breaks split variables, numeric values values indicating breaks. character, may one \"median\", \"mean\", \"quantile\", \"equal_length\", \"equal_range\". \"median\" \"mean\" return dichotomous variables, split mean median, respectively. \"quantile\" \"equal_length\" split variable n_groups groups, group refers interval specific range values. Thus, length interval based number groups. \"equal_range\" also splits variable multiple groups, however, length interval given, number resulting groups (hence, number breaks) determined many intervals can generated, based full range variable. n_groups split \"quantile\" \"equal_length\", defines number requested groups (.e. resulting number levels values) recoded variable(s). \"quantile\" define intervals based distribution variable, \"equal_length\" tries divide range variable pieces equal length. range split = \"equal_range\", defines range values recoded new value. lowest Minimum value recoded variable(s). NULL (default), numeric variables, minimum original input preserved. factors, default minimum 1. split = \"equal_range\", default minimum always 1, unless specified otherwise lowest. labels Character vector value labels. NULL, categorize() returns factors instead numeric variables, labels used labelling factor levels. Can also \"mean\" \"median\" factor labels mean/median groups. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode (or ","text":"x, recoded groups. default x numeric, unless labels specified. case, factor returned, factor levels (.e. recoded groups labelled accordingly.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"splits-and-breaks-cut-off-values-","dir":"Reference","previous_headings":"","what":"Splits and breaks (cut-off values)","title":"Recode (or ","text":"Breaks general exclusive, means values indicate lower bound next group interval begin. Take simple example, numeric variable values 1 9. median 5, thus first interval ranges 1-4 recoded 1, 5-9 turn 2 (compare cbind(1:9, categorize(1:9))). variable, using split = \"quantile\" n_groups = 3 define breaks 3.67 6.33 (see quantile(1:9, probs = c(1/3, 2/3))), means values 1 3 belong first interval recoded 1 (next interval starts 3.67), 4 6 2 7 9 3.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"recoding-into-groups-with-equal-size-or-range","dir":"Reference","previous_headings":"","what":"Recoding into groups with equal size or range","title":"Recode (or ","text":"split = \"equal_length\" split = \"equal_range\" try divide range x intervals similar () length. difference split = \"equal_length\" divide range x n_groups pieces thereby defining intervals used breaks (hence, equivalent cut(x, breaks = n_groups)),  split = \"equal_range\" cut x intervals length range, first interval defaults starts 1. lowest (starting) value interval can defined using lowest argument.","code":""},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Recode (or ","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/categorize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode (or ","text":"","code":"set.seed(123) x <- sample(1:10, size = 50, replace = TRUE)  table(x) #> x #>  1  2  3  4  5  6  7  8  9 10  #>  2  3  5  3  7  5  5  2 11  7   # by default, at median table(categorize(x)) #>  #>  1  2  #> 25 25   # into 3 groups, based on distribution (quantiles) table(categorize(x, split = \"quantile\", n_groups = 3)) #>  #>  1  2  3  #> 13 19 18   # into 3 groups, user-defined break table(categorize(x, split = c(3, 5))) #>  #>  1  2  3  #>  5  8 37   set.seed(123) x <- sample(1:100, size = 500, replace = TRUE)  # into 5 groups, try to recode into intervals of similar length, # i.e. the range within groups is the same for all groups table(categorize(x, split = \"equal_length\", n_groups = 5)) #>  #>   1   2   3   4   5  #>  89 116  96  94 105   # into 5 groups, try to return same range within groups # i.e. 1-20, 21-40, 41-60, etc. Since the range of \"x\" is # 1-100, and we have a range of 20, this results into 5 # groups, and thus is for this particular case identical # to the previous result. table(categorize(x, split = \"equal_range\", range = 20)) #>  #>   1   2   3   4   5  #>  89 116  96  94 105   # return factor with value labels instead of numeric value set.seed(123) x <- sample(1:10, size = 30, replace = TRUE) categorize(x, \"equal_length\", n_groups = 3) #>  [1] 1 1 3 1 2 2 2 2 3 3 2 1 3 3 3 1 3 3 3 3 3 1 2 1 3 2 3 3 3 3 categorize(x, \"equal_length\", n_groups = 3, labels = c(\"low\", \"mid\", \"high\")) #>  [1] low  low  high low  mid  mid  mid  mid  high high mid  low  high high high #> [16] low  high high high high high low  mid  low  high mid  high high high high #> Levels: low mid high  # cut numeric into groups with the mean or median as a label name x <- sample(1:10, size = 30, replace = TRUE) categorize(x, \"equal_length\", n_groups = 3, labels = \"mean\") #>  [1] 8.45 8.45 5.33 8.45 5.33 5.33 8.45 1.57 5.33 8.45 1.57 1.57 8.45 8.45 5.33 #> [16] 5.33 8.45 8.45 5.33 5.33 8.45 5.33 5.33 8.45 1.57 5.33 1.57 1.57 1.57 5.33 #> Levels: 1.57 5.33 8.45 categorize(x, \"equal_length\", n_groups = 3, labels = \"median\") #>  [1] 9.00 9.00 5.50 9.00 5.50 5.50 9.00 2.00 5.50 9.00 2.00 2.00 9.00 9.00 5.50 #> [16] 5.50 9.00 9.00 5.50 5.50 9.00 5.50 5.50 9.00 2.00 5.50 2.00 2.00 2.00 5.50 #> Levels: 2.00 5.50 9.00"},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":null,"dir":"Reference","previous_headings":"","what":"Centering (Grand-Mean Centering) — center","title":"Centering (Grand-Mean Centering) — center","text":"Performs grand-mean centering data.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centering (Grand-Mean Centering) — center","text":"","code":"center(x, ...)  centre(x, ...)  # S3 method for numeric center(   x,   robust = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   verbose = TRUE,   ... )  # S3 method for data.frame center(   x,   select = NULL,   exclude = NULL,   robust = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   force = FALSE,   remove_na = c(\"none\", \"selected\", \"all\"),   append = FALSE,   ignore_case = FALSE,   verbose = TRUE,   regex = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centering (Grand-Mean Centering) — center","text":"x (grouped) data frame, (numeric character) vector factor. ... Currently used. robust Logical, TRUE, centering done subtracting median variables. FALSE, variables centered subtracting mean. weights Can NULL (weighting), : data frames: numeric vector weights, character name column data.frame contains weights. numeric vectors: numeric vector weights. reference data frame variable centrality deviation computed instead input variable. Useful standardizing subset new data according another data frame. center Numeric value, can used alternative reference define reference centrality. center length 1, recycled match length selected variables centering. Else, center must length number selected variables. Values center matched selected variables provided order, unless named vector given. case, names matched names selected variables. verbose Toggle warnings messages. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. force Logical, TRUE, forces centering factors well. Factors converted numerical values, lowest level value 1 (unless factor numeric levels, converted corresponding numeric value). remove_na missing values (NA) treated: \"none\" (default): column's standardization done separately, ignoring NAs. Else, rows NA columns selected select / exclude (\"selected\") columns (\"\") dropped standardization, resulting data frame include cases. append Logical string. TRUE, centered variables get new column names (suffix \"_c\") appended (column bind) x, thus returning original centered variables. FALSE, original variables x overwritten centered versions. character value, centered variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centering (Grand-Mean Centering) — center","text":"centered variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Centering (Grand-Mean Centering) — center","text":"Difference centering standardizing: Standardized variables computed subtracting mean variable dividing standard deviation, centering variables involves subtraction.","code":""},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Centering (Grand-Mean Centering) — center","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/center.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centering (Grand-Mean Centering) — center","text":"","code":"data(iris)  # entire data frame or a vector head(iris$Sepal.Width) #> [1] 3.5 3.0 3.2 3.1 3.6 3.9 head(center(iris$Sepal.Width)) #> [1]  0.44266667 -0.05733333  0.14266667  0.04266667  0.54266667  0.84266667 head(center(iris)) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   -0.7433333  0.44266667       -2.358  -0.9993333  setosa #> 2   -0.9433333 -0.05733333       -2.358  -0.9993333  setosa #> 3   -1.1433333  0.14266667       -2.458  -0.9993333  setosa #> 4   -1.2433333  0.04266667       -2.258  -0.9993333  setosa #> 5   -0.8433333  0.54266667       -2.358  -0.9993333  setosa #> 6   -0.4433333  0.84266667       -2.058  -0.7993333  setosa head(center(iris, force = TRUE)) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   -0.7433333  0.44266667       -2.358  -0.9993333      -1 #> 2   -0.9433333 -0.05733333       -2.358  -0.9993333      -1 #> 3   -1.1433333  0.14266667       -2.458  -0.9993333      -1 #> 4   -1.2433333  0.04266667       -2.258  -0.9993333      -1 #> 5   -0.8433333  0.54266667       -2.358  -0.9993333      -1 #> 6   -0.4433333  0.84266667       -2.058  -0.7993333      -1  # only the selected columns from a data frame center(anscombe, select = c(\"x1\", \"x3\")) #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1   1 10  1  8  8.04 9.14  7.46  6.58 #> 2  -1  8 -1  8  6.95 8.14  6.77  5.76 #> 3   4 13  4  8  7.58 8.74 12.74  7.71 #> 4   0  9  0  8  8.81 8.77  7.11  8.84 #> 5   2 11  2  8  8.33 9.26  7.81  8.47 #> 6   5 14  5  8  9.96 8.10  8.84  7.04 #> 7  -3  6 -3  8  7.24 6.13  6.08  5.25 #> 8  -5  4 -5 19  4.26 3.10  5.39 12.50 #> 9   3 12  3  8 10.84 9.13  8.15  5.56 #> 10 -2  7 -2  8  4.82 7.26  6.42  7.91 #> 11 -4  5 -4  8  5.68 4.74  5.73  6.89 center(anscombe, exclude = c(\"x1\", \"x3\")) #>    x1 x2 x3 x4          y1         y2    y3         y4 #> 1  10  1 10 -1  0.53909091  1.6390909 -0.04 -0.9209091 #> 2   8 -1  8 -1 -0.55090909  0.6390909 -0.73 -1.7409091 #> 3  13  4 13 -1  0.07909091  1.2390909  5.24  0.2090909 #> 4   9  0  9 -1  1.30909091  1.2690909 -0.39  1.3390909 #> 5  11  2 11 -1  0.82909091  1.7590909  0.31  0.9690909 #> 6  14  5 14 -1  2.45909091  0.5990909  1.34 -0.4609091 #> 7   6 -3  6 -1 -0.26090909 -1.3709091 -1.42 -2.2509091 #> 8   4 -5  4 10 -3.24090909 -4.4009091 -2.11  4.9990909 #> 9  12  3 12 -1  3.33909091  1.6290909  0.65 -1.9409091 #> 10  7 -2  7 -1 -2.68090909 -0.2409091 -1.08  0.4090909 #> 11  5 -4  5 -1 -1.82090909 -2.7609091 -1.77 -0.6109091  # centering with reference center and scale d <- data.frame(   a = c(-2, -1, 0, 1, 2),   b = c(3, 4, 5, 6, 7) )  # default centering at mean center(d) #>    a  b #> 1 -2 -2 #> 2 -1 -1 #> 3  0  0 #> 4  1  1 #> 5  2  2  # centering, using 0 as mean center(d, center = 0) #>    a b #> 1 -2 3 #> 2 -1 4 #> 3  0 5 #> 4  1 6 #> 5  2 7  # centering, using -5 as mean center(d, center = -5) #>   a  b #> 1 3  8 #> 2 4  9 #> 3 5 10 #> 4 6 11 #> 5 7 12"},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the coefficient of variation — coef_var","title":"Compute the coefficient of variation — coef_var","text":"Compute coefficient variation (CV, ratio standard deviation mean, \\(\\sigma/\\mu\\)) set numeric values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the coefficient of variation — coef_var","text":"","code":"coef_var(x, ...)  distribution_coef_var(x, ...)  # S3 method for numeric coef_var(   x,   mu = NULL,   sigma = NULL,   method = c(\"standard\", \"unbiased\", \"median_mad\", \"qcd\"),   trim = 0,   remove_na = FALSE,   n = NULL,   na.rm = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the coefficient of variation — coef_var","text":"x numeric vector ratio scale (see details), vector values can coerced one. ... arguments passed computation functions. mu numeric vector mean values use compute coefficient variation. supplied, x used compute mean. sigma numeric vector standard deviation values use compute coefficient variation. supplied, x used compute SD. method Method use compute CV. Can \"standard\" compute dividing standard deviation mean, \"unbiased\" unbiased estimator normally distributed data, one two robust alternatives: \"median_mad\" divide median stats::mad(), \"qcd\" (quartile coefficient dispersion, interquartile range divided sum quartiles [twice midhinge]: \\((Q_3 - Q_1)/(Q_3 + Q_1)\\). trim fraction (0 0.5) values trimmed end x mean standard deviation (measures) computed. Values trim outside range (0 0.5) taken nearest endpoint. remove_na Logical. NA values removed computing (TRUE) (FALSE, default)? n method = \"unbiased\" mu sigma provided (computed x), sample size use adjust computed CV small-sample bias? na.rm Deprecated. Please use remove_na instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the coefficient of variation — coef_var","text":"computed coefficient variation x.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute the coefficient of variation — coef_var","text":"CV applicable values taken ratio scale: values fixed meaningfully defined 0 (either lowest highest possible value), ratios interpretable example, many sandwiches eaten week? 0 means \"none\" 20 sandwiches 4 times 5 sandwiches. center number sandwiches, longer ratio scale (0 \"none\" mean, ratio 4 -2 meaningful). Scaling ratio scale still results ratio scale. can re define \"many half sandwiches eat week ( = sandwiches * 0.5) 0 still mean \"none\", 20 half-sandwiches still 4 times 5 half-sandwiches. means CV invariant shifting, scaling:","code":"sandwiches <- c(0, 4, 15, 0, 0, 5, 2, 7) coef_var(sandwiches) #> [1] 1.239094  coef_var(sandwiches / 2) # same #> [1] 1.239094  coef_var(sandwiches + 4) # different! 0 is no longer meaningful! #> [1] 0.6290784"},{"path":"https://easystats.github.io/datawizard/reference/coef_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the coefficient of variation — coef_var","text":"","code":"coef_var(1:10) #> [1] 0.5504819 coef_var(c(1:10, 100), method = \"median_mad\") #> [1] 0.7413 coef_var(c(1:10, 100), method = \"qcd\") #> [1] 0.4166667 coef_var(mu = 10, sigma = 20) #> [1] 2 coef_var(mu = 10, sigma = 20, method = \"unbiased\", n = 30) #> [1] 2.250614"},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to Numeric (if possible) — coerce_to_numeric","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"Tries convert vector numeric possible (warnings errors). Otherwise, leaves .","code":""},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"","code":"coerce_to_numeric(x)"},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"x vector converted.","code":""},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"Numeric vector (possible)","code":""},{"path":"https://easystats.github.io/datawizard/reference/coerce_to_numeric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to Numeric (if possible) — coerce_to_numeric","text":"","code":"coerce_to_numeric(c(\"1\", \"2\")) #> [1] 1 2 coerce_to_numeric(c(\"1\", \"2\", \"A\")) #> [1] \"1\" \"2\" \"A\""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for working with column names — row_to_colnames","title":"Tools for working with column names — row_to_colnames","text":"Tools working column names","code":""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for working with column names — row_to_colnames","text":"","code":"row_to_colnames(x, row = 1, na_prefix = \"x\", verbose = TRUE)  colnames_to_row(x, prefix = \"x\")"},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for working with column names — row_to_colnames","text":"x data frame. row Row use column names. na_prefix Prefix give column name row NA. Default 'x', incremented NA (x1, x2, etc.). verbose Toggle warnings. prefix Prefix give column name. Default 'x', incremented column (x1, x2, etc.).","code":""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tools for working with column names — row_to_colnames","text":"row_to_colnames() colnames_to_row() return data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/colnames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for working with column names — row_to_colnames","text":"","code":"# Convert a row to column names -------------------------------- test <- data.frame(   a = c(\"iso\", 2, 5),   b = c(\"year\", 3, 6),   c = c(\"value\", 5, 7) ) test #>     a    b     c #> 1 iso year value #> 2   2    3     5 #> 3   5    6     7 row_to_colnames(test) #>   iso year value #> 2   2    3     5 #> 3   5    6     7  # Convert column names to row -------------------------------- test <- data.frame(   ARG = c(\"BRA\", \"FRA\"),   `1960` = c(1960, 1960),   `2000` = c(2000, 2000) ) test #>   ARG X1960 X2000 #> 1 BRA  1960  2000 #> 2 FRA  1960  2000 colnames_to_row(test) #>    x1    x2    x3 #> 1 ARG X1960 X2000 #> 2 BRA  1960  2000 #> 3 FRA  1960  2000"},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":null,"dir":"Reference","previous_headings":"","what":"Deviation Contrast Matrix — contr.deviation","title":"Deviation Contrast Matrix — contr.deviation","text":"Build deviation contrast matrix, type effects contrast matrix.","code":""},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deviation Contrast Matrix — contr.deviation","text":"","code":"contr.deviation(n, base = 1, contrasts = TRUE, sparse = FALSE)"},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deviation Contrast Matrix — contr.deviation","text":"n vector levels factor, number levels. base integer specifying group considered     baseline group. Ignored contrasts FALSE. contrasts logical indicating whether contrasts     computed. sparse logical indicating result sparse     (class dgCMatrix), using     package Matrix.","code":""},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Deviation Contrast Matrix — contr.deviation","text":"effects coding, unlike treatment/dummy coding (stats::contr.treatment()), contrast sums 0. regressions models, results intercept represents (unweighted) average group means. ANOVA settings, also guarantees lower order effects represent main effects (simple conditional effects, case using R's default stats::contr.treatment()).  Deviation coding (contr.deviation) type effects coding. deviation coding, coefficients factor variables interpreted difference factor level base level (interpretation treatment/dummy coding). example, factor group levels \"\", \"B\", \"C\", contr.devation, intercept represents overall mean (average group means 3 groups), coefficients groupB groupC represent differences group mean B C group means, respectively.  Sum coding (stats::contr.sum()) another type effects coding. sum coding, coefficients factor variables interpreted difference factor level grand (across-groups) mean. example, factor group levels \"\", \"B\", \"C\", contr.sum, intercept represents overall mean (average group means 3 groups), coefficients group1 group2 represent differences B group means overall mean, respectively.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/contr.deviation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deviation Contrast Matrix — contr.deviation","text":"","code":"if (FALSE) {   data(\"mtcars\")    mtcars <- data_modify(mtcars, cyl = factor(cyl))    c.treatment <- cbind(Intercept = 1, contrasts(mtcars$cyl))   solve(c.treatment)   #>            4 6 8   #> Intercept  1 0 0  # mean of the 1st level   #> 6         -1 1 0  # 2nd level - 1st level   #> 8         -1 0 1  # 3rd level - 1st level    contrasts(mtcars$cyl) <- contr.sum   c.sum <- cbind(Intercept = 1, contrasts(mtcars$cyl))   solve(c.sum)   #>                4      6      8   #> Intercept  0.333  0.333  0.333   # overall mean   #>            0.667 -0.333 -0.333   # deviation of 1st from overall mean   #>           -0.333  0.667 -0.333   # deviation of 2nd from overall mean     contrasts(mtcars$cyl) <- contr.deviation   c.deviation <- cbind(Intercept = 1, contrasts(mtcars$cyl))   solve(c.deviation)   #>                4     6     8   #> Intercept  0.333 0.333 0.333   # overall mean   #> 6         -1.000 1.000 0.000   # 2nd level - 1st level   #> 8         -1.000 0.000 1.000   # 3rd level - 1st level    ## With Interactions -----------------------------------------   mtcars <- data_modify(mtcars, am = C(am, contr = contr.deviation))   mtcars <- data_arrange(mtcars, select = c(\"cyl\", \"am\"))    mm <- unique(model.matrix(~ cyl * am, data = mtcars))   rownames(mm) <- c(     \"cyl4.am0\", \"cyl4.am1\", \"cyl6.am0\",     \"cyl6.am1\", \"cyl8.am0\", \"cyl8.am1\"   )    solve(mm)   #>             cyl4.am0 cyl4.am1 cyl6.am0 cyl6.am1 cyl8.am0 cyl8.am1   #> (Intercept)    0.167    0.167    0.167    0.167    0.167    0.167  # overall mean   #> cyl6          -0.500   -0.500    0.500    0.500    0.000    0.000  # cyl MAIN eff: 2nd - 1st   #> cyl8          -0.500   -0.500    0.000    0.000    0.500    0.500  # cyl MAIN eff: 2nd - 1st   #> am1           -0.333    0.333   -0.333    0.333   -0.333    0.333  # am MAIN eff   #> cyl6:am1       1.000   -1.000   -1.000    1.000    0.000    0.000   #> cyl8:am1       1.000   -1.000    0.000    0.000   -1.000    1.000 }"},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace missing values in a variable or a data frame. — convert_na_to","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"Replace missing values variable data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"","code":"convert_na_to(x, ...)  # S3 method for numeric convert_na_to(x, replacement = NULL, verbose = TRUE, ...)  # S3 method for character convert_na_to(x, replacement = NULL, verbose = TRUE, ...)  # S3 method for data.frame convert_na_to(   x,   select = NULL,   exclude = NULL,   replacement = NULL,   replace_num = replacement,   replace_char = replacement,   replace_fac = replacement,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"x numeric, factor, character vector, data frame. ... used. replacement Numeric character value used replace NA. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. replace_num Value replace NA variable type numeric. replace_char Value replace NA variable type character. replace_fac Value replace NA variable type factor. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"x, NA values replaced replacement.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_na_to.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Replace missing values in a variable or a data frame. — convert_na_to","text":"","code":"# Convert NA to 0 in a numeric vector convert_na_to(   c(9, 3, NA, 2, 3, 1, NA, 8),   replacement = 0 ) #> [1] 9 3 0 2 3 1 0 8  # Convert NA to \"missing\" in a character vector convert_na_to(   c(\"a\", NA, \"d\", \"z\", NA, \"t\"),   replacement = \"missing\" ) #> [1] \"a\"       \"missing\" \"d\"       \"z\"       \"missing\" \"t\"        ### For data frames  test_df <- data.frame(   x = c(1, 2, NA),   x2 = c(4, 5, NA),   y = c(\"a\", \"b\", NA) )  # Convert all NA to 0 in numeric variables, and all NA to \"missing\" in # character variables convert_na_to(   test_df,   replace_num = 0,   replace_char = \"missing\" ) #>   x x2       y #> 1 1  4       a #> 2 2  5       b #> 3 0  0 missing  # Convert a specific variable in the data frame convert_na_to(   test_df,   replace_num = 0,   replace_char = \"missing\",   select = \"x\" ) #>   x x2    y #> 1 1  4    a #> 2 2  5    b #> 3 0 NA <NA>  # Convert all variables starting with \"x\" convert_na_to(   test_df,   replace_num = 0,   replace_char = \"missing\",   select = starts_with(\"x\") ) #>   x x2    y #> 1 1  4    a #> 2 2  5    b #> 3 0  0 <NA>  # Convert NA to 1 in variable 'x2' and to 0 in all other numeric # variables convert_na_to(   test_df,   replace_num = 0,   select = list(x2 = 1) ) #>   x x2    y #> 1 1  4    a #> 2 2  5    b #> 3 0  1 <NA>"},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert non-missing values in a variable into missing values. — convert_to_na","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"Convert non-missing values variable missing values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"","code":"convert_to_na(x, ...)  # S3 method for numeric convert_to_na(x, na = NULL, verbose = TRUE, ...)  # S3 method for factor convert_to_na(x, na = NULL, drop_levels = FALSE, verbose = TRUE, ...)  # S3 method for data.frame convert_to_na(   x,   select = NULL,   exclude = NULL,   na = NULL,   drop_levels = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"x vector, factor data frame. ... used. na Numeric, character vector logical (list numeric, character vectors logicals) values converted NA. Numeric values applied numeric vectors, character values used factors, character vectors date variables, logical values logical vectors. verbose Toggle warnings. drop_levels Logical, factors, specific levels replaced NA, unused levels dropped? select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"x, values na converted NA.","code":""},{"path":"https://easystats.github.io/datawizard/reference/convert_to_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert non-missing values in a variable into missing values. — convert_to_na","text":"","code":"x <- sample(1:6, size = 30, replace = TRUE) x #>  [1] 5 5 6 3 1 4 6 1 6 1 3 6 4 1 6 6 3 6 5 3 6 2 5 5 3 2 2 2 4 2 # values 4 and 5 to NA convert_to_na(x, na = 4:5) #>  [1] NA NA  6  3  1 NA  6  1  6  1  3  6 NA  1  6  6  3  6 NA  3  6  2 NA NA  3 #> [26]  2  2  2 NA  2  # data frames set.seed(123) x <- data.frame(   a = sample(1:6, size = 20, replace = TRUE),   b = sample(letters[1:6], size = 20, replace = TRUE),   c = sample(c(30:33, 99), size = 20, replace = TRUE) ) # for all numerics, convert 5 to NA. Character/factor will be ignored. convert_to_na(x, na = 5) #> Could not convert values into `NA` for a factor or character variable. #>   To do this, `na` needs to be a character vector, or a list that contains #>   character vector elements. #>     a b  c #> 1   3 a 33 #> 2   6 e 99 #> 3   3 c 99 #> 4   2 b 32 #> 5   2 b 30 #> 6   6 a 31 #> 7   3 f 99 #> 8  NA c 99 #> 9   4 d 33 #> 10  6 f 99 #> 11  6 a 31 #> 12  1 c 30 #> 13  2 e 30 #> 14  3 d 32 #> 15 NA b 30 #> 16  3 e 99 #> 17  3 a 30 #> 18  1 a 31 #> 19  4 b 33 #> 20  1 c 33  # for numerics, 5 to NA, for character/factor, \"f\" to NA convert_to_na(x, na = list(6, \"f\")) #>     a    b  c #> 1   3    a 33 #> 2  NA    e 99 #> 3   3    c 99 #> 4   2    b 32 #> 5   2    b 30 #> 6  NA    a 31 #> 7   3 <NA> 99 #> 8   5    c 99 #> 9   4    d 33 #> 10 NA <NA> 99 #> 11 NA    a 31 #> 12  1    c 30 #> 13  2    e 30 #> 14  3    d 32 #> 15  5    b 30 #> 16  3    e 99 #> 17  3    a 30 #> 18  1    a 31 #> 19  4    b 33 #> 20  1    c 33  # select specific variables convert_to_na(x, select = c(\"a\", \"b\"), na = list(6, \"f\")) #>     a    b  c #> 1   3    a 33 #> 2  NA    e 99 #> 3   3    c 99 #> 4   2    b 32 #> 5   2    b 30 #> 6  NA    a 31 #> 7   3 <NA> 99 #> 8   5    c 99 #> 9   4    d 33 #> 10 NA <NA> 99 #> 11 NA    a 31 #> 12  1    c 30 #> 13  2    e 30 #> 14  3    d 32 #> 15  5    b 30 #> 16  3    e 99 #> 17  3    a 30 #> 18  1    a 31 #> 19  4    b 33 #> 20  1    c 33"},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange rows by column values — data_arrange","title":"Arrange rows by column values — data_arrange","text":"data_arrange() orders rows data frame values selected columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange rows by column values — data_arrange","text":"","code":"data_arrange(data, select = NULL, safe = TRUE)"},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange rows by column values — data_arrange","text":"data data frame, object can coerced data frame. select Character vector column names. Use dash just column name arrange decreasing order, example \"-x1\". safe throw error one variables specified exist.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange rows by column values — data_arrange","text":"data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_arrange.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange rows by column values — data_arrange","text":"","code":"# Arrange using several variables data_arrange(head(mtcars), c(\"gear\", \"carb\")) #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  # Arrange in decreasing order data_arrange(head(mtcars), \"-carb\") #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  # Throw an error if one of the variables specified doesn't exist try(data_arrange(head(mtcars), c(\"gear\", \"foo\"), safe = FALSE)) #> Error : The following column(s) don't exist in the dataset: foo. #>   Possibly misspelled?"},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a codebook of a data frame. — data_codebook","title":"Generate a codebook of a data frame. — data_codebook","text":"data_codebook() generates codebooks data frames, .e. overviews variables information variable (like labels, values value range, frequencies, amount missing values).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a codebook of a data frame. — data_codebook","text":"","code":"data_codebook(   data,   select = NULL,   exclude = NULL,   variable_label_width = NULL,   value_label_width = NULL,   max_values = 10,   range_at = 6,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  # S3 method for data_codebook print_html(   x,   font_size = \"100%\",   line_padding = 3,   row_color = \"#eeeeee\",   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a codebook of a data frame. — data_codebook","text":"data data frame, object can coerced data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. variable_label_width Length variable labels. Longer labels wrapped variable_label_width chars. NULL, longer labels split multiple lines. applies labelled data. value_label_width Length value labels. Longer labels shortened, remaining part truncated. applies labelled data factor levels. max_values Number maximum values displayed. Can used avoid many rows variables lots unique values. range_at Indicates many unique values numeric vector needed order print range variable instead frequency table numeric values. Can useful data contains numeric variables unique values full frequency tables instead value ranges displayed. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings messages . ... Arguments passed methods. x (grouped) data frame, vector statistical model (unstandardize() model). font_size HTML tables, font size. line_padding HTML tables, distance (pixel) lines. row_color HTML tables, fill color odd rows.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a codebook of a data frame. — data_codebook","text":"formatted data frame, summarizing content data frame. Returned columns include column index variables original data frame (ID), column name, variable label (data labelled), type variable, number missing values, unique values (value range), value labels (labelled data), frequency table (N value). columns formatted character vectors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate a codebook of a data frame. — data_codebook","text":"methods print() data frame nicer output, well methods printing markdown HTML format (print_md() print_html()).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_codebook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a codebook of a data frame. — data_codebook","text":"","code":"data(iris) data_codebook(iris, select = starts_with(\"Sepal\")) #> iris (150 rows and 5 variables, 2 shown) #>  #> ID | Name         | Type    | Missings |     Values |   N #> ---+--------------+---------+----------+------------+---- #> 1  | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150 #> ---+--------------+---------+----------+------------+---- #> 2  | Sepal.Width  | numeric | 0 (0.0%) |   [2, 4.4] | 150 #> ---------------------------------------------------------  data(efc) data_codebook(efc) #> efc (100 rows and 5 variables, 5 shown) #>  #> ID | Name     | Label                                    | Type        |   Missings |   Values | Value Labels                    |          N #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 1  | c12hour  | average number of hours of care per week | numeric     |   2 (2.0%) | [5, 168] |                                 |         98 #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 2  | e16sex   | elder's gender                           | numeric     |   0 (0.0%) |        1 | male                            | 46 (46.0%) #>    |          |                                          |             |            |        2 | female                          | 54 (54.0%) #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 3  | e42dep   | elder's dependency                       | categorical |   3 (3.0%) |        1 | independent                     |  2 ( 2.1%) #>    |          |                                          |             |            |        2 | slightly dependent              |  4 ( 4.1%) #>    |          |                                          |             |            |        3 | moderately dependent            | 28 (28.9%) #>    |          |                                          |             |            |        4 | severely dependent              | 63 (64.9%) #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 4  | c172code | carer's level of education               | numeric     | 10 (10.0%) |        1 | low level of education          |  8 ( 8.9%) #>    |          |                                          |             |            |        2 | intermediate level of education | 66 (73.3%) #>    |          |                                          |             |            |        3 | high level of education         | 16 (17.8%) #> ---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+----------- #> 5  | neg_c_7  | Negative impact with 7 items             | numeric     |   3 (3.0%) |  [7, 28] |                                 |         97 #> ---------------------------------------------------------------------------------------------------------------------------------------------  # shorten labels data_codebook(efc, variable_label_width = 20, value_label_width = 15) #> efc (100 rows and 5 variables, 5 shown) #>  #> ID | Name     | Label              | Type        |   Missings |   Values | Value Labels     |          N #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 1  | c12hour  | average number of  | numeric     |   2 (2.0%) | [5, 168] |                  |         98 #>    |          | hours of care per  |             |            |          |                  |            #>    |          | week               |             |            |          |                  |            #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 2  | e16sex   | elder's gender     | numeric     |   0 (0.0%) |        1 | male             | 46 (46.0%) #>    |          |                    |             |            |        2 | female           | 54 (54.0%) #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 3  | e42dep   | elder's dependency | categorical |   3 (3.0%) |        1 | independent      |  2 ( 2.1%) #>    |          |                    |             |            |        2 | slightly...      |  4 ( 4.1%) #>    |          |                    |             |            |        3 | moderately...    | 28 (28.9%) #>    |          |                    |             |            |        4 | severely...      | 63 (64.9%) #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 4  | c172code | carer's level of   | numeric     | 10 (10.0%) |        1 | low level of...  |  8 ( 8.9%) #>    |          | education          |             |            |        2 | intermediate...  | 66 (73.3%) #>    |          |                    |             |            |        3 | high level of... | 16 (17.8%) #> ---+----------+--------------------+-------------+------------+----------+------------------+----------- #> 5  | neg_c_7  | Negative impact    | numeric     |   3 (3.0%) |  [7, 28] |                  |         97 #>    |          | with 7 items       |             |            |          |                  |            #> --------------------------------------------------------------------------------------------------------  # automatic range for numerics at more than 5 unique values data(mtcars) data_codebook(mtcars, select = starts_with(\"c\")) #> mtcars (32 rows and 11 variables, 2 shown) #>  #> ID | Name | Type    | Missings | Values |          N #> ---+------+---------+----------+--------+----------- #> 2  | cyl  | numeric | 0 (0.0%) |      4 | 11 (34.4%) #>    |      |         |          |      6 |  7 (21.9%) #>    |      |         |          |      8 | 14 (43.8%) #> ---+------+---------+----------+--------+----------- #> 11 | carb | numeric | 0 (0.0%) | [1, 8] |         32 #> ----------------------------------------------------  # force all values to be displayed data_codebook(mtcars, select = starts_with(\"c\"), range_at = 100) #> mtcars (32 rows and 11 variables, 2 shown) #>  #> ID | Name | Type    | Missings | Values |          N #> ---+------+---------+----------+--------+----------- #> 2  | cyl  | numeric | 0 (0.0%) |      4 | 11 (34.4%) #>    |      |         |          |      6 |  7 (21.9%) #>    |      |         |          |      8 | 14 (43.8%) #> ---+------+---------+----------+--------+----------- #> 11 | carb | numeric | 0 (0.0%) |      1 |  7 (21.9%) #>    |      |         |          |      2 | 10 (31.2%) #>    |      |         |          |      3 |  3 ( 9.4%) #>    |      |         |          |      4 | 10 (31.2%) #>    |      |         |          |      6 |  1 ( 3.1%) #>    |      |         |          |      8 |  1 ( 3.1%) #> ----------------------------------------------------"},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract all duplicates — data_duplicated","title":"Extract all duplicates — data_duplicated","text":"Extract duplicates, visual inspection. Note also contains first occurrence future duplicates, unlike duplicated() dplyr::distinct()). Also contains additional column reporting number missing values row, help decision-making selecting duplicates keep.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract all duplicates — data_duplicated","text":"","code":"data_duplicated(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract all duplicates — data_duplicated","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract all duplicates — data_duplicated","text":"dataframe, containing duplicates.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_duplicated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract all duplicates — data_duplicated","text":"","code":"df1 <- data.frame(   id = c(1, 2, 3, 1, 3),   year = c(2022, 2022, 2022, 2022, 2000),   item1 = c(NA, 1, 1, 2, 3),   item2 = c(NA, 1, 1, 2, 3),   item3 = c(NA, 1, 1, 2, 3) )  data_duplicated(df1, select = \"id\") #>   Row id year item1 item2 item3 count_na #> 1   1  1 2022    NA    NA    NA        3 #> 4   4  1 2022     2     2     2        0 #> 3   3  3 2022     1     1     1        0 #> 5   5  3 2000     3     3     3        0  data_duplicated(df1, select = c(\"id\", \"year\")) #>   Row id year item1 item2 item3 count_na #> 1   1  1 2022    NA    NA    NA        3 #> 4   4  1 2022     2     2     2        0  # Filter to exclude duplicates df2 <- df1[-c(1, 5), ] df2 #>   id year item1 item2 item3 #> 2  2 2022     1     1     1 #> 3  3 2022     1     1     1 #> 4  1 2022     2     2     2"},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract one or more columns or elements from an object — data_extract","title":"Extract one or more columns or elements from an object — data_extract","text":"data_extract() (alias extract()) similar $. extracts either single column element object (e.g., data frame, list), multiple columns resp. elements.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract one or more columns or elements from an object — data_extract","text":"","code":"data_extract(data, select, ...)  # S3 method for data.frame data_extract(   data,   select,   name = NULL,   extract = \"all\",   as_data_frame = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract one or more columns or elements from an object — data_extract","text":"data object subset. Methods currently available data frames data frame extensions (e.g., tibbles). select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". ... use future methods. name optional argument specifies column used names vector elements extraction. Must specified either literal variable name (e.g., column_name) string (\"column_name\"). name ignored data frame returned. extract String, indicating element extracted select matches multiple variables. Can \"\" (default) return matched variables, \"first\" \"last\" return first last match, \"odd\" \"even\" return odd-numbered even-numbered matches. Note \"first\" \"last\" return vector (unless as_data_frame = TRUE), \"\" can return vector (one match found) data frame (one match). Type safe return values possible extract \"first\" \"last\" (always return vector) as_data_frame = TRUE (always returns data frame). as_data_frame Logical, TRUE, always return data frame, even one variable matched. FALSE, either returns vector data frame. See extract details. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract one or more columns or elements from an object — data_extract","text":"vector (data frame) containing extracted element, NULL matching variable found.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract one or more columns or elements from an object — data_extract","text":"data_extract() can used select multiple variables pull single variable data frame. Thus, return value default type safe - data_extract() either returns vector data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"extracting-single-variables-vectors-","dir":"Reference","previous_headings":"","what":"Extracting single variables (vectors)","title":"Extract one or more columns or elements from an object — data_extract","text":"select name single column, select matches one column, vector returned. single variable also returned extract either \"first \"last\". Setting as_data_frame TRUE overrides behaviour always returns data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"extracting-a-data-frame-of-variables","dir":"Reference","previous_headings":"","what":"Extracting a data frame of variables","title":"Extract one or more columns or elements from an object — data_extract","text":"select character vector containing one column name (numeric vector one valid column indices), select uses one supported select-helpers match multiple columns, data frame returned. Setting as_data_frame TRUE always returns data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_extract.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract one or more columns or elements from an object — data_extract","text":"","code":"# single variable data_extract(mtcars, cyl, name = gear) #> 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4  #> 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4  data_extract(mtcars, \"cyl\", name = gear) #> 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4  #> 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4  data_extract(mtcars, -1, name = gear) #>                     cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4             6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag         6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710            4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive        6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Hornet Sportabout     8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Valiant               6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Duster 360            8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 240D             4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> Merc 230              4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> Merc 280              6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C             6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Merc 450SE            8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL            8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC           8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood    8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial     8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Fiat 128              4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic           4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla        4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Toyota Corona         4 120.1  97 3.70 2.465 20.01  1  0    3    1 #> Dodge Challenger      8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin           8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28            8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird      8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Fiat X1-9             4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2         4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa          4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L        8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino          6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora         8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E            4 121.0 109 4.11 2.780 18.60  1  1    4    2 data_extract(mtcars, cyl, name = 0) #>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive  #>                   6                   6                   4                   6  #>   Hornet Sportabout             Valiant          Duster 360           Merc 240D  #>                   8                   6                   8                   4  #>            Merc 230            Merc 280           Merc 280C          Merc 450SE  #>                   4                   6                   6                   8  #>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental  #>                   8                   8                   8                   8  #>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla  #>                   8                   4                   4                   4  #>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28  #>                   4                   8                   8                   8  #>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa  #>                   8                   4                   4                   4  #>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E  #>                   8                   6                   8                   4  data_extract(mtcars, cyl, name = \"row.names\") #>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive  #>                   6                   6                   4                   6  #>   Hornet Sportabout             Valiant          Duster 360           Merc 240D  #>                   8                   6                   8                   4  #>            Merc 230            Merc 280           Merc 280C          Merc 450SE  #>                   4                   6                   6                   8  #>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental  #>                   8                   8                   8                   8  #>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla  #>                   8                   4                   4                   4  #>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28  #>                   4                   8                   8                   8  #>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa  #>                   8                   4                   4                   4  #>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E  #>                   8                   6                   8                   4   # selecting multiple variables head(data_extract(iris, starts_with(\"Sepal\"))) #>   Sepal.Length Sepal.Width #> 1          5.1         3.5 #> 2          4.9         3.0 #> 3          4.7         3.2 #> 4          4.6         3.1 #> 5          5.0         3.6 #> 6          5.4         3.9 head(data_extract(iris, ends_with(\"Width\"))) #>   Sepal.Width Petal.Width #> 1         3.5         0.2 #> 2         3.0         0.2 #> 3         3.2         0.2 #> 4         3.1         0.2 #> 5         3.6         0.2 #> 6         3.9         0.4 head(data_extract(iris, 2:4)) #>   Sepal.Width Petal.Length Petal.Width #> 1         3.5          1.4         0.2 #> 2         3.0          1.4         0.2 #> 3         3.2          1.3         0.2 #> 4         3.1          1.5         0.2 #> 5         3.6          1.4         0.2 #> 6         3.9          1.7         0.4  # select first of multiple variables data_extract(iris, starts_with(\"Sepal\"), extract = \"first\") #>   [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1 #>  [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0 #>  [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5 #>  [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1 #>  [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5 #>  [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 #> [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2 #> [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 #> [145] 6.7 6.7 6.3 6.5 6.2 5.9  # select first of multiple variables, return as data frame head(data_extract(iris, starts_with(\"Sepal\"), extract = \"first\", as_data_frame = TRUE)) #>   Sepal.Length #> 1          5.1 #> 2          4.9 #> 3          4.7 #> 4          4.6 #> 5          5.0 #> 6          5.4"},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a grouped data frame — data_group","title":"Create a grouped data frame — data_group","text":"function comparable dplyr::group_by(), just following datawizard function design. data_ungroup() removes grouping information grouped data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a grouped data frame — data_group","text":"","code":"data_group(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_ungroup(data, verbose = TRUE, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a grouped data frame — data_group","text":"data data frame select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings. ... Arguments passed functions. Mostly used yet.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a grouped data frame — data_group","text":"grouped data frame, .e. data frame additional information grouping structure saved attributes.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_group.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a grouped data frame — data_group","text":"","code":"data(efc) suppressPackageStartupMessages(library(poorman, quietly = TRUE))  # total mean efc %>%   summarize(mean_hours = mean(c12hour, na.rm = TRUE)) #>   mean_hours #> 1   85.65306  # mean by educational level efc %>%   data_group(c172code) %>%   summarize(mean_hours = mean(c12hour, na.rm = TRUE)) #> # A tibble: 3 × 2 #> # Groups:   c172code [3] #>   c172code mean_hours #>      <dbl>      <dbl> #> 1        1       87.1 #> 2        2       94.0 #> 3        3       75"},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":null,"dir":"Reference","previous_headings":"","what":"Return filtered or sliced data frame, or row indices — data_match","title":"Return filtered or sliced data frame, or row indices — data_match","text":"Return filtered (sliced) data frame row indices data frame match specific condition. data_filter() works like data_match(), works logical expressions row indices data frame specify matching conditions.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return filtered or sliced data frame, or row indices — data_match","text":"","code":"data_match(x, to, match = \"and\", return_indices = FALSE, drop_na = TRUE, ...)  data_filter(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return filtered or sliced data frame, or row indices — data_match","text":"x data frame. data frame matching specified conditions. Note match value \"\", original row order might changed. See 'Details'. match String, indicating logical operation matching conditions combined. Can \"\" (\"&\"), \"\" (\"|\") \"\" (\"!\"). return_indices Logical, FALSE, return vector rows can used filter original data frame. FALSE (default), returns directly filtered data frame instead row indices. drop_na Logical, TRUE, missing values (NAs) removed filtering data. default behaviour, however, sometimes row indices requested (.e. return_indices=TRUE), might useful preserve NA values, returned row indices match row indices original data frame. ... sequence logical expressions indicating rows keep, numeric vector indicating row indices rows keep. Can also string representation logical expression (e.g. \"x > 4\"), character vector (e.g. c(\"x > 4\", \"y == 2\")) variable contains string representation logical expression. might useful used packages avoid defining undefined global variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return filtered or sliced data frame, or row indices — data_match","text":"filtered data frame, row indices match specified configuration.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Return filtered or sliced data frame, or row indices — data_match","text":"data_match(), match either \"\" \"\", original row order x might changed. preserving row order required, use data_filter() instead.   data_match() works data frames match conditions , data_filter() basically wrapper around subset(subset = <filter>). However, unlike subset(), preserves label attributes useful working labelled data.","code":"# mimics subset() behaviour, preserving original row order head(data_filter(mtcars[c(\"mpg\", \"vs\", \"am\")], vs == 0 | am == 1)) #>                    mpg vs am #> Mazda RX4         21.0  0  1 #> Mazda RX4 Wag     21.0  0  1 #> Datsun 710        22.8  1  1 #> Hornet Sportabout 18.7  0  0 #> Duster 360        14.3  0  0 #> Merc 450SE        16.4  0  0  # re-sorting rows head(data_match(mtcars[c(\"mpg\", \"vs\", \"am\")],                 data.frame(vs = 0, am = 1),                 match = \"or\")) #>                    mpg vs am #> Mazda RX4         21.0  0  1 #> Mazda RX4 Wag     21.0  0  1 #> Hornet Sportabout 18.7  0  0 #> Duster 360        14.3  0  0 #> Merc 450SE        16.4  0  0 #> Merc 450SL        17.3  0  0"},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_match.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return filtered or sliced data frame, or row indices — data_match","text":"","code":"data_match(mtcars, data.frame(vs = 0, am = 1)) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 data_match(mtcars, data.frame(vs = 0, am = c(0, 1))) #>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8  # observations where \"vs\" is NOT 0 AND \"am\" is NOT 1 data_match(mtcars, data.frame(vs = 0, am = 1), match = \"not\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1 # equivalent to data_filter(mtcars, vs != 0 & am != 1) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  # observations where EITHER \"vs\" is 0 OR \"am\" is 1 data_match(mtcars, data.frame(vs = 0, am = 1), match = \"or\") #>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2 # equivalent to data_filter(mtcars, vs == 0 | am == 1) #>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  # slice data frame by row indices data_filter(mtcars, 5:10) #>                    mpg cyl  disp  hp drat   wt  qsec vs am gear carb #> Hornet Sportabout 18.7   8 360.0 175 3.15 3.44 17.02  0  0    3    2 #> Valiant           18.1   6 225.0 105 2.76 3.46 20.22  1  0    3    1 #> Duster 360        14.3   8 360.0 245 3.21 3.57 15.84  0  0    3    4 #> Merc 240D         24.4   4 146.7  62 3.69 3.19 20.00  1  0    4    2 #> Merc 230          22.8   4 140.8  95 3.92 3.15 22.90  1  0    4    2 #> Merc 280          19.2   6 167.6 123 3.92 3.44 18.30  1  0    4    4  # Define a custom function containing data_filter() my_filter <- function(data, variable) {   data_filter(data, variable) } my_filter(mtcars, \"cyl == 6\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  # Pass complete filter-condition as string. my_filter <- function(data, condition) {   data_filter(data, condition) } my_filter(mtcars, \"am != 0\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  # string can also be used directly as argument data_filter(mtcars, \"am != 0\") #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  # or as variable fl <- \"am != 0\" data_filter(mtcars, fl) #>                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2"},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge (join) two data frames, or a list of data frames — data_merge","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Merge (join) two data frames, list data frames. However, unlike base R's merge(), data_merge() offers methods join data frames, drop data frame column attributes.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"","code":"data_merge(x, ...)  data_join(x, ...)  # S3 method for data.frame data_merge(x, y, join = \"left\", by = NULL, id = NULL, verbose = TRUE, ...)  # S3 method for list data_merge(x, join = \"left\", by = NULL, id = NULL, verbose = TRUE, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"x, y data frame merge. x may also list data frames merged. Note list-method y argument. ... used. join Character vector, indicating method joining data frames. Can \"full\", \"left\" (default), \"right\", \"inner\", \"anti\", \"semi\" \"bind\". See details . Specifications columns used merging. id Optional name ID column created indicate source data frames appended rows. applies join = \"bind\". verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"merged data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"merging-data-frames","dir":"Reference","previous_headings":"","what":"Merging data frames","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Merging data frames performed adding rows (cases), columns (variables) source data frame (y) target data frame (x). usually requires one variables included data frames used merging, typically indicated argument. contains variable present data frames, cases matched filtered identical values x y.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"left-and-right-joins","dir":"Reference","previous_headings":"","what":"Left- and right-joins","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Left- right joins usually add new rows (cases), new columns (variables) existing cases x. join = \"left\" join = \"right\" work, must indicate one columns included data frames. join = \"left\", identifier variable, included x y, variables y copied x, cases y matching values identifier variable x (.e. cases x also found y get related values new columns y). match identifiers x y, copied variable y get NA value particular case. variables occur x y, used identifiers (), renamed avoid multiple identical variable names. Cases y values identifier match x's identifier removed. join = \"right\" works similar way join = \"left\", just cases x matching values identifier variable y chosen.  base R, equivalent merge(x, y, .x = TRUE) merge(x, y, .y = TRUE).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"full-joins","dir":"Reference","previous_headings":"","what":"Full joins","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Full joins copy cases y x. matching cases data frames, values new variables copied y x. cases y present x, added new rows x. Thus, full joins add new columns (variables), also might add new rows (cases).  base R, equivalent merge(x, y, = TRUE).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"inner-joins","dir":"Reference","previous_headings":"","what":"Inner joins","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"Inner joins merge two data frames, however, rows (cases) kept present data frames. Thus, inner joins usually add new columns (variables), also remove rows (cases) occur one data frame.  base R, equivalent merge(x, y).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"binds","dir":"Reference","previous_headings":"","what":"Binds","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"join = \"bind\" row-binds complete second data frame y x. Unlike simple rbind(), requires columns data frames, join = \"bind\" bind shared columns y x, add new columns y x.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_merge.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge (join) two data frames, or a list of data frames — data_merge","text":"","code":"x <- data.frame(a = 1:3, b = c(\"a\", \"b\", \"c\"), c = 5:7, id = 1:3) y <- data.frame(c = 6:8, d = c(\"f\", \"g\", \"h\"), e = 100:102, id = 2:4)  x #>   a b c id #> 1 1 a 5  1 #> 2 2 b 6  2 #> 3 3 c 7  3 y #>   c d   e id #> 1 6 f 100  2 #> 2 7 g 101  3 #> 3 8 h 102  4  # \"by\" will default to all shared columns, i.e. \"c\" and \"id\". new columns # \"d\" and \"e\" will be copied from \"y\" to \"x\", but there are only two cases # in \"x\" that have the same values for \"c\" and \"id\" in \"y\". only those cases # have values in the copied columns, the other case gets \"NA\". data_merge(x, y, join = \"left\") #>   a b c id    d   e #> 3 1 a 5  1 <NA>  NA #> 1 2 b 6  2    f 100 #> 2 3 c 7  3    g 101  # we change the id-value here x <- data.frame(a = 1:3, b = c(\"a\", \"b\", \"c\"), c = 5:7, id = 1:3) y <- data.frame(c = 6:8, d = c(\"f\", \"g\", \"h\"), e = 100:102, id = 3:5)  x #>   a b c id #> 1 1 a 5  1 #> 2 2 b 6  2 #> 3 3 c 7  3 y #>   c d   e id #> 1 6 f 100  3 #> 2 7 g 101  4 #> 3 8 h 102  5  # no cases in \"y\" have the same matching \"c\" and \"id\" as in \"x\", thus # copied variables from \"y\" to \"x\" copy no values, all get NA. data_merge(x, y, join = \"left\") #>   a b c id    d  e #> 1 1 a 5  1 <NA> NA #> 2 2 b 6  2 <NA> NA #> 3 3 c 7  3 <NA> NA  # one case in \"y\" has a match in \"id\" with \"x\", thus values for this # case from the remaining variables in \"y\" are copied to \"x\", all other # values (cases) in those remaining variables get NA data_merge(x, y, join = \"left\", by = \"id\") #>   a b id    d   e c.x c.y #> 2 1 a  1 <NA>  NA   5  NA #> 3 2 b  2 <NA>  NA   6  NA #> 1 3 c  3    f 100   7   6  data(mtcars) x <- mtcars[1:5, 1:3] y <- mtcars[28:32, 4:6]  # add ID common column x$id <- 1:5 y$id <- 3:7  # left-join, add new variables and copy values from y to x, # where \"id\" values match data_merge(x, y) #>    mpg cyl disp id  hp drat    wt #> 4 21.0   6  160  1  NA   NA    NA #> 5 21.0   6  160  2  NA   NA    NA #> 1 22.8   4  108  3 113 3.77 1.513 #> 2 21.4   6  258  4 264 4.22 3.170 #> 3 18.7   8  360  5 175 3.62 2.770  # right-join, add new variables and copy values from x to y, # where \"id\" values match data_merge(x, y, join = \"right\") #>    mpg cyl disp id  hp drat    wt #> 1 22.8   4  108  3 113 3.77 1.513 #> 2 21.4   6  258  4 264 4.22 3.170 #> 3 18.7   8  360  5 175 3.62 2.770 #> 4   NA  NA   NA  6 335 3.54 3.570 #> 5   NA  NA   NA  7 109 4.11 2.780  # full-join data_merge(x, y, join = \"full\") #>    mpg cyl disp id  hp drat    wt #> 4 21.0   6  160  1  NA   NA    NA #> 5 21.0   6  160  2  NA   NA    NA #> 1 22.8   4  108  3 113 3.77 1.513 #> 2 21.4   6  258  4 264 4.22 3.170 #> 3 18.7   8  360  5 175 3.62 2.770 #> 6   NA  NA   NA  6 335 3.54 3.570 #> 7   NA  NA   NA  7 109 4.11 2.780   data(mtcars) x <- mtcars[1:5, 1:3] y <- mtcars[28:32, c(1, 4:5)]  # add ID common column x$id <- 1:5 y$id <- 3:7  # left-join, no matching rows (because columns \"id\" and \"disp\" are used) # new variables get all NA values data_merge(x, y) #>    mpg cyl disp id hp drat #> 1 21.0   6  160  1 NA   NA #> 2 21.0   6  160  2 NA   NA #> 3 22.8   4  108  3 NA   NA #> 4 21.4   6  258  4 NA   NA #> 5 18.7   8  360  5 NA   NA  # one common value in \"mpg\", so one row from y is copied to x data_merge(x, y, by = \"mpg\") #>    mpg cyl disp  hp drat id.x id.y #> 2 21.0   6  160  NA   NA    1   NA #> 3 21.0   6  160  NA   NA    2   NA #> 4 22.8   4  108  NA   NA    3   NA #> 1 21.4   6  258 109 4.11    4    7 #> 5 18.7   8  360  NA   NA    5   NA  # only keep rows with matching values in by-column data_merge(x, y, join = \"semi\", by = \"mpg\") #>                 mpg cyl disp id #> Hornet 4 Drive 21.4   6  258  4  # only keep rows with non-matching values in by-column data_merge(x, y, join = \"anti\", by = \"mpg\") #>                    mpg cyl disp id #> Mazda RX4         21.0   6  160  1 #> Mazda RX4 Wag     21.0   6  160  2 #> Datsun 710        22.8   4  108  3 #> Hornet Sportabout 18.7   8  360  5  # merge list of data frames. can be of different rows x <- mtcars[1:5, 1:3] y <- mtcars[28:31, 3:5] z <- mtcars[11:18, c(1, 3:4, 6:8)] x$id <- 1:5 y$id <- 4:7 z$id <- 3:10 data_merge(list(x, y, z), join = \"bind\", by = \"id\", id = \"source\") #>     mpg cyl  disp id  hp drat    wt  qsec vs source #> 1  21.0   6 160.0  1  NA   NA    NA    NA NA      1 #> 2  21.0   6 160.0  2  NA   NA    NA    NA NA      1 #> 3  22.8   4 108.0  3  NA   NA    NA    NA NA      1 #> 4  21.4   6 258.0  4  NA   NA    NA    NA NA      1 #> 5  18.7   8 360.0  5  NA   NA    NA    NA NA      1 #> 6    NA  NA  95.1  4 113 3.77    NA    NA NA      2 #> 7    NA  NA 351.0  5 264 4.22    NA    NA NA      2 #> 8    NA  NA 145.0  6 175 3.62    NA    NA NA      2 #> 9    NA  NA 301.0  7 335 3.54    NA    NA NA      2 #> 10 17.8  NA 167.6  3 123   NA 3.440 18.90  1      3 #> 11 16.4  NA 275.8  4 180   NA 4.070 17.40  0      3 #> 12 17.3  NA 275.8  5 180   NA 3.730 17.60  0      3 #> 13 15.2  NA 275.8  6 180   NA 3.780 18.00  0      3 #> 14 10.4  NA 472.0  7 205   NA 5.250 17.98  0      3 #> 15 10.4  NA 460.0  8 215   NA 5.424 17.82  0      3 #> 16 14.7  NA 440.0  9 230   NA 5.345 17.42  0      3 #> 17 32.4  NA  78.7 10  66   NA 2.200 19.47  1      3"},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":null,"dir":"Reference","previous_headings":"","what":"Create new variables in a data frame — data_modify","title":"Create new variables in a data frame — data_modify","text":"Create new variables modify existing variables data frame. Unlike base::transform(), data_modify() can used grouped data frames, newly created variables can directly used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create new variables in a data frame — data_modify","text":"","code":"data_modify(data, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create new variables in a data frame — data_modify","text":"data data frame ... One expressions define new variable name values recoding new variables. expressions can one : sequence named, literal expressions, left-hand side refers name new variable, right-hand side represent values new variable. Example: Sepal.Width = center(Sepal.Width). sequence string values, representing expressions. variable contains string representation expression. Example:   character vector expressions. Example: c(\"SW_double = 2 * Sepal.Width\", \"SW_fraction = SW_double / 10\"). type expression mixed expressions, .e. character vector provided, may add elements .... Using NULL right-hand side removes variable data frame. Example: Petal.Width = NULL. Note newly created variables can used subsequent expressions. See also 'Examples'.","code":"a <- \"2 * Sepal.Width\" data_modify(iris, a)"},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create new variables in a data frame — data_modify","text":"data_modify() can also used inside functions. However, recommended pass recode-expression character vector list characters.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_modify.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create new variables in a data frame — data_modify","text":"","code":"data(efc) new_efc <- data_modify(   efc,   c12hour_c = center(c12hour),   c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),   c12hour_z2 = standardize(c12hour) ) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z c12hour_z2 #> 1      16      2      3        2      12 -69.65306 -1.0466657 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657 -1.0466657  # using strings instead of literal expressions new_efc <- data_modify(   efc,   \"c12hour_c = center(c12hour)\",   \"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)\",   \"c12hour_z2 = standardize(c12hour)\" ) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z c12hour_z2 #> 1      16      2      3        2      12 -69.65306 -1.0466657 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657 -1.0466657  # using character strings, provided as variable stand <- \"c12hour_c / sd(c12hour, na.rm = TRUE)\" new_efc <- data_modify(   efc,   c12hour_c = center(c12hour),   c12hour_z = stand ) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z #> 1      16      2      3        2      12 -69.65306 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657  # providing expressions as character vector new_exp <- c(   \"c12hour_c = center(c12hour)\",   \"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)\" ) new_efc <- data_modify(efc, new_exp) head(new_efc) #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c  c12hour_z #> 1      16      2      3        2      12 -69.65306 -1.0466657 #> 2     148      2      3        2      20  62.34694  0.9368777 #> 3      70      2      3        1      11 -15.65306 -0.2352161 #> 4      NA      2   <NA>        2      10        NA         NA #> 5     168      2      4        2      12  82.34694  1.2374146 #> 6      16      2      4        2      19 -69.65306 -1.0466657  # attributes - in this case, value and variable labels - are preserved str(new_efc) #> 'data.frame':\t100 obs. of  7 variables: #>  $ c12hour  : num  16 148 70 NA 168 16 161 110 28 40 ... #>   ..- attr(*, \"label\")= chr \"average number of hours of care per week\" #>  $ e16sex   : num  2 2 2 2 2 2 1 2 2 2 ... #>   ..- attr(*, \"label\")= chr \"elder's gender\" #>   ..- attr(*, \"labels\")= Named num [1:2] 1 2 #>   .. ..- attr(*, \"names\")= chr [1:2] \"male\" \"female\" #>  $ e42dep   : Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 3 3 3 NA 4 4 4 4 4 4 ... #>   ..- attr(*, \"labels\")= Named num [1:4] 1 2 3 4 #>   .. ..- attr(*, \"names\")= chr [1:4] \"independent\" \"slightly dependent\" \"moderately dependent\" \"severely dependent\" #>   ..- attr(*, \"label\")= chr \"elder's dependency\" #>  $ c172code : num  2 2 1 2 2 2 2 2 NA 2 ... #>   ..- attr(*, \"label\")= chr \"carer's level of education\" #>   ..- attr(*, \"labels\")= Named num [1:3] 1 2 3 #>   .. ..- attr(*, \"names\")= chr [1:3] \"low level of education\" \"intermediate level of education\" \"high level of education\" #>  $ neg_c_7  : num  12 20 11 10 12 19 15 11 15 10 ... #>   ..- attr(*, \"label\")= chr \"Negative impact with 7 items\" #>  $ c12hour_c: 'dw_transformer' num  -69.7 62.3 -15.7 NA 82.3 ... #>   ..- attr(*, \"center\")= num 85.7 #>   ..- attr(*, \"scale\")= num 1 #>   ..- attr(*, \"robust\")= logi FALSE #>   ..- attr(*, \"label\")= chr \"average number of hours of care per week\" #>  $ c12hour_z: 'dw_transformer' num  -1.047 0.937 -0.235 NA 1.237 ... #>   ..- attr(*, \"center\")= num 85.7 #>   ..- attr(*, \"scale\")= num 1 #>   ..- attr(*, \"robust\")= logi FALSE #>   ..- attr(*, \"label\")= chr \"average number of hours of care per week\"  # overwrite existing variable, remove old variable out <- data_modify(iris, Petal.Length = 1 / Sepal.Length, Sepal.Length = NULL) head(out) #>   Sepal.Width Petal.Length Petal.Width Species #> 1         3.5    0.1960784         0.2  setosa #> 2         3.0    0.2040816         0.2  setosa #> 3         3.2    0.2127660         0.2  setosa #> 4         3.1    0.2173913         0.2  setosa #> 5         3.6    0.2000000         0.2  setosa #> 6         3.9    0.1851852         0.4  setosa  # works on grouped data grouped_efc <- data_group(efc, \"c172code\") new_efc <- data_modify(   grouped_efc,   c12hour_c = center(c12hour),   c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),   c12hour_z2 = standardize(c12hour) ) head(new_efc) #> # A tibble: 6 × 8 #> # Groups:   c172code [2] #>   c12hour e16sex e42dep c172code neg_c_7 c12hour_c c12hour_z c12hour_z2 #>     <dbl>  <dbl> <fct>     <dbl>   <dbl>     <dbl>     <dbl>      <dbl> #> 1      16      2 3             2      12     -78.0    -1.16      -1.16  #> 2     148      2 3             2      20      54.0     0.804      0.804 #> 3      70      2 3             1      11     -17.1    -0.250     -0.250 #> 4      NA      2 NA            2      10      NA      NA         NA     #> 5     168      2 4             2      12      74.0     1.10       1.10  #> 6      16      2 4             2      19     -78.0    -1.16      -1.16   # works from inside functions foo <- function(data, z) {   head(data_modify(data, z)) } foo(iris, \"var_a = Sepal.Width / 10\") #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species var_a #> 1          5.1         3.5          1.4         0.2  setosa  0.35 #> 2          4.9         3.0          1.4         0.2  setosa  0.30 #> 3          4.7         3.2          1.3         0.2  setosa  0.32 #> 4          4.6         3.1          1.5         0.2  setosa  0.31 #> 5          5.0         3.6          1.4         0.2  setosa  0.36 #> 6          5.4         3.9          1.7         0.4  setosa  0.39  new_exp <- c(\"SW_double = 2 * Sepal.Width\", \"SW_fraction = SW_double / 10\") foo(iris, new_exp) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species SW_double #> 1          5.1         3.5          1.4         0.2  setosa       7.0 #> 2          4.9         3.0          1.4         0.2  setosa       6.0 #> 3          4.7         3.2          1.3         0.2  setosa       6.4 #> 4          4.6         3.1          1.5         0.2  setosa       6.2 #> 5          5.0         3.6          1.4         0.2  setosa       7.2 #> 6          5.4         3.9          1.7         0.4  setosa       7.8 #>   SW_fraction #> 1        0.70 #> 2        0.60 #> 3        0.64 #> 4        0.62 #> 5        0.72 #> 6        0.78"},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":null,"dir":"Reference","previous_headings":"","what":"Partition data — data_partition","title":"Partition data — data_partition","text":"Creates data partitions (instance, training test set) based data frame can also stratified (.e., evenly spread given factor) using group argument.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partition data — data_partition","text":"","code":"data_partition(   data,   proportion = 0.7,   group = NULL,   seed = NULL,   row_id = \".row_id\",   verbose = TRUE,   training_proportion = proportion,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partition data — data_partition","text":"data data frame, object can coerced data frame. proportion Scalar (0 1) numeric vector, indicating proportion(s) training set(s). sum proportion must greater 1. remaining part used test set. group character vector indicating name(s) column(s) used stratified partitioning. seed random number generator seed. Enter integer (e.g. 123) random sampling time run function. row_id Character string, indicating name column contains row-id's. verbose Toggle messages warnings. training_proportion Deprecated, please use proportion. ... arguments passed functions.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partition data — data_partition","text":"list data frames. list includes one training set per given proportion remaining data test set. List elements training sets named given proportions (e.g., $p_0.7), test set named $test.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_partition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partition data — data_partition","text":"","code":"data(iris) out <- data_partition(iris, proportion = 0.9) out$test #>    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species .row_id #> 1           4.8         3.4          1.6         0.2     setosa      12 #> 2           5.8         4.0          1.2         0.2     setosa      15 #> 3           4.8         3.1          1.6         0.2     setosa      31 #> 4           5.0         3.5          1.3         0.3     setosa      41 #> 5           6.0         2.2          4.0         1.0 versicolor      63 #> 6           5.6         2.9          3.6         1.3 versicolor      65 #> 7           6.7         3.1          4.4         1.4 versicolor      66 #> 8           6.3         2.5          4.9         1.5 versicolor      73 #> 9           5.5         2.4          3.8         1.1 versicolor      81 #> 10          5.7         2.9          4.2         1.3 versicolor      97 #> 11          6.3         3.3          6.0         2.5  virginica     101 #> 12          6.3         2.9          5.6         1.8  virginica     104 #> 13          6.5         3.0          5.8         2.2  virginica     105 #> 14          5.7         2.5          5.0         2.0  virginica     114 #> 15          6.9         3.1          5.1         2.3  virginica     142 nrow(out$p_0.9) #> [1] 135  # Stratify by group (equal proportions of each species) out <- data_partition(iris, proportion = 0.9, group = \"Species\") out$test #>    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species .row_id #> 1           5.8         4.0          1.2         0.2     setosa      15 #> 2           5.7         4.4          1.5         0.4     setosa      16 #> 3           5.7         3.8          1.7         0.3     setosa      19 #> 4           5.1         3.7          1.5         0.4     setosa      22 #> 5           4.4         3.0          1.3         0.2     setosa      39 #> 6           7.0         3.2          4.7         1.4 versicolor      51 #> 7           6.6         2.9          4.6         1.3 versicolor      59 #> 8           5.6         2.9          3.6         1.3 versicolor      65 #> 9           6.2         2.2          4.5         1.5 versicolor      69 #> 10          6.6         3.0          4.4         1.4 versicolor      76 #> 11          6.3         3.3          6.0         2.5  virginica     101 #> 12          6.5         3.0          5.8         2.2  virginica     105 #> 13          6.3         2.7          4.9         1.8  virginica     124 #> 14          7.2         3.2          6.0         1.8  virginica     126 #> 15          6.7         3.0          5.2         2.3  virginica     146  # Create multiple partitions out <- data_partition(iris, proportion = c(0.3, 0.3)) lapply(out, head) #> $p_0.3 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id #> 1          4.7         3.2          1.3         0.2  setosa       3 #> 2          5.0         3.4          1.5         0.2  setosa       8 #> 3          4.4         2.9          1.4         0.2  setosa       9 #> 4          4.9         3.1          1.5         0.1  setosa      10 #> 5          5.4         3.7          1.5         0.2  setosa      11 #> 6          4.8         3.4          1.6         0.2  setosa      12 #>  #> $p_0.3 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id #> 1          5.0         3.6          1.4         0.2  setosa       5 #> 2          5.4         3.9          1.7         0.4  setosa       6 #> 3          4.6         3.4          1.4         0.3  setosa       7 #> 4          4.3         3.0          1.1         0.1  setosa      14 #> 5          5.4         3.9          1.3         0.4  setosa      17 #> 6          5.7         3.8          1.7         0.3  setosa      19 #>  #> $test #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id #> 1          5.1         3.5          1.4         0.2  setosa       1 #> 2          4.9         3.0          1.4         0.2  setosa       2 #> 3          4.6         3.1          1.5         0.2  setosa       4 #> 4          4.8         3.0          1.4         0.1  setosa      13 #> 5          5.8         4.0          1.2         0.2  setosa      15 #> 6          5.1         3.8          1.5         0.3  setosa      20 #>   # Create multiple partitions, stratified by group - 30% equally sampled # from species in first training set, 50% in second training set and # remaining 20% equally sampled from each species in test set. out <- data_partition(iris, proportion = c(0.3, 0.5), group = \"Species\") lapply(out, function(i) table(i$Species)) #> $p_0.3 #>  #>     setosa versicolor  virginica  #>         15         15         15  #>  #> $p_0.5 #>  #>     setosa versicolor  virginica  #>         25         25         25  #>  #> $test #>  #>     setosa versicolor  virginica  #>         10         10         10  #>"},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":null,"dir":"Reference","previous_headings":"","what":"Peek at values and type of variables in a data frame — data_peek","title":"Peek at values and type of variables in a data frame — data_peek","text":"function creates table data frame, showing column names, variable types first values (many fit screen).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Peek at values and type of variables in a data frame — data_peek","text":"","code":"data_peek(x, ...)  # S3 method for data.frame data_peek(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   width = NULL,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Peek at values and type of variables in a data frame — data_peek","text":"x data frame. ... used. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. width Maximum width line length display. NULL, width determined using options()$width. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Peek at values and type of variables in a data frame — data_peek","text":"data frame three columns, containing information name, type first values input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Peek at values and type of variables in a data frame — data_peek","text":"show specific limited number variables, use select argument, e.g. select = 1:5 show first five variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_peek.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Peek at values and type of variables in a data frame — data_peek","text":"","code":"data(efc) data_peek(efc) #> Data frame with 100 rows and 5 variables #>  #> Variable | Type    | Values                                            #> ---------------------------------------------------------------------- #> c12hour  | numeric | 16, 148, 70, NA, 168, 16, 161, 110, 28, 40, ...   #> e16sex   | numeric | 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, ...  #> e42dep   | factor  | 3, 3, 3, NA, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 3, ... #> c172code | numeric | 2, 2, 1, 2, 2, 2, 2, 2, NA, 2, 2, 2, 3, 1, 3, ... #> neg_c_7  | numeric | 12, 20, 11, 10, 12, 19, 15, 11, 15, 10, 28, ...   # show variables two to four data_peek(efc, select = 2:4) #> Data frame with 100 rows and 5 variables #>  #> Variable | Type    | Values                                            #> ---------------------------------------------------------------------- #> e16sex   | numeric | 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, ...  #> e42dep   | factor  | 3, 3, 3, NA, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 3, ... #> c172code | numeric | 2, 2, 1, 2, 2, 2, 2, 2, NA, 2, 2, 2, 3, 1, 3, ..."},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":null,"dir":"Reference","previous_headings":"","what":"Read (import) data files from various sources — data_read","title":"Read (import) data files from various sources — data_read","text":"functions imports data various file types. small wrapper around haven::read_spss(), haven::read_stata(), haven::read_sas(), readxl::read_excel() data.table::fread() resp. readr::read_delim() (latter package data.table installed). Thus, supported file types importing data data files SPSS, SAS Stata, Excel files text files (like '.csv' files). file types passed rio::import(). data_write() works similar way.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read (import) data files from various sources — data_read","text":"","code":"data_read(   path,   path_catalog = NULL,   encoding = NULL,   convert_factors = TRUE,   verbose = TRUE,   ... )  data_write(   data,   path,   delimiter = \",\",   convert_factors = FALSE,   save_labels = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read (import) data files from various sources — data_read","text":"path Character string, file path data file. path_catalog Character string, path catalog file. relevant SAS data files. encoding character encoding used file. Usually needed. convert_factors TRUE (default), numeric variables, values value label, assumed categorical converted factors. FALSE, variable types guessed conversion numeric variables factors performed. See also section 'Differences packages'. data_write(), argument applies text (e.g. .txt .csv) spreadsheet file formats (like .xlsx). Converting factors might useful formats labelled numeric variables converted factors exported character columns - else, value labels lost numeric values written file. verbose Toggle warnings messages. ... Arguments passed related read_*() write_*() functions. data data frame written file. delimiter CSV-files, specifies delimiter. Defaults \",\", particular European regions, \";\" might useful alternative, especially exported CSV-files opened Excel. save_labels applies CSV files. TRUE, value variable labels () saved additional CSV file. file file name exported CSV file, includes \"_labels\" suffix (.e. file name \"mydat.csv\", additional file value variable labels named \"mydat_labels.csv\").","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read (import) data files from various sources — data_read","text":"data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"supported-file-types","dir":"Reference","previous_headings":"","what":"Supported file types","title":"Read (import) data files from various sources — data_read","text":"data_read() wrapper around haven, data.table, readr readxl rio packages. Currently supported file types .txt, .csv, .xls, .xlsx, .sav, .por, .dta .sas (related files). file types passed rio::import(). data_write() wrapper around haven, readr rio packages, supports writing files formats supported packages.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"compressed-files-zip-and-urls","dir":"Reference","previous_headings":"","what":"Compressed files (zip) and URLs","title":"Read (import) data files from various sources — data_read","text":"data_read() can also read mentioned files URLs inside zip-compressed files. Thus, path can also URL file like \"http://www.url.com/file.csv\". path points zip-compressed file, multiple files inside zip-archive, first supported file extracted loaded.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"general-behaviour","dir":"Reference","previous_headings":"","what":"General behaviour","title":"Read (import) data files from various sources — data_read","text":"data_read() detects appropriate read_*() function based file-extension data file. Thus, cases enough specify path argument. However, control needed, arguments ... passed related read_*() function. applies data_write(), .e. based file extension provided path, appropriate write_*() function used automatically.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"spss-specific-behaviour","dir":"Reference","previous_headings":"","what":"SPSS specific behaviour","title":"Read (import) data files from various sources — data_read","text":"data_read() import user-defined (\"tagged\") NA values SPSS, .e. argument user_na always set FALSE importing SPSS data haven package. Use convert_to_na() define missing values imported data, necessary. Furthermore, data_write() compresses SPSS files default. causes problems (older) SPSS versions, use compress = \"none\", example data_write(data, \"myfile.sav\", compress = \"none\").","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_read.html","id":"differences-to-other-packages-that-read-foreign-data-formats","dir":"Reference","previous_headings":"","what":"Differences to other packages that read foreign data formats","title":"Read (import) data files from various sources — data_read","text":"data_read() comparable rio::import(). data files SPSS, SAS Stata, support labelled data, variables converted appropriate type. major difference rio::import() data_read() automatically converts fully labelled numeric variables factors, imported value labels set factor levels. numeric variable value labels less value labels values, converted factor. case, value labels preserved \"labels\" attribute. Character vectors preserved.  Use convert_factors = FALSE remove automatic conversion numeric variables factors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":null,"dir":"Reference","previous_headings":"","what":"Relocate (reorder) columns of a data frame — data_relocate","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"data_relocate() reorder columns specific positions, indicated . data_reorder() instead move selected columns beginning data frame. Finally, data_remove() removes columns data frame. functions support select-helpers allow flexible specification search pattern find matching columns, reordered removed.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"","code":"data_relocate(   data,   select,   before = NULL,   after = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_reorder(   data,   select,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_remove(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". , Destination columns. Supplying neither move columns left-hand side; specifying error. Can character vector, indicating name destination column, numeric value, indicating index number destination column. -1, added last column. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings. ... Arguments passed functions. Mostly used yet. exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"data frame reordered columns.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_relocate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Relocate (reorder) columns of a data frame — data_relocate","text":"","code":"# Reorder columns head(data_relocate(iris, select = \"Species\", before = \"Sepal.Length\")) #>   Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1  setosa          5.1         3.5          1.4         0.2 #> 2  setosa          4.9         3.0          1.4         0.2 #> 3  setosa          4.7         3.2          1.3         0.2 #> 4  setosa          4.6         3.1          1.5         0.2 #> 5  setosa          5.0         3.6          1.4         0.2 #> 6  setosa          5.4         3.9          1.7         0.4 head(data_relocate(iris, select = \"Species\", before = \"Sepal.Width\")) #>   Sepal.Length Species Sepal.Width Petal.Length Petal.Width #> 1          5.1  setosa         3.5          1.4         0.2 #> 2          4.9  setosa         3.0          1.4         0.2 #> 3          4.7  setosa         3.2          1.3         0.2 #> 4          4.6  setosa         3.1          1.5         0.2 #> 5          5.0  setosa         3.6          1.4         0.2 #> 6          5.4  setosa         3.9          1.7         0.4 head(data_relocate(iris, select = \"Sepal.Width\", after = \"Species\")) #>   Sepal.Length Petal.Length Petal.Width Species Sepal.Width #> 1          5.1          1.4         0.2  setosa         3.5 #> 2          4.9          1.4         0.2  setosa         3.0 #> 3          4.7          1.3         0.2  setosa         3.2 #> 4          4.6          1.5         0.2  setosa         3.1 #> 5          5.0          1.4         0.2  setosa         3.6 #> 6          5.4          1.7         0.4  setosa         3.9 # which is same as head(data_relocate(iris, select = \"Sepal.Width\", after = -1)) #>   Sepal.Length Petal.Length Petal.Width Species Sepal.Width #> 1          5.1          1.4         0.2  setosa         3.5 #> 2          4.9          1.4         0.2  setosa         3.0 #> 3          4.7          1.3         0.2  setosa         3.2 #> 4          4.6          1.5         0.2  setosa         3.1 #> 5          5.0          1.4         0.2  setosa         3.6 #> 6          5.4          1.7         0.4  setosa         3.9  # Reorder multiple columns head(data_relocate(iris, select = c(\"Species\", \"Petal.Length\"), after = \"Sepal.Width\")) #>   Sepal.Length Sepal.Width Species Petal.Length Petal.Width #> 1          5.1         3.5  setosa          1.4         0.2 #> 2          4.9         3.0  setosa          1.4         0.2 #> 3          4.7         3.2  setosa          1.3         0.2 #> 4          4.6         3.1  setosa          1.5         0.2 #> 5          5.0         3.6  setosa          1.4         0.2 #> 6          5.4         3.9  setosa          1.7         0.4 # which is same as head(data_relocate(iris, select = c(\"Species\", \"Petal.Length\"), after = 2)) #>   Sepal.Length Sepal.Width Species Petal.Length Petal.Width #> 1          5.1         3.5  setosa          1.4         0.2 #> 2          4.9         3.0  setosa          1.4         0.2 #> 3          4.7         3.2  setosa          1.3         0.2 #> 4          4.6         3.1  setosa          1.5         0.2 #> 5          5.0         3.6  setosa          1.4         0.2 #> 6          5.4         3.9  setosa          1.7         0.4  # Reorder columns head(data_reorder(iris, c(\"Species\", \"Sepal.Length\"))) #>   Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1  setosa          5.1         3.5          1.4         0.2 #> 2  setosa          4.9         3.0          1.4         0.2 #> 3  setosa          4.7         3.2          1.3         0.2 #> 4  setosa          4.6         3.1          1.5         0.2 #> 5  setosa          5.0         3.6          1.4         0.2 #> 6  setosa          5.4         3.9          1.7         0.4  # Remove columns head(data_remove(iris, \"Sepal.Length\")) #>   Sepal.Width Petal.Length Petal.Width Species #> 1         3.5          1.4         0.2  setosa #> 2         3.0          1.4         0.2  setosa #> 3         3.2          1.3         0.2  setosa #> 4         3.1          1.5         0.2  setosa #> 5         3.6          1.4         0.2  setosa #> 6         3.9          1.7         0.4  setosa head(data_remove(iris, starts_with(\"Sepal\"))) #>   Petal.Length Petal.Width Species #> 1          1.4         0.2  setosa #> 2          1.4         0.2  setosa #> 3          1.3         0.2  setosa #> 4          1.5         0.2  setosa #> 5          1.4         0.2  setosa #> 6          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":null,"dir":"Reference","previous_headings":"","what":"Rename columns and variable names — data_addprefix","title":"Rename columns and variable names — data_addprefix","text":"Safe intuitive functions rename variables rows data frames. data_rename() rename column names, .e. facilitates renaming variables data_addprefix() data_addsuffix() add prefixes suffixes column names. data_rename_rows() convenient shortcut add rename row names data frame, unlike row.names(), input output data frame, thus, integrating smoothly possible pipe-workflow.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rename columns and variable names — data_addprefix","text":"","code":"data_addprefix(   data,   pattern,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_addsuffix(   data,   pattern,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_rename(   data,   pattern = NULL,   replacement = NULL,   safe = TRUE,   verbose = TRUE,   ... )  data_rename_rows(data, rows = NULL)"},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rename columns and variable names — data_addprefix","text":"data data frame, object can coerced data frame. pattern Character vector. data_rename(), indicates columns selected renaming. Can NULL (case columns selected). data_addprefix() data_addsuffix(), character string, added prefix suffix column names. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings messages. ... arguments passed functions. replacement Character vector. Indicates new name columns selected pattern. Can NULL (case column numbered sequential order). NULL, pattern replacement must length. safe throw error instance variable renamed/removed exist. rows Vector row names.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rename columns and variable names — data_addprefix","text":"modified data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_rename.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rename columns and variable names — data_addprefix","text":"","code":"# Add prefix / suffix to all columns head(data_addprefix(iris, \"NEW_\")) #>   NEW_Sepal.Length NEW_Sepal.Width NEW_Petal.Length NEW_Petal.Width NEW_Species #> 1              5.1             3.5              1.4             0.2      setosa #> 2              4.9             3.0              1.4             0.2      setosa #> 3              4.7             3.2              1.3             0.2      setosa #> 4              4.6             3.1              1.5             0.2      setosa #> 5              5.0             3.6              1.4             0.2      setosa #> 6              5.4             3.9              1.7             0.4      setosa head(data_addsuffix(iris, \"_OLD\")) #>   Sepal.Length_OLD Sepal.Width_OLD Petal.Length_OLD Petal.Width_OLD Species_OLD #> 1              5.1             3.5              1.4             0.2      setosa #> 2              4.9             3.0              1.4             0.2      setosa #> 3              4.7             3.2              1.3             0.2      setosa #> 4              4.6             3.1              1.5             0.2      setosa #> 5              5.0             3.6              1.4             0.2      setosa #> 6              5.4             3.9              1.7             0.4      setosa  # Rename columns head(data_rename(iris, \"Sepal.Length\", \"length\")) #>   length Sepal.Width Petal.Length Petal.Width Species #> 1    5.1         3.5          1.4         0.2  setosa #> 2    4.9         3.0          1.4         0.2  setosa #> 3    4.7         3.2          1.3         0.2  setosa #> 4    4.6         3.1          1.5         0.2  setosa #> 5    5.0         3.6          1.4         0.2  setosa #> 6    5.4         3.9          1.7         0.4  setosa # data_rename(iris, \"FakeCol\", \"length\", safe=FALSE)  # This fails head(data_rename(iris, \"FakeCol\", \"length\")) # This doesn't #> Variable `FakeCol` is not in your data frame :/ #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa head(data_rename(iris, c(\"Sepal.Length\", \"Sepal.Width\"), c(\"length\", \"width\"))) #>   length width Petal.Length Petal.Width Species #> 1    5.1   3.5          1.4         0.2  setosa #> 2    4.9   3.0          1.4         0.2  setosa #> 3    4.7   3.2          1.3         0.2  setosa #> 4    4.6   3.1          1.5         0.2  setosa #> 5    5.0   3.6          1.4         0.2  setosa #> 6    5.4   3.9          1.7         0.4  setosa  # Reset names head(data_rename(iris, NULL)) #>     1   2   3   4      5 #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa  # Change all head(data_rename(iris, replacement = paste0(\"Var\", 1:5))) #>   Var1 Var2 Var3 Var4   Var5 #> 1  5.1  3.5  1.4  0.2 setosa #> 2  4.9  3.0  1.4  0.2 setosa #> 3  4.7  3.2  1.3  0.2 setosa #> 4  4.6  3.1  1.5  0.2 setosa #> 5  5.0  3.6  1.4  0.2 setosa #> 6  5.4  3.9  1.7  0.4 setosa"},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":null,"dir":"Reference","previous_headings":"","what":"Restore the type of columns according to a reference data frame — data_restoretype","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"Restore type columns according reference data frame","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"","code":"data_restoretype(data, reference = NULL, ...)"},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"data data frame pivot. reference reference data frame find correct column types. NULL, column converted numeric generate NAs. example, c(\"1\", \"2\") can converted numeric c(\"Sepal.Length\"). ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"data frame columns whose types restored based reference data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_restoretype.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Restore the type of columns according to a reference data frame — data_restoretype","text":"","code":"data <- data.frame(   Sepal.Length = c(\"1\", \"3\", \"2\"),   Species = c(\"setosa\", \"versicolor\", \"setosa\"),   New = c(\"1\", \"3\", \"4\") )  fixed <- data_restoretype(data, reference = iris) summary(fixed) #>   Sepal.Length       Species      New            #>  Min.   :1.0   setosa    :2   Length:3           #>  1st Qu.:1.5   versicolor:1   Class :character   #>  Median :2.0   virginica :0   Mode  :character   #>  Mean   :2.0                                     #>  3rd Qu.:2.5                                     #>  Max.   :3.0"},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":null,"dir":"Reference","previous_headings":"","what":"Rotate a data frame — data_rotate","title":"Rotate a data frame — data_rotate","text":"function rotates data frame, .e. columns become rows vice versa. equivalent using t() restores data.frame class, preserves attributes prints warning data type modified (see example).","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rotate a data frame — data_rotate","text":"","code":"data_rotate(data, rownames = NULL, colnames = FALSE, verbose = TRUE)  data_transpose(data, rownames = NULL, colnames = FALSE, verbose = TRUE)"},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rotate a data frame — data_rotate","text":"data data frame. rownames Character vector (optional). NULL, data frame's rownames added (first) column output, rownames name column. colnames Logical character vector (optional). TRUE, values first column x used column names rotated data frame. character vector, values column used column names. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rotate a data frame — data_rotate","text":"(rotated) data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_rotate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rotate a data frame — data_rotate","text":"","code":"x <- mtcars[1:3, 1:4]  x #>                mpg cyl disp  hp #> Mazda RX4     21.0   6  160 110 #> Mazda RX4 Wag 21.0   6  160 110 #> Datsun 710    22.8   4  108  93  data_rotate(x) #>      Mazda RX4 Mazda RX4 Wag Datsun 710 #> mpg         21            21       22.8 #> cyl          6             6        4.0 #> disp       160           160      108.0 #> hp         110           110       93.0 data_rotate(x, rownames = \"property\") #>   property Mazda RX4 Mazda RX4 Wag Datsun 710 #> 1      mpg        21            21       22.8 #> 2      cyl         6             6        4.0 #> 3     disp       160           160      108.0 #> 4       hp       110           110       93.0  # use values in 1. column as column name data_rotate(x, colnames = TRUE) #>       21  21 22.8 #> cyl    6   6    4 #> disp 160 160  108 #> hp   110 110   93 data_rotate(x, rownames = \"property\", colnames = TRUE) #>   property  21  21 22.8 #> 1      cyl   6   6    4 #> 2     disp 160 160  108 #> 3       hp 110 110   93  # use either first column or specific column for column names x <- data.frame(a = 1:5, b = 11:15, c = 21:25) data_rotate(x, colnames = TRUE) #>    1  2  3  4  5 #> b 11 12 13 14 15 #> c 21 22 23 24 25 data_rotate(x, colnames = \"c\") #>   21 22 23 24 25 #> a  1  2  3  4  5 #> b 11 12 13 14 15"},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":null,"dir":"Reference","previous_headings":"","what":"Find variables by their names, variable or value labels — data_seek","title":"Find variables by their names, variable or value labels — data_seek","text":"functions seeks variables data frame, based patterns either match variable name (column name), variable labels, value labels factor levels. Matching variable value labels works \"labelled\" data, .e. variables either label attribute labels attribute. data_seek() particular useful larger data frames labelled data - finding correct variable name can challenge. function helps find required variables, certain patterns variable names labels known.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find variables by their names, variable or value labels — data_seek","text":"","code":"data_seek(data, pattern, seek = c(\"names\", \"labels\"), fuzzy = FALSE)"},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find variables by their names, variable or value labels — data_seek","text":"data data frame. pattern Character string (regular expression) matched data. May also character vector length > 1. pattern searched column names, variable label value labels attributes, factor levels variables data. seek Character vector, indicating pattern sought. Use one following options: \"names\": Searches column names. \"column_names\" \"columns\" aliases \"names\". \"labels\": Searches variable labels. applies label attribute set variable. \"values\": Searches value labels factor levels. applies labels attribute set variable, variable factor. \"levels\" alias \"values\". \"\": Searches . fuzzy Logical. TRUE, \"fuzzy matching\" (partial close distance matching) used find pattern.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find variables by their names, variable or value labels — data_seek","text":"data frame three columns: column index, column name - available - variable label matched variables data.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_seek.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find variables by their names, variable or value labels — data_seek","text":"","code":"# seek variables with \"Length\" in variable name or labels data_seek(iris, \"Length\") #> index |       column |       labels #> ----------------------------------- #>     1 | Sepal.Length | Sepal.Length #>     3 | Petal.Length | Petal.Length  # seek variables with \"dependency\" in names or labels # column \"e42dep\" has a label-attribute \"elder's dependency\" data(efc) data_seek(efc, \"dependency\") #> index | column |             labels #> ----------------------------------- #>     3 | e42dep | elder's dependency  # \"female\" only appears as value label attribute - default search is in # variable names and labels only, so no match data_seek(efc, \"female\") #> No matches found. # when we seek in all sources, we find the variable \"e16sex\" data_seek(efc, \"female\", seek = \"all\") #> index | column |         labels #> ------------------------------- #>     2 | e16sex | elder's gender  # typo, no match data_seek(iris, \"Lenght\") #> No matches found. # typo, fuzzy match data_seek(iris, \"Lenght\", fuzzy = TRUE) #> index |       column |       labels #> ----------------------------------- #>     1 | Sepal.Length | Sepal.Length #>     3 | Petal.Length | Petal.Length"},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":null,"dir":"Reference","previous_headings":"","what":"Separate single variable into multiple variables — data_separate","title":"Separate single variable into multiple variables — data_separate","text":"Separates single variable multiple new variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Separate single variable into multiple variables — data_separate","text":"","code":"data_separate(   data,   select = NULL,   new_columns = NULL,   separator = \"[^[:alnum:]]+\",   guess_columns = NULL,   merge_multiple = FALSE,   merge_separator = \"\",   fill = \"right\",   extra = \"drop_right\",   convert_na = TRUE,   exclude = NULL,   append = FALSE,   ignore_case = FALSE,   verbose = TRUE,   regex = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Separate single variable into multiple variables — data_separate","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". new_columns names new columns, character vector. one variable selected (select), new names prefixed name original column. new_columns can also list (named) character vectors multiple variables separated. See 'Examples'. separator Separator columns. Can character vector, treated regular expression, numeric vector indicates positions string values split. guess_columns new_columns given, required number new columns guessed based results value splitting. example, variable split three new columns, considered required number new columns, columns named \"split_1\", \"split_2\" \"split_3\". values variable split different amount new columns, guess_column can either \"mode\" (number new columns based common number splits), \"min\" \"max\" use minimum resp. maximum number possible splits required number columns. merge_multiple Logical, TRUE one variable selected separating, new columns can merged. Value pairs split variables merged. merge_separator Separator string merge_multiple = TRUE. Defines string used merge values together. fill deal values return fewer new columns splitting? Can \"left\" (fill missing columns left NA), \"right\" (fill missing columns right NA) \"value_left\" \"value_right\" fill missing columns left right left-right-values. extra deal values return many new columns splitting? Can \"drop_left\" \"drop_right\" drop left-right-values, \"merge_left\" \"merge_right\" merge left- right-value together, keeping remaining values . convert_na Logical, TRUE, character \"NA\" values converted real NA values. exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical, FALSE (default), removes original columns separated. TRUE, columns preserved new columns appended data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. verbose Toggle warnings. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Separate single variable into multiple variables — data_separate","text":"data frame newly created variable(s), - append = TRUE - data including new variables.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_separate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Separate single variable into multiple variables — data_separate","text":"","code":"# simple case d <- data.frame(   x = c(\"1.a.6\", \"2.b.7\", \"3.c.8\"),   stringsAsFactors = FALSE ) d #>       x #> 1 1.a.6 #> 2 2.b.7 #> 3 3.c.8 data_separate(d, new_columns = c(\"a\", \"b\", \"c\")) #>   a b c #> 1 1 a 6 #> 2 2 b 7 #> 3 3 c 8  # guess number of columns d <- data.frame(   x = c(\"1.a.6\", NA, \"2.b.6.7\", \"3.c\", \"x.y.z\"),   stringsAsFactors = FALSE ) d #>         x #> 1   1.a.6 #> 2    <NA> #> 3 2.b.6.7 #> 4     3.c #> 5   x.y.z data_separate(d, guess_columns = \"mode\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been dropped. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b    6 #> 4    3    c <NA> #> 5    x    y    z  data_separate(d, guess_columns = \"max\") #> Column `x` had different number of values after splitting. Variable was #>   split into 4 columns. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3  x_4 #> 1    1    a    6 <NA> #> 2 <NA> <NA> <NA> <NA> #> 3    2    b    6    7 #> 4    3    c <NA> <NA> #> 5    x    y    z <NA>  # drop left-most column data_separate(d, guess_columns = \"mode\", extra = \"drop_left\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Left-most #>   columns have been dropped. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    b    6    7 #> 4    3    c <NA> #> 5    x    y    z  # merge right-most column data_separate(d, guess_columns = \"mode\", extra = \"merge_right\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been merged together. #> `x`returned fewer columns than expected after splitting. Right-most #>   columns were filled with `NA`. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b  6 7 #> 4    3    c <NA> #> 5    x    y    z  # fill columns with fewer values with left-most values data_separate(d, guess_columns = \"mode\", fill = \"value_left\") #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been dropped. #> `x`returned fewer columns than expected after splitting. Left-most #>   columns were filled with first value. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b    6 #> 4    3    3    c #> 5    x    y    z  # fill and merge data_separate(   d,   guess_columns = \"mode\",   fill = \"value_left\",   extra = \"merge_right\" ) #> Column `x` had different number of values after splitting. Variable was #>   split into 3 columns. #> `x` returned more columns than expected after splitting. Right-most #>   columns have been merged together. #> `x`returned fewer columns than expected after splitting. Left-most #>   columns were filled with first value. #>    x_1  x_2  x_3 #> 1    1    a    6 #> 2 <NA> <NA> <NA> #> 3    2    b  6 7 #> 4    3    3    c #> 5    x    y    z  # multiple columns to split d <- data.frame(   x = c(\"1.a.6\", \"2.b.7\", \"3.c.8\"),   y = c(\"x.y.z\", \"10.11.12\", \"m.n.o\"),   stringsAsFactors = FALSE ) d #>       x        y #> 1 1.a.6    x.y.z #> 2 2.b.7 10.11.12 #> 3 3.c.8    m.n.o # split two columns, default column names data_separate(d, guess_columns = \"mode\") #>   x_1 x_2 x_3 y_1 y_2 y_3 #> 1   1   a   6   x   y   z #> 2   2   b   7  10  11  12 #> 3   3   c   8   m   n   o  # split into new named columns, repeating column names data_separate(d, new_columns = c(\"a\", \"b\", \"c\")) #>   x_a x_b x_c y_a y_b y_c #> 1   1   a   6   x   y   z #> 2   2   b   7  10  11  12 #> 3   3   c   8   m   n   o  # split selected variable new columns data_separate(d, select = \"y\", new_columns = c(\"a\", \"b\", \"c\")) #>       x  a  b  c #> 1 1.a.6  x  y  z #> 2 2.b.7 10 11 12 #> 3 3.c.8  m  n  o  # merge multiple split columns data_separate(   d,   new_columns = c(\"a\", \"b\", \"c\"),   merge_multiple = TRUE ) #>     a   b   c #> 1  1x  ay  6z #> 2 210 b11 712 #> 3  3m  cn  8o  # merge multiple split columns data_separate(   d,   new_columns = c(\"a\", \"b\", \"c\"),   merge_multiple = TRUE,   merge_separator = \"-\" ) #>      a    b    c #> 1  1-x  a-y  6-z #> 2 2-10 b-11 7-12 #> 3  3-m  c-n  8-o  # separate multiple columns, give proper column names d_sep <- data.frame(   x = c(\"1.a.6\", \"2.b.7.d\", \"3.c.8\", \"5.j\"),   y = c(\"m.n.99.22\", \"77.f.g.34\", \"44.9\", NA),   stringsAsFactors = FALSE )  data_separate(   d_sep,   select = c(\"x\", \"y\"),   new_columns = list(     x = c(\"A\", \"B\", \"C\"), # separate \"x\" into three columns     y = c(\"EE\", \"FF\", \"GG\", \"HH\") # separate \"y\" into four columns   ),   verbose = FALSE ) #>   A B    C   EE   FF   GG   HH #> 1 1 a    6    m    n   99   22 #> 2 2 b    7   77    f    g   34 #> 3 3 c    8   44    9 <NA> <NA> #> 4 5 j <NA> <NA> <NA> <NA> <NA>"},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Create frequency tables of variables — data_tabulate","title":"Create frequency tables of variables — data_tabulate","text":"function creates frequency tables variables, including number levels/values well distribution raw, valid cumulative percentages.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create frequency tables of variables — data_tabulate","text":"","code":"data_tabulate(x, ...)  # S3 method for default data_tabulate(x, drop_levels = FALSE, name = NULL, verbose = TRUE, ...)  # S3 method for data.frame data_tabulate(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   collapse = FALSE,   drop_levels = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create frequency tables of variables — data_tabulate","text":"x (grouped) data frame, vector factor. ... used. drop_levels Logical, TRUE, factor levels occur data included table (frequency zero), else unused factor levels dropped frequency table. name Optional character string, includes name used printing. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. collapse Logical, TRUE collapses multiple tables one larger table printing. affects printing, returned object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create frequency tables of variables — data_tabulate","text":"data frame, list data frames, one frequency table data frame per variable.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_tabulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create frequency tables of variables — data_tabulate","text":"","code":"data(efc)  # vector/factor data_tabulate(efc$c172code) #> carer's level of education (efc$c172code) <numeric> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  8 |  8.00 |    8.89 |         8.89 #> 2     | 66 | 66.00 |   73.33 |        82.22 #> 3     | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>  # data frame data_tabulate(efc, c(\"e42dep\", \"c172code\")) #> elder's dependency (e42dep) <categorical> #> # total N=100 valid N=97 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  2 |  2.00 |    2.06 |         2.06 #> 2     |  4 |  4.00 |    4.12 |         6.19 #> 3     | 28 | 28.00 |   28.87 |        35.05 #> 4     | 63 | 63.00 |   64.95 |       100.00 #> <NA>  |  3 |  3.00 |    <NA> |         <NA> #>  #> carer's level of education (c172code) <numeric> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  8 |  8.00 |    8.89 |         8.89 #> 2     | 66 | 66.00 |   73.33 |        82.22 #> 3     | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>  # grouped data frame suppressPackageStartupMessages(library(poorman, quietly = TRUE)) efc %>%   group_by(c172code) %>%   data_tabulate(\"e16sex\") #> elder's gender (e16sex) <numeric> #> Grouped by c172code (1) #> # total N=8 valid N=8 #>  #> Value | N | Raw % | Valid % | Cumulative % #> ------+---+-------+---------+------------- #> 1     | 5 | 62.50 |   62.50 |        62.50 #> 2     | 3 | 37.50 |   37.50 |       100.00 #> <NA>  | 0 |  0.00 |    <NA> |         <NA> #>  #> elder's gender (e16sex) <numeric> #> Grouped by c172code (2) #> # total N=66 valid N=66 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     | 32 | 48.48 |   48.48 |        48.48 #> 2     | 34 | 51.52 |   51.52 |       100.00 #> <NA>  |  0 |  0.00 |    <NA> |         <NA> #>  #> elder's gender (e16sex) <numeric> #> Grouped by c172code (3) #> # total N=16 valid N=16 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  4 | 25.00 |   25.00 |        25.00 #> 2     | 12 | 75.00 |   75.00 |       100.00 #> <NA>  |  0 |  0.00 |    <NA> |         <NA> #>  #> elder's gender (e16sex) <numeric> #> Grouped by c172code (NA) #> # total N=10 valid N=10 #>  #> Value | N | Raw % | Valid % | Cumulative % #> ------+---+-------+---------+------------- #> 1     | 5 | 50.00 |   50.00 |        50.00 #> 2     | 5 | 50.00 |   50.00 |       100.00 #> <NA>  | 0 |  0.00 |    <NA> |         <NA>  # collapse tables efc %>%   group_by(c172code) %>%   data_tabulate(\"e16sex\", collapse = TRUE) #> # Frequency Table #>  #> Variable |         Group | Value |  N | Raw % | Valid % | Cumulative % #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   |  c172code (1) |     1 |  5 | 62.50 |   62.50 |        62.50 #>          |               |     2 |  3 | 37.50 |   37.50 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   |  c172code (2) |     1 | 32 | 48.48 |   48.48 |        48.48 #>          |               |     2 | 34 | 51.52 |   51.52 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   |  c172code (3) |     1 |  4 | 25.00 |   25.00 |        25.00 #>          |               |     2 | 12 | 75.00 |   75.00 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex   | c172code (NA) |     1 |  5 | 50.00 |   50.00 |        50.00 #>          |               |     2 |  5 | 50.00 |   50.00 |       100.00 #>          |               |  <NA> |  0 |  0.00 |    <NA> |         <NA> #> ----------------------------------------------------------------------  # for larger N's (> 100000), a big mark is automatically added set.seed(123) x <- sample(1:3, 1e6, TRUE) data_tabulate(x, name = \"Large Number\") #> Large Number (x) <integer> #> # total N=1,000,000 valid N=1,000,000 #>  #> Value |       N | Raw % | Valid % | Cumulative % #> ------+---------+-------+---------+------------- #> 1     | 333,852 | 33.39 |   33.39 |        33.39 #> 2     | 332,910 | 33.29 |   33.29 |        66.68 #> 3     | 333,238 | 33.32 |   33.32 |       100.00 #> <NA>  |       0 |  0.00 |    <NA> |         <NA>  # to remove the big mark, use \"print(..., big_mark = \"\")\" print(data_tabulate(x), big_mark = \"\") #> x <integer> #> # total N=1000000 valid N=1000000 #>  #> Value |      N | Raw % | Valid % | Cumulative % #> ------+--------+-------+---------+------------- #> 1     | 333852 | 33.39 |   33.39 |        33.39 #> 2     | 332910 | 33.29 |   33.29 |        66.68 #> 3     | 333238 | 33.32 |   33.32 |       100.00 #> <NA>  |      0 |  0.00 |    <NA> |         <NA>"},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape (pivot) data from wide to long — data_to_long","title":"Reshape (pivot) data from wide to long — data_to_long","text":"function \"lengthens\" data, increasing number rows decreasing number columns. dependency-free base-R equivalent tidyr::pivot_longer().","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape (pivot) data from wide to long — data_to_long","text":"","code":"data_to_long(   data,   select = \"all\",   names_to = \"name\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   values_to = \"value\",   values_drop_na = FALSE,   rows_to = NULL,   ignore_case = FALSE,   regex = FALSE,   ...,   cols,   colnames_to )  reshape_longer(   data,   select = \"all\",   names_to = \"name\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   values_to = \"value\",   values_drop_na = FALSE,   rows_to = NULL,   ignore_case = FALSE,   regex = FALSE,   ...,   cols,   colnames_to )"},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape (pivot) data from wide to long — data_to_long","text":"data data frame pivot. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". names_to name new column contain column names. names_prefix regular expression used remove matching text start variable name. names_sep, names_pattern names_to contains multiple values, argument controls column name broken . names_pattern takes regular expression containing matching groups, .e. \"()\". values_to name new column contain values pivoted variables. values_drop_na TRUE, drop rows contain NA values_to column. effectively converts explicit missing values implicit missing values, generally used missing values data created structure. rows_to name column contain row names row numbers original data. NULL, removed. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. ... Currently used. cols Identical select. argument ensure compatibility tidyr::pivot_longer(). select cols provided, cols used. colnames_to Deprecated. Use names_to instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reshape (pivot) data from wide to long — data_to_long","text":"tibble provided input, reshape_longer() also returns tibble. Otherwise, returns data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_to_long.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape (pivot) data from wide to long — data_to_long","text":"","code":"wide_data <- data.frame(replicate(5, rnorm(10)))  # Default behaviour (equivalent to tidyr::pivot_longer(wide_data, cols = 1:5)) data_to_long(wide_data) #>    name       value #> 1    X1  0.96438796 #> 2    X2 -0.05906520 #> 3    X3 -0.49945619 #> 4    X4 -1.18164198 #> 5    X5 -0.26924131 #> 6    X1  0.78540975 #> 7    X2 -0.66621750 #> 8    X3  1.11673935 #> 9    X4 -0.43727481 #> 10   X5 -0.28183866 #> 11   X1 -0.68673736 #> 12   X2 -0.83318830 #> 13   X3  0.17915927 #> 14   X4 -0.27775661 #> 15   X5  1.62361443 #> 16   X1 -0.35491405 #> 17   X2 -1.41166020 #> 18   X3 -1.14038587 #> 19   X4 -0.65407266 #> 20   X5  0.82933164 #> 21   X1 -0.25090623 #> 22   X2  0.22522310 #> 23   X3  0.80261795 #> 24   X4  0.18375250 #> 25   X5 -0.79579710 #> 26   X1  0.71122444 #> 27   X2  1.30430074 #> 28   X3 -0.01983587 #> 29   X4 -0.67286229 #> 30   X5 -0.52205496 #> 31   X1  1.69081622 #> 32   X2  0.28000624 #> 33   X3  0.73973690 #> 34   X4  1.98316979 #> 35   X5 -0.02966633 #> 36   X1  0.21996840 #> 37   X2  1.21381633 #> 38   X3 -1.76563045 #> 39   X4  0.98711564 #> 40   X5  0.72787721 #> 41   X1 -0.88773194 #> 42   X2 -0.53360955 #> 43   X3  0.74375283 #> 44   X4 -0.49170812 #> 45   X5 -2.91160978 #> 46   X1 -0.91386940 #> 47   X2  0.78988333 #> 48   X3 -0.44083106 #> 49   X4 -0.35785520 #> 50   X5 -1.48411421  # Customizing the names data_to_long(wide_data,   select = c(1, 2),   names_to = \"Column\",   values_to = \"Numbers\",   rows_to = \"Row\" ) #>             X3         X4          X5 Row Column    Numbers #> 1  -0.49945619 -1.1816420 -0.26924131   1     X1  0.9643880 #> 2  -0.49945619 -1.1816420 -0.26924131   1     X2 -0.0590652 #> 3   1.11673935 -0.4372748 -0.28183866   2     X1  0.7854097 #> 4   1.11673935 -0.4372748 -0.28183866   2     X2 -0.6662175 #> 5   0.17915927 -0.2777566  1.62361443   3     X1 -0.6867374 #> 6   0.17915927 -0.2777566  1.62361443   3     X2 -0.8331883 #> 7  -1.14038587 -0.6540727  0.82933164   4     X1 -0.3549140 #> 8  -1.14038587 -0.6540727  0.82933164   4     X2 -1.4116602 #> 9   0.80261795  0.1837525 -0.79579710   5     X1 -0.2509062 #> 10  0.80261795  0.1837525 -0.79579710   5     X2  0.2252231 #> 11 -0.01983587 -0.6728623 -0.52205496   6     X1  0.7112244 #> 12 -0.01983587 -0.6728623 -0.52205496   6     X2  1.3043007 #> 13  0.73973690  1.9831698 -0.02966633   7     X1  1.6908162 #> 14  0.73973690  1.9831698 -0.02966633   7     X2  0.2800062 #> 15 -1.76563045  0.9871156  0.72787721   8     X1  0.2199684 #> 16 -1.76563045  0.9871156  0.72787721   8     X2  1.2138163 #> 17  0.74375283 -0.4917081 -2.91160978   9     X1 -0.8877319 #> 18  0.74375283 -0.4917081 -2.91160978   9     X2 -0.5336095 #> 19 -0.44083106 -0.3578552 -1.48411421  10     X1 -0.9138694 #> 20 -0.44083106 -0.3578552 -1.48411421  10     X2  0.7898833  # Full example # ------------------ data <- psych::bfi # Wide format with one row per participant's personality test  # Pivot long format data_to_long(data,   select = regex(\"\\\\d\"), # Select all columns that contain a digit   names_to = \"Item\",   values_to = \"Score\",   rows_to = \"Participant\" ) #>       gender education age Participant Item Score #> 1          1        NA  16       61617   A1     2 #> 2          1        NA  16       61617   A2     4 #> 3          1        NA  16       61617   A3     3 #> 4          1        NA  16       61617   A4     4 #> 5          1        NA  16       61617   A5     4 #> 6          1        NA  16       61617   C1     2 #> 7          1        NA  16       61617   C2     3 #> 8          1        NA  16       61617   C3     3 #> 9          1        NA  16       61617   C4     4 #> 10         1        NA  16       61617   C5     4 #> 11         1        NA  16       61617   E1     3 #> 12         1        NA  16       61617   E2     3 #> 13         1        NA  16       61617   E3     3 #> 14         1        NA  16       61617   E4     4 #> 15         1        NA  16       61617   E5     4 #> 16         1        NA  16       61617   N1     3 #> 17         1        NA  16       61617   N2     4 #> 18         1        NA  16       61617   N3     2 #> 19         1        NA  16       61617   N4     2 #> 20         1        NA  16       61617   N5     3 #> 21         1        NA  16       61617   O1     3 #> 22         1        NA  16       61617   O2     6 #> 23         1        NA  16       61617   O3     3 #> 24         1        NA  16       61617   O4     4 #> 25         1        NA  16       61617   O5     3 #> 26         2        NA  18       61618   A1     2 #> 27         2        NA  18       61618   A2     4 #> 28         2        NA  18       61618   A3     5 #> 29         2        NA  18       61618   A4     2 #> 30         2        NA  18       61618   A5     5 #> 31         2        NA  18       61618   C1     5 #> 32         2        NA  18       61618   C2     4 #> 33         2        NA  18       61618   C3     4 #> 34         2        NA  18       61618   C4     3 #> 35         2        NA  18       61618   C5     4 #> 36         2        NA  18       61618   E1     1 #> 37         2        NA  18       61618   E2     1 #> 38         2        NA  18       61618   E3     6 #> 39         2        NA  18       61618   E4     4 #> 40         2        NA  18       61618   E5     3 #> 41         2        NA  18       61618   N1     3 #> 42         2        NA  18       61618   N2     3 #> 43         2        NA  18       61618   N3     3 #> 44         2        NA  18       61618   N4     5 #> 45         2        NA  18       61618   N5     5 #> 46         2        NA  18       61618   O1     4 #> 47         2        NA  18       61618   O2     2 #> 48         2        NA  18       61618   O3     4 #> 49         2        NA  18       61618   O4     3 #> 50         2        NA  18       61618   O5     3 #> 51         2        NA  17       61620   A1     5 #> 52         2        NA  17       61620   A2     4 #> 53         2        NA  17       61620   A3     5 #> 54         2        NA  17       61620   A4     4 #> 55         2        NA  17       61620   A5     4 #> 56         2        NA  17       61620   C1     4 #> 57         2        NA  17       61620   C2     5 #> 58         2        NA  17       61620   C3     4 #> 59         2        NA  17       61620   C4     2 #> 60         2        NA  17       61620   C5     5 #> 61         2        NA  17       61620   E1     2 #> 62         2        NA  17       61620   E2     4 #> 63         2        NA  17       61620   E3     4 #> 64         2        NA  17       61620   E4     4 #> 65         2        NA  17       61620   E5     5 #> 66         2        NA  17       61620   N1     4 #> 67         2        NA  17       61620   N2     5 #> 68         2        NA  17       61620   N3     4 #> 69         2        NA  17       61620   N4     2 #> 70         2        NA  17       61620   N5     3 #> 71         2        NA  17       61620   O1     4 #> 72         2        NA  17       61620   O2     2 #> 73         2        NA  17       61620   O3     5 #> 74         2        NA  17       61620   O4     5 #> 75         2        NA  17       61620   O5     2 #> 76         2        NA  17       61621   A1     4 #> 77         2        NA  17       61621   A2     4 #> 78         2        NA  17       61621   A3     6 #> 79         2        NA  17       61621   A4     5 #> 80         2        NA  17       61621   A5     5 #> 81         2        NA  17       61621   C1     4 #> 82         2        NA  17       61621   C2     4 #> 83         2        NA  17       61621   C3     3 #> 84         2        NA  17       61621   C4     5 #> 85         2        NA  17       61621   C5     5 #> 86         2        NA  17       61621   E1     5 #> 87         2        NA  17       61621   E2     3 #> 88         2        NA  17       61621   E3     4 #> 89         2        NA  17       61621   E4     4 #> 90         2        NA  17       61621   E5     4 #> 91         2        NA  17       61621   N1     2 #> 92         2        NA  17       61621   N2     5 #> 93         2        NA  17       61621   N3     2 #> 94         2        NA  17       61621   N4     4 #> 95         2        NA  17       61621   N5     1 #> 96         2        NA  17       61621   O1     3 #> 97         2        NA  17       61621   O2     3 #> 98         2        NA  17       61621   O3     4 #> 99         2        NA  17       61621   O4     3 #> 100        2        NA  17       61621   O5     5 #> 101        1        NA  17       61622   A1     2 #> 102        1        NA  17       61622   A2     3 #> 103        1        NA  17       61622   A3     3 #> 104        1        NA  17       61622   A4     4 #> 105        1        NA  17       61622   A5     5 #> 106        1        NA  17       61622   C1     4 #> 107        1        NA  17       61622   C2     4 #> 108        1        NA  17       61622   C3     5 #> 109        1        NA  17       61622   C4     3 #> 110        1        NA  17       61622   C5     2 #> 111        1        NA  17       61622   E1     2 #> 112        1        NA  17       61622   E2     2 #> 113        1        NA  17       61622   E3     5 #> 114        1        NA  17       61622   E4     4 #> 115        1        NA  17       61622   E5     5 #> 116        1        NA  17       61622   N1     2 #> 117        1        NA  17       61622   N2     3 #> 118        1        NA  17       61622   N3     4 #> 119        1        NA  17       61622   N4     4 #> 120        1        NA  17       61622   N5     3 #> 121        1        NA  17       61622   O1     3 #> 122        1        NA  17       61622   O2     3 #> 123        1        NA  17       61622   O3     4 #> 124        1        NA  17       61622   O4     3 #> 125        1        NA  17       61622   O5     3 #> 126        2         3  21       61623   A1     6 #> 127        2         3  21       61623   A2     6 #> 128        2         3  21       61623   A3     5 #> 129        2         3  21       61623   A4     6 #> 130        2         3  21       61623   A5     5 #> 131        2         3  21       61623   C1     6 #> 132        2         3  21       61623   C2     6 #> 133        2         3  21       61623   C3     6 #> 134        2         3  21       61623   C4     1 #> 135        2         3  21       61623   C5     3 #> 136        2         3  21       61623   E1     2 #> 137        2         3  21       61623   E2     1 #> 138        2         3  21       61623   E3     6 #> 139        2         3  21       61623   E4     5 #> 140        2         3  21       61623   E5     6 #> 141        2         3  21       61623   N1     3 #> 142        2         3  21       61623   N2     5 #> 143        2         3  21       61623   N3     2 #> 144        2         3  21       61623   N4     2 #> 145        2         3  21       61623   N5     3 #> 146        2         3  21       61623   O1     4 #> 147        2         3  21       61623   O2     3 #> 148        2         3  21       61623   O3     5 #> 149        2         3  21       61623   O4     6 #> 150        2         3  21       61623   O5     1 #> 151        1        NA  18       61624   A1     2 #> 152        1        NA  18       61624   A2     5 #> 153        1        NA  18       61624   A3     5 #> 154        1        NA  18       61624   A4     3 #> 155        1        NA  18       61624   A5     5 #> 156        1        NA  18       61624   C1     5 #> 157        1        NA  18       61624   C2     4 #> 158        1        NA  18       61624   C3     4 #> 159        1        NA  18       61624   C4     2 #> 160        1        NA  18       61624   C5     3 #> 161        1        NA  18       61624   E1     4 #> 162        1        NA  18       61624   E2     3 #> 163        1        NA  18       61624   E3     4 #> 164        1        NA  18       61624   E4     5 #> 165        1        NA  18       61624   E5     5 #> 166        1        NA  18       61624   N1     1 #> 167        1        NA  18       61624   N2     2 #> 168        1        NA  18       61624   N3     2 #> 169        1        NA  18       61624   N4     1 #> 170        1        NA  18       61624   N5     1 #> 171        1        NA  18       61624   O1     5 #> 172        1        NA  18       61624   O2     2 #> 173        1        NA  18       61624   O3     5 #> 174        1        NA  18       61624   O4     6 #> 175        1        NA  18       61624   O5     1 #> 176        1         2  19       61629   A1     4 #> 177        1         2  19       61629   A2     3 #> 178        1         2  19       61629   A3     1 #> 179        1         2  19       61629   A4     5 #> 180        1         2  19       61629   A5     1 #> 181        1         2  19       61629   C1     3 #> 182        1         2  19       61629   C2     2 #> 183        1         2  19       61629   C3     4 #> 184        1         2  19       61629   C4     2 #> 185        1         2  19       61629   C5     4 #> 186        1         2  19       61629   E1     3 #> 187        1         2  19       61629   E2     6 #> 188        1         2  19       61629   E3     4 #> 189        1         2  19       61629   E4     2 #> 190        1         2  19       61629   E5     1 #> 191        1         2  19       61629   N1     6 #> 192        1         2  19       61629   N2     3 #> 193        1         2  19       61629   N3     2 #> 194        1         2  19       61629   N4     6 #> 195        1         2  19       61629   N5     4 #> 196        1         2  19       61629   O1     3 #> 197        1         2  19       61629   O2     2 #> 198        1         2  19       61629   O3     4 #> 199        1         2  19       61629   O4     5 #> 200        1         2  19       61629   O5     3 #> 201        1         1  19       61630   A1     4 #> 202        1         1  19       61630   A2     3 #> 203        1         1  19       61630   A3     6 #> 204        1         1  19       61630   A4     3 #> 205        1         1  19       61630   A5     3 #> 206        1         1  19       61630   C1     6 #> 207        1         1  19       61630   C2     6 #> 208        1         1  19       61630   C3     3 #> 209        1         1  19       61630   C4     4 #> 210        1         1  19       61630   C5     5 #> 211        1         1  19       61630   E1     5 #> 212        1         1  19       61630   E2     3 #> 213        1         1  19       61630   E3    NA #> 214        1         1  19       61630   E4     4 #> 215        1         1  19       61630   E5     3 #> 216        1         1  19       61630   N1     5 #> 217        1         1  19       61630   N2     5 #> 218        1         1  19       61630   N3     2 #> 219        1         1  19       61630   N4     3 #> 220        1         1  19       61630   N5     3 #> 221        1         1  19       61630   O1     6 #> 222        1         1  19       61630   O2     6 #> 223        1         1  19       61630   O3     6 #> 224        1         1  19       61630   O4     6 #> 225        1         1  19       61630   O5     1 #> 226        2        NA  17       61633   A1     2 #> 227        2        NA  17       61633   A2     5 #> 228        2        NA  17       61633   A3     6 #> 229        2        NA  17       61633   A4     6 #> 230        2        NA  17       61633   A5     5 #> 231        2        NA  17       61633   C1     6 #> 232        2        NA  17       61633   C2     5 #> 233        2        NA  17       61633   C3     6 #> 234        2        NA  17       61633   C4     2 #> 235        2        NA  17       61633   C5     1 #> 236        2        NA  17       61633   E1     2 #> 237        2        NA  17       61633   E2     2 #> 238        2        NA  17       61633   E3     4 #> 239        2        NA  17       61633   E4     5 #> 240        2        NA  17       61633   E5     5 #> 241        2        NA  17       61633   N1     5 #> 242        2        NA  17       61633   N2     5 #> 243        2        NA  17       61633   N3     5 #> 244        2        NA  17       61633   N4     2 #> 245        2        NA  17       61633   N5     4 #> 246        2        NA  17       61633   O1     5 #> 247        2        NA  17       61633   O2     1 #> 248        2        NA  17       61633   O3     5 #> 249        2        NA  17       61633   O4     5 #> 250        2        NA  17       61633   O5     2 #> 251        1         1  21       61634   A1     4 #> 252        1         1  21       61634   A2     4 #> 253        1         1  21       61634   A3     5 #> 254        1         1  21       61634   A4     6 #> 255        1         1  21       61634   A5     5 #> 256        1         1  21       61634   C1     4 #> 257        1         1  21       61634   C2     3 #> 258        1         1  21       61634   C3     5 #> 259        1         1  21       61634   C4     3 #> 260        1         1  21       61634   C5     2 #> 261        1         1  21       61634   E1     1 #> 262        1         1  21       61634   E2     3 #> 263        1         1  21       61634   E3     2 #> 264        1         1  21       61634   E4     5 #> 265        1         1  21       61634   E5     4 #> 266        1         1  21       61634   N1     3 #> 267        1         1  21       61634   N2     3 #> 268        1         1  21       61634   N3     4 #> 269        1         1  21       61634   N4     2 #> 270        1         1  21       61634   N5     3 #> 271        1         1  21       61634   O1     5 #> 272        1         1  21       61634   O2     3 #> 273        1         1  21       61634   O3     5 #> 274        1         1  21       61634   O4     6 #> 275        1         1  21       61634   O5     3 #> 276        1        NA  16       61636   A1     2 #> 277        1        NA  16       61636   A2     5 #> 278        1        NA  16       61636   A3     5 #> 279        1        NA  16       61636   A4     5 #> 280        1        NA  16       61636   A5     5 #> 281        1        NA  16       61636   C1     5 #> 282        1        NA  16       61636   C2     4 #> 283        1        NA  16       61636   C3     5 #> 284        1        NA  16       61636   C4     4 #> 285        1        NA  16       61636   C5     5 #> 286        1        NA  16       61636   E1     3 #> 287        1        NA  16       61636   E2     3 #> 288        1        NA  16       61636   E3     4 #> 289        1        NA  16       61636   E4     5 #> 290        1        NA  16       61636   E5     4 #> 291        1        NA  16       61636   N1     4 #> 292        1        NA  16       61636   N2     5 #> 293        1        NA  16       61636   N3     3 #> 294        1        NA  16       61636   N4     2 #> 295        1        NA  16       61636   N5    NA #> 296        1        NA  16       61636   O1     4 #> 297        1        NA  16       61636   O2     6 #> 298        1        NA  16       61636   O3     4 #> 299        1        NA  16       61636   O4     5 #> 300        1        NA  16       61636   O5     4 #> 301        2        NA  16       61637   A1     5 #> 302        2        NA  16       61637   A2     5 #> 303        2        NA  16       61637   A3     5 #> 304        2        NA  16       61637   A4     6 #> 305        2        NA  16       61637   A5     4 #> 306        2        NA  16       61637   C1     5 #> 307        2        NA  16       61637   C2     4 #> 308        2        NA  16       61637   C3     3 #> 309        2        NA  16       61637   C4     2 #> 310        2        NA  16       61637   C5     2 #> 311        2        NA  16       61637   E1     3 #> 312        2        NA  16       61637   E2     3 #> 313        2        NA  16       61637   E3     3 #> 314        2        NA  16       61637   E4     2 #> 315        2        NA  16       61637   E5     4 #> 316        2        NA  16       61637   N1     1 #> 317        2        NA  16       61637   N2     2 #> 318        2        NA  16       61637   N3     2 #> 319        2        NA  16       61637   N4     2 #> 320        2        NA  16       61637   N5     2 #> 321        2        NA  16       61637   O1     4 #> 322        2        NA  16       61637   O2     2 #> 323        2        NA  16       61637   O3     4 #> 324        2        NA  16       61637   O4     5 #> 325        2        NA  16       61637   O5     2 #> 326        1        NA  16       61639   A1     5 #> 327        1        NA  16       61639   A2     5 #> 328        1        NA  16       61639   A3     5 #> 329        1        NA  16       61639   A4     6 #> 330        1        NA  16       61639   A5     6 #> 331        1        NA  16       61639   C1     4 #> 332        1        NA  16       61639   C2     4 #> 333        1        NA  16       61639   C3     4 #> 334        1        NA  16       61639   C4     2 #> 335        1        NA  16       61639   C5     1 #> 336        1        NA  16       61639   E1     2 #> 337        1        NA  16       61639   E2     2 #> 338        1        NA  16       61639   E3     4 #> 339        1        NA  16       61639   E4     6 #> 340        1        NA  16       61639   E5     5 #> 341        1        NA  16       61639   N1     1 #> 342        1        NA  16       61639   N2     1 #> 343        1        NA  16       61639   N3     1 #> 344        1        NA  16       61639   N4     2 #> 345        1        NA  16       61639   N5     1 #> 346        1        NA  16       61639   O1     5 #> 347        1        NA  16       61639   O2     3 #> 348        1        NA  16       61639   O3     4 #> 349        1        NA  16       61639   O4     4 #> 350        1        NA  16       61639   O5     4 #> 351        1         1  17       61640   A1     4 #> 352        1         1  17       61640   A2     5 #> 353        1         1  17       61640   A3     2 #> 354        1         1  17       61640   A4     2 #> 355        1         1  17       61640   A5     1 #> 356        1         1  17       61640   C1     5 #> 357        1         1  17       61640   C2     5 #> 358        1         1  17       61640   C3     5 #> 359        1         1  17       61640   C4     2 #> 360        1         1  17       61640   C5     2 #> 361        1         1  17       61640   E1     3 #> 362        1         1  17       61640   E2     4 #> 363        1         1  17       61640   E3     3 #> 364        1         1  17       61640   E4     6 #> 365        1         1  17       61640   E5     5 #> 366        1         1  17       61640   N1     2 #> 367        1         1  17       61640   N2     4 #> 368        1         1  17       61640   N3     2 #> 369        1         1  17       61640   N4     2 #> 370        1         1  17       61640   N5     3 #> 371        1         1  17       61640   O1     5 #> 372        1         1  17       61640   O2     2 #> 373        1         1  17       61640   O3     5 #> 374        1         1  17       61640   O4     5 #> 375        1         1  17       61640   O5     5 #> 376        1        NA  17       61643   A1     4 #> 377        1        NA  17       61643   A2     3 #> 378        1        NA  17       61643   A3     6 #> 379        1        NA  17       61643   A4     6 #> 380        1        NA  17       61643   A5     3 #> 381        1        NA  17       61643   C1     5 #> 382        1        NA  17       61643   C2     5 #> 383        1        NA  17       61643   C3     5 #> 384        1        NA  17       61643   C4     3 #> 385        1        NA  17       61643   C5     5 #> 386        1        NA  17       61643   E1     1 #> 387        1        NA  17       61643   E2     1 #> 388        1        NA  17       61643   E3     6 #> 389        1        NA  17       61643   E4     6 #> 390        1        NA  17       61643   E5     4 #> 391        1        NA  17       61643   N1     4 #> 392        1        NA  17       61643   N2     5 #> 393        1        NA  17       61643   N3     4 #> 394        1        NA  17       61643   N4     5 #> 395        1        NA  17       61643   N5     5 #> 396        1        NA  17       61643   O1     6 #> 397        1        NA  17       61643   O2     6 #> 398        1        NA  17       61643   O3     6 #> 399        1        NA  17       61643   O4     3 #> 400        1        NA  17       61643   O5     2 #> 401        2        NA  17       61650   A1     4 #> 402        2        NA  17       61650   A2     6 #> 403        2        NA  17       61650   A3     6 #> 404        2        NA  17       61650   A4     2 #> 405        2        NA  17       61650   A5     5 #> 406        2        NA  17       61650   C1     4 #> 407        2        NA  17       61650   C2     4 #> 408        2        NA  17       61650   C3     4 #> 409        2        NA  17       61650   C4     4 #> 410        2        NA  17       61650   C5     4 #> 411        2        NA  17       61650   E1     1 #> 412        2        NA  17       61650   E2     2 #> 413        2        NA  17       61650   E3     5 #> 414        2        NA  17       61650   E4     5 #> 415        2        NA  17       61650   E5     5 #> 416        2        NA  17       61650   N1     4 #> 417        2        NA  17       61650   N2     4 #> 418        2        NA  17       61650   N3     4 #> 419        2        NA  17       61650   N4     4 #> 420        2        NA  17       61650   N5     5 #> 421        2        NA  17       61650   O1     5 #> 422        2        NA  17       61650   O2     1 #> 423        2        NA  17       61650   O3     5 #> 424        2        NA  17       61650   O4     6 #> 425        2        NA  17       61650   O5     3 #> 426        1        NA  17       61651   A1     5 #> 427        1        NA  17       61651   A2     5 #> 428        1        NA  17       61651   A3     5 #> 429        1        NA  17       61651   A4     4 #> 430        1        NA  17       61651   A5     5 #> 431        1        NA  17       61651   C1     5 #> 432        1        NA  17       61651   C2     5 #> 433        1        NA  17       61651   C3     5 #> 434        1        NA  17       61651   C4     4 #> 435        1        NA  17       61651   C5     3 #> 436        1        NA  17       61651   E1     2 #> 437        1        NA  17       61651   E2     2 #> 438        1        NA  17       61651   E3     4 #> 439        1        NA  17       61651   E4     6 #> 440        1        NA  17       61651   E5     6 #> 441        1        NA  17       61651   N1     6 #> 442        1        NA  17       61651   N2     5 #> 443        1        NA  17       61651   N3     5 #> 444        1        NA  17       61651   N4     4 #> 445        1        NA  17       61651   N5     4 #> 446        1        NA  17       61651   O1     5 #> 447        1        NA  17       61651   O2     1 #> 448        1        NA  17       61651   O3     4 #> 449        1        NA  17       61651   O4     5 #> 450        1        NA  17       61651   O5     4 #> 451        2        NA  16       61653   A1     4 #> 452        2        NA  16       61653   A2     4 #> 453        2        NA  16       61653   A3     5 #> 454        2        NA  16       61653   A4     4 #> 455        2        NA  16       61653   A5     3 #> 456        2        NA  16       61653   C1     5 #> 457        2        NA  16       61653   C2     4 #> 458        2        NA  16       61653   C3     5 #> 459        2        NA  16       61653   C4     4 #> 460        2        NA  16       61653   C5     6 #> 461        2        NA  16       61653   E1     1 #> 462        2        NA  16       61653   E2     2 #> 463        2        NA  16       61653   E3     4 #> 464        2        NA  16       61653   E4     5 #> 465        2        NA  16       61653   E5     5 #> 466        2        NA  16       61653   N1     5 #> 467        2        NA  16       61653   N2     6 #> 468        2        NA  16       61653   N3     5 #> 469        2        NA  16       61653   N4     5 #> 470        2        NA  16       61653   N5     2 #> 471        2        NA  16       61653   O1     4 #> 472        2        NA  16       61653   O2     2 #> 473        2        NA  16       61653   O3     2 #> 474        2        NA  16       61653   O4     4 #> 475        2        NA  16       61653   O5     2 #> 476        2        NA  17       61654   A1     4 #> 477        2        NA  17       61654   A2     4 #> 478        2        NA  17       61654   A3     6 #> 479        2        NA  17       61654   A4     5 #> 480        2        NA  17       61654   A5     5 #> 481        2        NA  17       61654   C1     1 #> 482        2        NA  17       61654   C2     1 #> 483        2        NA  17       61654   C3     1 #> 484        2        NA  17       61654   C4     5 #> 485        2        NA  17       61654   C5     6 #> 486        2        NA  17       61654   E1     1 #> 487        2        NA  17       61654   E2     1 #> 488        2        NA  17       61654   E3     4 #> 489        2        NA  17       61654   E4     5 #> 490        2        NA  17       61654   E5     6 #> 491        2        NA  17       61654   N1     5 #> 492        2        NA  17       61654   N2     5 #> 493        2        NA  17       61654   N3     5 #> 494        2        NA  17       61654   N4     1 #> 495        2        NA  17       61654   N5     1 #> 496        2        NA  17       61654   O1     4 #> 497        2        NA  17       61654   O2     1 #> 498        2        NA  17       61654   O3     5 #> 499        2        NA  17       61654   O4     3 #> 500        2        NA  17       61654   O5     2 #> 501        1        NA  17       61656   A1     5 #> 502        1        NA  17       61656   A2     4 #> 503        1        NA  17       61656   A3     2 #> 504        1        NA  17       61656   A4     1 #> 505        1        NA  17       61656   A5     2 #> 506        1        NA  17       61656   C1     4 #> 507        1        NA  17       61656   C2     6 #> 508        1        NA  17       61656   C3     5 #> 509        1        NA  17       61656   C4     5 #> 510        1        NA  17       61656   C5     4 #> 511        1        NA  17       61656   E1     3 #> 512        1        NA  17       61656   E2     3 #> 513        1        NA  17       61656   E3     5 #> 514        1        NA  17       61656   E4     5 #> 515        1        NA  17       61656   E5     4 #> 516        1        NA  17       61656   N1     1 #> 517        1        NA  17       61656   N2     3 #> 518        1        NA  17       61656   N3     3 #> 519        1        NA  17       61656   N4     2 #> 520        1        NA  17       61656   N5     1 #> 521        1        NA  17       61656   O1     6 #> 522        1        NA  17       61656   O2     1 #> 523        1        NA  17       61656   O3     3 #> 524        1        NA  17       61656   O4     2 #> 525        1        NA  17       61656   O5     4 #> 526        2        NA  17       61659   A1     1 #> 527        2        NA  17       61659   A2     6 #> 528        2        NA  17       61659   A3     6 #> 529        2        NA  17       61659   A4     1 #> 530        2        NA  17       61659   A5     5 #> 531        2        NA  17       61659   C1     5 #> 532        2        NA  17       61659   C2     4 #> 533        2        NA  17       61659   C3     4 #> 534        2        NA  17       61659   C4     2 #> 535        2        NA  17       61659   C5     3 #> 536        2        NA  17       61659   E1     1 #> 537        2        NA  17       61659   E2     2 #> 538        2        NA  17       61659   E3     4 #> 539        2        NA  17       61659   E4     3 #> 540        2        NA  17       61659   E5     4 #> 541        2        NA  17       61659   N1     2 #> 542        2        NA  17       61659   N2     5 #> 543        2        NA  17       61659   N3     5 #> 544        2        NA  17       61659   N4     4 #> 545        2        NA  17       61659   N5     6 #> 546        2        NA  17       61659   O1     5 #> 547        2        NA  17       61659   O2     1 #> 548        2        NA  17       61659   O3     6 #> 549        2        NA  17       61659   O4     6 #> 550        2        NA  17       61659   O5     2 #> 551        1         5  68       61661   A1     1 #> 552        1         5  68       61661   A2     5 #> 553        1         5  68       61661   A3     6 #> 554        1         5  68       61661   A4     5 #> 555        1         5  68       61661   A5     6 #> 556        1         5  68       61661   C1     4 #> 557        1         5  68       61661   C2     3 #> 558        1         5  68       61661   C3     2 #> 559        1         5  68       61661   C4     4 #> 560        1         5  68       61661   C5     5 #> 561        1         5  68       61661   E1     2 #> 562        1         5  68       61661   E2     1 #> 563        1         5  68       61661   E3     2 #> 564        1         5  68       61661   E4     5 #> 565        1         5  68       61661   E5     2 #> 566        1         5  68       61661   N1     2 #> 567        1         5  68       61661   N2     2 #> 568        1         5  68       61661   N3     2 #> 569        1         5  68       61661   N4     2 #> 570        1         5  68       61661   N5     2 #> 571        1         5  68       61661   O1     6 #> 572        1         5  68       61661   O2     1 #> 573        1         5  68       61661   O3     5 #> 574        1         5  68       61661   O4     5 #> 575        1         5  68       61661   O5     2 #> 576        2         2  27       61664   A1     2 #> 577        2         2  27       61664   A2     6 #> 578        2         2  27       61664   A3     5 #> 579        2         2  27       61664   A4     6 #> 580        2         2  27       61664   A5     5 #> 581        2         2  27       61664   C1     3 #> 582        2         2  27       61664   C2     5 #> 583        2         2  27       61664   C3     6 #> 584        2         2  27       61664   C4     3 #> 585        2         2  27       61664   C5     6 #> 586        2         2  27       61664   E1     2 #> 587        2         2  27       61664   E2     2 #> 588        2         2  27       61664   E3     4 #> 589        2         2  27       61664   E4     6 #> 590        2         2  27       61664   E5     6 #> 591        2         2  27       61664   N1     4 #> 592        2         2  27       61664   N2     4 #> 593        2         2  27       61664   N3     4 #> 594        2         2  27       61664   N4     6 #> 595        2         2  27       61664   N5     6 #> 596        2         2  27       61664   O1     6 #> 597        2         2  27       61664   O2     1 #> 598        2         2  27       61664   O3     5 #> 599        2         2  27       61664   O4     6 #> 600        2         2  27       61664   O5     1 #> 601        1         1  18       61667   A1     4 #> 602        1         1  18       61667   A2     5 #> 603        1         1  18       61667   A3     5 #> 604        1         1  18       61667   A4     6 #> 605        1         1  18       61667   A5     5 #> 606        1         1  18       61667   C1     5 #> 607        1         1  18       61667   C2     5 #> 608        1         1  18       61667   C3     4 #> 609        1         1  18       61667   C4     1 #> 610        1         1  18       61667   C5     1 #> 611        1         1  18       61667   E1     3 #> 612        1         1  18       61667   E2     2 #> 613        1         1  18       61667   E3     5 #> 614        1         1  18       61667   E4     5 #> 615        1         1  18       61667   E5     6 #> 616        1         1  18       61667   N1     2 #> 617        1         1  18       61667   N2     3 #> 618        1         1  18       61667   N3     3 #> 619        1         1  18       61667   N4     1 #> 620        1         1  18       61667   N5     1 #> 621        1         1  18       61667   O1     6 #> 622        1         1  18       61667   O2     2 #> 623        1         1  18       61667   O3     5 #> 624        1         1  18       61667   O4     6 #> 625        1         1  18       61667   O5     2 #> 626        2         3  20       61668   A1     1 #> 627        2         3  20       61668   A2     6 #> 628        2         3  20       61668   A3     6 #> 629        2         3  20       61668   A4     1 #> 630        2         3  20       61668   A5     6 #> 631        2         3  20       61668   C1     5 #> 632        2         3  20       61668   C2     2 #> 633        2         3  20       61668   C3     5 #> 634        2         3  20       61668   C4     1 #> 635        2         3  20       61668   C5     1 #> 636        2         3  20       61668   E1     1 #> 637        2         3  20       61668   E2     1 #> 638        2         3  20       61668   E3     6 #> 639        2         3  20       61668   E4     6 #> 640        2         3  20       61668   E5     6 #> 641        2         3  20       61668   N1     2 #> 642        2         3  20       61668   N2     3 #> 643        2         3  20       61668   N3     1 #> 644        2         3  20       61668   N4     2 #> 645        2         3  20       61668   N5     1 #> 646        2         3  20       61668   O1     6 #> 647        2         3  20       61668   O2     4 #> 648        2         3  20       61668   O3     5 #> 649        2         3  20       61668   O4     5 #> 650        2         3  20       61668   O5     3 #> 651        2         5  51       61669   A1     2 #> 652        2         5  51       61669   A2     4 #> 653        2         5  51       61669   A3     4 #> 654        2         5  51       61669   A4     4 #> 655        2         5  51       61669   A5     3 #> 656        2         5  51       61669   C1     6 #> 657        2         5  51       61669   C2     5 #> 658        2         5  51       61669   C3     6 #> 659        2         5  51       61669   C4     1 #> 660        2         5  51       61669   C5     1 #> 661        2         5  51       61669   E1     2 #> 662        2         5  51       61669   E2     4 #> 663        2         5  51       61669   E3     4 #> 664        2         5  51       61669   E4     2 #> 665        2         5  51       61669   E5     6 #> 666        2         5  51       61669   N1     3 #> 667        2         5  51       61669   N2     3 #> 668        2         5  51       61669   N3     5 #> 669        2         5  51       61669   N4     3 #> 670        2         5  51       61669   N5     2 #> 671        2         5  51       61669   O1     5 #> 672        2         5  51       61669   O2     2 #> 673        2         5  51       61669   O3     6 #> 674        2         5  51       61669   O4     6 #> 675        2         5  51       61669   O5     1 #> 676        2        NA  14       61670   A1     2 #> 677        2        NA  14       61670   A2     5 #> 678        2        NA  14       61670   A3     6 #> 679        2        NA  14       61670   A4     6 #> 680        2        NA  14       61670   A5     6 #> 681        2        NA  14       61670   C1     4 #> 682        2        NA  14       61670   C2     5 #> 683        2        NA  14       61670   C3     4 #> 684        2        NA  14       61670   C4     3 #> 685        2        NA  14       61670   C5     4 #> 686        2        NA  14       61670   E1     1 #> 687        2        NA  14       61670   E2     2 #> 688        2        NA  14       61670   E3     6 #> 689        2        NA  14       61670   E4     6 #> 690        2        NA  14       61670   E5     6 #> 691        2        NA  14       61670   N1     4 #> 692        2        NA  14       61670   N2     4 #> 693        2        NA  14       61670   N3     5 #> 694        2        NA  14       61670   N4     2 #> 695        2        NA  14       61670   N5     3 #> 696        2        NA  14       61670   O1     6 #> 697        2        NA  14       61670   O2     1 #> 698        2        NA  14       61670   O3     6 #> 699        2        NA  14       61670   O4     4 #> 700        2        NA  14       61670   O5     3 #> 701        2         3  33       61672   A1     2 #> 702        2         3  33       61672   A2     5 #> 703        2         3  33       61672   A3     1 #> 704        2         3  33       61672   A4     3 #> 705        2         3  33       61672   A5     5 #> 706        2         3  33       61672   C1     5 #> 707        2         3  33       61672   C2     4 #> 708        2         3  33       61672   C3     5 #> 709        2         3  33       61672   C4     2 #> 710        2         3  33       61672   C5     5 #> 711        2         3  33       61672   E1     1 #> 712        2         3  33       61672   E2     2 #> 713        2         3  33       61672   E3     6 #> 714        2         3  33       61672   E4     5 #> 715        2         3  33       61672   E5     4 #> 716        2         3  33       61672   N1     1 #> 717        2         3  33       61672   N2     4 #> 718        2         3  33       61672   N3     2 #> 719        2         3  33       61672   N4     2 #> 720        2         3  33       61672   N5     5 #> 721        2         3  33       61672   O1     2 #> 722        2         3  33       61672   O2     4 #> 723        2         3  33       61672   O3     5 #> 724        2         3  33       61672   O4     4 #> 725        2         3  33       61672   O5     1 #> 726        2         3  18       61673   A1     4 #> 727        2         3  18       61673   A2     5 #> 728        2         3  18       61673   A3     6 #> 729        2         3  18       61673   A4     5 #> 730        2         3  18       61673   A5     5 #> 731        2         3  18       61673   C1     5 #> 732        2         3  18       61673   C2     5 #> 733        2         3  18       61673   C3     3 #> 734        2         3  18       61673   C4     5 #> 735        2         3  18       61673   C5     4 #> 736        2         3  18       61673   E1     1 #> 737        2         3  18       61673   E2     2 #> 738        2         3  18       61673   E3     6 #> 739        2         3  18       61673   E4     5 #> 740        2         3  18       61673   E5     5 #> 741        2         3  18       61673   N1     5 #> 742        2         3  18       61673   N2     4 #> 743        2         3  18       61673   N3     4 #> 744        2         3  18       61673   N4     3 #> 745        2         3  18       61673   N5     1 #> 746        2         3  18       61673   O1     4 #> 747        2         3  18       61673   O2     4 #> 748        2         3  18       61673   O3     6 #> 749        2         3  18       61673   O4     5 #> 750        2         3  18       61673   O5     1 #> 751        2        NA  17       61678   A1     1 #> 752        2        NA  17       61678   A2     6 #> 753        2        NA  17       61678   A3     5 #> 754        2        NA  17       61678   A4     6 #> 755        2        NA  17       61678   A5     3 #> 756        2        NA  17       61678   C1     5 #> 757        2        NA  17       61678   C2     5 #> 758        2        NA  17       61678   C3     5 #> 759        2        NA  17       61678   C4     4 #> 760        2        NA  17       61678   C5     3 #> 761        2        NA  17       61678   E1     2 #> 762        2        NA  17       61678   E2     5 #> 763        2        NA  17       61678   E3     1 #> 764        2        NA  17       61678   E4     5 #> 765        2        NA  17       61678   E5     3 #> 766        2        NA  17       61678   N1     5 #> 767        2        NA  17       61678   N2     5 #> 768        2        NA  17       61678   N3     5 #> 769        2        NA  17       61678   N4     6 #> 770        2        NA  17       61678   N5     6 #> 771        2        NA  17       61678   O1     4 #> 772        2        NA  17       61678   O2     3 #> 773        2        NA  17       61678   O3     3 #> 774        2        NA  17       61678   O4     6 #> 775        2        NA  17       61678   O5     5 #> 776        2         3  41       61679   A1     2 #> 777        2         3  41       61679   A2     5 #> 778        2         3  41       61679   A3     6 #> 779        2         3  41       61679   A4     6 #> 780        2         3  41       61679   A5     6 #> 781        2         3  41       61679   C1     5 #> 782        2         3  41       61679   C2     5 #> 783        2         3  41       61679   C3     5 #> 784        2         3  41       61679   C4     2 #> 785        2         3  41       61679   C5     4 #> 786        2         3  41       61679   E1     1 #> 787        2         3  41       61679   E2     2 #> 788        2         3  41       61679   E3     4 #> 789        2         3  41       61679   E4     5 #> 790        2         3  41       61679   E5     5 #> 791        2         3  41       61679   N1     3 #> 792        2         3  41       61679   N2     2 #> 793        2         3  41       61679   N3     4 #> 794        2         3  41       61679   N4     1 #> 795        2         3  41       61679   N5     2 #> 796        2         3  41       61679   O1     5 #> 797        2         3  41       61679   O2     2 #> 798        2         3  41       61679   O3     5 #> 799        2         3  41       61679   O4     5 #> 800        2         3  41       61679   O5     2 #> 801        1         5  23       61682   A1     1 #> 802        1         5  23       61682   A2     5 #> 803        1         5  23       61682   A3     6 #> 804        1         5  23       61682   A4     5 #> 805        1         5  23       61682   A5     4 #> 806        1         5  23       61682   C1     1 #> 807        1         5  23       61682   C2     5 #> 808        1         5  23       61682   C3     6 #> 809        1         5  23       61682   C4     4 #> 810        1         5  23       61682   C5     6 #> 811        1         5  23       61682   E1     6 #> 812        1         5  23       61682   E2     6 #> 813        1         5  23       61682   E3     2 #> 814        1         5  23       61682   E4     1 #> 815        1         5  23       61682   E5     1 #> 816        1         5  23       61682   N1     1 #> 817        1         5  23       61682   N2     2 #> 818        1         5  23       61682   N3     1 #> 819        1         5  23       61682   N4     3 #> 820        1         5  23       61682   N5     6 #> 821        1         5  23       61682   O1     6 #> 822        1         5  23       61682   O2     6 #> 823        1         5  23       61682   O3     5 #> 824        1         5  23       61682   O4     6 #> 825        1         5  23       61682   O5     1 #> 826        2        NA  17       61683   A1     2 #> 827        2        NA  17       61683   A2     4 #> 828        2        NA  17       61683   A3     5 #> 829        2        NA  17       61683   A4     6 #> 830        2        NA  17       61683   A5     5 #> 831        2        NA  17       61683   C1     4 #> 832        2        NA  17       61683   C2     6 #> 833        2        NA  17       61683   C3     4 #> 834        2        NA  17       61683   C4     2 #> 835        2        NA  17       61683   C5     4 #> 836        2        NA  17       61683   E1     2 #> 837        2        NA  17       61683   E2     2 #> 838        2        NA  17       61683   E3     3 #> 839        2        NA  17       61683   E4     5 #> 840        2        NA  17       61683   E5     3 #> 841        2        NA  17       61683   N1     2 #> 842        2        NA  17       61683   N2     2 #> 843        2        NA  17       61683   N3     4 #> 844        2        NA  17       61683   N4     1 #> 845        2        NA  17       61683   N5     3 #> 846        2        NA  17       61683   O1     5 #> 847        2        NA  17       61683   O2     5 #> 848        2        NA  17       61683   O3     5 #> 849        2        NA  17       61683   O4     4 #> 850        2        NA  17       61683   O5     2 #> 851        1         3  20       61684   A1     4 #> 852        1         3  20       61684   A2     4 #> 853        1         3  20       61684   A3     4 #> 854        1         3  20       61684   A4     4 #> 855        1         3  20       61684   A5     4 #> 856        1         3  20       61684   C1     4 #> 857        1         3  20       61684   C2     3 #> 858        1         3  20       61684   C3     3 #> 859        1         3  20       61684   C4     3 #> 860        1         3  20       61684   C5     4 #> 861        1         3  20       61684   E1     2 #> 862        1         3  20       61684   E2     3 #> 863        1         3  20       61684   E3     4 #> 864        1         3  20       61684   E4     2 #> 865        1         3  20       61684   E5     3 #> 866        1         3  20       61684   N1    NA #> 867        1         3  20       61684   N2     2 #> 868        1         3  20       61684   N3     1 #> 869        1         3  20       61684   N4     2 #> 870        1         3  20       61684   N5     2 #> 871        1         3  20       61684   O1     4 #> 872        1         3  20       61684   O2     3 #> 873        1         3  20       61684   O3     5 #> 874        1         3  20       61684   O4     5 #> 875        1         3  20       61684   O5     3 #> 876        1         3  23       61685   A1     5 #> 877        1         3  23       61685   A2     3 #> 878        1         3  23       61685   A3     5 #> 879        1         3  23       61685   A4     4 #> 880        1         3  23       61685   A5     2 #> 881        1         3  23       61685   C1     2 #> 882        1         3  23       61685   C2     2 #> 883        1         3  23       61685   C3     4 #> 884        1         3  23       61685   C4     3 #> 885        1         3  23       61685   C5     4 #> 886        1         3  23       61685   E1     3 #> 887        1         3  23       61685   E2     4 #> 888        1         3  23       61685   E3     3 #> 889        1         3  23       61685   E4     2 #> 890        1         3  23       61685   E5     3 #> 891        1         3  23       61685   N1     5 #> 892        1         3  23       61685   N2     3 #> 893        1         3  23       61685   N3     4 #> 894        1         3  23       61685   N4     4 #> 895        1         3  23       61685   N5     3 #> 896        1         3  23       61685   O1     4 #> 897        1         3  23       61685   O2     5 #> 898        1         3  23       61685   O3     4 #> 899        1         3  23       61685   O4     4 #> 900        1         3  23       61685   O5     3 #> 901        1         3  20       61686   A1     1 #> 902        1         3  20       61686   A2     6 #> 903        1         3  20       61686   A3     4 #> 904        1         3  20       61686   A4     6 #> 905        1         3  20       61686   A5     4 #> 906        1         3  20       61686   C1     5 #> 907        1         3  20       61686   C2     6 #> 908        1         3  20       61686   C3     3 #> 909        1         3  20       61686   C4     1 #> 910        1         3  20       61686   C5     5 #> 911        1         3  20       61686   E1     6 #> 912        1         3  20       61686   E2     6 #> 913        1         3  20       61686   E3     3 #> 914        1         3  20       61686   E4     2 #> 915        1         3  20       61686   E5     2 #> 916        1         3  20       61686   N1     2 #> 917        1         3  20       61686   N2     2 #> 918        1         3  20       61686   N3     2 #> 919        1         3  20       61686   N4     4 #> 920        1         3  20       61686   N5     1 #> 921        1         3  20       61686   O1     5 #> 922        1         3  20       61686   O2     5 #> 923        1         3  20       61686   O3     4 #> 924        1         3  20       61686   O4     5 #> 925        1         3  20       61686   O5     3 #> 926        1         3  21       61687   A1     1 #> 927        1         3  21       61687   A2     4 #> 928        1         3  21       61687   A3     4 #> 929        1         3  21       61687   A4     2 #> 930        1         3  21       61687   A5     3 #> 931        1         3  21       61687   C1     6 #> 932        1         3  21       61687   C2     5 #> 933        1         3  21       61687   C3     6 #> 934        1         3  21       61687   C4     3 #> 935        1         3  21       61687   C5     4 #> 936        1         3  21       61687   E1     3 #> 937        1         3  21       61687   E2     4 #> 938        1         3  21       61687   E3     3 #> 939        1         3  21       61687   E4     3 #> 940        1         3  21       61687   E5     5 #> 941        1         3  21       61687   N1     5 #> 942        1         3  21       61687   N2     6 #> 943        1         3  21       61687   N3     5 #> 944        1         3  21       61687   N4     5 #> 945        1         3  21       61687   N5     4 #> 946        1         3  21       61687   O1     5 #> 947        1         3  21       61687   O2     5 #> 948        1         3  21       61687   O3     4 #> 949        1         3  21       61687   O4     5 #> 950        1         3  21       61687   O5     2 #> 951        1        NA  30       61688   A1     1 #> 952        1        NA  30       61688   A2     6 #> 953        1        NA  30       61688   A3     6 #> 954        1        NA  30       61688   A4     6 #> 955        1        NA  30       61688   A5     6 #> 956        1        NA  30       61688   C1     6 #> 957        1        NA  30       61688   C2     6 #> 958        1        NA  30       61688   C3     6 #> 959        1        NA  30       61688   C4     1 #> 960        1        NA  30       61688   C5     1 #> 961        1        NA  30       61688   E1     1 #> 962        1        NA  30       61688   E2     1 #> 963        1        NA  30       61688   E3     1 #> 964        1        NA  30       61688   E4     6 #> 965        1        NA  30       61688   E5     6 #> 966        1        NA  30       61688   N1     1 #> 967        1        NA  30       61688   N2     1 #> 968        1        NA  30       61688   N3     1 #> 969        1        NA  30       61688   N4     1 #> 970        1        NA  30       61688   N5     1 #> 971        1        NA  30       61688   O1     6 #> 972        1        NA  30       61688   O2     1 #> 973        1        NA  30       61688   O3     6 #> 974        1        NA  30       61688   O4     6 #> 975        1        NA  30       61688   O5     1 #> 976        2         5  48       61691   A1     1 #> 977        2         5  48       61691   A2     5 #> 978        2         5  48       61691   A3     4 #> 979        2         5  48       61691   A4     3 #> 980        2         5  48       61691   A5     5 #> 981        2         5  48       61691   C1     6 #> 982        2         5  48       61691   C2     5 #> 983        2         5  48       61691   C3     5 #> 984        2         5  48       61691   C4     2 #> 985        2         5  48       61691   C5     2 #> 986        2         5  48       61691   E1     3 #> 987        2         5  48       61691   E2     2 #> 988        2         5  48       61691   E3     3 #> 989        2         5  48       61691   E4     6 #> 990        2         5  48       61691   E5     5 #> 991        2         5  48       61691   N1     1 #> 992        2         5  48       61691   N2     2 #> 993        2         5  48       61691   N3     1 #> 994        2         5  48       61691   N4     2 #> 995        2         5  48       61691   N5     1 #> 996        2         5  48       61691   O1     5 #> 997        2         5  48       61691   O2     1 #> 998        2         5  48       61691   O3     6 #> 999        2         5  48       61691   O4     6 #> 1000       2         5  48       61691   O5     1 #> 1001       2         3  40       61692   A1     1 #> 1002       2         3  40       61692   A2     5 #> 1003       2         3  40       61692   A3     5 #> 1004       2         3  40       61692   A4     6 #> 1005       2         3  40       61692   A5     5 #> 1006       2         3  40       61692   C1     4 #> 1007       2         3  40       61692   C2     4 #> 1008       2         3  40       61692   C3     4 #> 1009       2         3  40       61692   C4     3 #> 1010       2         3  40       61692   C5     4 #> 1011       2         3  40       61692   E1     4 #> 1012       2         3  40       61692   E2     3 #> 1013       2         3  40       61692   E3     4 #> 1014       2         3  40       61692   E4     4 #> 1015       2         3  40       61692   E5     4 #> 1016       2         3  40       61692   N1     2 #> 1017       2         3  40       61692   N2     2 #> 1018       2         3  40       61692   N3     3 #> 1019       2         3  40       61692   N4     3 #> 1020       2         3  40       61692   N5     3 #> 1021       2         3  40       61692   O1     4 #> 1022       2         3  40       61692   O2     3 #> 1023       2         3  40       61692   O3     2 #> 1024       2         3  40       61692   O4     5 #> 1025       2         3  40       61692   O5     2 #> 1026       2         4  27       61693   A1     5 #> 1027       2         4  27       61693   A2     4 #> 1028       2         4  27       61693   A3     3 #> 1029       2         4  27       61693   A4     6 #> 1030       2         4  27       61693   A5     4 #> 1031       2         4  27       61693   C1     5 #> 1032       2         4  27       61693   C2     2 #> 1033       2         4  27       61693   C3     5 #> 1034       2         4  27       61693   C4     2 #> 1035       2         4  27       61693   C5     4 #> 1036       2         4  27       61693   E1     6 #> 1037       2         4  27       61693   E2     4 #> 1038       2         4  27       61693   E3     2 #> 1039       2         4  27       61693   E4     4 #> 1040       2         4  27       61693   E5     4 #> 1041       2         4  27       61693   N1     1 #> 1042       2         4  27       61693   N2     2 #> 1043       2         4  27       61693   N3     1 #> 1044       2         4  27       61693   N4     2 #> 1045       2         4  27       61693   N5    NA #> 1046       2         4  27       61693   O1     3 #> 1047       2         4  27       61693   O2     3 #> 1048       2         4  27       61693   O3     2 #> 1049       2         4  27       61693   O4     2 #> 1050       2         4  27       61693   O5     5 #> 1051       1         1  18       61696   A1     1 #> 1052       1         1  18       61696   A2     5 #> 1053       1         1  18       61696   A3     4 #> 1054       1         1  18       61696   A4     4 #> 1055       1         1  18       61696   A5     5 #> 1056       1         1  18       61696   C1     4 #> 1057       1         1  18       61696   C2     5 #> 1058       1         1  18       61696   C3     4 #> 1059       1         1  18       61696   C4     3 #> 1060       1         1  18       61696   C5     3 #> 1061       1         1  18       61696   E1     3 #> 1062       1         1  18       61696   E2     3 #> 1063       1         1  18       61696   E3     2 #> 1064       1         1  18       61696   E4     5 #> 1065       1         1  18       61696   E5     4 #> 1066       1         1  18       61696   N1     2 #> 1067       1         1  18       61696   N2     3 #> 1068       1         1  18       61696   N3     1 #> 1069       1         1  18       61696   N4     3 #> 1070       1         1  18       61696   N5     2 #> 1071       1         1  18       61696   O1     4 #> 1072       1         1  18       61696   O2     3 #> 1073       1         1  18       61696   O3     5 #> 1074       1         1  18       61696   O4     4 #> 1075       1         1  18       61696   O5     3 #> 1076       1         4  20       61698   A1     5 #> 1077       1         4  20       61698   A2     6 #> 1078       1         4  20       61698   A3     4 #> 1079       1         4  20       61698   A4     3 #> 1080       1         4  20       61698   A5     5 #> 1081       1         4  20       61698   C1     5 #> 1082       1         4  20       61698   C2     3 #> 1083       1         4  20       61698   C3     3 #> 1084       1         4  20       61698   C4     4 #> 1085       1         4  20       61698   C5     5 #> 1086       1         4  20       61698   E1     6 #> 1087       1         4  20       61698   E2     4 #> 1088       1         4  20       61698   E3     4 #> 1089       1         4  20       61698   E4     4 #> 1090       1         4  20       61698   E5     3 #> 1091       1         4  20       61698   N1     2 #> 1092       1         4  20       61698   N2     2 #> 1093       1         4  20       61698   N3     3 #> 1094       1         4  20       61698   N4     4 #> 1095       1         4  20       61698   N5     5 #> 1096       1         4  20       61698   O1     3 #> 1097       1         4  20       61698   O2     5 #> 1098       1         4  20       61698   O3     4 #> 1099       1         4  20       61698   O4     4 #> 1100       1         4  20       61698   O5     4 #> 1101       2         5  24       61700   A1     2 #> 1102       2         5  24       61700   A2     6 #> 1103       2         5  24       61700   A3     6 #> 1104       2         5  24       61700   A4     6 #> 1105       2         5  24       61700   A5     6 #> 1106       2         5  24       61700   C1     5 #> 1107       2         5  24       61700   C2     4 #> 1108       2         5  24       61700   C3     5 #> 1109       2         5  24       61700   C4     3 #> 1110       2         5  24       61700   C5     4 #> 1111       2         5  24       61700   E1     2 #> 1112       2         5  24       61700   E2     2 #> 1113       2         5  24       61700   E3     4 #> 1114       2         5  24       61700   E4     5 #> 1115       2         5  24       61700   E5     5 #> 1116       2         5  24       61700   N1     2 #> 1117       2         5  24       61700   N2     2 #> 1118       2         5  24       61700   N3     2 #> 1119       2         5  24       61700   N4     2 #> 1120       2         5  24       61700   N5     3 #> 1121       2         5  24       61700   O1     5 #> 1122       2         5  24       61700   O2     2 #> 1123       2         5  24       61700   O3     5 #> 1124       2         5  24       61700   O4     5 #> 1125       2         5  24       61700   O5     1 #> 1126       1         3  25       61701   A1     1 #> 1127       1         3  25       61701   A2     6 #> 1128       1         3  25       61701   A3     6 #> 1129       1         3  25       61701   A4     6 #> 1130       1         3  25       61701   A5     6 #> 1131       1         3  25       61701   C1     5 #> 1132       1         3  25       61701   C2     2 #> 1133       1         3  25       61701   C3     1 #> 1134       1         3  25       61701   C4     2 #> 1135       1         3  25       61701   C5     1 #> 1136       1         3  25       61701   E1     6 #> 1137       1         3  25       61701   E2     5 #> 1138       1         3  25       61701   E3     6 #> 1139       1         3  25       61701   E4     6 #> 1140       1         3  25       61701   E5     5 #> 1141       1         3  25       61701   N1     2 #> 1142       1         3  25       61701   N2     1 #> 1143       1         3  25       61701   N3     4 #> 1144       1         3  25       61701   N4     6 #> 1145       1         3  25       61701   N5     5 #> 1146       1         3  25       61701   O1     6 #> 1147       1         3  25       61701   O2     5 #> 1148       1         3  25       61701   O3     6 #> 1149       1         3  25       61701   O4     6 #> 1150       1         3  25       61701   O5     1 #> 1151       1         2  22       61702   A1     5 #> 1152       1         2  22       61702   A2     5 #> 1153       1         2  22       61702   A3     3 #> 1154       1         2  22       61702   A4     4 #> 1155       1         2  22       61702   A5     3 #> 1156       1         2  22       61702   C1     4 #> 1157       1         2  22       61702   C2     4 #> 1158       1         2  22       61702   C3     3 #> 1159       1         2  22       61702   C4     4 #> 1160       1         2  22       61702   C5     5 #> 1161       1         2  22       61702   E1     4 #> 1162       1         2  22       61702   E2     4 #> 1163       1         2  22       61702   E3     5 #> 1164       1         2  22       61702   E4     2 #> 1165       1         2  22       61702   E5     4 #> 1166       1         2  22       61702   N1     4 #> 1167       1         2  22       61702   N2     5 #> 1168       1         2  22       61702   N3     3 #> 1169       1         2  22       61702   N4     5 #> 1170       1         2  22       61702   N5     2 #> 1171       1         2  22       61702   O1     3 #> 1172       1         2  22       61702   O2     5 #> 1173       1         2  22       61702   O3     4 #> 1174       1         2  22       61702   O4     4 #> 1175       1         2  22       61702   O5     2 #> 1176       2         1  18       61703   A1     2 #> 1177       2         1  18       61703   A2     6 #> 1178       2         1  18       61703   A3     4 #> 1179       2         1  18       61703   A4     5 #> 1180       2         1  18       61703   A5     5 #> 1181       2         1  18       61703   C1     3 #> 1182       2         1  18       61703   C2     2 #> 1183       2         1  18       61703   C3     3 #> 1184       2         1  18       61703   C4     4 #> 1185       2         1  18       61703   C5     6 #> 1186       2         1  18       61703   E1     2 #> 1187       2         1  18       61703   E2     4 #> 1188       2         1  18       61703   E3     2 #> 1189       2         1  18       61703   E4     4 #> 1190       2         1  18       61703   E5     4 #> 1191       2         1  18       61703   N1     3 #> 1192       2         1  18       61703   N2     4 #> 1193       2         1  18       61703   N3     2 #> 1194       2         1  18       61703   N4     2 #> 1195       2         1  18       61703   N5     4 #> 1196       2         1  18       61703   O1     5 #> 1197       2         1  18       61703   O2     4 #> 1198       2         1  18       61703   O3     5 #> 1199       2         1  18       61703   O4     3 #> 1200       2         1  18       61703   O5     2 #> 1201       2         1  43       61713   A1     1 #> 1202       2         1  43       61713   A2     5 #> 1203       2         1  43       61713   A3     3 #> 1204       2         1  43       61713   A4     2 #> 1205       2         1  43       61713   A5     3 #> 1206       2         1  43       61713   C1     3 #> 1207       2         1  43       61713   C2     6 #> 1208       2         1  43       61713   C3     3 #> 1209       2         1  43       61713   C4     1 #> 1210       2         1  43       61713   C5     3 #> 1211       2         1  43       61713   E1     5 #> 1212       2         1  43       61713   E2     5 #> 1213       2         1  43       61713   E3     5 #> 1214       2         1  43       61713   E4     5 #> 1215       2         1  43       61713   E5     3 #> 1216       2         1  43       61713   N1     5 #> 1217       2         1  43       61713   N2     5 #> 1218       2         1  43       61713   N3     5 #> 1219       2         1  43       61713   N4     5 #> 1220       2         1  43       61713   N5     3 #> 1221       2         1  43       61713   O1     3 #> 1222       2         1  43       61713   O2     3 #> 1223       2         1  43       61713   O3     2 #> 1224       2         1  43       61713   O4     5 #> 1225       2         1  43       61713   O5     1 #> 1226       1         3  20       61715   A1     1 #> 1227       1         3  20       61715   A2     6 #> 1228       1         3  20       61715   A3     6 #> 1229       1         3  20       61715   A4     6 #> 1230       1         3  20       61715   A5     6 #> 1231       1         3  20       61715   C1     5 #> 1232       1         3  20       61715   C2     5 #> 1233       1         3  20       61715   C3     4 #> 1234       1         3  20       61715   C4     1 #> 1235       1         3  20       61715   C5     2 #> 1236       1         3  20       61715   E1     1 #> 1237       1         3  20       61715   E2     1 #> 1238       1         3  20       61715   E3     6 #> 1239       1         3  20       61715   E4     6 #> 1240       1         3  20       61715   E5     6 #> 1241       1         3  20       61715   N1     4 #> 1242       1         3  20       61715   N2     4 #> 1243       1         3  20       61715   N3     1 #> 1244       1         3  20       61715   N4     1 #> 1245       1         3  20       61715   N5     1 #> 1246       1         3  20       61715   O1     6 #> 1247       1         3  20       61715   O2     3 #> 1248       1         3  20       61715   O3     6 #> 1249       1         3  20       61715   O4     6 #> 1250       1         3  20       61715   O5     1 #> 1251       2         3  24       61716   A1     1 #> 1252       2         3  24       61716   A2     6 #> 1253       2         3  24       61716   A3     6 #> 1254       2         3  24       61716   A4     6 #> 1255       2         3  24       61716   A5     4 #> 1256       2         3  24       61716   C1     4 #> 1257       2         3  24       61716   C2     3 #> 1258       2         3  24       61716   C3     1 #> 1259       2         3  24       61716   C4     4 #> 1260       2         3  24       61716   C5     2 #> 1261       2         3  24       61716   E1     2 #> 1262       2         3  24       61716   E2     2 #> 1263       2         3  24       61716   E3     5 #> 1264       2         3  24       61716   E4     4 #> 1265       2         3  24       61716   E5     4 #> 1266       2         3  24       61716   N1     6 #> 1267       2         3  24       61716   N2     6 #> 1268       2         3  24       61716   N3     6 #> 1269       2         3  24       61716   N4     3 #> 1270       2         3  24       61716   N5     3 #> 1271       2         3  24       61716   O1     3 #> 1272       2         3  24       61716   O2     1 #> 1273       2         3  24       61716   O3     4 #> 1274       2         3  24       61716   O4     6 #> 1275       2         3  24       61716   O5     2 #> 1276       2         4  26       61723   A1     1 #> 1277       2         4  26       61723   A2     5 #> 1278       2         4  26       61723   A3     6 #> 1279       2         4  26       61723   A4     5 #> 1280       2         4  26       61723   A5     4 #> 1281       2         4  26       61723   C1     6 #> 1282       2         4  26       61723   C2     6 #> 1283       2         4  26       61723   C3     6 #> 1284       2         4  26       61723   C4     6 #> 1285       2         4  26       61723   C5     2 #> 1286       2         4  26       61723   E1     4 #> 1287       2         4  26       61723   E2     4 #> 1288       2         4  26       61723   E3     4 #> 1289       2         4  26       61723   E4     3 #> 1290       2         4  26       61723   E5     6 #> 1291       2         4  26       61723   N1     1 #> 1292       2         4  26       61723   N2     1 #> 1293       2         4  26       61723   N3     1 #> 1294       2         4  26       61723   N4     6 #> 1295       2         4  26       61723   N5     5 #> 1296       2         4  26       61723   O1     5 #> 1297       2         4  26       61723   O2     6 #> 1298       2         4  26       61723   O3     3 #> 1299       2         4  26       61723   O4     6 #> 1300       2         4  26       61723   O5     3 #> 1301       1         4  26       61724   A1     3 #> 1302       1         4  26       61724   A2     6 #> 1303       1         4  26       61724   A3     4 #> 1304       1         4  26       61724   A4     4 #> 1305       1         4  26       61724   A5     4 #> 1306       1         4  26       61724   C1     5 #> 1307       1         4  26       61724   C2     5 #> 1308       1         4  26       61724   C3     3 #> 1309       1         4  26       61724   C4     2 #> 1310       1         4  26       61724   C5     5 #> 1311       1         4  26       61724   E1     1 #> 1312       1         4  26       61724   E2     1 #> 1313       1         4  26       61724   E3     4 #> 1314       1         4  26       61724   E4     6 #> 1315       1         4  26       61724   E5     5 #> 1316       1         4  26       61724   N1     2 #> 1317       1         4  26       61724   N2     2 #> 1318       1         4  26       61724   N3     1 #> 1319       1         4  26       61724   N4     1 #> 1320       1         4  26       61724   N5     1 #> 1321       1         4  26       61724   O1     5 #> 1322       1         4  26       61724   O2     1 #> 1323       1         4  26       61724   O3     5 #> 1324       1         4  26       61724   O4     5 #> 1325       1         4  26       61724   O5     6 #> 1326       2         3  25       61725   A1     4 #> 1327       2         3  25       61725   A2     3 #> 1328       2         3  25       61725   A3     5 #> 1329       2         3  25       61725   A4     6 #> 1330       2         3  25       61725   A5     3 #> 1331       2         3  25       61725   C1     5 #> 1332       2         3  25       61725   C2     6 #> 1333       2         3  25       61725   C3     2 #> 1334       2         3  25       61725   C4     5 #> 1335       2         3  25       61725   C5     2 #> 1336       2         3  25       61725   E1     3 #> 1337       2         3  25       61725   E2     5 #> 1338       2         3  25       61725   E3     2 #> 1339       2         3  25       61725   E4     6 #> 1340       2         3  25       61725   E5     2 #> 1341       2         3  25       61725   N1     6 #> 1342       2         3  25       61725   N2     5 #> 1343       2         3  25       61725   N3     5 #> 1344       2         3  25       61725   N4     5 #> 1345       2         3  25       61725   N5     6 #> 1346       2         3  25       61725   O1     2 #> 1347       2         3  25       61725   O2     5 #> 1348       2         3  25       61725   O3     2 #> 1349       2         3  25       61725   O4     6 #> 1350       2         3  25       61725   O5     4 #> 1351       1         4  25       61728   A1     1 #> 1352       1         4  25       61728   A2     6 #> 1353       1         4  25       61728   A3     6 #> 1354       1         4  25       61728   A4     6 #> 1355       1         4  25       61728   A5     6 #> 1356       1         4  25       61728   C1     6 #> 1357       1         4  25       61728   C2     5 #> 1358       1         4  25       61728   C3     5 #> 1359       1         4  25       61728   C4     2 #> 1360       1         4  25       61728   C5     2 #> 1361       1         4  25       61728   E1     1 #> 1362       1         4  25       61728   E2     2 #> 1363       1         4  25       61728   E3     5 #> 1364       1         4  25       61728   E4     6 #> 1365       1         4  25       61728   E5     5 #> 1366       1         4  25       61728   N1     2 #> 1367       1         4  25       61728   N2     3 #> 1368       1         4  25       61728   N3     2 #> 1369       1         4  25       61728   N4     3 #> 1370       1         4  25       61728   N5     2 #> 1371       1         4  25       61728   O1     5 #> 1372       1         4  25       61728   O2     3 #> 1373       1         4  25       61728   O3     5 #> 1374       1         4  25       61728   O4     5 #> 1375       1         4  25       61728   O5     2 #> 1376       1         5  26       61730   A1     1 #> 1377       1         5  26       61730   A2     4 #> 1378       1         5  26       61730   A3     3 #> 1379       1         5  26       61730   A4     5 #> 1380       1         5  26       61730   A5     5 #> 1381       1         5  26       61730   C1     5 #> 1382       1         5  26       61730   C2     5 #> 1383       1         5  26       61730   C3     4 #> 1384       1         5  26       61730   C4     4 #> 1385       1         5  26       61730   C5     5 #> 1386       1         5  26       61730   E1     2 #> 1387       1         5  26       61730   E2     5 #> 1388       1         5  26       61730   E3     4 #> 1389       1         5  26       61730   E4     5 #> 1390       1         5  26       61730   E5     5 #> 1391       1         5  26       61730   N1     2 #> 1392       1         5  26       61730   N2     4 #> 1393       1         5  26       61730   N3     4 #> 1394       1         5  26       61730   N4     5 #> 1395       1         5  26       61730   N5     3 #> 1396       1         5  26       61730   O1     5 #> 1397       1         5  26       61730   O2     1 #> 1398       1         5  26       61730   O3     6 #> 1399       1         5  26       61730   O4     6 #> 1400       1         5  26       61730   O5     1 #> 1401       2         3  21       61731   A1     1 #> 1402       2         3  21       61731   A2     4 #> 1403       2         3  21       61731   A3     2 #> 1404       2         3  21       61731   A4     2 #> 1405       2         3  21       61731   A5     2 #> 1406       2         3  21       61731   C1     5 #> 1407       2         3  21       61731   C2     5 #> 1408       2         3  21       61731   C3     5 #> 1409       2         3  21       61731   C4     5 #> 1410       2         3  21       61731   C5     1 #> 1411       2         3  21       61731   E1     4 #> 1412       2         3  21       61731   E2     5 #> 1413       2         3  21       61731   E3     4 #> 1414       2         3  21       61731   E4     3 #> 1415       2         3  21       61731   E5     4 #> 1416       2         3  21       61731   N1     3 #> 1417       2         3  21       61731   N2     4 #> 1418       2         3  21       61731   N3     5 #> 1419       2         3  21       61731   N4     5 #> 1420       2         3  21       61731   N5     5 #> 1421       2         3  21       61731   O1     4 #> 1422       2         3  21       61731   O2     4 #> 1423       2         3  21       61731   O3     6 #> 1424       2         3  21       61731   O4     6 #> 1425       2         3  21       61731   O5     3 #> 1426       1         5  24       61732   A1     3 #> 1427       1         5  24       61732   A2     4 #> 1428       1         5  24       61732   A3     5 #> 1429       1         5  24       61732   A4     2 #> 1430       1         5  24       61732   A5     4 #> 1431       1         5  24       61732   C1     5 #> 1432       1         5  24       61732   C2     4 #> 1433       1         5  24       61732   C3     5 #> 1434       1         5  24       61732   C4     2 #> 1435       1         5  24       61732   C5     4 #> 1436       1         5  24       61732   E1     5 #> 1437       1         5  24       61732   E2     5 #> 1438       1         5  24       61732   E3     5 #> 1439       1         5  24       61732   E4     4 #> 1440       1         5  24       61732   E5     5 #> 1441       1         5  24       61732   N1     5 #> 1442       1         5  24       61732   N2     5 #> 1443       1         5  24       61732   N3     5 #> 1444       1         5  24       61732   N4     3 #> 1445       1         5  24       61732   N5     2 #> 1446       1         5  24       61732   O1     5 #> 1447       1         5  24       61732   O2     2 #> 1448       1         5  24       61732   O3     5 #> 1449       1         5  24       61732   O4     5 #> 1450       1         5  24       61732   O5     5 #> 1451       2         2  50       61740   A1     1 #> 1452       2         2  50       61740   A2     6 #> 1453       2         2  50       61740   A3     5 #> 1454       2         2  50       61740   A4     4 #> 1455       2         2  50       61740   A5     4 #> 1456       2         2  50       61740   C1     6 #> 1457       2         2  50       61740   C2     6 #> 1458       2         2  50       61740   C3     6 #> 1459       2         2  50       61740   C4     1 #> 1460       2         2  50       61740   C5     4 #> 1461       2         2  50       61740   E1     4 #> 1462       2         2  50       61740   E2     4 #> 1463       2         2  50       61740   E3     1 #> 1464       2         2  50       61740   E4     2 #> 1465       2         2  50       61740   E5     5 #> 1466       2         2  50       61740   N1     3 #> 1467       2         2  50       61740   N2     4 #> 1468       2         2  50       61740   N3     4 #> 1469       2         2  50       61740   N4     4 #> 1470       2         2  50       61740   N5     4 #> 1471       2         2  50       61740   O1     4 #> 1472       2         2  50       61740   O2     4 #> 1473       2         2  50       61740   O3     4 #> 1474       2         2  50       61740   O4     2 #> 1475       2         2  50       61740   O5     1 #> 1476       1         5  29       61742   A1     3 #> 1477       1         5  29       61742   A2     3 #> 1478       1         5  29       61742   A3     5 #> 1479       1         5  29       61742   A4     4 #> 1480       1         5  29       61742   A5     5 #> 1481       1         5  29       61742   C1     6 #> 1482       1         5  29       61742   C2     4 #> 1483       1         5  29       61742   C3     4 #> 1484       1         5  29       61742   C4     2 #> 1485       1         5  29       61742   C5     2 #> 1486       1         5  29       61742   E1     2 #> 1487       1         5  29       61742   E2     1 #> 1488       1         5  29       61742   E3     4 #> 1489       1         5  29       61742   E4     6 #> 1490       1         5  29       61742   E5     4 #> 1491       1         5  29       61742   N1     1 #> 1492       1         5  29       61742   N2     2 #> 1493       1         5  29       61742   N3     1 #> 1494       1         5  29       61742   N4     4 #> 1495       1         5  29       61742   N5     1 #> 1496       1         5  29       61742   O1     4 #> 1497       1         5  29       61742   O2     3 #> 1498       1         5  29       61742   O3     5 #> 1499       1         5  29       61742   O4     5 #> 1500       1         5  29       61742   O5     2 #> 1501       1         1  32       61748   A1     2 #> 1502       1         1  32       61748   A2     3 #> 1503       1         1  32       61748   A3     4 #> 1504       1         1  32       61748   A4     4 #> 1505       1         1  32       61748   A5     5 #> 1506       1         1  32       61748   C1     5 #> 1507       1         1  32       61748   C2     3 #> 1508       1         1  32       61748   C3     2 #> 1509       1         1  32       61748   C4     4 #> 1510       1         1  32       61748   C5     6 #> 1511       1         1  32       61748   E1     4 #> 1512       1         1  32       61748   E2     4 #> 1513       1         1  32       61748   E3     3 #> 1514       1         1  32       61748   E4     5 #> 1515       1         1  32       61748   E5     2 #> 1516       1         1  32       61748   N1     4 #> 1517       1         1  32       61748   N2     4 #> 1518       1         1  32       61748   N3     6 #> 1519       1         1  32       61748   N4     5 #> 1520       1         1  32       61748   N5     2 #> 1521       1         1  32       61748   O1     2 #> 1522       1         1  32       61748   O2     4 #> 1523       1         1  32       61748   O3     3 #> 1524       1         1  32       61748   O4     5 #> 1525       1         1  32       61748   O5     5 #> 1526       1         1  18       61749   A1     2 #> 1527       1         1  18       61749   A2     4 #> 1528       1         1  18       61749   A3     4 #> 1529       1         1  18       61749   A4     5 #> 1530       1         1  18       61749   A5     3 #> 1531       1         1  18       61749   C1     5 #> 1532       1         1  18       61749   C2     5 #> 1533       1         1  18       61749   C3     4 #> 1534       1         1  18       61749   C4     3 #> 1535       1         1  18       61749   C5     4 #> 1536       1         1  18       61749   E1     6 #> 1537       1         1  18       61749   E2     5 #> 1538       1         1  18       61749   E3     4 #> 1539       1         1  18       61749   E4     3 #> 1540       1         1  18       61749   E5     4 #> 1541       1         1  18       61749   N1     4 #> 1542       1         1  18       61749   N2     5 #> 1543       1         1  18       61749   N3     4 #> 1544       1         1  18       61749   N4     5 #> 1545       1         1  18       61749   N5     5 #> 1546       1         1  18       61749   O1     6 #> 1547       1         1  18       61749   O2     6 #> 1548       1         1  18       61749   O3     4 #> 1549       1         1  18       61749   O4     6 #> 1550       1         1  18       61749   O5     2 #> 1551       2         4  32       61754   A1     1 #> 1552       2         4  32       61754   A2     4 #> 1553       2         4  32       61754   A3     6 #> 1554       2         4  32       61754   A4     6 #> 1555       2         4  32       61754   A5     6 #> 1556       2         4  32       61754   C1    NA #> 1557       2         4  32       61754   C2     6 #> 1558       2         4  32       61754   C3     6 #> 1559       2         4  32       61754   C4     2 #> 1560       2         4  32       61754   C5     3 #> 1561       2         4  32       61754   E1     1 #> 1562       2         4  32       61754   E2     1 #> 1563       2         4  32       61754   E3     5 #> 1564       2         4  32       61754   E4     6 #> 1565       2         4  32       61754   E5     6 #> 1566       2         4  32       61754   N1     4 #> 1567       2         4  32       61754   N2     4 #> 1568       2         4  32       61754   N3     3 #> 1569       2         4  32       61754   N4     1 #> 1570       2         4  32       61754   N5     3 #> 1571       2         4  32       61754   O1     5 #> 1572       2         4  32       61754   O2     3 #> 1573       2         4  32       61754   O3     3 #> 1574       2         4  32       61754   O4     6 #> 1575       2         4  32       61754   O5     3 #> 1576       2         3  26       61756   A1     4 #> 1577       2         3  26       61756   A2     5 #> 1578       2         3  26       61756   A3     3 #> 1579       2         3  26       61756   A4     5 #> 1580       2         3  26       61756   A5     4 #> 1581       2         3  26       61756   C1     6 #> 1582       2         3  26       61756   C2     5 #> 1583       2         3  26       61756   C3     5 #> 1584       2         3  26       61756   C4     2 #> 1585       2         3  26       61756   C5     2 #> 1586       2         3  26       61756   E1     1 #> 1587       2         3  26       61756   E2     1 #> 1588       2         3  26       61756   E3     5 #> 1589       2         3  26       61756   E4     6 #> 1590       2         3  26       61756   E5     6 #> 1591       2         3  26       61756   N1     3 #> 1592       2         3  26       61756   N2     5 #> 1593       2         3  26       61756   N3     3 #> 1594       2         3  26       61756   N4     1 #> 1595       2         3  26       61756   N5     3 #> 1596       2         3  26       61756   O1     4 #> 1597       2         3  26       61756   O2     1 #> 1598       2         3  26       61756   O3     4 #> 1599       2         3  26       61756   O4     6 #> 1600       2         3  26       61756   O5     1 #> 1601       2         5  27       61757   A1     2 #> 1602       2         5  27       61757   A2     6 #> 1603       2         5  27       61757   A3     6 #> 1604       2         5  27       61757   A4     6 #> 1605       2         5  27       61757   A5     6 #> 1606       2         5  27       61757   C1     4 #> 1607       2         5  27       61757   C2     6 #> 1608       2         5  27       61757   C3     6 #> 1609       2         5  27       61757   C4     1 #> 1610       2         5  27       61757   C5     4 #> 1611       2         5  27       61757   E1     3 #> 1612       2         5  27       61757   E2     6 #> 1613       2         5  27       61757   E3     5 #> 1614       2         5  27       61757   E4     4 #> 1615       2         5  27       61757   E5     4 #> 1616       2         5  27       61757   N1     4 #> 1617       2         5  27       61757   N2     3 #> 1618       2         5  27       61757   N3     6 #> 1619       2         5  27       61757   N4     6 #> 1620       2         5  27       61757   N5     4 #> 1621       2         5  27       61757   O1     4 #> 1622       2         5  27       61757   O2     1 #> 1623       2         5  27       61757   O3     6 #> 1624       2         5  27       61757   O4     6 #> 1625       2         5  27       61757   O5     3 #> 1626       2         3  19       61759   A1     2 #> 1627       2         3  19       61759   A2    NA #> 1628       2         3  19       61759   A3     4 #> 1629       2         3  19       61759   A4     6 #> 1630       2         3  19       61759   A5     4 #> 1631       2         3  19       61759   C1     5 #> 1632       2         3  19       61759   C2     4 #> 1633       2         3  19       61759   C3     5 #> 1634       2         3  19       61759   C4     2 #> 1635       2         3  19       61759   C5     1 #> 1636       2         3  19       61759   E1     5 #> 1637       2         3  19       61759   E2     5 #> 1638       2         3  19       61759   E3     3 #> 1639       2         3  19       61759   E4     3 #> 1640       2         3  19       61759   E5     3 #> 1641       2         3  19       61759   N1     1 #> 1642       2         3  19       61759   N2     1 #> 1643       2         3  19       61759   N3     1 #> 1644       2         3  19       61759   N4    NA #> 1645       2         3  19       61759   N5     1 #> 1646       2         3  19       61759   O1     2 #> 1647       2         3  19       61759   O2     1 #> 1648       2         3  19       61759   O3     4 #> 1649       2         3  19       61759   O4     6 #> 1650       2         3  19       61759   O5     1 #> 1651       1         4  21       61761   A1     1 #> 1652       1         4  21       61761   A2     5 #> 1653       1         4  21       61761   A3     4 #> 1654       1         4  21       61761   A4     2 #> 1655       1         4  21       61761   A5     5 #> 1656       1         4  21       61761   C1     1 #> 1657       1         4  21       61761   C2     2 #> 1658       1         4  21       61761   C3     2 #> 1659       1         4  21       61761   C4     2 #> 1660       1         4  21       61761   C5     6 #> 1661       1         4  21       61761   E1     2 #> 1662       1         4  21       61761   E2     5 #> 1663       1         4  21       61761   E3     2 #> 1664       1         4  21       61761   E4     2 #> 1665       1         4  21       61761   E5     1 #> 1666       1         4  21       61761   N1     2 #> 1667       1         4  21       61761   N2     5 #> 1668       1         4  21       61761   N3     5 #> 1669       1         4  21       61761   N4     4 #> 1670       1         4  21       61761   N5     2 #> 1671       1         4  21       61761   O1     5 #> 1672       1         4  21       61761   O2     4 #> 1673       1         4  21       61761   O3     4 #> 1674       1         4  21       61761   O4     6 #> 1675       1         4  21       61761   O5     1 #> 1676       1         3  21       61762   A1     4 #> 1677       1         3  21       61762   A2     3 #> 1678       1         3  21       61762   A3     2 #> 1679       1         3  21       61762   A4     2 #> 1680       1         3  21       61762   A5     2 #> 1681       1         3  21       61762   C1     4 #> 1682       1         3  21       61762   C2     2 #> 1683       1         3  21       61762   C3     2 #> 1684       1         3  21       61762   C4     4 #> 1685       1         3  21       61762   C5     5 #> 1686       1         3  21       61762   E1     4 #> 1687       1         3  21       61762   E2     3 #> 1688       1         3  21       61762   E3     4 #> 1689       1         3  21       61762   E4     2 #> 1690       1         3  21       61762   E5     4 #> 1691       1         3  21       61762   N1     1 #> 1692       1         3  21       61762   N2     2 #> 1693       1         3  21       61762   N3     1 #> 1694       1         3  21       61762   N4     5 #> 1695       1         3  21       61762   N5     2 #> 1696       1         3  21       61762   O1     6 #> 1697       1         3  21       61762   O2     1 #> 1698       1         3  21       61762   O3     6 #> 1699       1         3  21       61762   O4     6 #> 1700       1         3  21       61762   O5     1 #> 1701       2         5  36       61763   A1     2 #> 1702       2         5  36       61763   A2     3 #> 1703       2         5  36       61763   A3     4 #> 1704       2         5  36       61763   A4     5 #> 1705       2         5  36       61763   A5     6 #> 1706       2         5  36       61763   C1     5 #> 1707       2         5  36       61763   C2     5 #> 1708       2         5  36       61763   C3     4 #> 1709       2         5  36       61763   C4     2 #> 1710       2         5  36       61763   C5     2 #> 1711       2         5  36       61763   E1     1 #> 1712       2         5  36       61763   E2     2 #> 1713       2         5  36       61763   E3     4 #> 1714       2         5  36       61763   E4     4 #> 1715       2         5  36       61763   E5     4 #> 1716       2         5  36       61763   N1     1 #> 1717       2         5  36       61763   N2     2 #> 1718       2         5  36       61763   N3     2 #> 1719       2         5  36       61763   N4     4 #> 1720       2         5  36       61763   N5     2 #> 1721       2         5  36       61763   O1     3 #> 1722       2         5  36       61763   O2     2 #> 1723       2         5  36       61763   O3     5 #> 1724       2         5  36       61763   O4     5 #> 1725       2         5  36       61763   O5     2 #> 1726       2         2  48       61764   A1     1 #> 1727       2         2  48       61764   A2     6 #> 1728       2         2  48       61764   A3     6 #> 1729       2         2  48       61764   A4     3 #> 1730       2         2  48       61764   A5     6 #> 1731       2         2  48       61764   C1     6 #> 1732       2         2  48       61764   C2     5 #> 1733       2         2  48       61764   C3     6 #> 1734       2         2  48       61764   C4     1 #> 1735       2         2  48       61764   C5     4 #> 1736       2         2  48       61764   E1     4 #> 1737       2         2  48       61764   E2     4 #> 1738       2         2  48       61764   E3     2 #> 1739       2         2  48       61764   E4     3 #> 1740       2         2  48       61764   E5     3 #> 1741       2         2  48       61764   N1     1 #> 1742       2         2  48       61764   N2     2 #> 1743       2         2  48       61764   N3     2 #> 1744       2         2  48       61764   N4     5 #> 1745       2         2  48       61764   N5     2 #> 1746       2         2  48       61764   O1     6 #> 1747       2         2  48       61764   O2     2 #> 1748       2         2  48       61764   O3     3 #> 1749       2         2  48       61764   O4     5 #> 1750       2         2  48       61764   O5     2 #> 1751       2         3  22       61771   A1     4 #> 1752       2         3  22       61771   A2     5 #> 1753       2         3  22       61771   A3     6 #> 1754       2         3  22       61771   A4     6 #> 1755       2         3  22       61771   A5     4 #> 1756       2         3  22       61771   C1     4 #> 1757       2         3  22       61771   C2     6 #> 1758       2         3  22       61771   C3     6 #> 1759       2         3  22       61771   C4     1 #> 1760       2         3  22       61771   C5     2 #> 1761       2         3  22       61771   E1     4 #> 1762       2         3  22       61771   E2     3 #> 1763       2         3  22       61771   E3     5 #> 1764       2         3  22       61771   E4     5 #> 1765       2         3  22       61771   E5     5 #> 1766       2         3  22       61771   N1     2 #> 1767       2         3  22       61771   N2     3 #> 1768       2         3  22       61771   N3     3 #> 1769       2         3  22       61771   N4     1 #> 1770       2         3  22       61771   N5     3 #> 1771       2         3  22       61771   O1     4 #> 1772       2         3  22       61771   O2     1 #> 1773       2         3  22       61771   O3     4 #> 1774       2         3  22       61771   O4     6 #> 1775       2         3  22       61771   O5     2 #> 1776       2         2  23       61772   A1     2 #> 1777       2         2  23       61772   A2     4 #> 1778       2         2  23       61772   A3     6 #> 1779       2         2  23       61772   A4     2 #> 1780       2         2  23       61772   A5     5 #> 1781       2         2  23       61772   C1     2 #> 1782       2         2  23       61772   C2     4 #> 1783       2         2  23       61772   C3     4 #> 1784       2         2  23       61772   C4     1 #> 1785       2         2  23       61772   C5     1 #> 1786       2         2  23       61772   E1     1 #> 1787       2         2  23       61772   E2     1 #> 1788       2         2  23       61772   E3     4 #> 1789       2         2  23       61772   E4     4 #> 1790       2         2  23       61772   E5    NA #> 1791       2         2  23       61772   N1     1 #> 1792       2         2  23       61772   N2     3 #> 1793       2         2  23       61772   N3     2 #> 1794       2         2  23       61772   N4     2 #> 1795       2         2  23       61772   N5     1 #> 1796       2         2  23       61772   O1     6 #> 1797       2         2  23       61772   O2     2 #> 1798       2         2  23       61772   O3     4 #> 1799       2         2  23       61772   O4     5 #> 1800       2         2  23       61772   O5     3 #> 1801       1         3  21       61773   A1     4 #> 1802       1         3  21       61773   A2     4 #> 1803       1         3  21       61773   A3     4 #> 1804       1         3  21       61773   A4     5 #> 1805       1         3  21       61773   A5     3 #> 1806       1         3  21       61773   C1     5 #> 1807       1         3  21       61773   C2     4 #> 1808       1         3  21       61773   C3     6 #> 1809       1         3  21       61773   C4     2 #> 1810       1         3  21       61773   C5     4 #> 1811       1         3  21       61773   E1     2 #> 1812       1         3  21       61773   E2     2 #> 1813       1         3  21       61773   E3     4 #> 1814       1         3  21       61773   E4     6 #> 1815       1         3  21       61773   E5     4 #> 1816       1         3  21       61773   N1     5 #> 1817       1         3  21       61773   N2     5 #> 1818       1         3  21       61773   N3     4 #> 1819       1         3  21       61773   N4     3 #> 1820       1         3  21       61773   N5     5 #> 1821       1         3  21       61773   O1     4 #> 1822       1         3  21       61773   O2     1 #> 1823       1         3  21       61773   O3     4 #> 1824       1         3  21       61773   O4     2 #> 1825       1         3  21       61773   O5     3 #> 1826       2         3  20       61775   A1     1 #> 1827       2         3  20       61775   A2     5 #> 1828       2         3  20       61775   A3     5 #> 1829       2         3  20       61775   A4     5 #> 1830       2         3  20       61775   A5     4 #> 1831       2         3  20       61775   C1     5 #> 1832       2         3  20       61775   C2     5 #> 1833       2         3  20       61775   C3     5 #> 1834       2         3  20       61775   C4     1 #> 1835       2         3  20       61775   C5     2 #> 1836       2         3  20       61775   E1     4 #> 1837       2         3  20       61775   E2     4 #> 1838       2         3  20       61775   E3     3 #> 1839       2         3  20       61775   E4     2 #> 1840       2         3  20       61775   E5     5 #> 1841       2         3  20       61775   N1     1 #> 1842       2         3  20       61775   N2     2 #> 1843       2         3  20       61775   N3     4 #> 1844       2         3  20       61775   N4     4 #> 1845       2         3  20       61775   N5     4 #> 1846       2         3  20       61775   O1     5 #> 1847       2         3  20       61775   O2     1 #> 1848       2         3  20       61775   O3     5 #> 1849       2         3  20       61775   O4     4 #> 1850       2         3  20       61775   O5     2 #> 1851       2         3  23       61776   A1     3 #> 1852       2         3  23       61776   A2     5 #> 1853       2         3  23       61776   A3     5 #> 1854       2         3  23       61776   A4     5 #> 1855       2         3  23       61776   A5     4 #> 1856       2         3  23       61776   C1     5 #> 1857       2         3  23       61776   C2     5 #> 1858       2         3  23       61776   C3     5 #> 1859       2         3  23       61776   C4     1 #> 1860       2         3  23       61776   C5     1 #> 1861       2         3  23       61776   E1     2 #> 1862       2         3  23       61776   E2     3 #> 1863       2         3  23       61776   E3     5 #> 1864       2         3  23       61776   E4     5 #> 1865       2         3  23       61776   E5     4 #> 1866       2         3  23       61776   N1     2 #> 1867       2         3  23       61776   N2     1 #> 1868       2         3  23       61776   N3     2 #> 1869       2         3  23       61776   N4     2 #> 1870       2         3  23       61776   N5     3 #> 1871       2         3  23       61776   O1     5 #> 1872       2         3  23       61776   O2     1 #> 1873       2         3  23       61776   O3     5 #> 1874       2         3  23       61776   O4     4 #> 1875       2         3  23       61776   O5     2 #> 1876       2         4  43       61777   A1     2 #> 1877       2         4  43       61777   A2     5 #> 1878       2         4  43       61777   A3     5 #> 1879       2         4  43       61777   A4     6 #> 1880       2         4  43       61777   A5     5 #> 1881       2         4  43       61777   C1     5 #> 1882       2         4  43       61777   C2     4 #> 1883       2         4  43       61777   C3     6 #> 1884       2         4  43       61777   C4     3 #> 1885       2         4  43       61777   C5     2 #> 1886       2         4  43       61777   E1     1 #> 1887       2         4  43       61777   E2     1 #> 1888       2         4  43       61777   E3     4 #> 1889       2         4  43       61777   E4     6 #> 1890       2         4  43       61777   E5     5 #> 1891       2         4  43       61777   N1     2 #> 1892       2         4  43       61777   N2     2 #> 1893       2         4  43       61777   N3     3 #> 1894       2         4  43       61777   N4     3 #> 1895       2         4  43       61777   N5     2 #> 1896       2         4  43       61777   O1     5 #> 1897       2         4  43       61777   O2     1 #> 1898       2         4  43       61777   O3     4 #> 1899       2         4  43       61777   O4     3 #> 1900       2         4  43       61777   O5     4 #> 1901       2        NA  16       61778   A1     2 #> 1902       2        NA  16       61778   A2     6 #> 1903       2        NA  16       61778   A3     6 #> 1904       2        NA  16       61778   A4     6 #> 1905       2        NA  16       61778   A5     6 #> 1906       2        NA  16       61778   C1     5 #> 1907       2        NA  16       61778   C2     4 #> 1908       2        NA  16       61778   C3     5 #> 1909       2        NA  16       61778   C4     1 #> 1910       2        NA  16       61778   C5     2 #> 1911       2        NA  16       61778   E1     1 #> 1912       2        NA  16       61778   E2     1 #> 1913       2        NA  16       61778   E3     6 #> 1914       2        NA  16       61778   E4     6 #> 1915       2        NA  16       61778   E5     5 #> 1916       2        NA  16       61778   N1     2 #> 1917       2        NA  16       61778   N2     4 #> 1918       2        NA  16       61778   N3     2 #> 1919       2        NA  16       61778   N4     1 #> 1920       2        NA  16       61778   N5     1 #> 1921       2        NA  16       61778   O1     6 #> 1922       2        NA  16       61778   O2     3 #> 1923       2        NA  16       61778   O3     5 #> 1924       2        NA  16       61778   O4     4 #> 1925       2        NA  16       61778   O5     1 #> 1926       2        NA  14       61780   A1     5 #> 1927       2        NA  14       61780   A2     6 #> 1928       2        NA  14       61780   A3     6 #> 1929       2        NA  14       61780   A4     6 #> 1930       2        NA  14       61780   A5     5 #> 1931       2        NA  14       61780   C1     5 #> 1932       2        NA  14       61780   C2     5 #> 1933       2        NA  14       61780   C3     6 #> 1934       2        NA  14       61780   C4     3 #> 1935       2        NA  14       61780   C5     4 #> 1936       2        NA  14       61780   E1     1 #> 1937       2        NA  14       61780   E2     2 #> 1938       2        NA  14       61780   E3     5 #> 1939       2        NA  14       61780   E4     6 #> 1940       2        NA  14       61780   E5     6 #> 1941       2        NA  14       61780   N1     4 #> 1942       2        NA  14       61780   N2     3 #> 1943       2        NA  14       61780   N3     4 #> 1944       2        NA  14       61780   N4     4 #> 1945       2        NA  14       61780   N5     6 #> 1946       2        NA  14       61780   O1     5 #> 1947       2        NA  14       61780   O2     4 #> 1948       2        NA  14       61780   O3     5 #> 1949       2        NA  14       61780   O4     6 #> 1950       2        NA  14       61780   O5     3 #> 1951       2         3  54       61782   A1     1 #> 1952       2         3  54       61782   A2     2 #> 1953       2         3  54       61782   A3     2 #> 1954       2         3  54       61782   A4     4 #> 1955       2         3  54       61782   A5     2 #> 1956       2         3  54       61782   C1     2 #> 1957       2         3  54       61782   C2     4 #> 1958       2         3  54       61782   C3     2 #> 1959       2         3  54       61782   C4     5 #> 1960       2         3  54       61782   C5     1 #> 1961       2         3  54       61782   E1     2 #> 1962       2         3  54       61782   E2     2 #> 1963       2         3  54       61782   E3     4 #> 1964       2         3  54       61782   E4     2 #> 1965       2         3  54       61782   E5     2 #> 1966       2         3  54       61782   N1     4 #> 1967       2         3  54       61782   N2     2 #> 1968       2         3  54       61782   N3     2 #> 1969       2         3  54       61782   N4     2 #> 1970       2         3  54       61782   N5     4 #> 1971       2         3  54       61782   O1     2 #> 1972       2         3  54       61782   O2     3 #> 1973       2         3  54       61782   O3     4 #> 1974       2         3  54       61782   O4     2 #> 1975       2         3  54       61782   O5     4 #> 1976       1         2  20       61783   A1     2 #> 1977       1         2  20       61783   A2     5 #> 1978       1         2  20       61783   A3     5 #> 1979       1         2  20       61783   A4     5 #> 1980       1         2  20       61783   A5     5 #> 1981       1         2  20       61783   C1     4 #> 1982       1         2  20       61783   C2     2 #> 1983       1         2  20       61783   C3     3 #> 1984       1         2  20       61783   C4     5 #> 1985       1         2  20       61783   C5     4 #> 1986       1         2  20       61783   E1     4 #> 1987       1         2  20       61783   E2     4 #> 1988       1         2  20       61783   E3     4 #> 1989       1         2  20       61783   E4     5 #> 1990       1         2  20       61783   E5     4 #> 1991       1         2  20       61783   N1     2 #> 1992       1         2  20       61783   N2     3 #> 1993       1         2  20       61783   N3     4 #> 1994       1         2  20       61783   N4     5 #> 1995       1         2  20       61783   N5     2 #> 1996       1         2  20       61783   O1     4 #> 1997       1         2  20       61783   O2     4 #> 1998       1         2  20       61783   O3     3 #> 1999       1         2  20       61783   O4     5 #> 2000       1         2  20       61783   O5     1 #> 2001       1         4  28       61784   A1     1 #> 2002       1         4  28       61784   A2     5 #> 2003       1         4  28       61784   A3     6 #> 2004       1         4  28       61784   A4     5 #> 2005       1         4  28       61784   A5     3 #> 2006       1         4  28       61784   C1     6 #> 2007       1         4  28       61784   C2     5 #> 2008       1         4  28       61784   C3     4 #> 2009       1         4  28       61784   C4     4 #> 2010       1         4  28       61784   C5     3 #> 2011       1         4  28       61784   E1     4 #> 2012       1         4  28       61784   E2     5 #> 2013       1         4  28       61784   E3     4 #> 2014       1         4  28       61784   E4     4 #> 2015       1         4  28       61784   E5     5 #> 2016       1         4  28       61784   N1     3 #> 2017       1         4  28       61784   N2     3 #> 2018       1         4  28       61784   N3     3 #> 2019       1         4  28       61784   N4     4 #> 2020       1         4  28       61784   N5     3 #> 2021       1         4  28       61784   O1     6 #> 2022       1         4  28       61784   O2     2 #> 2023       1         4  28       61784   O3     5 #> 2024       1         4  28       61784   O4     6 #> 2025       1         4  28       61784   O5     2 #> 2026       2         4  38       61788   A1     1 #> 2027       2         4  38       61788   A2     6 #> 2028       2         4  38       61788   A3     3 #> 2029       2         4  38       61788   A4     3 #> 2030       2         4  38       61788   A5     1 #> 2031       2         4  38       61788   C1     6 #> 2032       2         4  38       61788   C2     6 #> 2033       2         4  38       61788   C3     5 #> 2034       2         4  38       61788   C4     1 #> 2035       2         4  38       61788   C5     6 #> 2036       2         4  38       61788   E1     4 #> 2037       2         4  38       61788   E2     5 #> 2038       2         4  38       61788   E3     3 #> 2039       2         4  38       61788   E4     5 #> 2040       2         4  38       61788   E5     1 #> 2041       2         4  38       61788   N1     4 #> 2042       2         4  38       61788   N2     4 #> 2043       2         4  38       61788   N3     6 #> 2044       2         4  38       61788   N4     6 #> 2045       2         4  38       61788   N5     5 #> 2046       2         4  38       61788   O1     6 #> 2047       2         4  38       61788   O2     1 #> 2048       2         4  38       61788   O3     6 #> 2049       2         4  38       61788   O4     6 #> 2050       2         4  38       61788   O5     1 #> 2051       1        NA  38       61789   A1     1 #> 2052       1        NA  38       61789   A2     4 #> 2053       1        NA  38       61789   A3     6 #> 2054       1        NA  38       61789   A4     2 #> 2055       1        NA  38       61789   A5     6 #> 2056       1        NA  38       61789   C1     3 #> 2057       1        NA  38       61789   C2     3 #> 2058       1        NA  38       61789   C3     4 #> 2059       1        NA  38       61789   C4     4 #> 2060       1        NA  38       61789   C5     5 #> 2061       1        NA  38       61789   E1     6 #> 2062       1        NA  38       61789   E2     4 #> 2063       1        NA  38       61789   E3     5 #> 2064       1        NA  38       61789   E4     6 #> 2065       1        NA  38       61789   E5     2 #> 2066       1        NA  38       61789   N1     3 #> 2067       1        NA  38       61789   N2     5 #> 2068       1        NA  38       61789   N3     4 #> 2069       1        NA  38       61789   N4     6 #> 2070       1        NA  38       61789   N5     4 #> 2071       1        NA  38       61789   O1     6 #> 2072       1        NA  38       61789   O2     2 #> 2073       1        NA  38       61789   O3     5 #> 2074       1        NA  38       61789   O4     6 #> 2075       1        NA  38       61789   O5     1 #> 2076       1         3  27       61793   A1     1 #> 2077       1         3  27       61793   A2     6 #> 2078       1         3  27       61793   A3     6 #> 2079       1         3  27       61793   A4     5 #> 2080       1         3  27       61793   A5     6 #> 2081       1         3  27       61793   C1     5 #> 2082       1         3  27       61793   C2     6 #> 2083       1         3  27       61793   C3     5 #> 2084       1         3  27       61793   C4     1 #> 2085       1         3  27       61793   C5     1 #> 2086       1         3  27       61793   E1     3 #> 2087       1         3  27       61793   E2     2 #> 2088       1         3  27       61793   E3     5 #> 2089       1         3  27       61793   E4     5 #> 2090       1         3  27       61793   E5     5 #> 2091       1         3  27       61793   N1     3 #> 2092       1         3  27       61793   N2     3 #> 2093       1         3  27       61793   N3     2 #> 2094       1         3  27       61793   N4     2 #> 2095       1         3  27       61793   N5     2 #> 2096       1         3  27       61793   O1     4 #> 2097       1         3  27       61793   O2     5 #> 2098       1         3  27       61793   O3     4 #> 2099       1         3  27       61793   O4     5 #> 2100       1         3  27       61793   O5     2 #> 2101       2         1  18       61794   A1     1 #> 2102       2         1  18       61794   A2     6 #> 2103       2         1  18       61794   A3     5 #> 2104       2         1  18       61794   A4     2 #> 2105       2         1  18       61794   A5     6 #> 2106       2         1  18       61794   C1     4 #> 2107       2         1  18       61794   C2     5 #> 2108       2         1  18       61794   C3     5 #> 2109       2         1  18       61794   C4     2 #> 2110       2         1  18       61794   C5     3 #> 2111       2         1  18       61794   E1     3 #> 2112       2         1  18       61794   E2     4 #> 2113       2         1  18       61794   E3     4 #> 2114       2         1  18       61794   E4     5 #> 2115       2         1  18       61794   E5     3 #> 2116       2         1  18       61794   N1     2 #> 2117       2         1  18       61794   N2     4 #> 2118       2         1  18       61794   N3     5 #> 2119       2         1  18       61794   N4     6 #> 2120       2         1  18       61794   N5     6 #> 2121       2         1  18       61794   O1     6 #> 2122       2         1  18       61794   O2     5 #> 2123       2         1  18       61794   O3     4 #> 2124       2         1  18       61794   O4     6 #> 2125       2         1  18       61794   O5     1 #> 2126       1         3  29       61797   A1     4 #> 2127       1         3  29       61797   A2     4 #> 2128       1         3  29       61797   A3     4 #> 2129       1         3  29       61797   A4     6 #> 2130       1         3  29       61797   A5     5 #> 2131       1         3  29       61797   C1     2 #> 2132       1         3  29       61797   C2     5 #> 2133       1         3  29       61797   C3     4 #> 2134       1         3  29       61797   C4     4 #> 2135       1         3  29       61797   C5     3 #> 2136       1         3  29       61797   E1     5 #> 2137       1         3  29       61797   E2     3 #> 2138       1         3  29       61797   E3     3 #> 2139       1         3  29       61797   E4     3 #> 2140       1         3  29       61797   E5     5 #> 2141       1         3  29       61797   N1     2 #> 2142       1         3  29       61797   N2     2 #> 2143       1         3  29       61797   N3     4 #> 2144       1         3  29       61797   N4     2 #> 2145       1         3  29       61797   N5     4 #> 2146       1         3  29       61797   O1     3 #> 2147       1         3  29       61797   O2     4 #> 2148       1         3  29       61797   O3     3 #> 2149       1         3  29       61797   O4     4 #> 2150       1         3  29       61797   O5     4 #> 2151       2         4  50       61798   A1     1 #> 2152       2         4  50       61798   A2     5 #> 2153       2         4  50       61798   A3     5 #> 2154       2         4  50       61798   A4     5 #> 2155       2         4  50       61798   A5     5 #> 2156       2         4  50       61798   C1     4 #> 2157       2         4  50       61798   C2     4 #> 2158       2         4  50       61798   C3     5 #> 2159       2         4  50       61798   C4     2 #> 2160       2         4  50       61798   C5     2 #> 2161       2         4  50       61798   E1     5 #> 2162       2         4  50       61798   E2     2 #> 2163       2         4  50       61798   E3     2 #> 2164       2         4  50       61798   E4     5 #> 2165       2         4  50       61798   E5     5 #> 2166       2         4  50       61798   N1     3 #> 2167       2         4  50       61798   N2     3 #> 2168       2         4  50       61798   N3     2 #> 2169       2         4  50       61798   N4     2 #> 2170       2         4  50       61798   N5     2 #> 2171       2         4  50       61798   O1     4 #> 2172       2         4  50       61798   O2     5 #> 2173       2         4  50       61798   O3     5 #> 2174       2         4  50       61798   O4     4 #> 2175       2         4  50       61798   O5     4 #> 2176       2         3  50       61801   A1     1 #> 2177       2         3  50       61801   A2     6 #> 2178       2         3  50       61801   A3     5 #> 2179       2         3  50       61801   A4     4 #> 2180       2         3  50       61801   A5     6 #> 2181       2         3  50       61801   C1     4 #> 2182       2         3  50       61801   C2     5 #> 2183       2         3  50       61801   C3     4 #> 2184       2         3  50       61801   C4     4 #> 2185       2         3  50       61801   C5     5 #> 2186       2         3  50       61801   E1     2 #> 2187       2         3  50       61801   E2     2 #> 2188       2         3  50       61801   E3     4 #> 2189       2         3  50       61801   E4     5 #> 2190       2         3  50       61801   E5     5 #> 2191       2         3  50       61801   N1     1 #> 2192       2         3  50       61801   N2     3 #> 2193       2         3  50       61801   N3     2 #> 2194       2         3  50       61801   N4     2 #> 2195       2         3  50       61801   N5     2 #> 2196       2         3  50       61801   O1     5 #> 2197       2         3  50       61801   O2     1 #> 2198       2         3  50       61801   O3     4 #> 2199       2         3  50       61801   O4     5 #> 2200       2         3  50       61801   O5     2 #> 2201       2         3  20       61808   A1     1 #> 2202       2         3  20       61808   A2     6 #> 2203       2         3  20       61808   A3     4 #> 2204       2         3  20       61808   A4     6 #> 2205       2         3  20       61808   A5     4 #> 2206       2         3  20       61808   C1     4 #> 2207       2         3  20       61808   C2     4 #> 2208       2         3  20       61808   C3     5 #> 2209       2         3  20       61808   C4     2 #> 2210       2         3  20       61808   C5     4 #> 2211       2         3  20       61808   E1     1 #> 2212       2         3  20       61808   E2     2 #> 2213       2         3  20       61808   E3     4 #> 2214       2         3  20       61808   E4     5 #> 2215       2         3  20       61808   E5     5 #> 2216       2         3  20       61808   N1     2 #> 2217       2         3  20       61808   N2     3 #> 2218       2         3  20       61808   N3     2 #> 2219       2         3  20       61808   N4     2 #> 2220       2         3  20       61808   N5     2 #> 2221       2         3  20       61808   O1     4 #> 2222       2         3  20       61808   O2     2 #> 2223       2         3  20       61808   O3     3 #> 2224       2         3  20       61808   O4     4 #> 2225       2         3  20       61808   O5     2 #> 2226       2         3  19       61812   A1     3 #> 2227       2         3  19       61812   A2     4 #> 2228       2         3  19       61812   A3     5 #> 2229       2         3  19       61812   A4     5 #> 2230       2         3  19       61812   A5     4 #> 2231       2         3  19       61812   C1     3 #> 2232       2         3  19       61812   C2     3 #> 2233       2         3  19       61812   C3    NA #> 2234       2         3  19       61812   C4     3 #> 2235       2         3  19       61812   C5     3 #> 2236       2         3  19       61812   E1     4 #> 2237       2         3  19       61812   E2     4 #> 2238       2         3  19       61812   E3     4 #> 2239       2         3  19       61812   E4     3 #> 2240       2         3  19       61812   E5     4 #> 2241       2         3  19       61812   N1     3 #> 2242       2         3  19       61812   N2     3 #> 2243       2         3  19       61812   N3     4 #> 2244       2         3  19       61812   N4     4 #> 2245       2         3  19       61812   N5     3 #> 2246       2         3  19       61812   O1     4 #> 2247       2         3  19       61812   O2     2 #> 2248       2         3  19       61812   O3     4 #> 2249       2         3  19       61812   O4     4 #> 2250       2         3  19       61812   O5     3 #> 2251       2         4  56       61813   A1     1 #> 2252       2         4  56       61813   A2     5 #> 2253       2         4  56       61813   A3     5 #> 2254       2         4  56       61813   A4     5 #> 2255       2         4  56       61813   A5     5 #> 2256       2         4  56       61813   C1     5 #> 2257       2         4  56       61813   C2     2 #> 2258       2         4  56       61813   C3     4 #> 2259       2         4  56       61813   C4     1 #> 2260       2         4  56       61813   C5     2 #> 2261       2         4  56       61813   E1     4 #> 2262       2         4  56       61813   E2     1 #> 2263       2         4  56       61813   E3     5 #> 2264       2         4  56       61813   E4     5 #> 2265       2         4  56       61813   E5     5 #> 2266       2         4  56       61813   N1     1 #> 2267       2         4  56       61813   N2     1 #> 2268       2         4  56       61813   N3     1 #> 2269       2         4  56       61813   N4     4 #> 2270       2         4  56       61813   N5     1 #> 2271       2         4  56       61813   O1     6 #> 2272       2         4  56       61813   O2     1 #> 2273       2         4  56       61813   O3     5 #> 2274       2         4  56       61813   O4     5 #> 2275       2         4  56       61813   O5     2 #> 2276       2         4  29       61816   A1     1 #> 2277       2         4  29       61816   A2     5 #> 2278       2         4  29       61816   A3     6 #> 2279       2         4  29       61816   A4     2 #> 2280       2         4  29       61816   A5     5 #> 2281       2         4  29       61816   C1     5 #> 2282       2         4  29       61816   C2     4 #> 2283       2         4  29       61816   C3     5 #> 2284       2         4  29       61816   C4     3 #> 2285       2         4  29       61816   C5     4 #> 2286       2         4  29       61816   E1     3 #> 2287       2         4  29       61816   E2     5 #> 2288       2         4  29       61816   E3     4 #> 2289       2         4  29       61816   E4     4 #> 2290       2         4  29       61816   E5     4 #> 2291       2         4  29       61816   N1     3 #> 2292       2         4  29       61816   N2     3 #> 2293       2         4  29       61816   N3     4 #> 2294       2         4  29       61816   N4     3 #> 2295       2         4  29       61816   N5     4 #> 2296       2         4  29       61816   O1     6 #> 2297       2         4  29       61816   O2     1 #> 2298       2         4  29       61816   O3     5 #> 2299       2         4  29       61816   O4     6 #> 2300       2         4  29       61816   O5     2 #> 2301       2         5  29       61818   A1     2 #> 2302       2         5  29       61818   A2     4 #> 2303       2         5  29       61818   A3     5 #> 2304       2         5  29       61818   A4     4 #> 2305       2         5  29       61818   A5     5 #> 2306       2         5  29       61818   C1     2 #> 2307       2         5  29       61818   C2     2 #> 2308       2         5  29       61818   C3     3 #> 2309       2         5  29       61818   C4     4 #> 2310       2         5  29       61818   C5     4 #> 2311       2         5  29       61818   E1     1 #> 2312       2         5  29       61818   E2     5 #> 2313       2         5  29       61818   E3     2 #> 2314       2         5  29       61818   E4     6 #> 2315       2         5  29       61818   E5     2 #> 2316       2         5  29       61818   N1     2 #> 2317       2         5  29       61818   N2     3 #> 2318       2         5  29       61818   N3     6 #> 2319       2         5  29       61818   N4     4 #> 2320       2         5  29       61818   N5     6 #> 2321       2         5  29       61818   O1     3 #> 2322       2         5  29       61818   O2     2 #> 2323       2         5  29       61818   O3     2 #> 2324       2         5  29       61818   O4     2 #> 2325       2         5  29       61818   O5     5 #> 2326       2         5  48       61819   A1     1 #> 2327       2         5  48       61819   A2     6 #> 2328       2         5  48       61819   A3     6 #> 2329       2         5  48       61819   A4     3 #> 2330       2         5  48       61819   A5     6 #> 2331       2         5  48       61819   C1     6 #> 2332       2         5  48       61819   C2     6 #> 2333       2         5  48       61819   C3     5 #> 2334       2         5  48       61819   C4     1 #> 2335       2         5  48       61819   C5     1 #> 2336       2         5  48       61819   E1     1 #> 2337       2         5  48       61819   E2     3 #> 2338       2         5  48       61819   E3     5 #> 2339       2         5  48       61819   E4     6 #> 2340       2         5  48       61819   E5     6 #> 2341       2         5  48       61819   N1     2 #> 2342       2         5  48       61819   N2     5 #> 2343       2         5  48       61819   N3     4 #> 2344       2         5  48       61819   N4     6 #> 2345       2         5  48       61819   N5     1 #> 2346       2         5  48       61819   O1     6 #> 2347       2         5  48       61819   O2     2 #> 2348       2         5  48       61819   O3     5 #> 2349       2         5  48       61819   O4     6 #> 2350       2         5  48       61819   O5     1 #> 2351       2         1  30       61821   A1     2 #> 2352       2         1  30       61821   A2     6 #> 2353       2         1  30       61821   A3     4 #> 2354       2         1  30       61821   A4     6 #> 2355       2         1  30       61821   A5     4 #> 2356       2         1  30       61821   C1     4 #> 2357       2         1  30       61821   C2     5 #> 2358       2         1  30       61821   C3     6 #> 2359       2         1  30       61821   C4     3 #> 2360       2         1  30       61821   C5     1 #> 2361       2         1  30       61821   E1     2 #> 2362       2         1  30       61821   E2     4 #> 2363       2         1  30       61821   E3     2 #> 2364       2         1  30       61821   E4     5 #> 2365       2         1  30       61821   E5     4 #> 2366       2         1  30       61821   N1     4 #> 2367       2         1  30       61821   N2     5 #> 2368       2         1  30       61821   N3     2 #> 2369       2         1  30       61821   N4     3 #> 2370       2         1  30       61821   N5     6 #> 2371       2         1  30       61821   O1     3 #> 2372       2         1  30       61821   O2     4 #> 2373       2         1  30       61821   O3     3 #> 2374       2         1  30       61821   O4     5 #> 2375       2         1  30       61821   O5     6 #> 2376       2         2  21       61822   A1     2 #> 2377       2         2  21       61822   A2     3 #> 2378       2         2  21       61822   A3     3 #> 2379       2         2  21       61822   A4     4 #> 2380       2         2  21       61822   A5     3 #> 2381       2         2  21       61822   C1     5 #> 2382       2         2  21       61822   C2     6 #> 2383       2         2  21       61822   C3     4 #> 2384       2         2  21       61822   C4     1 #> 2385       2         2  21       61822   C5     4 #> 2386       2         2  21       61822   E1     5 #> 2387       2         2  21       61822   E2     6 #> 2388       2         2  21       61822   E3     2 #> 2389       2         2  21       61822   E4     2 #> 2390       2         2  21       61822   E5     4 #> 2391       2         2  21       61822   N1     3 #> 2392       2         2  21       61822   N2     4 #> 2393       2         2  21       61822   N3     6 #> 2394       2         2  21       61822   N4     6 #> 2395       2         2  21       61822   N5     6 #> 2396       2         2  21       61822   O1     5 #> 2397       2         2  21       61822   O2     2 #> 2398       2         2  21       61822   O3     3 #> 2399       2         2  21       61822   O4     5 #> 2400       2         2  21       61822   O5     4 #> 2401       1         3  19       61825   A1     3 #> 2402       1         3  19       61825   A2     1 #> 2403       1         3  19       61825   A3     2 #> 2404       1         3  19       61825   A4     2 #> 2405       1         3  19       61825   A5     2 #> 2406       1         3  19       61825   C1     2 #> 2407       1         3  19       61825   C2     1 #> 2408       1         3  19       61825   C3     2 #> 2409       1         3  19       61825   C4     6 #> 2410       1         3  19       61825   C5     6 #> 2411       1         3  19       61825   E1     6 #> 2412       1         3  19       61825   E2     6 #> 2413       1         3  19       61825   E3     1 #> 2414       1         3  19       61825   E4     1 #> 2415       1         3  19       61825   E5     1 #> 2416       1         3  19       61825   N1     3 #> 2417       1         3  19       61825   N2     5 #> 2418       1         3  19       61825   N3     4 #> 2419       1         3  19       61825   N4     4 #> 2420       1         3  19       61825   N5     4 #> 2421       1         3  19       61825   O1     5 #> 2422       1         3  19       61825   O2     1 #> 2423       1         3  19       61825   O3     3 #> 2424       1         3  19       61825   O4     6 #> 2425       1         3  19       61825   O5     1 #> 2426       1         5  42       61826   A1     1 #> 2427       1         5  42       61826   A2     4 #> 2428       1         5  42       61826   A3     5 #> 2429       1         5  42       61826   A4     5 #> 2430       1         5  42       61826   A5     6 #> 2431       1         5  42       61826   C1     6 #> 2432       1         5  42       61826   C2     4 #> 2433       1         5  42       61826   C3     4 #> 2434       1         5  42       61826   C4     1 #> 2435       1         5  42       61826   C5     1 #> 2436       1         5  42       61826   E1     5 #> 2437       1         5  42       61826   E2     1 #> 2438       1         5  42       61826   E3     2 #> 2439       1         5  42       61826   E4     3 #> 2440       1         5  42       61826   E5     5 #> 2441       1         5  42       61826   N1     1 #> 2442       1         5  42       61826   N2     2 #> 2443       1         5  42       61826   N3     1 #> 2444       1         5  42       61826   N4     1 #> 2445       1         5  42       61826   N5     1 #> 2446       1         5  42       61826   O1     5 #> 2447       1         5  42       61826   O2     1 #> 2448       1         5  42       61826   O3     5 #> 2449       1         5  42       61826   O4     6 #> 2450       1         5  42       61826   O5     1 #> 2451       2         3  18       61829   A1     4 #> 2452       2         3  18       61829   A2     3 #> 2453       2         3  18       61829   A3     2 #> 2454       2         3  18       61829   A4     6 #> 2455       2         3  18       61829   A5     5 #> 2456       2         3  18       61829   C1     4 #> 2457       2         3  18       61829   C2     5 #> 2458       2         3  18       61829   C3     6 #> 2459       2         3  18       61829   C4     2 #> 2460       2         3  18       61829   C5     6 #> 2461       2         3  18       61829   E1     5 #> 2462       2         3  18       61829   E2     5 #> 2463       2         3  18       61829   E3     1 #> 2464       2         3  18       61829   E4     5 #> 2465       2         3  18       61829   E5     5 #> 2466       2         3  18       61829   N1     4 #> 2467       2         3  18       61829   N2     5 #> 2468       2         3  18       61829   N3     6 #> 2469       2         3  18       61829   N4     6 #> 2470       2         3  18       61829   N5     6 #> 2471       2         3  18       61829   O1     5 #> 2472       2         3  18       61829   O2     1 #> 2473       2         3  18       61829   O3     2 #> 2474       2         3  18       61829   O4     6 #> 2475       2         3  18       61829   O5     4 #> 2476       2         4  50       61831   A1     2 #> 2477       2         4  50       61831   A2     6 #> 2478       2         4  50       61831   A3     2 #> 2479       2         4  50       61831   A4     3 #> 2480       2         4  50       61831   A5     4 #> 2481       2         4  50       61831   C1     4 #> 2482       2         4  50       61831   C2     2 #> 2483       2         4  50       61831   C3     5 #> 2484       2         4  50       61831   C4     3 #> 2485       2         4  50       61831   C5     5 #> 2486       2         4  50       61831   E1     5 #> 2487       2         4  50       61831   E2     3 #> 2488       2         4  50       61831   E3     2 #> 2489       2         4  50       61831   E4     3 #> 2490       2         4  50       61831   E5     5 #> 2491       2         4  50       61831   N1     1 #> 2492       2         4  50       61831   N2     1 #> 2493       2         4  50       61831   N3     1 #> 2494       2         4  50       61831   N4     2 #> 2495       2         4  50       61831   N5     2 #> 2496       2         4  50       61831   O1     2 #> 2497       2         4  50       61831   O2     1 #> 2498       2         4  50       61831   O3     5 #> 2499       2         4  50       61831   O4     3 #> 2500       2         4  50       61831   O5     3 #> 2501       2         3  24       61834   A1     2 #> 2502       2         3  24       61834   A2     5 #> 2503       2         3  24       61834   A3     5 #> 2504       2         3  24       61834   A4     3 #> 2505       2         3  24       61834   A5     5 #> 2506       2         3  24       61834   C1     4 #> 2507       2         3  24       61834   C2     3 #> 2508       2         3  24       61834   C3     4 #> 2509       2         3  24       61834   C4     3 #> 2510       2         3  24       61834   C5     5 #> 2511       2         3  24       61834   E1     3 #> 2512       2         3  24       61834   E2     5 #> 2513       2         3  24       61834   E3     4 #> 2514       2         3  24       61834   E4     5 #> 2515       2         3  24       61834   E5     5 #> 2516       2         3  24       61834   N1    NA #> 2517       2         3  24       61834   N2     5 #> 2518       2         3  24       61834   N3     6 #> 2519       2         3  24       61834   N4     4 #> 2520       2         3  24       61834   N5     4 #> 2521       2         3  24       61834   O1     6 #> 2522       2         3  24       61834   O2     2 #> 2523       2         3  24       61834   O3     5 #> 2524       2         3  24       61834   O4     6 #> 2525       2         3  24       61834   O5     1 #> 2526       1         5  25       61835   A1     3 #> 2527       1         5  25       61835   A2     4 #> 2528       1         5  25       61835   A3     4 #> 2529       1         5  25       61835   A4     2 #> 2530       1         5  25       61835   A5     4 #> 2531       1         5  25       61835   C1     4 #> 2532       1         5  25       61835   C2     3 #> 2533       1         5  25       61835   C3     4 #> 2534       1         5  25       61835   C4     4 #> 2535       1         5  25       61835   C5     2 #> 2536       1         5  25       61835   E1     2 #> 2537       1         5  25       61835   E2     3 #> 2538       1         5  25       61835   E3     4 #> 2539       1         5  25       61835   E4     3 #> 2540       1         5  25       61835   E5     5 #> 2541       1         5  25       61835   N1     4 #> 2542       1         5  25       61835   N2     4 #> 2543       1         5  25       61835   N3     2 #> 2544       1         5  25       61835   N4     2 #> 2545       1         5  25       61835   N5     4 #> 2546       1         5  25       61835   O1     4 #> 2547       1         5  25       61835   O2     2 #> 2548       1         5  25       61835   O3     5 #> 2549       1         5  25       61835   O4     4 #> 2550       1         5  25       61835   O5     2 #> 2551       1         5  47       61838   A1     2 #> 2552       1         5  47       61838   A2     5 #> 2553       1         5  47       61838   A3     5 #> 2554       1         5  47       61838   A4     5 #> 2555       1         5  47       61838   A5     2 #> 2556       1         5  47       61838   C1     6 #> 2557       1         5  47       61838   C2     6 #> 2558       1         5  47       61838   C3     4 #> 2559       1         5  47       61838   C4     1 #> 2560       1         5  47       61838   C5     5 #> 2561       1         5  47       61838   E1     3 #> 2562       1         5  47       61838   E2     2 #> 2563       1         5  47       61838   E3     2 #> 2564       1         5  47       61838   E4     2 #> 2565       1         5  47       61838   E5     5 #> 2566       1         5  47       61838   N1     4 #> 2567       1         5  47       61838   N2     5 #> 2568       1         5  47       61838   N3     5 #> 2569       1         5  47       61838   N4     5 #> 2570       1         5  47       61838   N5     4 #> 2571       1         5  47       61838   O1     6 #> 2572       1         5  47       61838   O2     2 #> 2573       1         5  47       61838   O3     5 #> 2574       1         5  47       61838   O4     6 #> 2575       1         5  47       61838   O5     1 #> 2576       2         1  18       61839   A1     1 #> 2577       2         1  18       61839   A2     4 #> 2578       2         1  18       61839   A3     5 #> 2579       2         1  18       61839   A4     2 #> 2580       2         1  18       61839   A5     6 #> 2581       2         1  18       61839   C1     2 #> 2582       2         1  18       61839   C2     1 #> 2583       2         1  18       61839   C3     2 #> 2584       2         1  18       61839   C4     4 #> 2585       2         1  18       61839   C5     6 #> 2586       2         1  18       61839   E1     6 #> 2587       2         1  18       61839   E2     5 #> 2588       2         1  18       61839   E3     3 #> 2589       2         1  18       61839   E4     3 #> 2590       2         1  18       61839   E5     3 #> 2591       2         1  18       61839   N1     2 #> 2592       2         1  18       61839   N2     1 #> 2593       2         1  18       61839   N3     3 #> 2594       2         1  18       61839   N4     3 #> 2595       2         1  18       61839   N5     1 #> 2596       2         1  18       61839   O1     4 #> 2597       2         1  18       61839   O2     3 #> 2598       2         1  18       61839   O3     4 #> 2599       2         1  18       61839   O4     5 #> 2600       2         1  18       61839   O5     6 #> 2601       2         4  37       61840   A1     1 #> 2602       2         4  37       61840   A2     6 #> 2603       2         4  37       61840   A3     4 #> 2604       2         4  37       61840   A4     1 #> 2605       2         4  37       61840   A5     1 #> 2606       2         4  37       61840   C1     6 #> 2607       2         4  37       61840   C2     4 #> 2608       2         4  37       61840   C3     5 #> 2609       2         4  37       61840   C4     2 #> 2610       2         4  37       61840   C5     1 #> 2611       2         4  37       61840   E1     6 #> 2612       2         4  37       61840   E2     6 #> 2613       2         4  37       61840   E3     3 #> 2614       2         4  37       61840   E4     1 #> 2615       2         4  37       61840   E5     1 #> 2616       2         4  37       61840   N1     4 #> 2617       2         4  37       61840   N2     3 #> 2618       2         4  37       61840   N3     6 #> 2619       2         4  37       61840   N4     6 #> 2620       2         4  37       61840   N5     5 #> 2621       2         4  37       61840   O1     2 #> 2622       2         4  37       61840   O2     3 #> 2623       2         4  37       61840   O3     3 #> 2624       2         4  37       61840   O4     6 #> 2625       2         4  37       61840   O5     3 #> 2626       2         4  33       61841   A1     1 #> 2627       2         4  33       61841   A2     6 #> 2628       2         4  33       61841   A3     5 #> 2629       2         4  33       61841   A4     6 #> 2630       2         4  33       61841   A5     6 #> 2631       2         4  33       61841   C1     5 #> 2632       2         4  33       61841   C2     3 #> 2633       2         4  33       61841   C3     4 #> 2634       2         4  33       61841   C4     6 #> 2635       2         4  33       61841   C5     5 #> 2636       2         4  33       61841   E1     2 #> 2637       2         4  33       61841   E2     4 #> 2638       2         4  33       61841   E3     3 #> 2639       2         4  33       61841   E4     4 #> 2640       2         4  33       61841   E5     5 #> 2641       2         4  33       61841   N1     4 #> 2642       2         4  33       61841   N2     5 #> 2643       2         4  33       61841   N3     4 #> 2644       2         4  33       61841   N4     3 #> 2645       2         4  33       61841   N5     4 #> 2646       2         4  33       61841   O1     5 #> 2647       2         4  33       61841   O2     4 #> 2648       2         4  33       61841   O3     4 #> 2649       2         4  33       61841   O4     6 #> 2650       2         4  33       61841   O5     2 #> 2651       2         5  40       61847   A1     1 #> 2652       2         5  40       61847   A2     5 #> 2653       2         5  40       61847   A3     5 #> 2654       2         5  40       61847   A4     5 #> 2655       2         5  40       61847   A5     2 #> 2656       2         5  40       61847   C1     6 #> 2657       2         5  40       61847   C2    NA #> 2658       2         5  40       61847   C3     6 #> 2659       2         5  40       61847   C4     1 #> 2660       2         5  40       61847   C5     1 #> 2661       2         5  40       61847   E1     3 #> 2662       2         5  40       61847   E2     2 #> 2663       2         5  40       61847   E3     1 #> 2664       2         5  40       61847   E4     2 #> 2665       2         5  40       61847   E5     6 #> 2666       2         5  40       61847   N1     5 #> 2667       2         5  40       61847   N2    NA #> 2668       2         5  40       61847   N3     5 #> 2669       2         5  40       61847   N4     4 #> 2670       2         5  40       61847   N5     3 #> 2671       2         5  40       61847   O1     4 #> 2672       2         5  40       61847   O2     1 #> 2673       2         5  40       61847   O3     6 #> 2674       2         5  40       61847   O4     6 #> 2675       2         5  40       61847   O5     1 #> 2676       1        NA  18       61848   A1     6 #> 2677       1        NA  18       61848   A2     1 #> 2678       1        NA  18       61848   A3     1 #> 2679       1        NA  18       61848   A4     1 #> 2680       1        NA  18       61848   A5     2 #> 2681       1        NA  18       61848   C1     3 #> 2682       1        NA  18       61848   C2     6 #> 2683       1        NA  18       61848   C3     3 #> 2684       1        NA  18       61848   C4     3 #> 2685       1        NA  18       61848   C5     4 #> 2686       1        NA  18       61848   E1     1 #> 2687       1        NA  18       61848   E2     4 #> 2688       1        NA  18       61848   E3     5 #> 2689       1        NA  18       61848   E4     2 #> 2690       1        NA  18       61848   E5     6 #> 2691       1        NA  18       61848   N1     6 #> 2692       1        NA  18       61848   N2     6 #> 2693       1        NA  18       61848   N3     3 #> 2694       1        NA  18       61848   N4     2 #> 2695       1        NA  18       61848   N5     5 #> 2696       1        NA  18       61848   O1     6 #> 2697       1        NA  18       61848   O2     1 #> 2698       1        NA  18       61848   O3     6 #> 2699       1        NA  18       61848   O4     5 #> 2700       1        NA  18       61848   O5     2 #> 2701       2         3  21       61851   A1     2 #> 2702       2         3  21       61851   A2     6 #> 2703       2         3  21       61851   A3     5 #> 2704       2         3  21       61851   A4     6 #> 2705       2         3  21       61851   A5     5 #> 2706       2         3  21       61851   C1     5 #> 2707       2         3  21       61851   C2     4 #> 2708       2         3  21       61851   C3     4 #> 2709       2         3  21       61851   C4     4 #> 2710       2         3  21       61851   C5     6 #> 2711       2         3  21       61851   E1     1 #> 2712       2         3  21       61851   E2     3 #> 2713       2         3  21       61851   E3     4 #> 2714       2         3  21       61851   E4     6 #> 2715       2         3  21       61851   E5     4 #> 2716       2         3  21       61851   N1     5 #> 2717       2         3  21       61851   N2     6 #> 2718       2         3  21       61851   N3     2 #> 2719       2         3  21       61851   N4     4 #> 2720       2         3  21       61851   N5     6 #> 2721       2         3  21       61851   O1     6 #> 2722       2         3  21       61851   O2     5 #> 2723       2         3  21       61851   O3     6 #> 2724       2         3  21       61851   O4     6 #> 2725       2         3  21       61851   O5     2 #> 2726       2         3  27       61852   A1     3 #> 2727       2         3  27       61852   A2     6 #> 2728       2         3  27       61852   A3     5 #> 2729       2         3  27       61852   A4     6 #> 2730       2         3  27       61852   A5     4 #> 2731       2         3  27       61852   C1     3 #> 2732       2         3  27       61852   C2     5 #> 2733       2         3  27       61852   C3     2 #> 2734       2         3  27       61852   C4     3 #> 2735       2         3  27       61852   C5     4 #> 2736       2         3  27       61852   E1     2 #> 2737       2         3  27       61852   E2     5 #> 2738       2         3  27       61852   E3     6 #> 2739       2         3  27       61852   E4     6 #> 2740       2         3  27       61852   E5     5 #> 2741       2         3  27       61852   N1     6 #> 2742       2         3  27       61852   N2     6 #> 2743       2         3  27       61852   N3     6 #> 2744       2         3  27       61852   N4     4 #> 2745       2         3  27       61852   N5     6 #> 2746       2         3  27       61852   O1     5 #> 2747       2         3  27       61852   O2     4 #> 2748       2         3  27       61852   O3     4 #> 2749       2         3  27       61852   O4     6 #> 2750       2         3  27       61852   O5     4 #> 2751       2         3  19       61854   A1     1 #> 2752       2         3  19       61854   A2     6 #> 2753       2         3  19       61854   A3     6 #> 2754       2         3  19       61854   A4     6 #> 2755       2         3  19       61854   A5     6 #> 2756       2         3  19       61854   C1     4 #> 2757       2         3  19       61854   C2     4 #> 2758       2         3  19       61854   C3     5 #> 2759       2         3  19       61854   C4     3 #> 2760       2         3  19       61854   C5     4 #> 2761       2         3  19       61854   E1     3 #> 2762       2         3  19       61854   E2     2 #> 2763       2         3  19       61854   E3     4 #> 2764       2         3  19       61854   E4     5 #> 2765       2         3  19       61854   E5     4 #> 2766       2         3  19       61854   N1     2 #> 2767       2         3  19       61854   N2     3 #> 2768       2         3  19       61854   N3     2 #> 2769       2         3  19       61854   N4     3 #> 2770       2         3  19       61854   N5     4 #> 2771       2         3  19       61854   O1     4 #> 2772       2         3  19       61854   O2     1 #> 2773       2         3  19       61854   O3     5 #> 2774       2         3  19       61854   O4     6 #> 2775       2         3  19       61854   O5     1 #> 2776       2         4  36       61856   A1    NA #> 2777       2         4  36       61856   A2     4 #> 2778       2         4  36       61856   A3     5 #> 2779       2         4  36       61856   A4     6 #> 2780       2         4  36       61856   A5     4 #> 2781       2         4  36       61856   C1     5 #> 2782       2         4  36       61856   C2     5 #> 2783       2         4  36       61856   C3     5 #> 2784       2         4  36       61856   C4     1 #> 2785       2         4  36       61856   C5     5 #> 2786       2         4  36       61856   E1     3 #> 2787       2         4  36       61856   E2     4 #> 2788       2         4  36       61856   E3     3 #> 2789       2         4  36       61856   E4     2 #> 2790       2         4  36       61856   E5     3 #> 2791       2         4  36       61856   N1     1 #> 2792       2         4  36       61856   N2     2 #> 2793       2         4  36       61856   N3     1 #> 2794       2         4  36       61856   N4     4 #> 2795       2         4  36       61856   N5     2 #> 2796       2         4  36       61856   O1     3 #> 2797       2         4  36       61856   O2     6 #> 2798       2         4  36       61856   O3     1 #> 2799       2         4  36       61856   O4     3 #> 2800       2         4  36       61856   O5     3 #> 2801       2         3  19       61857   A1     1 #> 2802       2         3  19       61857   A2     6 #> 2803       2         3  19       61857   A3     6 #> 2804       2         3  19       61857   A4     5 #> 2805       2         3  19       61857   A5     4 #> 2806       2         3  19       61857   C1     5 #> 2807       2         3  19       61857   C2     3 #> 2808       2         3  19       61857   C3     5 #> 2809       2         3  19       61857   C4     1 #> 2810       2         3  19       61857   C5     2 #> 2811       2         3  19       61857   E1     2 #> 2812       2         3  19       61857   E2     4 #> 2813       2         3  19       61857   E3     4 #> 2814       2         3  19       61857   E4     5 #> 2815       2         3  19       61857   E5     4 #> 2816       2         3  19       61857   N1     1 #> 2817       2         3  19       61857   N2     1 #> 2818       2         3  19       61857   N3     1 #> 2819       2         3  19       61857   N4     2 #> 2820       2         3  19       61857   N5     2 #> 2821       2         3  19       61857   O1     4 #> 2822       2         3  19       61857   O2     2 #> 2823       2         3  19       61857   O3     4 #> 2824       2         3  19       61857   O4     6 #> 2825       2         3  19       61857   O5     2 #> 2826       2         5  29       61861   A1     1 #> 2827       2         5  29       61861   A2     6 #> 2828       2         5  29       61861   A3     6 #> 2829       2         5  29       61861   A4     6 #> 2830       2         5  29       61861   A5     6 #> 2831       2         5  29       61861   C1     6 #> 2832       2         5  29       61861   C2     6 #> 2833       2         5  29       61861   C3     5 #> 2834       2         5  29       61861   C4     1 #> 2835       2         5  29       61861   C5     6 #> 2836       2         5  29       61861   E1     5 #> 2837       2         5  29       61861   E2     5 #> 2838       2         5  29       61861   E3     2 #> 2839       2         5  29       61861   E4     5 #> 2840       2         5  29       61861   E5     5 #> 2841       2         5  29       61861   N1     1 #> 2842       2         5  29       61861   N2     5 #> 2843       2         5  29       61861   N3     5 #> 2844       2         5  29       61861   N4     5 #> 2845       2         5  29       61861   N5     6 #> 2846       2         5  29       61861   O1     6 #> 2847       2         5  29       61861   O2     1 #> 2848       2         5  29       61861   O3     6 #> 2849       2         5  29       61861   O4     6 #> 2850       2         5  29       61861   O5     2 #> 2851       2         5  23       61862   A1     1 #> 2852       2         5  23       61862   A2     6 #> 2853       2         5  23       61862   A3     5 #> 2854       2         5  23       61862   A4     4 #> 2855       2         5  23       61862   A5     5 #> 2856       2         5  23       61862   C1     5 #> 2857       2         5  23       61862   C2     4 #> 2858       2         5  23       61862   C3     5 #> 2859       2         5  23       61862   C4     2 #> 2860       2         5  23       61862   C5     2 #> 2861       2         5  23       61862   E1     1 #> 2862       2         5  23       61862   E2     1 #> 2863       2         5  23       61862   E3     6 #> 2864       2         5  23       61862   E4     6 #> 2865       2         5  23       61862   E5     5 #> 2866       2         5  23       61862   N1     2 #> 2867       2         5  23       61862   N2     2 #> 2868       2         5  23       61862   N3     2 #> 2869       2         5  23       61862   N4     2 #> 2870       2         5  23       61862   N5     2 #> 2871       2         5  23       61862   O1     6 #> 2872       2         5  23       61862   O2     1 #> 2873       2         5  23       61862   O3     5 #> 2874       2         5  23       61862   O4     4 #> 2875       2         5  23       61862   O5     1 #> 2876       1         5  23       61865   A1     4 #> 2877       1         5  23       61865   A2     2 #> 2878       1         5  23       61865   A3     1 #> 2879       1         5  23       61865   A4     2 #> 2880       1         5  23       61865   A5     2 #> 2881       1         5  23       61865   C1     4 #> 2882       1         5  23       61865   C2     1 #> 2883       1         5  23       61865   C3     2 #> 2884       1         5  23       61865   C4     6 #> 2885       1         5  23       61865   C5     6 #> 2886       1         5  23       61865   E1     5 #> 2887       1         5  23       61865   E2     5 #> 2888       1         5  23       61865   E3     2 #> 2889       1         5  23       61865   E4     2 #> 2890       1         5  23       61865   E5     1 #> 2891       1         5  23       61865   N1     2 #> 2892       1         5  23       61865   N2     4 #> 2893       1         5  23       61865   N3     2 #> 2894       1         5  23       61865   N4     4 #> 2895       1         5  23       61865   N5     2 #> 2896       1         5  23       61865   O1     2 #> 2897       1         5  23       61865   O2     6 #> 2898       1         5  23       61865   O3     3 #> 2899       1         5  23       61865   O4     5 #> 2900       1         5  23       61865   O5     5 #> 2901       1         3  20       61868   A1     1 #> 2902       1         3  20       61868   A2     5 #> 2903       1         3  20       61868   A3     4 #> 2904       1         3  20       61868   A4     3 #> 2905       1         3  20       61868   A5     4 #> 2906       1         3  20       61868   C1     5 #> 2907       1         3  20       61868   C2     6 #> 2908       1         3  20       61868   C3     4 #> 2909       1         3  20       61868   C4     1 #> 2910       1         3  20       61868   C5     4 #> 2911       1         3  20       61868   E1     3 #> 2912       1         3  20       61868   E2     2 #> 2913       1         3  20       61868   E3     4 #> 2914       1         3  20       61868   E4     5 #> 2915       1         3  20       61868   E5     6 #> 2916       1         3  20       61868   N1     1 #> 2917       1         3  20       61868   N2     2 #> 2918       1         3  20       61868   N3     2 #> 2919       1         3  20       61868   N4     4 #> 2920       1         3  20       61868   N5     1 #> 2921       1         3  20       61868   O1     4 #> 2922       1         3  20       61868   O2     1 #> 2923       1         3  20       61868   O3     6 #> 2924       1         3  20       61868   O4     5 #> 2925       1         3  20       61868   O5     1 #> 2926       1         4  24       61873   A1     5 #> 2927       1         4  24       61873   A2     2 #> 2928       1         4  24       61873   A3     2 #> 2929       1         4  24       61873   A4     2 #> 2930       1         4  24       61873   A5     1 #> 2931       1         4  24       61873   C1     2 #> 2932       1         4  24       61873   C2     2 #> 2933       1         4  24       61873   C3     2 #> 2934       1         4  24       61873   C4     3 #> 2935       1         4  24       61873   C5     5 #> 2936       1         4  24       61873   E1     5 #> 2937       1         4  24       61873   E2     6 #> 2938       1         4  24       61873   E3     2 #> 2939       1         4  24       61873   E4     1 #> 2940       1         4  24       61873   E5     2 #> 2941       1         4  24       61873   N1     4 #> 2942       1         4  24       61873   N2     4 #> 2943       1         4  24       61873   N3     2 #> 2944       1         4  24       61873   N4     2 #> 2945       1         4  24       61873   N5     2 #> 2946       1         4  24       61873   O1     5 #> 2947       1         4  24       61873   O2     1 #> 2948       1         4  24       61873   O3     5 #> 2949       1         4  24       61873   O4     5 #> 2950       1         4  24       61873   O5     2 #> 2951       1         5  24       61874   A1     2 #> 2952       1         5  24       61874   A2     5 #> 2953       1         5  24       61874   A3     4 #> 2954       1         5  24       61874   A4     3 #> 2955       1         5  24       61874   A5     4 #> 2956       1         5  24       61874   C1     5 #> 2957       1         5  24       61874   C2     4 #> 2958       1         5  24       61874   C3     5 #> 2959       1         5  24       61874   C4     2 #> 2960       1         5  24       61874   C5     3 #> 2961       1         5  24       61874   E1     1 #> 2962       1         5  24       61874   E2     2 #> 2963       1         5  24       61874   E3     4 #> 2964       1         5  24       61874   E4     5 #> 2965       1         5  24       61874   E5     5 #> 2966       1         5  24       61874   N1     4 #> 2967       1         5  24       61874   N2     4 #> 2968       1         5  24       61874   N3     3 #> 2969       1         5  24       61874   N4     2 #> 2970       1         5  24       61874   N5     3 #> 2971       1         5  24       61874   O1     6 #> 2972       1         5  24       61874   O2     1 #> 2973       1         5  24       61874   O3     5 #> 2974       1         5  24       61874   O4     5 #> 2975       1         5  24       61874   O5     2 #> 2976       1         5  26       61880   A1     1 #> 2977       1         5  26       61880   A2     5 #> 2978       1         5  26       61880   A3     6 #> 2979       1         5  26       61880   A4     6 #> 2980       1         5  26       61880   A5     6 #> 2981       1         5  26       61880   C1     6 #> 2982       1         5  26       61880   C2     5 #> 2983       1         5  26       61880   C3     4 #> 2984       1         5  26       61880   C4     2 #> 2985       1         5  26       61880   C5     6 #> 2986       1         5  26       61880   E1     1 #> 2987       1         5  26       61880   E2     2 #> 2988       1         5  26       61880   E3     5 #> 2989       1         5  26       61880   E4     6 #> 2990       1         5  26       61880   E5     6 #> 2991       1         5  26       61880   N1     1 #> 2992       1         5  26       61880   N2     2 #> 2993       1         5  26       61880   N3     2 #> 2994       1         5  26       61880   N4     2 #> 2995       1         5  26       61880   N5     4 #> 2996       1         5  26       61880   O1     6 #> 2997       1         5  26       61880   O2     1 #> 2998       1         5  26       61880   O3     6 #> 2999       1         5  26       61880   O4     6 #> 3000       1         5  26       61880   O5     1 #> 3001       2         3  25       61886   A1     4 #> 3002       2         3  25       61886   A2     6 #> 3003       2         3  25       61886   A3     5 #> 3004       2         3  25       61886   A4     5 #> 3005       2         3  25       61886   A5     6 #> 3006       2         3  25       61886   C1     4 #> 3007       2         3  25       61886   C2     6 #> 3008       2         3  25       61886   C3     5 #> 3009       2         3  25       61886   C4     5 #> 3010       2         3  25       61886   C5     2 #> 3011       2         3  25       61886   E1     1 #> 3012       2         3  25       61886   E2     3 #> 3013       2         3  25       61886   E3     6 #> 3014       2         3  25       61886   E4     6 #> 3015       2         3  25       61886   E5     6 #> 3016       2         3  25       61886   N1     4 #> 3017       2         3  25       61886   N2     3 #> 3018       2         3  25       61886   N3     1 #> 3019       2         3  25       61886   N4     3 #> 3020       2         3  25       61886   N5     4 #> 3021       2         3  25       61886   O1     6 #> 3022       2         3  25       61886   O2     3 #> 3023       2         3  25       61886   O3     6 #> 3024       2         3  25       61886   O4     5 #> 3025       2         3  25       61886   O5     3 #> 3026       1         4  24       61888   A1     2 #> 3027       1         4  24       61888   A2     5 #> 3028       1         4  24       61888   A3     4 #> 3029       1         4  24       61888   A4     4 #> 3030       1         4  24       61888   A5     4 #> 3031       1         4  24       61888   C1     4 #> 3032       1         4  24       61888   C2     5 #> 3033       1         4  24       61888   C3     5 #> 3034       1         4  24       61888   C4     5 #> 3035       1         4  24       61888   C5     5 #> 3036       1         4  24       61888   E1     5 #> 3037       1         4  24       61888   E2     6 #> 3038       1         4  24       61888   E3     3 #> 3039       1         4  24       61888   E4     2 #> 3040       1         4  24       61888   E5     2 #> 3041       1         4  24       61888   N1     3 #> 3042       1         4  24       61888   N2     4 #> 3043       1         4  24       61888   N3     2 #> 3044       1         4  24       61888   N4     5 #> 3045       1         4  24       61888   N5     6 #> 3046       1         4  24       61888   O1     2 #> 3047       1         4  24       61888   O2     4 #> 3048       1         4  24       61888   O3     4 #> 3049       1         4  24       61888   O4     6 #> 3050       1         4  24       61888   O5     4 #> 3051       1         5  24       61889   A1     6 #> 3052       1         5  24       61889   A2     5 #> 3053       1         5  24       61889   A3     5 #> 3054       1         5  24       61889   A4     2 #> 3055       1         5  24       61889   A5     5 #> 3056       1         5  24       61889   C1     5 #> 3057       1         5  24       61889   C2     5 #> 3058       1         5  24       61889   C3     6 #> 3059       1         5  24       61889   C4     1 #> 3060       1         5  24       61889   C5     1 #> 3061       1         5  24       61889   E1     1 #> 3062       1         5  24       61889   E2     2 #> 3063       1         5  24       61889   E3     5 #> 3064       1         5  24       61889   E4     5 #> 3065       1         5  24       61889   E5     6 #> 3066       1         5  24       61889   N1     5 #> 3067       1         5  24       61889   N2     4 #> 3068       1         5  24       61889   N3     4 #> 3069       1         5  24       61889   N4     3 #> 3070       1         5  24       61889   N5     1 #> 3071       1         5  24       61889   O1     6 #> 3072       1         5  24       61889   O2     2 #> 3073       1         5  24       61889   O3     6 #> 3074       1         5  24       61889   O4     5 #> 3075       1         5  24       61889   O5     1 #> 3076       1         5  28       61890   A1     2 #> 3077       1         5  28       61890   A2     5 #> 3078       1         5  28       61890   A3     5 #> 3079       1         5  28       61890   A4     1 #> 3080       1         5  28       61890   A5     6 #> 3081       1         5  28       61890   C1     5 #> 3082       1         5  28       61890   C2     5 #> 3083       1         5  28       61890   C3     5 #> 3084       1         5  28       61890   C4     3 #> 3085       1         5  28       61890   C5     6 #> 3086       1         5  28       61890   E1     2 #> 3087       1         5  28       61890   E2     4 #> 3088       1         5  28       61890   E3     6 #> 3089       1         5  28       61890   E4     6 #> 3090       1         5  28       61890   E5     5 #> 3091       1         5  28       61890   N1     4 #> 3092       1         5  28       61890   N2     4 #> 3093       1         5  28       61890   N3     3 #> 3094       1         5  28       61890   N4     3 #> 3095       1         5  28       61890   N5     1 #> 3096       1         5  28       61890   O1     5 #> 3097       1         5  28       61890   O2     3 #> 3098       1         5  28       61890   O3     6 #> 3099       1         5  28       61890   O4     4 #> 3100       1         5  28       61890   O5     2 #> 3101       1         4  22       61891   A1     2 #> 3102       1         4  22       61891   A2     4 #> 3103       1         4  22       61891   A3     5 #> 3104       1         4  22       61891   A4     5 #> 3105       1         4  22       61891   A5     5 #> 3106       1         4  22       61891   C1     5 #> 3107       1         4  22       61891   C2     5 #> 3108       1         4  22       61891   C3     4 #> 3109       1         4  22       61891   C4     6 #> 3110       1         4  22       61891   C5     6 #> 3111       1         4  22       61891   E1     5 #> 3112       1         4  22       61891   E2     5 #> 3113       1         4  22       61891   E3     5 #> 3114       1         4  22       61891   E4     5 #> 3115       1         4  22       61891   E5     3 #> 3116       1         4  22       61891   N1     1 #> 3117       1         4  22       61891   N2     2 #> 3118       1         4  22       61891   N3     2 #> 3119       1         4  22       61891   N4     4 #> 3120       1         4  22       61891   N5     2 #> 3121       1         4  22       61891   O1     5 #> 3122       1         4  22       61891   O2     2 #> 3123       1         4  22       61891   O3     5 #> 3124       1         4  22       61891   O4     6 #> 3125       1         4  22       61891   O5     1 #> 3126       1         5  26       61895   A1     2 #> 3127       1         5  26       61895   A2     5 #> 3128       1         5  26       61895   A3     5 #> 3129       1         5  26       61895   A4     5 #> 3130       1         5  26       61895   A5     2 #> 3131       1         5  26       61895   C1     5 #> 3132       1         5  26       61895   C2     5 #> 3133       1         5  26       61895   C3     5 #> 3134       1         5  26       61895   C4     2 #> 3135       1         5  26       61895   C5     6 #> 3136       1         5  26       61895   E1     5 #> 3137       1         5  26       61895   E2     5 #> 3138       1         5  26       61895   E3     3 #> 3139       1         5  26       61895   E4     2 #> 3140       1         5  26       61895   E5     2 #> 3141       1         5  26       61895   N1     2 #> 3142       1         5  26       61895   N2     3 #> 3143       1         5  26       61895   N3     2 #> 3144       1         5  26       61895   N4     5 #> 3145       1         5  26       61895   N5     5 #> 3146       1         5  26       61895   O1     5 #> 3147       1         5  26       61895   O2     1 #> 3148       1         5  26       61895   O3     6 #> 3149       1         5  26       61895   O4     6 #> 3150       1         5  26       61895   O5     1 #> 3151       1         5  25       61896   A1     2 #> 3152       1         5  25       61896   A2     5 #> 3153       1         5  25       61896   A3     6 #> 3154       1         5  25       61896   A4     5 #> 3155       1         5  25       61896   A5     6 #> 3156       1         5  25       61896   C1     5 #> 3157       1         5  25       61896   C2     4 #> 3158       1         5  25       61896   C3     5 #> 3159       1         5  25       61896   C4     3 #> 3160       1         5  25       61896   C5     2 #> 3161       1         5  25       61896   E1     2 #> 3162       1         5  25       61896   E2     2 #> 3163       1         5  25       61896   E3     4 #> 3164       1         5  25       61896   E4     5 #> 3165       1         5  25       61896   E5     5 #> 3166       1         5  25       61896   N1     4 #> 3167       1         5  25       61896   N2     4 #> 3168       1         5  25       61896   N3     2 #> 3169       1         5  25       61896   N4     2 #> 3170       1         5  25       61896   N5     2 #> 3171       1         5  25       61896   O1     4 #> 3172       1         5  25       61896   O2     3 #> 3173       1         5  25       61896   O3     5 #> 3174       1         5  25       61896   O4     4 #> 3175       1         5  25       61896   O5     2 #> 3176       1         4  23       61900   A1     2 #> 3177       1         4  23       61900   A2     5 #> 3178       1         4  23       61900   A3     4 #> 3179       1         4  23       61900   A4     4 #> 3180       1         4  23       61900   A5     5 #> 3181       1         4  23       61900   C1     5 #> 3182       1         4  23       61900   C2     4 #> 3183       1         4  23       61900   C3     5 #> 3184       1         4  23       61900   C4     3 #> 3185       1         4  23       61900   C5     5 #> 3186       1         4  23       61900   E1     2 #> 3187       1         4  23       61900   E2     3 #> 3188       1         4  23       61900   E3     5 #> 3189       1         4  23       61900   E4     4 #> 3190       1         4  23       61900   E5     5 #> 3191       1         4  23       61900   N1     2 #> 3192       1         4  23       61900   N2     3 #> 3193       1         4  23       61900   N3     3 #> 3194       1         4  23       61900   N4     4 #> 3195       1         4  23       61900   N5     2 #> 3196       1         4  23       61900   O1     5 #> 3197       1         4  23       61900   O2     2 #> 3198       1         4  23       61900   O3     6 #> 3199       1         4  23       61900   O4     6 #> 3200       1         4  23       61900   O5     2 #> 3201       1         3  22       61901   A1     2 #> 3202       1         3  22       61901   A2     5 #> 3203       1         3  22       61901   A3     5 #> 3204       1         3  22       61901   A4     3 #> 3205       1         3  22       61901   A5     2 #> 3206       1         3  22       61901   C1     2 #> 3207       1         3  22       61901   C2     3 #> 3208       1         3  22       61901   C3     4 #> 3209       1         3  22       61901   C4     5 #> 3210       1         3  22       61901   C5     6 #> 3211       1         3  22       61901   E1     2 #> 3212       1         3  22       61901   E2     5 #> 3213       1         3  22       61901   E3     2 #> 3214       1         3  22       61901   E4     2 #> 3215       1         3  22       61901   E5     2 #> 3216       1         3  22       61901   N1     2 #> 3217       1         3  22       61901   N2     4 #> 3218       1         3  22       61901   N3     4 #> 3219       1         3  22       61901   N4     4 #> 3220       1         3  22       61901   N5     2 #> 3221       1         3  22       61901   O1     5 #> 3222       1         3  22       61901   O2     2 #> 3223       1         3  22       61901   O3     4 #> 3224       1         3  22       61901   O4     6 #> 3225       1         3  22       61901   O5     2 #> 3226       2         3  19       61907   A1     4 #> 3227       2         3  19       61907   A2     5 #> 3228       2         3  19       61907   A3     4 #> 3229       2         3  19       61907   A4    NA #> 3230       2         3  19       61907   A5     3 #> 3231       2         3  19       61907   C1     5 #> 3232       2         3  19       61907   C2     3 #> 3233       2         3  19       61907   C3     4 #> 3234       2         3  19       61907   C4     2 #> 3235       2         3  19       61907   C5     5 #> 3236       2         3  19       61907   E1     1 #> 3237       2         3  19       61907   E2     4 #> 3238       2         3  19       61907   E3     3 #> 3239       2         3  19       61907   E4     5 #> 3240       2         3  19       61907   E5     4 #> 3241       2         3  19       61907   N1     4 #> 3242       2         3  19       61907   N2     5 #> 3243       2         3  19       61907   N3     5 #> 3244       2         3  19       61907   N4     4 #> 3245       2         3  19       61907   N5     5 #> 3246       2         3  19       61907   O1     4 #> 3247       2         3  19       61907   O2     4 #> 3248       2         3  19       61907   O3     3 #> 3249       2         3  19       61907   O4     6 #> 3250       2         3  19       61907   O5     3 #> 3251       2         4  25       61908   A1     2 #> 3252       2         4  25       61908   A2     4 #> 3253       2         4  25       61908   A3     4 #> 3254       2         4  25       61908   A4     4 #> 3255       2         4  25       61908   A5     4 #> 3256       2         4  25       61908   C1     4 #> 3257       2         4  25       61908   C2     4 #> 3258       2         4  25       61908   C3     4 #> 3259       2         4  25       61908   C4     4 #> 3260       2         4  25       61908   C5     5 #> 3261       2         4  25       61908   E1     4 #> 3262       2         4  25       61908   E2     3 #> 3263       2         4  25       61908   E3     4 #> 3264       2         4  25       61908   E4     6 #> 3265       2         4  25       61908   E5     5 #> 3266       2         4  25       61908   N1     4 #> 3267       2         4  25       61908   N2     5 #> 3268       2         4  25       61908   N3     3 #> 3269       2         4  25       61908   N4     5 #> 3270       2         4  25       61908   N5     4 #> 3271       2         4  25       61908   O1     4 #> 3272       2         4  25       61908   O2     2 #> 3273       2         4  25       61908   O3     4 #> 3274       2         4  25       61908   O4     6 #> 3275       2         4  25       61908   O5     3 #> 3276       1         3  25       61909   A1     3 #> 3277       1         3  25       61909   A2     5 #> 3278       1         3  25       61909   A3     4 #> 3279       1         3  25       61909   A4     5 #> 3280       1         3  25       61909   A5     4 #> 3281       1         3  25       61909   C1     4 #> 3282       1         3  25       61909   C2     4 #> 3283       1         3  25       61909   C3     5 #> 3284       1         3  25       61909   C4     3 #> 3285       1         3  25       61909   C5     5 #> 3286       1         3  25       61909   E1     2 #> 3287       1         3  25       61909   E2     4 #> 3288       1         3  25       61909   E3     5 #> 3289       1         3  25       61909   E4     5 #> 3290       1         3  25       61909   E5     4 #> 3291       1         3  25       61909   N1     5 #> 3292       1         3  25       61909   N2     5 #> 3293       1         3  25       61909   N3     5 #> 3294       1         3  25       61909   N4     5 #> 3295       1         3  25       61909   N5     5 #> 3296       1         3  25       61909   O1     6 #> 3297       1         3  25       61909   O2     3 #> 3298       1         3  25       61909   O3     4 #> 3299       1         3  25       61909   O4     5 #> 3300       1         3  25       61909   O5     2 #> 3301       1         5  27       61911   A1     4 #> 3302       1         5  27       61911   A2     3 #> 3303       1         5  27       61911   A3     3 #> 3304       1         5  27       61911   A4     3 #> 3305       1         5  27       61911   A5     3 #> 3306       1         5  27       61911   C1     6 #> 3307       1         5  27       61911   C2     5 #> 3308       1         5  27       61911   C3     5 #> 3309       1         5  27       61911   C4     2 #> 3310       1         5  27       61911   C5     3 #> 3311       1         5  27       61911   E1     5 #> 3312       1         5  27       61911   E2    NA #> 3313       1         5  27       61911   E3     3 #> 3314       1         5  27       61911   E4     2 #> 3315       1         5  27       61911   E5     3 #> 3316       1         5  27       61911   N1     3 #> 3317       1         5  27       61911   N2     3 #> 3318       1         5  27       61911   N3     3 #> 3319       1         5  27       61911   N4     4 #> 3320       1         5  27       61911   N5     4 #> 3321       1         5  27       61911   O1     5 #> 3322       1         5  27       61911   O2     1 #> 3323       1         5  27       61911   O3     5 #> 3324       1         5  27       61911   O4     6 #> 3325       1         5  27       61911   O5     6 #> 3326       2         3  20       61913   A1     2 #> 3327       2         3  20       61913   A2     4 #> 3328       2         3  20       61913   A3     3 #> 3329       2         3  20       61913   A4     3 #> 3330       2         3  20       61913   A5     3 #> 3331       2         3  20       61913   C1     4 #> 3332       2         3  20       61913   C2     4 #> 3333       2         3  20       61913   C3     4 #> 3334       2         3  20       61913   C4     3 #> 3335       2         3  20       61913   C5     6 #> 3336       2         3  20       61913   E1     2 #> 3337       2         3  20       61913   E2     5 #> 3338       2         3  20       61913   E3     3 #> 3339       2         3  20       61913   E4     2 #> 3340       2         3  20       61913   E5     5 #> 3341       2         3  20       61913   N1     4 #> 3342       2         3  20       61913   N2     6 #> 3343       2         3  20       61913   N3     4 #> 3344       2         3  20       61913   N4     4 #> 3345       2         3  20       61913   N5     5 #> 3346       2         3  20       61913   O1     3 #> 3347       2         3  20       61913   O2     5 #> 3348       2         3  20       61913   O3     3 #> 3349       2         3  20       61913   O4     5 #> 3350       2         3  20       61913   O5     3 #> 3351       2         3  25       61915   A1     1 #> 3352       2         3  25       61915   A2     6 #> 3353       2         3  25       61915   A3     6 #> 3354       2         3  25       61915   A4     6 #> 3355       2         3  25       61915   A5     6 #> 3356       2         3  25       61915   C1     6 #> 3357       2         3  25       61915   C2     6 #> 3358       2         3  25       61915   C3     3 #> 3359       2         3  25       61915   C4     6 #> 3360       2         3  25       61915   C5     5 #> 3361       2         3  25       61915   E1     6 #> 3362       2         3  25       61915   E2     5 #> 3363       2         3  25       61915   E3     5 #> 3364       2         3  25       61915   E4     6 #> 3365       2         3  25       61915   E5     6 #> 3366       2         3  25       61915   N1     6 #> 3367       2         3  25       61915   N2     6 #> 3368       2         3  25       61915   N3     4 #> 3369       2         3  25       61915   N4     5 #> 3370       2         3  25       61915   N5     6 #> 3371       2         3  25       61915   O1     5 #> 3372       2         3  25       61915   O2     6 #> 3373       2         3  25       61915   O3     6 #> 3374       2         3  25       61915   O4     6 #> 3375       2         3  25       61915   O5     2 #> 3376       2         3  49       61918   A1     1 #> 3377       2         3  49       61918   A2     6 #> 3378       2         3  49       61918   A3     6 #> 3379       2         3  49       61918   A4     5 #> 3380       2         3  49       61918   A5     6 #> 3381       2         3  49       61918   C1     6 #> 3382       2         3  49       61918   C2     1 #> 3383       2         3  49       61918   C3     3 #> 3384       2         3  49       61918   C4     4 #> 3385       2         3  49       61918   C5     5 #> 3386       2         3  49       61918   E1     3 #> 3387       2         3  49       61918   E2     4 #> 3388       2         3  49       61918   E3     4 #> 3389       2         3  49       61918   E4     6 #> 3390       2         3  49       61918   E5     3 #> 3391       2         3  49       61918   N1     5 #> 3392       2         3  49       61918   N2     5 #> 3393       2         3  49       61918   N3     2 #> 3394       2         3  49       61918   N4     5 #> 3395       2         3  49       61918   N5     2 #> 3396       2         3  49       61918   O1     4 #> 3397       2         3  49       61918   O2     4 #> 3398       2         3  49       61918   O3     6 #> 3399       2         3  49       61918   O4     6 #> 3400       2         3  49       61918   O5     1 #> 3401       2         3  26       61921   A1     1 #> 3402       2         3  26       61921   A2     6 #> 3403       2         3  26       61921   A3     6 #> 3404       2         3  26       61921   A4     4 #> 3405       2         3  26       61921   A5     6 #> 3406       2         3  26       61921   C1     1 #> 3407       2         3  26       61921   C2     1 #> 3408       2         3  26       61921   C3     3 #> 3409       2         3  26       61921   C4     1 #> 3410       2         3  26       61921   C5     1 #> 3411       2         3  26       61921   E1     1 #> 3412       2         3  26       61921   E2     1 #> 3413       2         3  26       61921   E3     6 #> 3414       2         3  26       61921   E4     6 #> 3415       2         3  26       61921   E5     6 #> 3416       2         3  26       61921   N1     4 #> 3417       2         3  26       61921   N2     4 #> 3418       2         3  26       61921   N3     1 #> 3419       2         3  26       61921   N4    NA #> 3420       2         3  26       61921   N5     2 #> 3421       2         3  26       61921   O1     4 #> 3422       2         3  26       61921   O2     1 #> 3423       2         3  26       61921   O3     6 #> 3424       2         3  26       61921   O4     2 #> 3425       2         3  26       61921   O5     2 #> 3426       2         3  25       61922   A1     1 #> 3427       2         3  25       61922   A2     6 #> 3428       2         3  25       61922   A3     5 #> 3429       2         3  25       61922   A4     6 #> 3430       2         3  25       61922   A5     5 #> 3431       2         3  25       61922   C1     6 #> 3432       2         3  25       61922   C2     6 #> 3433       2         3  25       61922   C3     4 #> 3434       2         3  25       61922   C4     1 #> 3435       2         3  25       61922   C5     1 #> 3436       2         3  25       61922   E1     1 #> 3437       2         3  25       61922   E2     2 #> 3438       2         3  25       61922   E3     5 #> 3439       2         3  25       61922   E4     4 #> 3440       2         3  25       61922   E5     5 #> 3441       2         3  25       61922   N1     4 #> 3442       2         3  25       61922   N2     6 #> 3443       2         3  25       61922   N3     2 #> 3444       2         3  25       61922   N4     1 #> 3445       2         3  25       61922   N5     2 #> 3446       2         3  25       61922   O1     6 #> 3447       2         3  25       61922   O2     1 #> 3448       2         3  25       61922   O3     6 #> 3449       2         3  25       61922   O4     5 #> 3450       2         3  25       61922   O5     3 #> 3451       2         3  25       61923   A1     1 #> 3452       2         3  25       61923   A2     6 #> 3453       2         3  25       61923   A3     5 #> 3454       2         3  25       61923   A4     6 #> 3455       2         3  25       61923   A5     5 #> 3456       2         3  25       61923   C1     5 #> 3457       2         3  25       61923   C2     5 #> 3458       2         3  25       61923   C3     4 #> 3459       2         3  25       61923   C4     1 #> 3460       2         3  25       61923   C5     4 #> 3461       2         3  25       61923   E1     2 #> 3462       2         3  25       61923   E2     3 #> 3463       2         3  25       61923   E3     6 #> 3464       2         3  25       61923   E4     6 #> 3465       2         3  25       61923   E5     5 #> 3466       2         3  25       61923   N1     4 #> 3467       2         3  25       61923   N2     4 #> 3468       2         3  25       61923   N3     5 #> 3469       2         3  25       61923   N4     4 #> 3470       2         3  25       61923   N5     5 #> 3471       2         3  25       61923   O1     5 #> 3472       2         3  25       61923   O2     2 #> 3473       2         3  25       61923   O3     5 #> 3474       2         3  25       61923   O4     6 #> 3475       2         3  25       61923   O5     4 #> 3476       1        NA  18       61925   A1     3 #> 3477       1        NA  18       61925   A2     6 #> 3478       1        NA  18       61925   A3     6 #> 3479       1        NA  18       61925   A4     4 #> 3480       1        NA  18       61925   A5     6 #> 3481       1        NA  18       61925   C1     2 #> 3482       1        NA  18       61925   C2     3 #> 3483       1        NA  18       61925   C3     2 #> 3484       1        NA  18       61925   C4     5 #> 3485       1        NA  18       61925   C5     5 #> 3486       1        NA  18       61925   E1     1 #> 3487       1        NA  18       61925   E2     1 #> 3488       1        NA  18       61925   E3     6 #> 3489       1        NA  18       61925   E4     6 #> 3490       1        NA  18       61925   E5     6 #> 3491       1        NA  18       61925   N1     4 #> 3492       1        NA  18       61925   N2     4 #> 3493       1        NA  18       61925   N3     4 #> 3494       1        NA  18       61925   N4     3 #> 3495       1        NA  18       61925   N5     3 #> 3496       1        NA  18       61925   O1     6 #> 3497       1        NA  18       61925   O2     3 #> 3498       1        NA  18       61925   O3     4 #> 3499       1        NA  18       61925   O4     5 #> 3500       1        NA  18       61925   O5     2 #> 3501       1         3  21       61926   A1     2 #> 3502       1         3  21       61926   A2     3 #> 3503       1         3  21       61926   A3     1 #> 3504       1         3  21       61926   A4     3 #> 3505       1         3  21       61926   A5     1 #> 3506       1         3  21       61926   C1     3 #> 3507       1         3  21       61926   C2     3 #> 3508       1         3  21       61926   C3     3 #> 3509       1         3  21       61926   C4     2 #> 3510       1         3  21       61926   C5     6 #> 3511       1         3  21       61926   E1     6 #> 3512       1         3  21       61926   E2     6 #> 3513       1         3  21       61926   E3     1 #> 3514       1         3  21       61926   E4     1 #> 3515       1         3  21       61926   E5     3 #> 3516       1         3  21       61926   N1     5 #> 3517       1         3  21       61926   N2     4 #> 3518       1         3  21       61926   N3     3 #> 3519       1         3  21       61926   N4     6 #> 3520       1         3  21       61926   N5     4 #> 3521       1         3  21       61926   O1     2 #> 3522       1         3  21       61926   O2     2 #> 3523       1         3  21       61926   O3     1 #> 3524       1         3  21       61926   O4     5 #> 3525       1         3  21       61926   O5     3 #> 3526       2         3  22       61928   A1     2 #> 3527       2         3  22       61928   A2     4 #> 3528       2         3  22       61928   A3     5 #> 3529       2         3  22       61928   A4     5 #> 3530       2         3  22       61928   A5     5 #> 3531       2         3  22       61928   C1     4 #> 3532       2         3  22       61928   C2     4 #> 3533       2         3  22       61928   C3     6 #> 3534       2         3  22       61928   C4     4 #> 3535       2         3  22       61928   C5     2 #> 3536       2         3  22       61928   E1     1 #> 3537       2         3  22       61928   E2     2 #> 3538       2         3  22       61928   E3     4 #> 3539       2         3  22       61928   E4     6 #> 3540       2         3  22       61928   E5     5 #> 3541       2         3  22       61928   N1     6 #> 3542       2         3  22       61928   N2     6 #> 3543       2         3  22       61928   N3     4 #> 3544       2         3  22       61928   N4     4 #> 3545       2         3  22       61928   N5     6 #> 3546       2         3  22       61928   O1     4 #> 3547       2         3  22       61928   O2     4 #> 3548       2         3  22       61928   O3     3 #> 3549       2         3  22       61928   O4     4 #> 3550       2         3  22       61928   O5     5 #> 3551       2         3  37       61932   A1     2 #> 3552       2         3  37       61932   A2     5 #> 3553       2         3  37       61932   A3     3 #> 3554       2         3  37       61932   A4     5 #> 3555       2         3  37       61932   A5     1 #> 3556       2         3  37       61932   C1     6 #> 3557       2         3  37       61932   C2     5 #> 3558       2         3  37       61932   C3     4 #> 3559       2         3  37       61932   C4     2 #> 3560       2         3  37       61932   C5     2 #> 3561       2         3  37       61932   E1     1 #> 3562       2         3  37       61932   E2     1 #> 3563       2         3  37       61932   E3     5 #> 3564       2         3  37       61932   E4     6 #> 3565       2         3  37       61932   E5     2 #> 3566       2         3  37       61932   N1     3 #> 3567       2         3  37       61932   N2     5 #> 3568       2         3  37       61932   N3     4 #> 3569       2         3  37       61932   N4     4 #> 3570       2         3  37       61932   N5     5 #> 3571       2         3  37       61932   O1     4 #> 3572       2         3  37       61932   O2     2 #> 3573       2         3  37       61932   O3     5 #> 3574       2         3  37       61932   O4     4 #> 3575       2         3  37       61932   O5     3 #> 3576       2         3  20       61935   A1     1 #> 3577       2         3  20       61935   A2     6 #> 3578       2         3  20       61935   A3     5 #> 3579       2         3  20       61935   A4     6 #> 3580       2         3  20       61935   A5     6 #> 3581       2         3  20       61935   C1     3 #> 3582       2         3  20       61935   C2     2 #> 3583       2         3  20       61935   C3     4 #> 3584       2         3  20       61935   C4     2 #> 3585       2         3  20       61935   C5     3 #> 3586       2         3  20       61935   E1     3 #> 3587       2         3  20       61935   E2     2 #> 3588       2         3  20       61935   E3     5 #> 3589       2         3  20       61935   E4     5 #> 3590       2         3  20       61935   E5     3 #> 3591       2         3  20       61935   N1     2 #> 3592       2         3  20       61935   N2     2 #> 3593       2         3  20       61935   N3     2 #> 3594       2         3  20       61935   N4     2 #> 3595       2         3  20       61935   N5     1 #> 3596       2         3  20       61935   O1     4 #> 3597       2         3  20       61935   O2     4 #> 3598       2         3  20       61935   O3     4 #> 3599       2         3  20       61935   O4     5 #> 3600       2         3  20       61935   O5     3 #> 3601       2         1  22       61936   A1     5 #> 3602       2         1  22       61936   A2     4 #> 3603       2         1  22       61936   A3     4 #> 3604       2         1  22       61936   A4     5 #> 3605       2         1  22       61936   A5     6 #> 3606       2         1  22       61936   C1     3 #> 3607       2         1  22       61936   C2     5 #> 3608       2         1  22       61936   C3     3 #> 3609       2         1  22       61936   C4     3 #> 3610       2         1  22       61936   C5     6 #> 3611       2         1  22       61936   E1     4 #> 3612       2         1  22       61936   E2     4 #> 3613       2         1  22       61936   E3     6 #> 3614       2         1  22       61936   E4     5 #> 3615       2         1  22       61936   E5     5 #> 3616       2         1  22       61936   N1     2 #> 3617       2         1  22       61936   N2     5 #> 3618       2         1  22       61936   N3     2 #> 3619       2         1  22       61936   N4     1 #> 3620       2         1  22       61936   N5     2 #> 3621       2         1  22       61936   O1     6 #> 3622       2         1  22       61936   O2     2 #> 3623       2         1  22       61936   O3     5 #> 3624       2         1  22       61936   O4     5 #> 3625       2         1  22       61936   O5     2 #> 3626       2         5  41       61939   A1     2 #> 3627       2         5  41       61939   A2     5 #> 3628       2         5  41       61939   A3     4 #> 3629       2         5  41       61939   A4     3 #> 3630       2         5  41       61939   A5     3 #> 3631       2         5  41       61939   C1     6 #> 3632       2         5  41       61939   C2     4 #> 3633       2         5  41       61939   C3     4 #> 3634       2         5  41       61939   C4     2 #> 3635       2         5  41       61939   C5     4 #> 3636       2         5  41       61939   E1     6 #> 3637       2         5  41       61939   E2     4 #> 3638       2         5  41       61939   E3     3 #> 3639       2         5  41       61939   E4     3 #> 3640       2         5  41       61939   E5     4 #> 3641       2         5  41       61939   N1     1 #> 3642       2         5  41       61939   N2     1 #> 3643       2         5  41       61939   N3     1 #> 3644       2         5  41       61939   N4     4 #> 3645       2         5  41       61939   N5     3 #> 3646       2         5  41       61939   O1     6 #> 3647       2         5  41       61939   O2     1 #> 3648       2         5  41       61939   O3     4 #> 3649       2         5  41       61939   O4     6 #> 3650       2         5  41       61939   O5     1 #> 3651       2         5  22       61944   A1     1 #> 3652       2         5  22       61944   A2     6 #> 3653       2         5  22       61944   A3     6 #> 3654       2         5  22       61944   A4     3 #> 3655       2         5  22       61944   A5     6 #> 3656       2         5  22       61944   C1     6 #> 3657       2         5  22       61944   C2     6 #> 3658       2         5  22       61944   C3     6 #> 3659       2         5  22       61944   C4     1 #> 3660       2         5  22       61944   C5     5 #> 3661       2         5  22       61944   E1     1 #> 3662       2         5  22       61944   E2     4 #> 3663       2         5  22       61944   E3     5 #> 3664       2         5  22       61944   E4     5 #> 3665       2         5  22       61944   E5     6 #> 3666       2         5  22       61944   N1     3 #> 3667       2         5  22       61944   N2     6 #> 3668       2         5  22       61944   N3     4 #> 3669       2         5  22       61944   N4     4 #> 3670       2         5  22       61944   N5     6 #> 3671       2         5  22       61944   O1     5 #> 3672       2         5  22       61944   O2     1 #> 3673       2         5  22       61944   O3     4 #> 3674       2         5  22       61944   O4     6 #> 3675       2         5  22       61944   O5     1 #> 3676       1         5  24       61945   A1     2 #> 3677       1         5  24       61945   A2     6 #> 3678       1         5  24       61945   A3     4 #> 3679       1         5  24       61945   A4     5 #> 3680       1         5  24       61945   A5     4 #> 3681       1         5  24       61945   C1     4 #> 3682       1         5  24       61945   C2     4 #> 3683       1         5  24       61945   C3     5 #> 3684       1         5  24       61945   C4     3 #> 3685       1         5  24       61945   C5     5 #> 3686       1         5  24       61945   E1     3 #> 3687       1         5  24       61945   E2     3 #> 3688       1         5  24       61945   E3     4 #> 3689       1         5  24       61945   E4     4 #> 3690       1         5  24       61945   E5     4 #> 3691       1         5  24       61945   N1     3 #> 3692       1         5  24       61945   N2     3 #> 3693       1         5  24       61945   N3     3 #> 3694       1         5  24       61945   N4     4 #> 3695       1         5  24       61945   N5     3 #> 3696       1         5  24       61945   O1     4 #> 3697       1         5  24       61945   O2     4 #> 3698       1         5  24       61945   O3     5 #> 3699       1         5  24       61945   O4     5 #> 3700       1         5  24       61945   O5     2 #> 3701       2         4  23       61949   A1     2 #> 3702       2         4  23       61949   A2     6 #> 3703       2         4  23       61949   A3     3 #> 3704       2         4  23       61949   A4     5 #> 3705       2         4  23       61949   A5     2 #> 3706       2         4  23       61949   C1     4 #> 3707       2         4  23       61949   C2     4 #> 3708       2         4  23       61949   C3     6 #> 3709       2         4  23       61949   C4     4 #> 3710       2         4  23       61949   C5     5 #> 3711       2         4  23       61949   E1     2 #> 3712       2         4  23       61949   E2     1 #> 3713       2         4  23       61949   E3     5 #> 3714       2         4  23       61949   E4     4 #> 3715       2         4  23       61949   E5     6 #> 3716       2         4  23       61949   N1     1 #> 3717       2         4  23       61949   N2     1 #> 3718       2         4  23       61949   N3     1 #> 3719       2         4  23       61949   N4     1 #> 3720       2         4  23       61949   N5     1 #> 3721       2         4  23       61949   O1     6 #> 3722       2         4  23       61949   O2     2 #> 3723       2         4  23       61949   O3     5 #> 3724       2         4  23       61949   O4     2 #> 3725       2         4  23       61949   O5     1 #> 3726       1         4  32       61952   A1     1 #> 3727       1         4  32       61952   A2     5 #> 3728       1         4  32       61952   A3     5 #> 3729       1         4  32       61952   A4     6 #> 3730       1         4  32       61952   A5     5 #> 3731       1         4  32       61952   C1     5 #> 3732       1         4  32       61952   C2     5 #> 3733       1         4  32       61952   C3     5 #> 3734       1         4  32       61952   C4     2 #> 3735       1         4  32       61952   C5     2 #> 3736       1         4  32       61952   E1     2 #> 3737       1         4  32       61952   E2     2 #> 3738       1         4  32       61952   E3     1 #> 3739       1         4  32       61952   E4     5 #> 3740       1         4  32       61952   E5     3 #> 3741       1         4  32       61952   N1     5 #> 3742       1         4  32       61952   N2     2 #> 3743       1         4  32       61952   N3     4 #> 3744       1         4  32       61952   N4     4 #> 3745       1         4  32       61952   N5     2 #> 3746       1         4  32       61952   O1     5 #> 3747       1         4  32       61952   O2     2 #> 3748       1         4  32       61952   O3     5 #> 3749       1         4  32       61952   O4     5 #> 3750       1         4  32       61952   O5     1 #> 3751       1         5  43       61953   A1     1 #> 3752       1         5  43       61953   A2     5 #> 3753       1         5  43       61953   A3     5 #> 3754       1         5  43       61953   A4     3 #> 3755       1         5  43       61953   A5     3 #> 3756       1         5  43       61953   C1     5 #> 3757       1         5  43       61953   C2     2 #> 3758       1         5  43       61953   C3     2 #> 3759       1         5  43       61953   C4     5 #> 3760       1         5  43       61953   C5     6 #> 3761       1         5  43       61953   E1     2 #> 3762       1         5  43       61953   E2     3 #> 3763       1         5  43       61953   E3     2 #> 3764       1         5  43       61953   E4     3 #> 3765       1         5  43       61953   E5     4 #> 3766       1         5  43       61953   N1     5 #> 3767       1         5  43       61953   N2     5 #> 3768       1         5  43       61953   N3     5 #> 3769       1         5  43       61953   N4     3 #> 3770       1         5  43       61953   N5     2 #> 3771       1         5  43       61953   O1     5 #> 3772       1         5  43       61953   O2     2 #> 3773       1         5  43       61953   O3     5 #> 3774       1         5  43       61953   O4     6 #> 3775       1         5  43       61953   O5     2 #> 3776       2         4  30       61954   A1     1 #> 3777       2         4  30       61954   A2     6 #> 3778       2         4  30       61954   A3     6 #> 3779       2         4  30       61954   A4     5 #> 3780       2         4  30       61954   A5     6 #> 3781       2         4  30       61954   C1     6 #> 3782       2         4  30       61954   C2     5 #> 3783       2         4  30       61954   C3     3 #> 3784       2         4  30       61954   C4     1 #> 3785       2         4  30       61954   C5     1 #> 3786       2         4  30       61954   E1     1 #> 3787       2         4  30       61954   E2     2 #> 3788       2         4  30       61954   E3     5 #> 3789       2         4  30       61954   E4     5 #> 3790       2         4  30       61954   E5     5 #> 3791       2         4  30       61954   N1     1 #> 3792       2         4  30       61954   N2     1 #> 3793       2         4  30       61954   N3     1 #> 3794       2         4  30       61954   N4     2 #> 3795       2         4  30       61954   N5     4 #> 3796       2         4  30       61954   O1     6 #> 3797       2         4  30       61954   O2     1 #> 3798       2         4  30       61954   O3     6 #> 3799       2         4  30       61954   O4     6 #> 3800       2         4  30       61954   O5     1 #> 3801       2         2  50       61957   A1     2 #> 3802       2         2  50       61957   A2     5 #> 3803       2         2  50       61957   A3     5 #> 3804       2         2  50       61957   A4     6 #> 3805       2         2  50       61957   A5     5 #> 3806       2         2  50       61957   C1     4 #> 3807       2         2  50       61957   C2     4 #> 3808       2         2  50       61957   C3     2 #> 3809       2         2  50       61957   C4     5 #> 3810       2         2  50       61957   C5     4 #> 3811       2         2  50       61957   E1     4 #> 3812       2         2  50       61957   E2     3 #> 3813       2         2  50       61957   E3     5 #> 3814       2         2  50       61957   E4     5 #> 3815       2         2  50       61957   E5     4 #> 3816       2         2  50       61957   N1     3 #> 3817       2         2  50       61957   N2     4 #> 3818       2         2  50       61957   N3     2 #> 3819       2         2  50       61957   N4     1 #> 3820       2         2  50       61957   N5     1 #> 3821       2         2  50       61957   O1     6 #> 3822       2         2  50       61957   O2     5 #> 3823       2         2  50       61957   O3     4 #> 3824       2         2  50       61957   O4     6 #> 3825       2         2  50       61957   O5     2 #> 3826       1         1  18       61958   A1     4 #> 3827       1         1  18       61958   A2     4 #> 3828       1         1  18       61958   A3     3 #> 3829       1         1  18       61958   A4     1 #> 3830       1         1  18       61958   A5     4 #> 3831       1         1  18       61958   C1     5 #> 3832       1         1  18       61958   C2     4 #> 3833       1         1  18       61958   C3     4 #> 3834       1         1  18       61958   C4     1 #> 3835       1         1  18       61958   C5     1 #> 3836       1         1  18       61958   E1     1 #> 3837       1         1  18       61958   E2     1 #> 3838       1         1  18       61958   E3     4 #> 3839       1         1  18       61958   E4     6 #> 3840       1         1  18       61958   E5     6 #> 3841       1         1  18       61958   N1     3 #> 3842       1         1  18       61958   N2     4 #> 3843       1         1  18       61958   N3     2 #> 3844       1         1  18       61958   N4     2 #> 3845       1         1  18       61958   N5     1 #> 3846       1         1  18       61958   O1     4 #> 3847       1         1  18       61958   O2     1 #> 3848       1         1  18       61958   O3     6 #> 3849       1         1  18       61958   O4     4 #> 3850       1         1  18       61958   O5     2 #> 3851       2         3  16       61965   A1     2 #> 3852       2         3  16       61965   A2     6 #> 3853       2         3  16       61965   A3     5 #> 3854       2         3  16       61965   A4     5 #> 3855       2         3  16       61965   A5     3 #> 3856       2         3  16       61965   C1     5 #> 3857       2         3  16       61965   C2     5 #> 3858       2         3  16       61965   C3     6 #> 3859       2         3  16       61965   C4     2 #> 3860       2         3  16       61965   C5     1 #> 3861       2         3  16       61965   E1     2 #> 3862       2         3  16       61965   E2     5 #> 3863       2         3  16       61965   E3     3 #> 3864       2         3  16       61965   E4     4 #> 3865       2         3  16       61965   E5     5 #> 3866       2         3  16       61965   N1     1 #> 3867       2         3  16       61965   N2     3 #> 3868       2         3  16       61965   N3     2 #> 3869       2         3  16       61965   N4     1 #> 3870       2         3  16       61965   N5     3 #> 3871       2         3  16       61965   O1     6 #> 3872       2         3  16       61965   O2     2 #> 3873       2         3  16       61965   O3     4 #> 3874       2         3  16       61965   O4     6 #> 3875       2         3  16       61965   O5     5 #> 3876       2         5  34       61967   A1     3 #> 3877       2         5  34       61967   A2     5 #> 3878       2         5  34       61967   A3     6 #> 3879       2         5  34       61967   A4     5 #> 3880       2         5  34       61967   A5     5 #> 3881       2         5  34       61967   C1     4 #> 3882       2         5  34       61967   C2     5 #> 3883       2         5  34       61967   C3     4 #> 3884       2         5  34       61967   C4     2 #> 3885       2         5  34       61967   C5     4 #> 3886       2         5  34       61967   E1     2 #> 3887       2         5  34       61967   E2     2 #> 3888       2         5  34       61967   E3     5 #> 3889       2         5  34       61967   E4     5 #> 3890       2         5  34       61967   E5     5 #> 3891       2         5  34       61967   N1     3 #> 3892       2         5  34       61967   N2     3 #> 3893       2         5  34       61967   N3     5 #> 3894       2         5  34       61967   N4     4 #> 3895       2         5  34       61967   N5     3 #> 3896       2         5  34       61967   O1     5 #> 3897       2         5  34       61967   O2     2 #> 3898       2         5  34       61967   O3     6 #> 3899       2         5  34       61967   O4     5 #> 3900       2         5  34       61967   O5     3 #> 3901       2         2  18       61968   A1     1 #> 3902       2         2  18       61968   A2     6 #> 3903       2         2  18       61968   A3     6 #> 3904       2         2  18       61968   A4     6 #> 3905       2         2  18       61968   A5     5 #> 3906       2         2  18       61968   C1     5 #> 3907       2         2  18       61968   C2     3 #> 3908       2         2  18       61968   C3     4 #> 3909       2         2  18       61968   C4     4 #> 3910       2         2  18       61968   C5     4 #> 3911       2         2  18       61968   E1     1 #> 3912       2         2  18       61968   E2     2 #> 3913       2         2  18       61968   E3     5 #> 3914       2         2  18       61968   E4     6 #> 3915       2         2  18       61968   E5     5 #> 3916       2         2  18       61968   N1     2 #> 3917       2         2  18       61968   N2     2 #> 3918       2         2  18       61968   N3     2 #> 3919       2         2  18       61968   N4     2 #> 3920       2         2  18       61968   N5     3 #> 3921       2         2  18       61968   O1     5 #> 3922       2         2  18       61968   O2     4 #> 3923       2         2  18       61968   O3     5 #> 3924       2         2  18       61968   O4     5 #> 3925       2         2  18       61968   O5     2 #> 3926       2         5  24       61969   A1     2 #> 3927       2         5  24       61969   A2     5 #> 3928       2         5  24       61969   A3     5 #> 3929       2         5  24       61969   A4     5 #> 3930       2         5  24       61969   A5     4 #> 3931       2         5  24       61969   C1     3 #> 3932       2         5  24       61969   C2     1 #> 3933       2         5  24       61969   C3     4 #> 3934       2         5  24       61969   C4     4 #> 3935       2         5  24       61969   C5     5 #> 3936       2         5  24       61969   E1     5 #> 3937       2         5  24       61969   E2     5 #> 3938       2         5  24       61969   E3     5 #> 3939       2         5  24       61969   E4     3 #> 3940       2         5  24       61969   E5     5 #> 3941       2         5  24       61969   N1     2 #> 3942       2         5  24       61969   N2     4 #> 3943       2         5  24       61969   N3     5 #> 3944       2         5  24       61969   N4     4 #> 3945       2         5  24       61969   N5     5 #> 3946       2         5  24       61969   O1     5 #> 3947       2         5  24       61969   O2     4 #> 3948       2         5  24       61969   O3     5 #> 3949       2         5  24       61969   O4     5 #> 3950       2         5  24       61969   O5     1 #> 3951       1         2  18       61971   A1     2 #> 3952       1         2  18       61971   A2     4 #> 3953       1         2  18       61971   A3     4 #> 3954       1         2  18       61971   A4     1 #> 3955       1         2  18       61971   A5     3 #> 3956       1         2  18       61971   C1     3 #> 3957       1         2  18       61971   C2     5 #> 3958       1         2  18       61971   C3     4 #> 3959       1         2  18       61971   C4     4 #> 3960       1         2  18       61971   C5     5 #> 3961       1         2  18       61971   E1     4 #> 3962       1         2  18       61971   E2     4 #> 3963       1         2  18       61971   E3     4 #> 3964       1         2  18       61971   E4     5 #> 3965       1         2  18       61971   E5     2 #> 3966       1         2  18       61971   N1     1 #> 3967       1         2  18       61971   N2     2 #> 3968       1         2  18       61971   N3     1 #> 3969       1         2  18       61971   N4     4 #> 3970       1         2  18       61971   N5     4 #> 3971       1         2  18       61971   O1     5 #> 3972       1         2  18       61971   O2     1 #> 3973       1         2  18       61971   O3     6 #> 3974       1         2  18       61971   O4     6 #> 3975       1         2  18       61971   O5     1 #> 3976       2         3  22       61972   A1     2 #> 3977       2         3  22       61972   A2     4 #> 3978       2         3  22       61972   A3     5 #> 3979       2         3  22       61972   A4     5 #> 3980       2         3  22       61972   A5     4 #> 3981       2         3  22       61972   C1     5 #> 3982       2         3  22       61972   C2     6 #> 3983       2         3  22       61972   C3     5 #> 3984       2         3  22       61972   C4     2 #> 3985       2         3  22       61972   C5     1 #> 3986       2         3  22       61972   E1     3 #> 3987       2         3  22       61972   E2     4 #> 3988       2         3  22       61972   E3     4 #> 3989       2         3  22       61972   E4     2 #> 3990       2         3  22       61972   E5     6 #> 3991       2         3  22       61972   N1     6 #> 3992       2         3  22       61972   N2     6 #> 3993       2         3  22       61972   N3     2 #> 3994       2         3  22       61972   N4     3 #> 3995       2         3  22       61972   N5     4 #> 3996       2         3  22       61972   O1     5 #> 3997       2         3  22       61972   O2     5 #> 3998       2         3  22       61972   O3     4 #> 3999       2         3  22       61972   O4     5 #> 4000       2         3  22       61972   O5     5 #> 4001       2         2  36       61973   A1     1 #> 4002       2         2  36       61973   A2     5 #> 4003       2         2  36       61973   A3     5 #> 4004       2         2  36       61973   A4     6 #> 4005       2         2  36       61973   A5     5 #> 4006       2         2  36       61973   C1     5 #> 4007       2         2  36       61973   C2     4 #> 4008       2         2  36       61973   C3     5 #> 4009       2         2  36       61973   C4     1 #> 4010       2         2  36       61973   C5     1 #> 4011       2         2  36       61973   E1     4 #> 4012       2         2  36       61973   E2     2 #> 4013       2         2  36       61973   E3     4 #> 4014       2         2  36       61973   E4     4 #> 4015       2         2  36       61973   E5     4 #> 4016       2         2  36       61973   N1     3 #> 4017       2         2  36       61973   N2     4 #> 4018       2         2  36       61973   N3     2 #> 4019       2         2  36       61973   N4    NA #> 4020       2         2  36       61973   N5     1 #> 4021       2         2  36       61973   O1     5 #> 4022       2         2  36       61973   O2     1 #> 4023       2         2  36       61973   O3     4 #> 4024       2         2  36       61973   O4     4 #> 4025       2         2  36       61973   O5     4 #> 4026       2         3  19       61974   A1     1 #> 4027       2         3  19       61974   A2     6 #> 4028       2         3  19       61974   A3     6 #> 4029       2         3  19       61974   A4     2 #> 4030       2         3  19       61974   A5     5 #> 4031       2         3  19       61974   C1     5 #> 4032       2         3  19       61974   C2     3 #> 4033       2         3  19       61974   C3     2 #> 4034       2         3  19       61974   C4     3 #> 4035       2         3  19       61974   C5     4 #> 4036       2         3  19       61974   E1     2 #> 4037       2         3  19       61974   E2     2 #> 4038       2         3  19       61974   E3     3 #> 4039       2         3  19       61974   E4     5 #> 4040       2         3  19       61974   E5     5 #> 4041       2         3  19       61974   N1     2 #> 4042       2         3  19       61974   N2     4 #> 4043       2         3  19       61974   N3     2 #> 4044       2         3  19       61974   N4     3 #> 4045       2         3  19       61974   N5     5 #> 4046       2         3  19       61974   O1     4 #> 4047       2         3  19       61974   O2     2 #> 4048       2         3  19       61974   O3     4 #> 4049       2         3  19       61974   O4     6 #> 4050       2         3  19       61974   O5     2 #> 4051       1         3  20       61975   A1     2 #> 4052       1         3  20       61975   A2     6 #> 4053       1         3  20       61975   A3     4 #> 4054       1         3  20       61975   A4     5 #> 4055       1         3  20       61975   A5     4 #> 4056       1         3  20       61975   C1     2 #> 4057       1         3  20       61975   C2     3 #> 4058       1         3  20       61975   C3     2 #> 4059       1         3  20       61975   C4     4 #> 4060       1         3  20       61975   C5     5 #> 4061       1         3  20       61975   E1     2 #> 4062       1         3  20       61975   E2     3 #> 4063       1         3  20       61975   E3     4 #> 4064       1         3  20       61975   E4     5 #> 4065       1         3  20       61975   E5     4 #> 4066       1         3  20       61975   N1     4 #> 4067       1         3  20       61975   N2     3 #> 4068       1         3  20       61975   N3     2 #> 4069       1         3  20       61975   N4     4 #> 4070       1         3  20       61975   N5     2 #> 4071       1         3  20       61975   O1     5 #> 4072       1         3  20       61975   O2     5 #> 4073       1         3  20       61975   O3     4 #> 4074       1         3  20       61975   O4     4 #> 4075       1         3  20       61975   O5     2 #> 4076       1         3  24       61976   A1     1 #> 4077       1         3  24       61976   A2     5 #> 4078       1         3  24       61976   A3     2 #> 4079       1         3  24       61976   A4     3 #> 4080       1         3  24       61976   A5     4 #> 4081       1         3  24       61976   C1     2 #> 4082       1         3  24       61976   C2     5 #> 4083       1         3  24       61976   C3     6 #> 4084       1         3  24       61976   C4     5 #> 4085       1         3  24       61976   C5     2 #> 4086       1         3  24       61976   E1     5 #> 4087       1         3  24       61976   E2     2 #> 4088       1         3  24       61976   E3     1 #> 4089       1         3  24       61976   E4     3 #> 4090       1         3  24       61976   E5     4 #> 4091       1         3  24       61976   N1     1 #> 4092       1         3  24       61976   N2     2 #> 4093       1         3  24       61976   N3     4 #> 4094       1         3  24       61976   N4     1 #> 4095       1         3  24       61976   N5     1 #> 4096       1         3  24       61976   O1     5 #> 4097       1         3  24       61976   O2     6 #> 4098       1         3  24       61976   O3     2 #> 4099       1         3  24       61976   O4     2 #> 4100       1         3  24       61976   O5     6 #> 4101       2         3  20       61978   A1     1 #> 4102       2         3  20       61978   A2     6 #> 4103       2         3  20       61978   A3     6 #> 4104       2         3  20       61978   A4     6 #> 4105       2         3  20       61978   A5     6 #> 4106       2         3  20       61978   C1     5 #> 4107       2         3  20       61978   C2     5 #> 4108       2         3  20       61978   C3     5 #> 4109       2         3  20       61978   C4     2 #> 4110       2         3  20       61978   C5     2 #> 4111       2         3  20       61978   E1     2 #> 4112       2         3  20       61978   E2     2 #> 4113       2         3  20       61978   E3     5 #> 4114       2         3  20       61978   E4     6 #> 4115       2         3  20       61978   E5     4 #> 4116       2         3  20       61978   N1     2 #> 4117       2         3  20       61978   N2     2 #> 4118       2         3  20       61978   N3     4 #> 4119       2         3  20       61978   N4     3 #> 4120       2         3  20       61978   N5     4 #> 4121       2         3  20       61978   O1     5 #> 4122       2         3  20       61978   O2     6 #> 4123       2         3  20       61978   O3     5 #> 4124       2         3  20       61978   O4     6 #> 4125       2         3  20       61978   O5     4 #> 4126       1         5  34       61979   A1     2 #> 4127       1         5  34       61979   A2     4 #> 4128       1         5  34       61979   A3     5 #> 4129       1         5  34       61979   A4     5 #> 4130       1         5  34       61979   A5     5 #> 4131       1         5  34       61979   C1     1 #> 4132       1         5  34       61979   C2     1 #> 4133       1         5  34       61979   C3     3 #> 4134       1         5  34       61979   C4     4 #> 4135       1         5  34       61979   C5     5 #> 4136       1         5  34       61979   E1     1 #> 4137       1         5  34       61979   E2     2 #> 4138       1         5  34       61979   E3     5 #> 4139       1         5  34       61979   E4     5 #> 4140       1         5  34       61979   E5     2 #> 4141       1         5  34       61979   N1     1 #> 4142       1         5  34       61979   N2     2 #> 4143       1         5  34       61979   N3     1 #> 4144       1         5  34       61979   N4     2 #> 4145       1         5  34       61979   N5     1 #> 4146       1         5  34       61979   O1     6 #> 4147       1         5  34       61979   O2     1 #> 4148       1         5  34       61979   O3     5 #> 4149       1         5  34       61979   O4     5 #> 4150       1         5  34       61979   O5     6 #> 4151       1         3  21       61983   A1     2 #> 4152       1         3  21       61983   A2     4 #> 4153       1         3  21       61983   A3     5 #> 4154       1         3  21       61983   A4     6 #> 4155       1         3  21       61983   A5     5 #> 4156       1         3  21       61983   C1     4 #> 4157       1         3  21       61983   C2     6 #> 4158       1         3  21       61983   C3     4 #> 4159       1         3  21       61983   C4     1 #> 4160       1         3  21       61983   C5     2 #> 4161       1         3  21       61983   E1     1 #> 4162       1         3  21       61983   E2     1 #> 4163       1         3  21       61983   E3     6 #> 4164       1         3  21       61983   E4     6 #> 4165       1         3  21       61983   E5     5 #> 4166       1         3  21       61983   N1     1 #> 4167       1         3  21       61983   N2     1 #> 4168       1         3  21       61983   N3     2 #> 4169       1         3  21       61983   N4     2 #> 4170       1         3  21       61983   N5     1 #> 4171       1         3  21       61983   O1     5 #> 4172       1         3  21       61983   O2     1 #> 4173       1         3  21       61983   O3     5 #> 4174       1         3  21       61983   O4     5 #> 4175       1         3  21       61983   O5     2 #> 4176       1         3  19       61986   A1     2 #> 4177       1         3  19       61986   A2     6 #> 4178       1         3  19       61986   A3     5 #> 4179       1         3  19       61986   A4     4 #> 4180       1         3  19       61986   A5     4 #> 4181       1         3  19       61986   C1     4 #> 4182       1         3  19       61986   C2     2 #> 4183       1         3  19       61986   C3     4 #> 4184       1         3  19       61986   C4     5 #> 4185       1         3  19       61986   C5     5 #> 4186       1         3  19       61986   E1     2 #> 4187       1         3  19       61986   E2    NA #> 4188       1         3  19       61986   E3     4 #> 4189       1         3  19       61986   E4     3 #> 4190       1         3  19       61986   E5     5 #> 4191       1         3  19       61986   N1     4 #> 4192       1         3  19       61986   N2     4 #> 4193       1         3  19       61986   N3     2 #> 4194       1         3  19       61986   N4     4 #> 4195       1         3  19       61986   N5     1 #> 4196       1         3  19       61986   O1     6 #> 4197       1         3  19       61986   O2     4 #> 4198       1         3  19       61986   O3     5 #> 4199       1         3  19       61986   O4     5 #> 4200       1         3  19       61986   O5     2 #> 4201       2         2  18       61987   A1     1 #> 4202       2         2  18       61987   A2     5 #> 4203       2         2  18       61987   A3     5 #> 4204       2         2  18       61987   A4     5 #> 4205       2         2  18       61987   A5     5 #> 4206       2         2  18       61987   C1     5 #> 4207       2         2  18       61987   C2     6 #> 4208       2         2  18       61987   C3     4 #> 4209       2         2  18       61987   C4     2 #> 4210       2         2  18       61987   C5     2 #> 4211       2         2  18       61987   E1     4 #> 4212       2         2  18       61987   E2     1 #> 4213       2         2  18       61987   E3     5 #> 4214       2         2  18       61987   E4     4 #> 4215       2         2  18       61987   E5     6 #> 4216       2         2  18       61987   N1     4 #> 4217       2         2  18       61987   N2     5 #> 4218       2         2  18       61987   N3     5 #> 4219       2         2  18       61987   N4     4 #> 4220       2         2  18       61987   N5     2 #> 4221       2         2  18       61987   O1     5 #> 4222       2         2  18       61987   O2     1 #> 4223       2         2  18       61987   O3     5 #> 4224       2         2  18       61987   O4     6 #> 4225       2         2  18       61987   O5     1 #> 4226       2        NA  25       61989   A1     1 #> 4227       2        NA  25       61989   A2     6 #> 4228       2        NA  25       61989   A3     6 #> 4229       2        NA  25       61989   A4     1 #> 4230       2        NA  25       61989   A5     3 #> 4231       2        NA  25       61989   C1     6 #> 4232       2        NA  25       61989   C2     6 #> 4233       2        NA  25       61989   C3     5 #> 4234       2        NA  25       61989   C4     1 #> 4235       2        NA  25       61989   C5     4 #> 4236       2        NA  25       61989   E1     5 #> 4237       2        NA  25       61989   E2     6 #> 4238       2        NA  25       61989   E3     1 #> 4239       2        NA  25       61989   E4     4 #> 4240       2        NA  25       61989   E5     1 #> 4241       2        NA  25       61989   N1     6 #> 4242       2        NA  25       61989   N2     6 #> 4243       2        NA  25       61989   N3     6 #> 4244       2        NA  25       61989   N4     5 #> 4245       2        NA  25       61989   N5     6 #> 4246       2        NA  25       61989   O1     5 #> 4247       2        NA  25       61989   O2     1 #> 4248       2        NA  25       61989   O3     2 #> 4249       2        NA  25       61989   O4     5 #> 4250       2        NA  25       61989   O5     1 #> 4251       2         5  27       61990   A1     3 #> 4252       2         5  27       61990   A2     4 #> 4253       2         5  27       61990   A3     4 #> 4254       2         5  27       61990   A4     3 #> 4255       2         5  27       61990   A5     4 #> 4256       2         5  27       61990   C1     3 #> 4257       2         5  27       61990   C2     3 #> 4258       2         5  27       61990   C3     5 #> 4259       2         5  27       61990   C4     3 #> 4260       2         5  27       61990   C5     6 #> 4261       2         5  27       61990   E1     2 #> 4262       2         5  27       61990   E2     5 #> 4263       2         5  27       61990   E3     5 #> 4264       2         5  27       61990   E4     3 #> 4265       2         5  27       61990   E5     4 #> 4266       2         5  27       61990   N1     5 #> 4267       2         5  27       61990   N2     4 #> 4268       2         5  27       61990   N3     2 #> 4269       2         5  27       61990   N4     2 #> 4270       2         5  27       61990   N5     4 #> 4271       2         5  27       61990   O1     5 #> 4272       2         5  27       61990   O2     4 #> 4273       2         5  27       61990   O3     4 #> 4274       2         5  27       61990   O4     4 #> 4275       2         5  27       61990   O5     5 #> 4276       1         5  33       61992   A1     4 #> 4277       1         5  33       61992   A2     4 #> 4278       1         5  33       61992   A3     5 #> 4279       1         5  33       61992   A4     6 #> 4280       1         5  33       61992   A5     4 #> 4281       1         5  33       61992   C1     5 #> 4282       1         5  33       61992   C2     6 #> 4283       1         5  33       61992   C3     6 #> 4284       1         5  33       61992   C4     4 #> 4285       1         5  33       61992   C5     2 #> 4286       1         5  33       61992   E1     2 #> 4287       1         5  33       61992   E2     2 #> 4288       1         5  33       61992   E3     5 #> 4289       1         5  33       61992   E4     5 #> 4290       1         5  33       61992   E5     5 #> 4291       1         5  33       61992   N1     4 #> 4292       1         5  33       61992   N2     2 #> 4293       1         5  33       61992   N3     4 #> 4294       1         5  33       61992   N4     5 #> 4295       1         5  33       61992   N5     4 #> 4296       1         5  33       61992   O1     4 #> 4297       1         5  33       61992   O2     2 #> 4298       1         5  33       61992   O3     5 #> 4299       1         5  33       61992   O4     3 #> 4300       1         5  33       61992   O5     3 #> 4301       1         3  20       61993   A1     1 #> 4302       1         3  20       61993   A2     5 #> 4303       1         3  20       61993   A3     5 #> 4304       1         3  20       61993   A4     5 #> 4305       1         3  20       61993   A5     5 #> 4306       1         3  20       61993   C1     4 #> 4307       1         3  20       61993   C2     4 #> 4308       1         3  20       61993   C3     4 #> 4309       1         3  20       61993   C4     4 #> 4310       1         3  20       61993   C5     5 #> 4311       1         3  20       61993   E1     2 #> 4312       1         3  20       61993   E2     2 #> 4313       1         3  20       61993   E3     5 #> 4314       1         3  20       61993   E4     5 #> 4315       1         3  20       61993   E5     5 #> 4316       1         3  20       61993   N1     3 #> 4317       1         3  20       61993   N2     3 #> 4318       1         3  20       61993   N3     3 #> 4319       1         3  20       61993   N4     3 #> 4320       1         3  20       61993   N5     4 #> 4321       1         3  20       61993   O1     6 #> 4322       1         3  20       61993   O2     2 #> 4323       1         3  20       61993   O3     5 #> 4324       1         3  20       61993   O4     4 #> 4325       1         3  20       61993   O5     5 #> 4326       2         3  19       61994   A1     2 #> 4327       2         3  19       61994   A2     6 #> 4328       2         3  19       61994   A3     5 #> 4329       2         3  19       61994   A4     5 #> 4330       2         3  19       61994   A5     4 #> 4331       2         3  19       61994   C1     5 #> 4332       2         3  19       61994   C2     4 #> 4333       2         3  19       61994   C3     5 #> 4334       2         3  19       61994   C4     3 #> 4335       2         3  19       61994   C5     4 #> 4336       2         3  19       61994   E1     4 #> 4337       2         3  19       61994   E2     6 #> 4338       2         3  19       61994   E3     5 #> 4339       2         3  19       61994   E4     6 #> 4340       2         3  19       61994   E5     6 #> 4341       2         3  19       61994   N1     3 #> 4342       2         3  19       61994   N2     3 #> 4343       2         3  19       61994   N3     2 #> 4344       2         3  19       61994   N4     2 #> 4345       2         3  19       61994   N5     2 #> 4346       2         3  19       61994   O1     5 #> 4347       2         3  19       61994   O2     6 #> 4348       2         3  19       61994   O3     6 #> 4349       2         3  19       61994   O4     5 #> 4350       2         3  19       61994   O5     1 #> 4351       2        NA  16       61999   A1     2 #> 4352       2        NA  16       61999   A2     6 #> 4353       2        NA  16       61999   A3     6 #> 4354       2        NA  16       61999   A4     6 #> 4355       2        NA  16       61999   A5     5 #> 4356       2        NA  16       61999   C1     5 #> 4357       2        NA  16       61999   C2     5 #> 4358       2        NA  16       61999   C3     5 #> 4359       2        NA  16       61999   C4     2 #> 4360       2        NA  16       61999   C5     1 #> 4361       2        NA  16       61999   E1     2 #> 4362       2        NA  16       61999   E2     1 #> 4363       2        NA  16       61999   E3     5 #> 4364       2        NA  16       61999   E4     5 #> 4365       2        NA  16       61999   E5     6 #> 4366       2        NA  16       61999   N1     1 #> 4367       2        NA  16       61999   N2     2 #> 4368       2        NA  16       61999   N3     2 #> 4369       2        NA  16       61999   N4     2 #> 4370       2        NA  16       61999   N5     3 #> 4371       2        NA  16       61999   O1     5 #> 4372       2        NA  16       61999   O2     1 #> 4373       2        NA  16       61999   O3     6 #> 4374       2        NA  16       61999   O4     6 #> 4375       2        NA  16       61999   O5     3 #> 4376       2         3  18       62001   A1     1 #> 4377       2         3  18       62001   A2     2 #> 4378       2         3  18       62001   A3     2 #> 4379       2         3  18       62001   A4     4 #> 4380       2         3  18       62001   A5     3 #> 4381       2         3  18       62001   C1     6 #> 4382       2         3  18       62001   C2     4 #> 4383       2         3  18       62001   C3     6 #> 4384       2         3  18       62001   C4     5 #> 4385       2         3  18       62001   C5     6 #> 4386       2         3  18       62001   E1     4 #> 4387       2         3  18       62001   E2     5 #> 4388       2         3  18       62001   E3     1 #> 4389       2         3  18       62001   E4     6 #> 4390       2         3  18       62001   E5     3 #> 4391       2         3  18       62001   N1     4 #> 4392       2         3  18       62001   N2     6 #> 4393       2         3  18       62001   N3     6 #> 4394       2         3  18       62001   N4     4 #> 4395       2         3  18       62001   N5     5 #> 4396       2         3  18       62001   O1     3 #> 4397       2         3  18       62001   O2     4 #> 4398       2         3  18       62001   O3     3 #> 4399       2         3  18       62001   O4     6 #> 4400       2         3  18       62001   O5     4 #> 4401       2         4  35       62003   A1     1 #> 4402       2         4  35       62003   A2     6 #> 4403       2         4  35       62003   A3     5 #> 4404       2         4  35       62003   A4     6 #> 4405       2         4  35       62003   A5     6 #> 4406       2         4  35       62003   C1     4 #> 4407       2         4  35       62003   C2     5 #> 4408       2         4  35       62003   C3     3 #> 4409       2         4  35       62003   C4     3 #> 4410       2         4  35       62003   C5     4 #> 4411       2         4  35       62003   E1     6 #> 4412       2         4  35       62003   E2     3 #> 4413       2         4  35       62003   E3     4 #> 4414       2         4  35       62003   E4     5 #> 4415       2         4  35       62003   E5     5 #> 4416       2         4  35       62003   N1     1 #> 4417       2         4  35       62003   N2     2 #> 4418       2         4  35       62003   N3     2 #> 4419       2         4  35       62003   N4     2 #> 4420       2         4  35       62003   N5     3 #> 4421       2         4  35       62003   O1     6 #> 4422       2         4  35       62003   O2     1 #> 4423       2         4  35       62003   O3     5 #> 4424       2         4  35       62003   O4     6 #> 4425       2         4  35       62003   O5     1 #> 4426       2         3  23       62004   A1     6 #> 4427       2         3  23       62004   A2     5 #> 4428       2         3  23       62004   A3     6 #> 4429       2         3  23       62004   A4     6 #> 4430       2         3  23       62004   A5     5 #> 4431       2         3  23       62004   C1     6 #> 4432       2         3  23       62004   C2     6 #> 4433       2         3  23       62004   C3     6 #> 4434       2         3  23       62004   C4     1 #> 4435       2         3  23       62004   C5     3 #> 4436       2         3  23       62004   E1     6 #> 4437       2         3  23       62004   E2     6 #> 4438       2         3  23       62004   E3     4 #> 4439       2         3  23       62004   E4     4 #> 4440       2         3  23       62004   E5     5 #> 4441       2         3  23       62004   N1     4 #> 4442       2         3  23       62004   N2     5 #> 4443       2         3  23       62004   N3     4 #> 4444       2         3  23       62004   N4     4 #> 4445       2         3  23       62004   N5     4 #> 4446       2         3  23       62004   O1     6 #> 4447       2         3  23       62004   O2     1 #> 4448       2         3  23       62004   O3     3 #> 4449       2         3  23       62004   O4     6 #> 4450       2         3  23       62004   O5     3 #> 4451       2         5  27       62005   A1     4 #> 4452       2         5  27       62005   A2     3 #> 4453       2         5  27       62005   A3     4 #> 4454       2         5  27       62005   A4     3 #> 4455       2         5  27       62005   A5     4 #> 4456       2         5  27       62005   C1     4 #> 4457       2         5  27       62005   C2     3 #> 4458       2         5  27       62005   C3     5 #> 4459       2         5  27       62005   C4     4 #> 4460       2         5  27       62005   C5     4 #> 4461       2         5  27       62005   E1     2 #> 4462       2         5  27       62005   E2     4 #> 4463       2         5  27       62005   E3     4 #> 4464       2         5  27       62005   E4     3 #> 4465       2         5  27       62005   E5     4 #> 4466       2         5  27       62005   N1     5 #> 4467       2         5  27       62005   N2     5 #> 4468       2         5  27       62005   N3     3 #> 4469       2         5  27       62005   N4     2 #> 4470       2         5  27       62005   N5     3 #> 4471       2         5  27       62005   O1     5 #> 4472       2         5  27       62005   O2     4 #> 4473       2         5  27       62005   O3     4 #> 4474       2         5  27       62005   O4     4 #> 4475       2         5  27       62005   O5     4 #> 4476       1         3  18       62007   A1     3 #> 4477       1         3  18       62007   A2     4 #> 4478       1         3  18       62007   A3     2 #> 4479       1         3  18       62007   A4     2 #> 4480       1         3  18       62007   A5     3 #> 4481       1         3  18       62007   C1     3 #> 4482       1         3  18       62007   C2     2 #> 4483       1         3  18       62007   C3     2 #> 4484       1         3  18       62007   C4     4 #> 4485       1         3  18       62007   C5     1 #> 4486       1         3  18       62007   E1     2 #> 4487       1         3  18       62007   E2     2 #> 4488       1         3  18       62007   E3     5 #> 4489       1         3  18       62007   E4     4 #> 4490       1         3  18       62007   E5     5 #> 4491       1         3  18       62007   N1     2 #> 4492       1         3  18       62007   N2     3 #> 4493       1         3  18       62007   N3     1 #> 4494       1         3  18       62007   N4     1 #> 4495       1         3  18       62007   N5     1 #> 4496       1         3  18       62007   O1     6 #> 4497       1         3  18       62007   O2     3 #> 4498       1         3  18       62007   O3     6 #> 4499       1         3  18       62007   O4     5 #> 4500       1         3  18       62007   O5     1 #> 4501       1         2  31       62009   A1     4 #> 4502       1         2  31       62009   A2     3 #> 4503       1         2  31       62009   A3     4 #> 4504       1         2  31       62009   A4     3 #> 4505       1         2  31       62009   A5     3 #> 4506       1         2  31       62009   C1     5 #> 4507       1         2  31       62009   C2     2 #> 4508       1         2  31       62009   C3     4 #> 4509       1         2  31       62009   C4     4 #> 4510       1         2  31       62009   C5     5 #> 4511       1         2  31       62009   E1     3 #> 4512       1         2  31       62009   E2     5 #> 4513       1         2  31       62009   E3     4 #> 4514       1         2  31       62009   E4     1 #> 4515       1         2  31       62009   E5     4 #> 4516       1         2  31       62009   N1     5 #> 4517       1         2  31       62009   N2     5 #> 4518       1         2  31       62009   N3     5 #> 4519       1         2  31       62009   N4     4 #> 4520       1         2  31       62009   N5     4 #> 4521       1         2  31       62009   O1     6 #> 4522       1         2  31       62009   O2     1 #> 4523       1         2  31       62009   O3     6 #> 4524       1         2  31       62009   O4     6 #> 4525       1         2  31       62009   O5     1 #> 4526       2        NA  17       62011   A1     1 #> 4527       2        NA  17       62011   A2     6 #> 4528       2        NA  17       62011   A3     5 #> 4529       2        NA  17       62011   A4     6 #> 4530       2        NA  17       62011   A5     6 #> 4531       2        NA  17       62011   C1     5 #> 4532       2        NA  17       62011   C2     4 #> 4533       2        NA  17       62011   C3     5 #> 4534       2        NA  17       62011   C4     2 #> 4535       2        NA  17       62011   C5     4 #> 4536       2        NA  17       62011   E1     3 #> 4537       2        NA  17       62011   E2     4 #> 4538       2        NA  17       62011   E3     5 #> 4539       2        NA  17       62011   E4     6 #> 4540       2        NA  17       62011   E5     4 #> 4541       2        NA  17       62011   N1     2 #> 4542       2        NA  17       62011   N2     3 #> 4543       2        NA  17       62011   N3     2 #> 4544       2        NA  17       62011   N4     1 #> 4545       2        NA  17       62011   N5     1 #> 4546       2        NA  17       62011   O1     5 #> 4547       2        NA  17       62011   O2     5 #> 4548       2        NA  17       62011   O3     3 #> 4549       2        NA  17       62011   O4     5 #> 4550       2        NA  17       62011   O5     3 #> 4551       1         1  53       62013   A1     3 #> 4552       1         1  53       62013   A2     5 #> 4553       1         1  53       62013   A3     4 #> 4554       1         1  53       62013   A4     4 #> 4555       1         1  53       62013   A5     4 #> 4556       1         1  53       62013   C1     3 #> 4557       1         1  53       62013   C2     3 #> 4558       1         1  53       62013   C3     4 #> 4559       1         1  53       62013   C4     4 #> 4560       1         1  53       62013   C5     4 #> 4561       1         1  53       62013   E1     5 #> 4562       1         1  53       62013   E2     4 #> 4563       1         1  53       62013   E3     3 #> 4564       1         1  53       62013   E4     2 #> 4565       1         1  53       62013   E5     4 #> 4566       1         1  53       62013   N1     2 #> 4567       1         1  53       62013   N2     3 #> 4568       1         1  53       62013   N3     2 #> 4569       1         1  53       62013   N4     3 #> 4570       1         1  53       62013   N5     2 #> 4571       1         1  53       62013   O1     5 #> 4572       1         1  53       62013   O2     4 #> 4573       1         1  53       62013   O3     4 #> 4574       1         1  53       62013   O4     3 #> 4575       1         1  53       62013   O5     4 #> 4576       1         5  29       62014   A1     4 #> 4577       1         5  29       62014   A2     2 #> 4578       1         5  29       62014   A3     2 #> 4579       1         5  29       62014   A4     3 #> 4580       1         5  29       62014   A5     3 #> 4581       1         5  29       62014   C1     4 #> 4582       1         5  29       62014   C2     3 #> 4583       1         5  29       62014   C3     3 #> 4584       1         5  29       62014   C4     4 #> 4585       1         5  29       62014   C5     5 #> 4586       1         5  29       62014   E1     4 #> 4587       1         5  29       62014   E2     5 #> 4588       1         5  29       62014   E3     2 #> 4589       1         5  29       62014   E4     2 #> 4590       1         5  29       62014   E5     5 #> 4591       1         5  29       62014   N1     5 #> 4592       1         5  29       62014   N2     4 #> 4593       1         5  29       62014   N3     4 #> 4594       1         5  29       62014   N4     5 #> 4595       1         5  29       62014   N5     4 #> 4596       1         5  29       62014   O1     5 #> 4597       1         5  29       62014   O2     2 #> 4598       1         5  29       62014   O3     5 #> 4599       1         5  29       62014   O4     6 #> 4600       1         5  29       62014   O5     2 #> 4601       1         2  19       62015   A1     3 #> 4602       1         2  19       62015   A2     4 #> 4603       1         2  19       62015   A3     4 #> 4604       1         2  19       62015   A4     3 #> 4605       1         2  19       62015   A5     3 #> 4606       1         2  19       62015   C1     5 #> 4607       1         2  19       62015   C2     5 #> 4608       1         2  19       62015   C3     4 #> 4609       1         2  19       62015   C4     3 #> 4610       1         2  19       62015   C5     2 #> 4611       1         2  19       62015   E1     3 #> 4612       1         2  19       62015   E2     4 #> 4613       1         2  19       62015   E3     4 #> 4614       1         2  19       62015   E4     2 #> 4615       1         2  19       62015   E5     3 #> 4616       1         2  19       62015   N1     4 #> 4617       1         2  19       62015   N2     4 #> 4618       1         2  19       62015   N3     5 #> 4619       1         2  19       62015   N4     4 #> 4620       1         2  19       62015   N5     4 #> 4621       1         2  19       62015   O1     6 #> 4622       1         2  19       62015   O2     1 #> 4623       1         2  19       62015   O3     6 #> 4624       1         2  19       62015   O4     6 #> 4625       1         2  19       62015   O5     1 #> 4626       2         1  29       62022   A1     1 #> 4627       2         1  29       62022   A2     6 #> 4628       2         1  29       62022   A3     5 #> 4629       2         1  29       62022   A4     6 #> 4630       2         1  29       62022   A5     3 #> 4631       2         1  29       62022   C1     3 #> 4632       2         1  29       62022   C2     5 #> 4633       2         1  29       62022   C3     1 #> 4634       2         1  29       62022   C4     1 #> 4635       2         1  29       62022   C5     6 #> 4636       2         1  29       62022   E1     1 #> 4637       2         1  29       62022   E2     3 #> 4638       2         1  29       62022   E3     3 #> 4639       2         1  29       62022   E4     3 #> 4640       2         1  29       62022   E5     6 #> 4641       2         1  29       62022   N1     6 #> 4642       2         1  29       62022   N2     6 #> 4643       2         1  29       62022   N3     6 #> 4644       2         1  29       62022   N4     2 #> 4645       2         1  29       62022   N5     6 #> 4646       2         1  29       62022   O1     3 #> 4647       2         1  29       62022   O2     6 #> 4648       2         1  29       62022   O3     1 #> 4649       2         1  29       62022   O4     1 #> 4650       2         1  29       62022   O5     6 #> 4651       1         5  41       62023   A1     1 #> 4652       1         5  41       62023   A2     5 #> 4653       1         5  41       62023   A3     5 #> 4654       1         5  41       62023   A4     4 #> 4655       1         5  41       62023   A5     5 #> 4656       1         5  41       62023   C1     4 #> 4657       1         5  41       62023   C2     4 #> 4658       1         5  41       62023   C3     4 #> 4659       1         5  41       62023   C4     4 #> 4660       1         5  41       62023   C5     4 #> 4661       1         5  41       62023   E1     4 #> 4662       1         5  41       62023   E2     1 #> 4663       1         5  41       62023   E3     5 #> 4664       1         5  41       62023   E4     5 #> 4665       1         5  41       62023   E5     4 #> 4666       1         5  41       62023   N1     1 #> 4667       1         5  41       62023   N2     2 #> 4668       1         5  41       62023   N3     1 #> 4669       1         5  41       62023   N4     1 #> 4670       1         5  41       62023   N5     2 #> 4671       1         5  41       62023   O1     6 #> 4672       1         5  41       62023   O2     4 #> 4673       1         5  41       62023   O3     6 #> 4674       1         5  41       62023   O4     6 #> 4675       1         5  41       62023   O5     1 #> 4676       2         5  31       62024   A1     2 #> 4677       2         5  31       62024   A2     2 #> 4678       2         5  31       62024   A3     1 #> 4679       2         5  31       62024   A4     6 #> 4680       2         5  31       62024   A5     3 #> 4681       2         5  31       62024   C1     6 #> 4682       2         5  31       62024   C2     6 #> 4683       2         5  31       62024   C3     6 #> 4684       2         5  31       62024   C4     1 #> 4685       2         5  31       62024   C5     1 #> 4686       2         5  31       62024   E1     2 #> 4687       2         5  31       62024   E2     2 #> 4688       2         5  31       62024   E3     5 #> 4689       2         5  31       62024   E4     6 #> 4690       2         5  31       62024   E5     6 #> 4691       2         5  31       62024   N1     1 #> 4692       2         5  31       62024   N2     3 #> 4693       2         5  31       62024   N3     1 #> 4694       2         5  31       62024   N4     1 #> 4695       2         5  31       62024   N5     1 #> 4696       2         5  31       62024   O1     6 #> 4697       2         5  31       62024   O2     1 #> 4698       2         5  31       62024   O3     6 #> 4699       2         5  31       62024   O4     6 #> 4700       2         5  31       62024   O5     2 #> 4701       2         3  45       62025   A1     2 #> 4702       2         3  45       62025   A2     4 #> 4703       2         3  45       62025   A3     4 #> 4704       2         3  45       62025   A4     5 #> 4705       2         3  45       62025   A5     5 #> 4706       2         3  45       62025   C1     5 #> 4707       2         3  45       62025   C2     5 #> 4708       2         3  45       62025   C3     5 #> 4709       2         3  45       62025   C4     1 #> 4710       2         3  45       62025   C5     2 #> 4711       2         3  45       62025   E1     6 #> 4712       2         3  45       62025   E2     5 #> 4713       2         3  45       62025   E3     2 #> 4714       2         3  45       62025   E4     5 #> 4715       2         3  45       62025   E5     5 #> 4716       2         3  45       62025   N1     5 #> 4717       2         3  45       62025   N2     5 #> 4718       2         3  45       62025   N3     5 #> 4719       2         3  45       62025   N4     6 #> 4720       2         3  45       62025   N5     3 #> 4721       2         3  45       62025   O1     2 #> 4722       2         3  45       62025   O2     2 #> 4723       2         3  45       62025   O3     3 #> 4724       2         3  45       62025   O4     5 #> 4725       2         3  45       62025   O5     2 #> 4726       2         2  47       62026   A1     1 #> 4727       2         2  47       62026   A2     6 #> 4728       2         2  47       62026   A3     6 #> 4729       2         2  47       62026   A4     2 #> 4730       2         2  47       62026   A5     5 #> 4731       2         2  47       62026   C1     5 #> 4732       2         2  47       62026   C2     5 #> 4733       2         2  47       62026   C3     5 #> 4734       2         2  47       62026   C4     1 #> 4735       2         2  47       62026   C5     2 #> 4736       2         2  47       62026   E1     2 #> 4737       2         2  47       62026   E2     2 #> 4738       2         2  47       62026   E3     6 #> 4739       2         2  47       62026   E4     6 #> 4740       2         2  47       62026   E5     6 #> 4741       2         2  47       62026   N1     6 #> 4742       2         2  47       62026   N2     6 #> 4743       2         2  47       62026   N3     4 #> 4744       2         2  47       62026   N4     2 #> 4745       2         2  47       62026   N5     4 #> 4746       2         2  47       62026   O1     6 #> 4747       2         2  47       62026   O2     1 #> 4748       2         2  47       62026   O3     5 #> 4749       2         2  47       62026   O4     5 #> 4750       2         2  47       62026   O5     1 #> 4751       1         4  27       62029   A1     2 #> 4752       1         4  27       62029   A2     3 #> 4753       1         4  27       62029   A3     4 #> 4754       1         4  27       62029   A4     4 #> 4755       1         4  27       62029   A5     4 #> 4756       1         4  27       62029   C1     3 #> 4757       1         4  27       62029   C2     1 #> 4758       1         4  27       62029   C3     1 #> 4759       1         4  27       62029   C4     3 #> 4760       1         4  27       62029   C5     2 #> 4761       1         4  27       62029   E1     3 #> 4762       1         4  27       62029   E2     3 #> 4763       1         4  27       62029   E3     3 #> 4764       1         4  27       62029   E4     3 #> 4765       1         4  27       62029   E5     4 #> 4766       1         4  27       62029   N1     2 #> 4767       1         4  27       62029   N2     3 #> 4768       1         4  27       62029   N3     2 #> 4769       1         4  27       62029   N4     3 #> 4770       1         4  27       62029   N5     2 #> 4771       1         4  27       62029   O1     6 #> 4772       1         4  27       62029   O2     2 #> 4773       1         4  27       62029   O3     4 #> 4774       1         4  27       62029   O4     5 #> 4775       1         4  27       62029   O5     2 #> 4776       2         2  24       62031   A1     1 #> 4777       2         2  24       62031   A2     6 #> 4778       2         2  24       62031   A3     4 #> 4779       2         2  24       62031   A4     3 #> 4780       2         2  24       62031   A5     6 #> 4781       2         2  24       62031   C1     4 #> 4782       2         2  24       62031   C2     4 #> 4783       2         2  24       62031   C3     5 #> 4784       2         2  24       62031   C4     2 #> 4785       2         2  24       62031   C5     5 #> 4786       2         2  24       62031   E1     2 #> 4787       2         2  24       62031   E2     5 #> 4788       2         2  24       62031   E3     4 #> 4789       2         2  24       62031   E4     5 #> 4790       2         2  24       62031   E5     6 #> 4791       2         2  24       62031   N1     4 #> 4792       2         2  24       62031   N2     5 #> 4793       2         2  24       62031   N3     5 #> 4794       2         2  24       62031   N4     2 #> 4795       2         2  24       62031   N5     6 #> 4796       2         2  24       62031   O1     5 #> 4797       2         2  24       62031   O2     2 #> 4798       2         2  24       62031   O3     6 #> 4799       2         2  24       62031   O4     6 #> 4800       2         2  24       62031   O5     1 #> 4801       1         4  20       62032   A1     2 #> 4802       1         4  20       62032   A2     3 #> 4803       1         4  20       62032   A3     4 #> 4804       1         4  20       62032   A4     5 #> 4805       1         4  20       62032   A5     3 #> 4806       1         4  20       62032   C1     4 #> 4807       1         4  20       62032   C2     5 #> 4808       1         4  20       62032   C3     4 #> 4809       1         4  20       62032   C4     2 #> 4810       1         4  20       62032   C5     5 #> 4811       1         4  20       62032   E1     5 #> 4812       1         4  20       62032   E2     3 #> 4813       1         4  20       62032   E3     3 #> 4814       1         4  20       62032   E4     4 #> 4815       1         4  20       62032   E5     4 #> 4816       1         4  20       62032   N1     2 #> 4817       1         4  20       62032   N2     2 #> 4818       1         4  20       62032   N3     4 #> 4819       1         4  20       62032   N4     4 #> 4820       1         4  20       62032   N5     3 #> 4821       1         4  20       62032   O1     4 #> 4822       1         4  20       62032   O2     3 #> 4823       1         4  20       62032   O3     4 #> 4824       1         4  20       62032   O4     5 #> 4825       1         4  20       62032   O5     3 #> 4826       1         2  23       62033   A1     5 #> 4827       1         2  23       62033   A2     4 #> 4828       1         2  23       62033   A3     3 #> 4829       1         2  23       62033   A4     5 #> 4830       1         2  23       62033   A5     4 #> 4831       1         2  23       62033   C1     4 #> 4832       1         2  23       62033   C2     4 #> 4833       1         2  23       62033   C3     4 #> 4834       1         2  23       62033   C4     4 #> 4835       1         2  23       62033   C5     5 #> 4836       1         2  23       62033   E1     1 #> 4837       1         2  23       62033   E2     5 #> 4838       1         2  23       62033   E3     5 #> 4839       1         2  23       62033   E4     6 #> 4840       1         2  23       62033   E5     4 #> 4841       1         2  23       62033   N1     1 #> 4842       1         2  23       62033   N2     1 #> 4843       1         2  23       62033   N3     1 #> 4844       1         2  23       62033   N4     4 #> 4845       1         2  23       62033   N5     5 #> 4846       1         2  23       62033   O1     5 #> 4847       1         2  23       62033   O2     4 #> 4848       1         2  23       62033   O3     4 #> 4849       1         2  23       62033   O4     4 #> 4850       1         2  23       62033   O5     5 #> 4851       1         5  28       62034   A1     1 #> 4852       1         5  28       62034   A2     5 #> 4853       1         5  28       62034   A3     4 #> 4854       1         5  28       62034   A4     4 #> 4855       1         5  28       62034   A5     4 #> 4856       1         5  28       62034   C1     5 #> 4857       1         5  28       62034   C2     5 #> 4858       1         5  28       62034   C3     5 #> 4859       1         5  28       62034   C4     2 #> 4860       1         5  28       62034   C5     4 #> 4861       1         5  28       62034   E1     3 #> 4862       1         5  28       62034   E2     4 #> 4863       1         5  28       62034   E3     2 #> 4864       1         5  28       62034   E4     4 #> 4865       1         5  28       62034   E5     4 #> 4866       1         5  28       62034   N1     4 #> 4867       1         5  28       62034   N2     4 #> 4868       1         5  28       62034   N3     4 #> 4869       1         5  28       62034   N4     3 #> 4870       1         5  28       62034   N5     4 #> 4871       1         5  28       62034   O1     4 #> 4872       1         5  28       62034   O2     2 #> 4873       1         5  28       62034   O3     3 #> 4874       1         5  28       62034   O4     5 #> 4875       1         5  28       62034   O5     2 #> 4876       2        NA  16       62038   A1     1 #> 4877       2        NA  16       62038   A2     6 #> 4878       2        NA  16       62038   A3     6 #> 4879       2        NA  16       62038   A4     5 #> 4880       2        NA  16       62038   A5     3 #> 4881       2        NA  16       62038   C1     1 #> 4882       2        NA  16       62038   C2     3 #> 4883       2        NA  16       62038   C3     2 #> 4884       2        NA  16       62038   C4     2 #> 4885       2        NA  16       62038   C5     5 #> 4886       2        NA  16       62038   E1     6 #> 4887       2        NA  16       62038   E2     6 #> 4888       2        NA  16       62038   E3     5 #> 4889       2        NA  16       62038   E4     1 #> 4890       2        NA  16       62038   E5     2 #> 4891       2        NA  16       62038   N1     6 #> 4892       2        NA  16       62038   N2     4 #> 4893       2        NA  16       62038   N3     6 #> 4894       2        NA  16       62038   N4     6 #> 4895       2        NA  16       62038   N5     3 #> 4896       2        NA  16       62038   O1     3 #> 4897       2        NA  16       62038   O2     1 #> 4898       2        NA  16       62038   O3     3 #> 4899       2        NA  16       62038   O4     3 #> 4900       2        NA  16       62038   O5     4 #> 4901       1         1  18       62039   A1     2 #> 4902       1         1  18       62039   A2     5 #> 4903       1         1  18       62039   A3     6 #> 4904       1         1  18       62039   A4     3 #> 4905       1         1  18       62039   A5     5 #> 4906       1         1  18       62039   C1     5 #> 4907       1         1  18       62039   C2     3 #> 4908       1         1  18       62039   C3     3 #> 4909       1         1  18       62039   C4     3 #> 4910       1         1  18       62039   C5     4 #> 4911       1         1  18       62039   E1     1 #> 4912       1         1  18       62039   E2     2 #> 4913       1         1  18       62039   E3     6 #> 4914       1         1  18       62039   E4     5 #> 4915       1         1  18       62039   E5     6 #> 4916       1         1  18       62039   N1     5 #> 4917       1         1  18       62039   N2     4 #> 4918       1         1  18       62039   N3     1 #> 4919       1         1  18       62039   N4     2 #> 4920       1         1  18       62039   N5     2 #> 4921       1         1  18       62039   O1     4 #> 4922       1         1  18       62039   O2     4 #> 4923       1         1  18       62039   O3     5 #> 4924       1         1  18       62039   O4     4 #> 4925       1         1  18       62039   O5     2 #> 4926       2        NA  16       62041   A1     2 #> 4927       2        NA  16       62041   A2     4 #> 4928       2        NA  16       62041   A3     5 #> 4929       2        NA  16       62041   A4     6 #> 4930       2        NA  16       62041   A5     5 #> 4931       2        NA  16       62041   C1     2 #> 4932       2        NA  16       62041   C2     4 #> 4933       2        NA  16       62041   C3     4 #> 4934       2        NA  16       62041   C4     3 #> 4935       2        NA  16       62041   C5     2 #> 4936       2        NA  16       62041   E1     4 #> 4937       2        NA  16       62041   E2     4 #> 4938       2        NA  16       62041   E3     3 #> 4939       2        NA  16       62041   E4     5 #> 4940       2        NA  16       62041   E5     2 #> 4941       2        NA  16       62041   N1     2 #> 4942       2        NA  16       62041   N2     4 #> 4943       2        NA  16       62041   N3     3 #> 4944       2        NA  16       62041   N4     2 #> 4945       2        NA  16       62041   N5     3 #> 4946       2        NA  16       62041   O1     3 #> 4947       2        NA  16       62041   O2     1 #> 4948       2        NA  16       62041   O3     3 #> 4949       2        NA  16       62041   O4     5 #> 4950       2        NA  16       62041   O5     3 #> 4951       1        NA  17       62042   A1     2 #> 4952       1        NA  17       62042   A2     6 #> 4953       1        NA  17       62042   A3     5 #> 4954       1        NA  17       62042   A4     5 #> 4955       1        NA  17       62042   A5     5 #> 4956       1        NA  17       62042   C1     2 #> 4957       1        NA  17       62042   C2     4 #> 4958       1        NA  17       62042   C3     3 #> 4959       1        NA  17       62042   C4     4 #> 4960       1        NA  17       62042   C5     6 #> 4961       1        NA  17       62042   E1     1 #> 4962       1        NA  17       62042   E2     3 #> 4963       1        NA  17       62042   E3     5 #> 4964       1        NA  17       62042   E4     6 #> 4965       1        NA  17       62042   E5     6 #> 4966       1        NA  17       62042   N1     1 #> 4967       1        NA  17       62042   N2     1 #> 4968       1        NA  17       62042   N3     2 #> 4969       1        NA  17       62042   N4     1 #> 4970       1        NA  17       62042   N5     1 #> 4971       1        NA  17       62042   O1     6 #> 4972       1        NA  17       62042   O2     3 #> 4973       1        NA  17       62042   O3     4 #> 4974       1        NA  17       62042   O4     5 #> 4975       1        NA  17       62042   O5     3 #> 4976       2         1  18       62043   A1     1 #> 4977       2         1  18       62043   A2     5 #> 4978       2         1  18       62043   A3     6 #> 4979       2         1  18       62043   A4     4 #> 4980       2         1  18       62043   A5     5 #> 4981       2         1  18       62043   C1     3 #> 4982       2         1  18       62043   C2     2 #> 4983       2         1  18       62043   C3     5 #> 4984       2         1  18       62043   C4     2 #> 4985       2         1  18       62043   C5     1 #> 4986       2         1  18       62043   E1     1 #> 4987       2         1  18       62043   E2     1 #> 4988       2         1  18       62043   E3     2 #> 4989       2         1  18       62043   E4     5 #> 4990       2         1  18       62043   E5     4 #> 4991       2         1  18       62043   N1     1 #> 4992       2         1  18       62043   N2     2 #> 4993       2         1  18       62043   N3     2 #> 4994       2         1  18       62043   N4     2 #> 4995       2         1  18       62043   N5     4 #> 4996       2         1  18       62043   O1     5 #> 4997       2         1  18       62043   O2     1 #> 4998       2         1  18       62043   O3     5 #> 4999       2         1  18       62043   O4     5 #> 5000       2         1  18       62043   O5     1 #> 5001       1        NA  16       62044   A1     2 #> 5002       1        NA  16       62044   A2     5 #> 5003       1        NA  16       62044   A3     5 #> 5004       1        NA  16       62044   A4     5 #> 5005       1        NA  16       62044   A5     5 #> 5006       1        NA  16       62044   C1     5 #> 5007       1        NA  16       62044   C2     6 #> 5008       1        NA  16       62044   C3     5 #> 5009       1        NA  16       62044   C4     1 #> 5010       1        NA  16       62044   C5     2 #> 5011       1        NA  16       62044   E1     2 #> 5012       1        NA  16       62044   E2     3 #> 5013       1        NA  16       62044   E3     3 #> 5014       1        NA  16       62044   E4     5 #> 5015       1        NA  16       62044   E5     4 #> 5016       1        NA  16       62044   N1     1 #> 5017       1        NA  16       62044   N2     3 #> 5018       1        NA  16       62044   N3     1 #> 5019       1        NA  16       62044   N4     2 #> 5020       1        NA  16       62044   N5     5 #> 5021       1        NA  16       62044   O1     4 #> 5022       1        NA  16       62044   O2     3 #> 5023       1        NA  16       62044   O3     4 #> 5024       1        NA  16       62044   O4     5 #> 5025       1        NA  16       62044   O5     5 #> 5026       2        NA  16       62047   A1     1 #> 5027       2        NA  16       62047   A2     5 #> 5028       2        NA  16       62047   A3     2 #> 5029       2        NA  16       62047   A4     2 #> 5030       2        NA  16       62047   A5     2 #> 5031       2        NA  16       62047   C1     2 #> 5032       2        NA  16       62047   C2     2 #> 5033       2        NA  16       62047   C3     6 #> 5034       2        NA  16       62047   C4     2 #> 5035       2        NA  16       62047   C5     3 #> 5036       2        NA  16       62047   E1     5 #> 5037       2        NA  16       62047   E2     6 #> 5038       2        NA  16       62047   E3     4 #> 5039       2        NA  16       62047   E4     1 #> 5040       2        NA  16       62047   E5     5 #> 5041       2        NA  16       62047   N1     3 #> 5042       2        NA  16       62047   N2     5 #> 5043       2        NA  16       62047   N3     5 #> 5044       2        NA  16       62047   N4     5 #> 5045       2        NA  16       62047   N5     4 #> 5046       2        NA  16       62047   O1     2 #> 5047       2        NA  16       62047   O2     5 #> 5048       2        NA  16       62047   O3     4 #> 5049       2        NA  16       62047   O4     6 #> 5050       2        NA  16       62047   O5     1 #> 5051       2         1  18       62048   A1     3 #> 5052       2         1  18       62048   A2     3 #> 5053       2         1  18       62048   A3     5 #> 5054       2         1  18       62048   A4     5 #> 5055       2         1  18       62048   A5     5 #> 5056       2         1  18       62048   C1     4 #> 5057       2         1  18       62048   C2     4 #> 5058       2         1  18       62048   C3     4 #> 5059       2         1  18       62048   C4     3 #> 5060       2         1  18       62048   C5     4 #> 5061       2         1  18       62048   E1     3 #> 5062       2         1  18       62048   E2     2 #> 5063       2         1  18       62048   E3     3 #> 5064       2         1  18       62048   E4     5 #> 5065       2         1  18       62048   E5     4 #> 5066       2         1  18       62048   N1     3 #> 5067       2         1  18       62048   N2     5 #> 5068       2         1  18       62048   N3     3 #> 5069       2         1  18       62048   N4     3 #> 5070       2         1  18       62048   N5     4 #> 5071       2         1  18       62048   O1     4 #> 5072       2         1  18       62048   O2     4 #> 5073       2         1  18       62048   O3     3 #> 5074       2         1  18       62048   O4     2 #> 5075       2         1  18       62048   O5     3 #> 5076       1        NA  17       62051   A1     2 #> 5077       1        NA  17       62051   A2     4 #> 5078       1        NA  17       62051   A3     5 #> 5079       1        NA  17       62051   A4     6 #> 5080       1        NA  17       62051   A5     5 #> 5081       1        NA  17       62051   C1     5 #> 5082       1        NA  17       62051   C2     6 #> 5083       1        NA  17       62051   C3     4 #> 5084       1        NA  17       62051   C4     1 #> 5085       1        NA  17       62051   C5     2 #> 5086       1        NA  17       62051   E1     2 #> 5087       1        NA  17       62051   E2     1 #> 5088       1        NA  17       62051   E3     4 #> 5089       1        NA  17       62051   E4     6 #> 5090       1        NA  17       62051   E5     5 #> 5091       1        NA  17       62051   N1     1 #> 5092       1        NA  17       62051   N2     2 #> 5093       1        NA  17       62051   N3     1 #> 5094       1        NA  17       62051   N4     1 #> 5095       1        NA  17       62051   N5     1 #> 5096       1        NA  17       62051   O1     5 #> 5097       1        NA  17       62051   O2     3 #> 5098       1        NA  17       62051   O3     4 #> 5099       1        NA  17       62051   O4     3 #> 5100       1        NA  17       62051   O5     2 #> 5101       1        NA  17       62052   A1     1 #> 5102       1        NA  17       62052   A2     6 #> 5103       1        NA  17       62052   A3     5 #> 5104       1        NA  17       62052   A4     5 #> 5105       1        NA  17       62052   A5     5 #> 5106       1        NA  17       62052   C1     5 #> 5107       1        NA  17       62052   C2     4 #> 5108       1        NA  17       62052   C3     4 #> 5109       1        NA  17       62052   C4     3 #> 5110       1        NA  17       62052   C5     4 #> 5111       1        NA  17       62052   E1     3 #> 5112       1        NA  17       62052   E2     2 #> 5113       1        NA  17       62052   E3     4 #> 5114       1        NA  17       62052   E4     5 #> 5115       1        NA  17       62052   E5     4 #> 5116       1        NA  17       62052   N1     1 #> 5117       1        NA  17       62052   N2     2 #> 5118       1        NA  17       62052   N3     4 #> 5119       1        NA  17       62052   N4     2 #> 5120       1        NA  17       62052   N5     1 #> 5121       1        NA  17       62052   O1     6 #> 5122       1        NA  17       62052   O2     2 #> 5123       1        NA  17       62052   O3     4 #> 5124       1        NA  17       62052   O4     5 #> 5125       1        NA  17       62052   O5     3 #> 5126       1        NA  16       62054   A1     2 #> 5127       1        NA  16       62054   A2     6 #> 5128       1        NA  16       62054   A3     4 #> 5129       1        NA  16       62054   A4     6 #> 5130       1        NA  16       62054   A5     2 #> 5131       1        NA  16       62054   C1     4 #> 5132       1        NA  16       62054   C2     5 #> 5133       1        NA  16       62054   C3     5 #> 5134       1        NA  16       62054   C4     1 #> 5135       1        NA  16       62054   C5     1 #> 5136       1        NA  16       62054   E1     6 #> 5137       1        NA  16       62054   E2     4 #> 5138       1        NA  16       62054   E3     5 #> 5139       1        NA  16       62054   E4     4 #> 5140       1        NA  16       62054   E5     6 #> 5141       1        NA  16       62054   N1     1 #> 5142       1        NA  16       62054   N2     2 #> 5143       1        NA  16       62054   N3     1 #> 5144       1        NA  16       62054   N4     5 #> 5145       1        NA  16       62054   N5     4 #> 5146       1        NA  16       62054   O1     5 #> 5147       1        NA  16       62054   O2     2 #> 5148       1        NA  16       62054   O3     1 #> 5149       1        NA  16       62054   O4     5 #> 5150       1        NA  16       62054   O5     1 #> 5151       2         1  18       62055   A1     2 #> 5152       2         1  18       62055   A2     5 #> 5153       2         1  18       62055   A3     5 #> 5154       2         1  18       62055   A4     6 #> 5155       2         1  18       62055   A5     4 #> 5156       2         1  18       62055   C1     2 #> 5157       2         1  18       62055   C2     4 #> 5158       2         1  18       62055   C3     2 #> 5159       2         1  18       62055   C4     2 #> 5160       2         1  18       62055   C5     4 #> 5161       2         1  18       62055   E1     5 #> 5162       2         1  18       62055   E2     4 #> 5163       2         1  18       62055   E3     3 #> 5164       2         1  18       62055   E4     3 #> 5165       2         1  18       62055   E5     5 #> 5166       2         1  18       62055   N1     4 #> 5167       2         1  18       62055   N2     4 #> 5168       2         1  18       62055   N3     4 #> 5169       2         1  18       62055   N4     3 #> 5170       2         1  18       62055   N5     2 #> 5171       2         1  18       62055   O1     3 #> 5172       2         1  18       62055   O2     2 #> 5173       2         1  18       62055   O3     3 #> 5174       2         1  18       62055   O4     4 #> 5175       2         1  18       62055   O5     3 #> 5176       2         5  50       62056   A1     1 #> 5177       2         5  50       62056   A2     4 #> 5178       2         5  50       62056   A3    NA #> 5179       2         5  50       62056   A4     6 #> 5180       2         5  50       62056   A5     6 #> 5181       2         5  50       62056   C1     6 #> 5182       2         5  50       62056   C2     4 #> 5183       2         5  50       62056   C3     4 #> 5184       2         5  50       62056   C4     1 #> 5185       2         5  50       62056   C5     1 #> 5186       2         5  50       62056   E1     6 #> 5187       2         5  50       62056   E2     6 #> 5188       2         5  50       62056   E3     6 #> 5189       2         5  50       62056   E4     1 #> 5190       2         5  50       62056   E5     6 #> 5191       2         5  50       62056   N1     1 #> 5192       2         5  50       62056   N2     1 #> 5193       2         5  50       62056   N3     1 #> 5194       2         5  50       62056   N4     1 #> 5195       2         5  50       62056   N5     1 #> 5196       2         5  50       62056   O1     6 #> 5197       2         5  50       62056   O2     1 #> 5198       2         5  50       62056   O3     4 #> 5199       2         5  50       62056   O4     6 #> 5200       2         5  50       62056   O5     1 #> 5201       2         4  25       62059   A1     1 #> 5202       2         4  25       62059   A2     5 #> 5203       2         4  25       62059   A3     6 #> 5204       2         4  25       62059   A4     5 #> 5205       2         4  25       62059   A5     5 #> 5206       2         4  25       62059   C1     3 #> 5207       2         4  25       62059   C2     3 #> 5208       2         4  25       62059   C3     3 #> 5209       2         4  25       62059   C4     4 #> 5210       2         4  25       62059   C5     4 #> 5211       2         4  25       62059   E1     1 #> 5212       2         4  25       62059   E2     1 #> 5213       2         4  25       62059   E3     5 #> 5214       2         4  25       62059   E4     5 #> 5215       2         4  25       62059   E5     5 #> 5216       2         4  25       62059   N1     3 #> 5217       2         4  25       62059   N2     4 #> 5218       2         4  25       62059   N3     4 #> 5219       2         4  25       62059   N4     2 #> 5220       2         4  25       62059   N5     3 #> 5221       2         4  25       62059   O1     4 #> 5222       2         4  25       62059   O2     3 #> 5223       2         4  25       62059   O3     5 #> 5224       2         4  25       62059   O4     4 #> 5225       2         4  25       62059   O5     3 #> 5226       1         5  48       62060   A1     2 #> 5227       1         5  48       62060   A2     3 #> 5228       1         5  48       62060   A3     2 #> 5229       1         5  48       62060   A4     1 #> 5230       1         5  48       62060   A5     4 #> 5231       1         5  48       62060   C1     1 #> 5232       1         5  48       62060   C2     2 #> 5233       1         5  48       62060   C3     2 #> 5234       1         5  48       62060   C4     1 #> 5235       1         5  48       62060   C5     5 #> 5236       1         5  48       62060   E1     1 #> 5237       1         5  48       62060   E2     5 #> 5238       1         5  48       62060   E3     2 #> 5239       1         5  48       62060   E4     4 #> 5240       1         5  48       62060   E5     5 #> 5241       1         5  48       62060   N1     1 #> 5242       1         5  48       62060   N2     5 #> 5243       1         5  48       62060   N3     1 #> 5244       1         5  48       62060   N4     2 #> 5245       1         5  48       62060   N5     1 #> 5246       1         5  48       62060   O1     4 #> 5247       1         5  48       62060   O2     1 #> 5248       1         5  48       62060   O3     2 #> 5249       1         5  48       62060   O4     5 #> 5250       1         5  48       62060   O5     1 #> 5251       2         4  27       62063   A1     2 #> 5252       2         4  27       62063   A2     5 #> 5253       2         4  27       62063   A3     5 #> 5254       2         4  27       62063   A4     5 #> 5255       2         4  27       62063   A5     5 #> 5256       2         4  27       62063   C1     6 #> 5257       2         4  27       62063   C2     2 #> 5258       2         4  27       62063   C3     5 #> 5259       2         4  27       62063   C4     2 #> 5260       2         4  27       62063   C5     2 #> 5261       2         4  27       62063   E1     2 #> 5262       2         4  27       62063   E2     2 #> 5263       2         4  27       62063   E3     4 #> 5264       2         4  27       62063   E4     6 #> 5265       2         4  27       62063   E5     5 #> 5266       2         4  27       62063   N1     2 #> 5267       2         4  27       62063   N2     2 #> 5268       2         4  27       62063   N3     2 #> 5269       2         4  27       62063   N4     1 #> 5270       2         4  27       62063   N5     2 #> 5271       2         4  27       62063   O1     5 #> 5272       2         4  27       62063   O2     1 #> 5273       2         4  27       62063   O3     5 #> 5274       2         4  27       62063   O4     6 #> 5275       2         4  27       62063   O5     1 #> 5276       2         3  22       62064   A1     1 #> 5277       2         3  22       62064   A2     6 #> 5278       2         3  22       62064   A3     5 #> 5279       2         3  22       62064   A4     6 #> 5280       2         3  22       62064   A5     6 #> 5281       2         3  22       62064   C1     4 #> 5282       2         3  22       62064   C2     5 #> 5283       2         3  22       62064   C3     4 #> 5284       2         3  22       62064   C4     5 #> 5285       2         3  22       62064   C5     6 #> 5286       2         3  22       62064   E1     3 #> 5287       2         3  22       62064   E2     4 #> 5288       2         3  22       62064   E3     6 #> 5289       2         3  22       62064   E4     5 #> 5290       2         3  22       62064   E5     5 #> 5291       2         3  22       62064   N1     5 #> 5292       2         3  22       62064   N2     5 #> 5293       2         3  22       62064   N3     6 #> 5294       2         3  22       62064   N4     2 #> 5295       2         3  22       62064   N5    NA #> 5296       2         3  22       62064   O1     6 #> 5297       2         3  22       62064   O2     1 #> 5298       2         3  22       62064   O3     1 #> 5299       2         3  22       62064   O4     5 #> 5300       2         3  22       62064   O5     2 #> 5301       1         3  20       62067   A1     5 #> 5302       1         3  20       62067   A2     4 #> 5303       1         3  20       62067   A3     4 #> 5304       1         3  20       62067   A4     4 #> 5305       1         3  20       62067   A5     5 #> 5306       1         3  20       62067   C1     5 #> 5307       1         3  20       62067   C2     4 #> 5308       1         3  20       62067   C3    NA #> 5309       1         3  20       62067   C4     4 #> 5310       1         3  20       62067   C5     4 #> 5311       1         3  20       62067   E1     4 #> 5312       1         3  20       62067   E2     4 #> 5313       1         3  20       62067   E3     4 #> 5314       1         3  20       62067   E4     5 #> 5315       1         3  20       62067   E5     3 #> 5316       1         3  20       62067   N1     2 #> 5317       1         3  20       62067   N2     2 #> 5318       1         3  20       62067   N3     2 #> 5319       1         3  20       62067   N4     2 #> 5320       1         3  20       62067   N5     1 #> 5321       1         3  20       62067   O1     5 #> 5322       1         3  20       62067   O2     3 #> 5323       1         3  20       62067   O3     4 #> 5324       1         3  20       62067   O4     5 #> 5325       1         3  20       62067   O5     4 #> 5326       2        NA  17       62070   A1     3 #> 5327       2        NA  17       62070   A2     4 #> 5328       2        NA  17       62070   A3     3 #> 5329       2        NA  17       62070   A4     3 #> 5330       2        NA  17       62070   A5     3 #> 5331       2        NA  17       62070   C1     3 #> 5332       2        NA  17       62070   C2     3 #> 5333       2        NA  17       62070   C3     3 #> 5334       2        NA  17       62070   C4     4 #> 5335       2        NA  17       62070   C5     6 #> 5336       2        NA  17       62070   E1     5 #> 5337       2        NA  17       62070   E2     5 #> 5338       2        NA  17       62070   E3     2 #> 5339       2        NA  17       62070   E4     2 #> 5340       2        NA  17       62070   E5     4 #> 5341       2        NA  17       62070   N1     6 #> 5342       2        NA  17       62070   N2     6 #> 5343       2        NA  17       62070   N3     6 #> 5344       2        NA  17       62070   N4     6 #> 5345       2        NA  17       62070   N5     3 #> 5346       2        NA  17       62070   O1     3 #> 5347       2        NA  17       62070   O2     4 #> 5348       2        NA  17       62070   O3     2 #> 5349       2        NA  17       62070   O4     4 #> 5350       2        NA  17       62070   O5     4 #> 5351       2         5  59       62073   A1     1 #> 5352       2         5  59       62073   A2     6 #> 5353       2         5  59       62073   A3     6 #> 5354       2         5  59       62073   A4     4 #> 5355       2         5  59       62073   A5     6 #> 5356       2         5  59       62073   C1     5 #> 5357       2         5  59       62073   C2     5 #> 5358       2         5  59       62073   C3     5 #> 5359       2         5  59       62073   C4     1 #> 5360       2         5  59       62073   C5     3 #> 5361       2         5  59       62073   E1     3 #> 5362       2         5  59       62073   E2     1 #> 5363       2         5  59       62073   E3     6 #> 5364       2         5  59       62073   E4     2 #> 5365       2         5  59       62073   E5    NA #> 5366       2         5  59       62073   N1     2 #> 5367       2         5  59       62073   N2     2 #> 5368       2         5  59       62073   N3     1 #> 5369       2         5  59       62073   N4     1 #> 5370       2         5  59       62073   N5     2 #> 5371       2         5  59       62073   O1     6 #> 5372       2         5  59       62073   O2     1 #> 5373       2         5  59       62073   O3     6 #> 5374       2         5  59       62073   O4     6 #> 5375       2         5  59       62073   O5     1 #> 5376       2         1  17       62075   A1     2 #> 5377       2         1  17       62075   A2     4 #> 5378       2         1  17       62075   A3     4 #> 5379       2         1  17       62075   A4     1 #> 5380       2         1  17       62075   A5     4 #> 5381       2         1  17       62075   C1     5 #> 5382       2         1  17       62075   C2     4 #> 5383       2         1  17       62075   C3     4 #> 5384       2         1  17       62075   C4     2 #> 5385       2         1  17       62075   C5     2 #> 5386       2         1  17       62075   E1     4 #> 5387       2         1  17       62075   E2     5 #> 5388       2         1  17       62075   E3     3 #> 5389       2         1  17       62075   E4     3 #> 5390       2         1  17       62075   E5     5 #> 5391       2         1  17       62075   N1     4 #> 5392       2         1  17       62075   N2     5 #> 5393       2         1  17       62075   N3     1 #> 5394       2         1  17       62075   N4     2 #> 5395       2         1  17       62075   N5     3 #> 5396       2         1  17       62075   O1     5 #> 5397       2         1  17       62075   O2     1 #> 5398       2         1  17       62075   O3     5 #> 5399       2         1  17       62075   O4     6 #> 5400       2         1  17       62075   O5     2 #> 5401       2         3  32       62077   A1     1 #> 5402       2         3  32       62077   A2     6 #> 5403       2         3  32       62077   A3     6 #> 5404       2         3  32       62077   A4     6 #> 5405       2         3  32       62077   A5     6 #> 5406       2         3  32       62077   C1     6 #> 5407       2         3  32       62077   C2     5 #> 5408       2         3  32       62077   C3     4 #> 5409       2         3  32       62077   C4     1 #> 5410       2         3  32       62077   C5     2 #> 5411       2         3  32       62077   E1     1 #> 5412       2         3  32       62077   E2     1 #> 5413       2         3  32       62077   E3     5 #> 5414       2         3  32       62077   E4     6 #> 5415       2         3  32       62077   E5     6 #> 5416       2         3  32       62077   N1     3 #> 5417       2         3  32       62077   N2     4 #> 5418       2         3  32       62077   N3     3 #> 5419       2         3  32       62077   N4     2 #> 5420       2         3  32       62077   N5     2 #> 5421       2         3  32       62077   O1     6 #> 5422       2         3  32       62077   O2     6 #> 5423       2         3  32       62077   O3     5 #> 5424       2         3  32       62077   O4     5 #> 5425       2         3  32       62077   O5     1 #> 5426       1         3  22       62079   A1     2 #> 5427       1         3  22       62079   A2     4 #> 5428       1         3  22       62079   A3     4 #> 5429       1         3  22       62079   A4     4 #> 5430       1         3  22       62079   A5     3 #> 5431       1         3  22       62079   C1     5 #> 5432       1         3  22       62079   C2     1 #> 5433       1         3  22       62079   C3     3 #> 5434       1         3  22       62079   C4     2 #> 5435       1         3  22       62079   C5     1 #> 5436       1         3  22       62079   E1     1 #> 5437       1         3  22       62079   E2     1 #> 5438       1         3  22       62079   E3     3 #> 5439       1         3  22       62079   E4     6 #> 5440       1         3  22       62079   E5     5 #> 5441       1         3  22       62079   N1     5 #> 5442       1         3  22       62079   N2     6 #> 5443       1         3  22       62079   N3     6 #> 5444       1         3  22       62079   N4     1 #> 5445       1         3  22       62079   N5     1 #> 5446       1         3  22       62079   O1     4 #> 5447       1         3  22       62079   O2     3 #> 5448       1         3  22       62079   O3     5 #> 5449       1         3  22       62079   O4     2 #> 5450       1         3  22       62079   O5     3 #> 5451       1         3  21       62082   A1     5 #> 5452       1         3  21       62082   A2     5 #> 5453       1         3  21       62082   A3     4 #> 5454       1         3  21       62082   A4     5 #> 5455       1         3  21       62082   A5     4 #> 5456       1         3  21       62082   C1     6 #> 5457       1         3  21       62082   C2     6 #> 5458       1         3  21       62082   C3     6 #> 5459       1         3  21       62082   C4     1 #> 5460       1         3  21       62082   C5     6 #> 5461       1         3  21       62082   E1     2 #> 5462       1         3  21       62082   E2     2 #> 5463       1         3  21       62082   E3     4 #> 5464       1         3  21       62082   E4     5 #> 5465       1         3  21       62082   E5     5 #> 5466       1         3  21       62082   N1     1 #> 5467       1         3  21       62082   N2     1 #> 5468       1         3  21       62082   N3     4 #> 5469       1         3  21       62082   N4     3 #> 5470       1         3  21       62082   N5     4 #> 5471       1         3  21       62082   O1     6 #> 5472       1         3  21       62082   O2     4 #> 5473       1         3  21       62082   O3     4 #> 5474       1         3  21       62082   O4     5 #> 5475       1         3  21       62082   O5     2 #> 5476       1         5  29       62084   A1     5 #> 5477       1         5  29       62084   A2     3 #> 5478       1         5  29       62084   A3     1 #> 5479       1         5  29       62084   A4     2 #> 5480       1         5  29       62084   A5     3 #> 5481       1         5  29       62084   C1     6 #> 5482       1         5  29       62084   C2     2 #> 5483       1         5  29       62084   C3     2 #> 5484       1         5  29       62084   C4     4 #> 5485       1         5  29       62084   C5     5 #> 5486       1         5  29       62084   E1     5 #> 5487       1         5  29       62084   E2     4 #> 5488       1         5  29       62084   E3     3 #> 5489       1         5  29       62084   E4     4 #> 5490       1         5  29       62084   E5     2 #> 5491       1         5  29       62084   N1     1 #> 5492       1         5  29       62084   N2     4 #> 5493       1         5  29       62084   N3     4 #> 5494       1         5  29       62084   N4     4 #> 5495       1         5  29       62084   N5     2 #> 5496       1         5  29       62084   O1     6 #> 5497       1         5  29       62084   O2     2 #> 5498       1         5  29       62084   O3     6 #> 5499       1         5  29       62084   O4     5 #> 5500       1         5  29       62084   O5     2 #> 5501       1         1  18       62090   A1     2 #> 5502       1         1  18       62090   A2     5 #> 5503       1         1  18       62090   A3     5 #> 5504       1         1  18       62090   A4     5 #> 5505       1         1  18       62090   A5     4 #> 5506       1         1  18       62090   C1     5 #> 5507       1         1  18       62090   C2     5 #> 5508       1         1  18       62090   C3     5 #> 5509       1         1  18       62090   C4     4 #> 5510       1         1  18       62090   C5     3 #> 5511       1         1  18       62090   E1     2 #> 5512       1         1  18       62090   E2     4 #> 5513       1         1  18       62090   E3     5 #> 5514       1         1  18       62090   E4     5 #> 5515       1         1  18       62090   E5     4 #> 5516       1         1  18       62090   N1     3 #> 5517       1         1  18       62090   N2     4 #> 5518       1         1  18       62090   N3     4 #> 5519       1         1  18       62090   N4     3 #> 5520       1         1  18       62090   N5     4 #> 5521       1         1  18       62090   O1    NA #> 5522       1         1  18       62090   O2     5 #> 5523       1         1  18       62090   O3     3 #> 5524       1         1  18       62090   O4     4 #> 5525       1         1  18       62090   O5     3 #> 5526       2         3  40       62092   A1     1 #> 5527       2         3  40       62092   A2     5 #> 5528       2         3  40       62092   A3    NA #> 5529       2         3  40       62092   A4     5 #> 5530       2         3  40       62092   A5     6 #> 5531       2         3  40       62092   C1     6 #> 5532       2         3  40       62092   C2     6 #> 5533       2         3  40       62092   C3     1 #> 5534       2         3  40       62092   C4     1 #> 5535       2         3  40       62092   C5     1 #> 5536       2         3  40       62092   E1     6 #> 5537       2         3  40       62092   E2     1 #> 5538       2         3  40       62092   E3     1 #> 5539       2         3  40       62092   E4     5 #> 5540       2         3  40       62092   E5     1 #> 5541       2         3  40       62092   N1     1 #> 5542       2         3  40       62092   N2     1 #> 5543       2         3  40       62092   N3     1 #> 5544       2         3  40       62092   N4     6 #> 5545       2         3  40       62092   N5     1 #> 5546       2         3  40       62092   O1     6 #> 5547       2         3  40       62092   O2     6 #> 5548       2         3  40       62092   O3     6 #> 5549       2         3  40       62092   O4     1 #> 5550       2         3  40       62092   O5     1 #> 5551       2         5  48       62094   A1     1 #> 5552       2         5  48       62094   A2     5 #> 5553       2         5  48       62094   A3     5 #> 5554       2         5  48       62094   A4     6 #> 5555       2         5  48       62094   A5     6 #> 5556       2         5  48       62094   C1     5 #> 5557       2         5  48       62094   C2     5 #> 5558       2         5  48       62094   C3     5 #> 5559       2         5  48       62094   C4     3 #> 5560       2         5  48       62094   C5     3 #> 5561       2         5  48       62094   E1     1 #> 5562       2         5  48       62094   E2     1 #> 5563       2         5  48       62094   E3     5 #> 5564       2         5  48       62094   E4     5 #> 5565       2         5  48       62094   E5     5 #> 5566       2         5  48       62094   N1     1 #> 5567       2         5  48       62094   N2     1 #> 5568       2         5  48       62094   N3     1 #> 5569       2         5  48       62094   N4     1 #> 5570       2         5  48       62094   N5     2 #> 5571       2         5  48       62094   O1     5 #> 5572       2         5  48       62094   O2     2 #> 5573       2         5  48       62094   O3     5 #> 5574       2         5  48       62094   O4     5 #> 5575       2         5  48       62094   O5     5 #> 5576       2         4  39       62099   A1     1 #> 5577       2         4  39       62099   A2     5 #> 5578       2         4  39       62099   A3     6 #> 5579       2         4  39       62099   A4     6 #> 5580       2         4  39       62099   A5     4 #> 5581       2         4  39       62099   C1     5 #> 5582       2         4  39       62099   C2     3 #> 5583       2         4  39       62099   C3     4 #> 5584       2         4  39       62099   C4     3 #> 5585       2         4  39       62099   C5     5 #> 5586       2         4  39       62099   E1     2 #> 5587       2         4  39       62099   E2     2 #> 5588       2         4  39       62099   E3     3 #> 5589       2         4  39       62099   E4     6 #> 5590       2         4  39       62099   E5     4 #> 5591       2         4  39       62099   N1     2 #> 5592       2         4  39       62099   N2     2 #> 5593       2         4  39       62099   N3     2 #> 5594       2         4  39       62099   N4     2 #> 5595       2         4  39       62099   N5     4 #> 5596       2         4  39       62099   O1     5 #> 5597       2         4  39       62099   O2     1 #> 5598       2         4  39       62099   O3     5 #> 5599       2         4  39       62099   O4     5 #> 5600       2         4  39       62099   O5     3 #> 5601       2         4  50       62101   A1     1 #> 5602       2         4  50       62101   A2     5 #> 5603       2         4  50       62101   A3     6 #> 5604       2         4  50       62101   A4     5 #> 5605       2         4  50       62101   A5     6 #> 5606       2         4  50       62101   C1     2 #> 5607       2         4  50       62101   C2     5 #> 5608       2         4  50       62101   C3     5 #> 5609       2         4  50       62101   C4     2 #> 5610       2         4  50       62101   C5     2 #> 5611       2         4  50       62101   E1     1 #> 5612       2         4  50       62101   E2     3 #> 5613       2         4  50       62101   E3     4 #> 5614       2         4  50       62101   E4     6 #> 5615       2         4  50       62101   E5     6 #> 5616       2         4  50       62101   N1     3 #> 5617       2         4  50       62101   N2     5 #> 5618       2         4  50       62101   N3     2 #> 5619       2         4  50       62101   N4     2 #> 5620       2         4  50       62101   N5     3 #> 5621       2         4  50       62101   O1     5 #> 5622       2         4  50       62101   O2     1 #> 5623       2         4  50       62101   O3     5 #> 5624       2         4  50       62101   O4     6 #> 5625       2         4  50       62101   O5     1 #> 5626       2         5  26       62102   A1     1 #> 5627       2         5  26       62102   A2     6 #> 5628       2         5  26       62102   A3     6 #> 5629       2         5  26       62102   A4     6 #> 5630       2         5  26       62102   A5     4 #> 5631       2         5  26       62102   C1     1 #> 5632       2         5  26       62102   C2     5 #> 5633       2         5  26       62102   C3     6 #> 5634       2         5  26       62102   C4     3 #> 5635       2         5  26       62102   C5     2 #> 5636       2         5  26       62102   E1     4 #> 5637       2         5  26       62102   E2     2 #> 5638       2         5  26       62102   E3     6 #> 5639       2         5  26       62102   E4     4 #> 5640       2         5  26       62102   E5     2 #> 5641       2         5  26       62102   N1     2 #> 5642       2         5  26       62102   N2     5 #> 5643       2         5  26       62102   N3     5 #> 5644       2         5  26       62102   N4     4 #> 5645       2         5  26       62102   N5     4 #> 5646       2         5  26       62102   O1     5 #> 5647       2         5  26       62102   O2     5 #> 5648       2         5  26       62102   O3     5 #> 5649       2         5  26       62102   O4     6 #> 5650       2         5  26       62102   O5     1 #> 5651       2         2  21       62103   A1     3 #> 5652       2         2  21       62103   A2     5 #> 5653       2         2  21       62103   A3     5 #> 5654       2         2  21       62103   A4     3 #> 5655       2         2  21       62103   A5     4 #> 5656       2         2  21       62103   C1     4 #> 5657       2         2  21       62103   C2     3 #> 5658       2         2  21       62103   C3     4 #> 5659       2         2  21       62103   C4     3 #> 5660       2         2  21       62103   C5     3 #> 5661       2         2  21       62103   E1     3 #> 5662       2         2  21       62103   E2     4 #> 5663       2         2  21       62103   E3     3 #> 5664       2         2  21       62103   E4     4 #> 5665       2         2  21       62103   E5     3 #> 5666       2         2  21       62103   N1     3 #> 5667       2         2  21       62103   N2     4 #> 5668       2         2  21       62103   N3     3 #> 5669       2         2  21       62103   N4     3 #> 5670       2         2  21       62103   N5     3 #> 5671       2         2  21       62103   O1     3 #> 5672       2         2  21       62103   O2     2 #> 5673       2         2  21       62103   O3     4 #> 5674       2         2  21       62103   O4     4 #> 5675       2         2  21       62103   O5     3 #> 5676       1         4  55       62105   A1     2 #> 5677       1         4  55       62105   A2     5 #> 5678       1         4  55       62105   A3     4 #> 5679       1         4  55       62105   A4     3 #> 5680       1         4  55       62105   A5     2 #> 5681       1         4  55       62105   C1     6 #> 5682       1         4  55       62105   C2     5 #> 5683       1         4  55       62105   C3     6 #> 5684       1         4  55       62105   C4     2 #> 5685       1         4  55       62105   C5     4 #> 5686       1         4  55       62105   E1     2 #> 5687       1         4  55       62105   E2     4 #> 5688       1         4  55       62105   E3     2 #> 5689       1         4  55       62105   E4     2 #> 5690       1         4  55       62105   E5     5 #> 5691       1         4  55       62105   N1     4 #> 5692       1         4  55       62105   N2     5 #> 5693       1         4  55       62105   N3     2 #> 5694       1         4  55       62105   N4     2 #> 5695       1         4  55       62105   N5     1 #> 5696       1         4  55       62105   O1     5 #> 5697       1         4  55       62105   O2     1 #> 5698       1         4  55       62105   O3    NA #> 5699       1         4  55       62105   O4     5 #> 5700       1         4  55       62105   O5     1 #> 5701       2         5  37       62106   A1     1 #> 5702       2         5  37       62106   A2     5 #> 5703       2         5  37       62106   A3     6 #> 5704       2         5  37       62106   A4     5 #> 5705       2         5  37       62106   A5     5 #> 5706       2         5  37       62106   C1     5 #> 5707       2         5  37       62106   C2     3 #> 5708       2         5  37       62106   C3     4 #> 5709       2         5  37       62106   C4     1 #> 5710       2         5  37       62106   C5     5 #> 5711       2         5  37       62106   E1     2 #> 5712       2         5  37       62106   E2     2 #> 5713       2         5  37       62106   E3     4 #> 5714       2         5  37       62106   E4     5 #> 5715       2         5  37       62106   E5     5 #> 5716       2         5  37       62106   N1     2 #> 5717       2         5  37       62106   N2     2 #> 5718       2         5  37       62106   N3     2 #> 5719       2         5  37       62106   N4     2 #> 5720       2         5  37       62106   N5     1 #> 5721       2         5  37       62106   O1     5 #> 5722       2         5  37       62106   O2     3 #> 5723       2         5  37       62106   O3     4 #> 5724       2         5  37       62106   O4     5 #> 5725       2         5  37       62106   O5     2 #> 5726       2         5  38       62107   A1     2 #> 5727       2         5  38       62107   A2     6 #> 5728       2         5  38       62107   A3     6 #> 5729       2         5  38       62107   A4     5 #> 5730       2         5  38       62107   A5     5 #> 5731       2         5  38       62107   C1     5 #> 5732       2         5  38       62107   C2     5 #> 5733       2         5  38       62107   C3     4 #> 5734       2         5  38       62107   C4     2 #> 5735       2         5  38       62107   C5     1 #> 5736       2         5  38       62107   E1     2 #> 5737       2         5  38       62107   E2     3 #> 5738       2         5  38       62107   E3     5 #> 5739       2         5  38       62107   E4     4 #> 5740       2         5  38       62107   E5     6 #> 5741       2         5  38       62107   N1     2 #> 5742       2         5  38       62107   N2     3 #> 5743       2         5  38       62107   N3     2 #> 5744       2         5  38       62107   N4     2 #> 5745       2         5  38       62107   N5     1 #> 5746       2         5  38       62107   O1     6 #> 5747       2         5  38       62107   O2     1 #> 5748       2         5  38       62107   O3     5 #> 5749       2         5  38       62107   O4     6 #> 5750       2         5  38       62107   O5     2 #> 5751       1         3  19       62111   A1     5 #> 5752       1         3  19       62111   A2     4 #> 5753       1         3  19       62111   A3     5 #> 5754       1         3  19       62111   A4     6 #> 5755       1         3  19       62111   A5     4 #> 5756       1         3  19       62111   C1     1 #> 5757       1         3  19       62111   C2     4 #> 5758       1         3  19       62111   C3     6 #> 5759       1         3  19       62111   C4     2 #> 5760       1         3  19       62111   C5     2 #> 5761       1         3  19       62111   E1     3 #> 5762       1         3  19       62111   E2     3 #> 5763       1         3  19       62111   E3     5 #> 5764       1         3  19       62111   E4     4 #> 5765       1         3  19       62111   E5     5 #> 5766       1         3  19       62111   N1     2 #> 5767       1         3  19       62111   N2     2 #> 5768       1         3  19       62111   N3     1 #> 5769       1         3  19       62111   N4     3 #> 5770       1         3  19       62111   N5     2 #> 5771       1         3  19       62111   O1     5 #> 5772       1         3  19       62111   O2     6 #> 5773       1         3  19       62111   O3     4 #> 5774       1         3  19       62111   O4     6 #> 5775       1         3  19       62111   O5     3 #> 5776       2        NA  17       62115   A1     2 #> 5777       2        NA  17       62115   A2     6 #> 5778       2        NA  17       62115   A3     4 #> 5779       2        NA  17       62115   A4     3 #> 5780       2        NA  17       62115   A5     5 #> 5781       2        NA  17       62115   C1     5 #> 5782       2        NA  17       62115   C2     3 #> 5783       2        NA  17       62115   C3     3 #> 5784       2        NA  17       62115   C4     2 #> 5785       2        NA  17       62115   C5     4 #> 5786       2        NA  17       62115   E1     4 #> 5787       2        NA  17       62115   E2     4 #> 5788       2        NA  17       62115   E3     3 #> 5789       2        NA  17       62115   E4     3 #> 5790       2        NA  17       62115   E5     3 #> 5791       2        NA  17       62115   N1     2 #> 5792       2        NA  17       62115   N2     3 #> 5793       2        NA  17       62115   N3     2 #> 5794       2        NA  17       62115   N4     2 #> 5795       2        NA  17       62115   N5     4 #> 5796       2        NA  17       62115   O1     6 #> 5797       2        NA  17       62115   O2     4 #> 5798       2        NA  17       62115   O3     4 #> 5799       2        NA  17       62115   O4     6 #> 5800       2        NA  17       62115   O5     3 #> 5801       2         3  20       62118   A1     5 #> 5802       2         3  20       62118   A2     4 #> 5803       2         3  20       62118   A3     5 #> 5804       2         3  20       62118   A4     6 #> 5805       2         3  20       62118   A5     2 #> 5806       2         3  20       62118   C1     5 #> 5807       2         3  20       62118   C2     6 #> 5808       2         3  20       62118   C3     4 #> 5809       2         3  20       62118   C4     1 #> 5810       2         3  20       62118   C5     1 #> 5811       2         3  20       62118   E1     1 #> 5812       2         3  20       62118   E2     1 #> 5813       2         3  20       62118   E3     3 #> 5814       2         3  20       62118   E4     6 #> 5815       2         3  20       62118   E5     3 #> 5816       2         3  20       62118   N1     2 #> 5817       2         3  20       62118   N2     4 #> 5818       2         3  20       62118   N3     2 #> 5819       2         3  20       62118   N4     3 #> 5820       2         3  20       62118   N5     1 #> 5821       2         3  20       62118   O1     6 #> 5822       2         3  20       62118   O2     2 #> 5823       2         3  20       62118   O3     6 #> 5824       2         3  20       62118   O4     3 #> 5825       2         3  20       62118   O5     2 #> 5826       2         5  34       62119   A1     3 #> 5827       2         5  34       62119   A2     5 #> 5828       2         5  34       62119   A3     2 #> 5829       2         5  34       62119   A4     4 #> 5830       2         5  34       62119   A5     5 #> 5831       2         5  34       62119   C1     5 #> 5832       2         5  34       62119   C2     4 #> 5833       2         5  34       62119   C3     6 #> 5834       2         5  34       62119   C4     3 #> 5835       2         5  34       62119   C5     3 #> 5836       2         5  34       62119   E1     3 #> 5837       2         5  34       62119   E2     5 #> 5838       2         5  34       62119   E3     2 #> 5839       2         5  34       62119   E4     3 #> 5840       2         5  34       62119   E5     2 #> 5841       2         5  34       62119   N1     1 #> 5842       2         5  34       62119   N2     3 #> 5843       2         5  34       62119   N3     3 #> 5844       2         5  34       62119   N4     2 #> 5845       2         5  34       62119   N5     4 #> 5846       2         5  34       62119   O1     5 #> 5847       2         5  34       62119   O2     2 #> 5848       2         5  34       62119   O3     5 #> 5849       2         5  34       62119   O4     5 #> 5850       2         5  34       62119   O5     2 #> 5851       2         3  38       62120   A1     3 #> 5852       2         3  38       62120   A2     6 #> 5853       2         3  38       62120   A3     5 #> 5854       2         3  38       62120   A4     6 #> 5855       2         3  38       62120   A5     6 #> 5856       2         3  38       62120   C1     6 #> 5857       2         3  38       62120   C2     5 #> 5858       2         3  38       62120   C3     5 #> 5859       2         3  38       62120   C4     1 #> 5860       2         3  38       62120   C5     1 #> 5861       2         3  38       62120   E1     1 #> 5862       2         3  38       62120   E2     2 #> 5863       2         3  38       62120   E3     4 #> 5864       2         3  38       62120   E4     6 #> 5865       2         3  38       62120   E5     6 #> 5866       2         3  38       62120   N1     2 #> 5867       2         3  38       62120   N2     2 #> 5868       2         3  38       62120   N3     1 #> 5869       2         3  38       62120   N4     2 #> 5870       2         3  38       62120   N5     3 #> 5871       2         3  38       62120   O1     5 #> 5872       2         3  38       62120   O2     2 #> 5873       2         3  38       62120   O3     4 #> 5874       2         3  38       62120   O4     6 #> 5875       2         3  38       62120   O5     1 #> 5876       2         3  18       62121   A1     2 #> 5877       2         3  18       62121   A2     5 #> 5878       2         3  18       62121   A3     4 #> 5879       2         3  18       62121   A4     4 #> 5880       2         3  18       62121   A5     4 #> 5881       2         3  18       62121   C1     3 #> 5882       2         3  18       62121   C2     3 #> 5883       2         3  18       62121   C3     4 #> 5884       2         3  18       62121   C4     5 #> 5885       2         3  18       62121   C5     4 #> 5886       2         3  18       62121   E1     2 #> 5887       2         3  18       62121   E2     2 #> 5888       2         3  18       62121   E3     4 #> 5889       2         3  18       62121   E4     4 #> 5890       2         3  18       62121   E5     4 #> 5891       2         3  18       62121   N1     5 #> 5892       2         3  18       62121   N2     5 #> 5893       2         3  18       62121   N3     4 #> 5894       2         3  18       62121   N4     4 #> 5895       2         3  18       62121   N5     4 #> 5896       2         3  18       62121   O1     3 #> 5897       2         3  18       62121   O2     5 #> 5898       2         3  18       62121   O3     4 #> 5899       2         3  18       62121   O4     3 #> 5900       2         3  18       62121   O5     4 #> 5901       2         5  32       62124   A1     1 #> 5902       2         5  32       62124   A2    NA #> 5903       2         5  32       62124   A3     4 #> 5904       2         5  32       62124   A4     6 #> 5905       2         5  32       62124   A5     5 #> 5906       2         5  32       62124   C1     5 #> 5907       2         5  32       62124   C2     5 #> 5908       2         5  32       62124   C3     2 #> 5909       2         5  32       62124   C4     3 #> 5910       2         5  32       62124   C5     3 #> 5911       2         5  32       62124   E1     2 #> 5912       2         5  32       62124   E2     2 #> 5913       2         5  32       62124   E3     4 #> 5914       2         5  32       62124   E4     2 #> 5915       2         5  32       62124   E5     4 #> 5916       2         5  32       62124   N1     2 #> 5917       2         5  32       62124   N2     3 #> 5918       2         5  32       62124   N3     3 #> 5919       2         5  32       62124   N4     4 #> 5920       2         5  32       62124   N5     3 #> 5921       2         5  32       62124   O1     4 #> 5922       2         5  32       62124   O2     3 #> 5923       2         5  32       62124   O3     5 #> 5924       2         5  32       62124   O4     6 #> 5925       2         5  32       62124   O5     2 #> 5926       1         3  19       62128   A1     1 #> 5927       1         3  19       62128   A2     6 #> 5928       1         3  19       62128   A3     6 #> 5929       1         3  19       62128   A4     6 #> 5930       1         3  19       62128   A5     5 #> 5931       1         3  19       62128   C1     4 #> 5932       1         3  19       62128   C2     3 #> 5933       1         3  19       62128   C3     4 #> 5934       1         3  19       62128   C4     5 #> 5935       1         3  19       62128   C5     6 #> 5936       1         3  19       62128   E1     2 #> 5937       1         3  19       62128   E2     2 #> 5938       1         3  19       62128   E3     4 #> 5939       1         3  19       62128   E4     5 #> 5940       1         3  19       62128   E5     3 #> 5941       1         3  19       62128   N1     1 #> 5942       1         3  19       62128   N2     2 #> 5943       1         3  19       62128   N3     4 #> 5944       1         3  19       62128   N4     4 #> 5945       1         3  19       62128   N5     4 #> 5946       1         3  19       62128   O1     4 #> 5947       1         3  19       62128   O2     3 #> 5948       1         3  19       62128   O3     5 #> 5949       1         3  19       62128   O4     5 #> 5950       1         3  19       62128   O5     3 #> 5951       1         3  19       62130   A1     3 #> 5952       1         3  19       62130   A2     4 #> 5953       1         3  19       62130   A3     4 #> 5954       1         3  19       62130   A4     6 #> 5955       1         3  19       62130   A5     3 #> 5956       1         3  19       62130   C1     5 #> 5957       1         3  19       62130   C2     5 #> 5958       1         3  19       62130   C3     5 #> 5959       1         3  19       62130   C4     2 #> 5960       1         3  19       62130   C5     4 #> 5961       1         3  19       62130   E1     6 #> 5962       1         3  19       62130   E2     4 #> 5963       1         3  19       62130   E3     3 #> 5964       1         3  19       62130   E4     3 #> 5965       1         3  19       62130   E5     4 #> 5966       1         3  19       62130   N1     1 #> 5967       1         3  19       62130   N2     3 #> 5968       1         3  19       62130   N3     2 #> 5969       1         3  19       62130   N4     2 #> 5970       1         3  19       62130   N5     1 #> 5971       1         3  19       62130   O1     6 #> 5972       1         3  19       62130   O2     2 #> 5973       1         3  19       62130   O3     3 #> 5974       1         3  19       62130   O4     5 #> 5975       1         3  19       62130   O5     2 #> 5976       1         3  21       62132   A1     3 #> 5977       1         3  21       62132   A2     5 #> 5978       1         3  21       62132   A3     6 #> 5979       1         3  21       62132   A4     5 #> 5980       1         3  21       62132   A5     6 #> 5981       1         3  21       62132   C1     5 #> 5982       1         3  21       62132   C2     6 #> 5983       1         3  21       62132   C3     5 #> 5984       1         3  21       62132   C4     2 #> 5985       1         3  21       62132   C5     1 #> 5986       1         3  21       62132   E1     3 #> 5987       1         3  21       62132   E2     1 #> 5988       1         3  21       62132   E3     4 #> 5989       1         3  21       62132   E4     6 #> 5990       1         3  21       62132   E5     5 #> 5991       1         3  21       62132   N1     3 #> 5992       1         3  21       62132   N2     5 #> 5993       1         3  21       62132   N3     3 #> 5994       1         3  21       62132   N4     3 #> 5995       1         3  21       62132   N5     1 #> 5996       1         3  21       62132   O1     4 #> 5997       1         3  21       62132   O2     1 #> 5998       1         3  21       62132   O3     4 #> 5999       1         3  21       62132   O4     4 #> 6000       1         3  21       62132   O5     3 #> 6001       2         3  22       62133   A1     1 #> 6002       2         3  22       62133   A2     5 #> 6003       2         3  22       62133   A3    NA #> 6004       2         3  22       62133   A4     6 #> 6005       2         3  22       62133   A5     5 #> 6006       2         3  22       62133   C1     5 #> 6007       2         3  22       62133   C2     5 #> 6008       2         3  22       62133   C3     6 #> 6009       2         3  22       62133   C4     1 #> 6010       2         3  22       62133   C5     1 #> 6011       2         3  22       62133   E1     1 #> 6012       2         3  22       62133   E2     2 #> 6013       2         3  22       62133   E3     4 #> 6014       2         3  22       62133   E4     6 #> 6015       2         3  22       62133   E5     6 #> 6016       2         3  22       62133   N1     4 #> 6017       2         3  22       62133   N2     4 #> 6018       2         3  22       62133   N3     3 #> 6019       2         3  22       62133   N4     2 #> 6020       2         3  22       62133   N5     2 #> 6021       2         3  22       62133   O1     6 #> 6022       2         3  22       62133   O2     1 #> 6023       2         3  22       62133   O3     5 #> 6024       2         3  22       62133   O4     6 #> 6025       2         3  22       62133   O5     2 #> 6026       1         3  19       62136   A1     2 #> 6027       1         3  19       62136   A2     4 #> 6028       1         3  19       62136   A3     4 #> 6029       1         3  19       62136   A4     6 #> 6030       1         3  19       62136   A5     3 #> 6031       1         3  19       62136   C1     4 #> 6032       1         3  19       62136   C2     5 #> 6033       1         3  19       62136   C3     4 #> 6034       1         3  19       62136   C4     2 #> 6035       1         3  19       62136   C5     4 #> 6036       1         3  19       62136   E1     6 #> 6037       1         3  19       62136   E2     4 #> 6038       1         3  19       62136   E3     2 #> 6039       1         3  19       62136   E4     3 #> 6040       1         3  19       62136   E5     4 #> 6041       1         3  19       62136   N1     1 #> 6042       1         3  19       62136   N2     1 #> 6043       1         3  19       62136   N3     1 #> 6044       1         3  19       62136   N4     1 #> 6045       1         3  19       62136   N5     1 #> 6046       1         3  19       62136   O1     6 #> 6047       1         3  19       62136   O2     2 #> 6048       1         3  19       62136   O3    NA #> 6049       1         3  19       62136   O4     5 #> 6050       1         3  19       62136   O5     2 #> 6051       2         5  41       62137   A1     4 #> 6052       2         5  41       62137   A2     5 #> 6053       2         5  41       62137   A3     5 #> 6054       2         5  41       62137   A4     5 #> 6055       2         5  41       62137   A5     5 #> 6056       2         5  41       62137   C1     5 #> 6057       2         5  41       62137   C2     4 #> 6058       2         5  41       62137   C3     5 #> 6059       2         5  41       62137   C4     2 #> 6060       2         5  41       62137   C5     4 #> 6061       2         5  41       62137   E1     2 #> 6062       2         5  41       62137   E2     2 #> 6063       2         5  41       62137   E3     4 #> 6064       2         5  41       62137   E4     5 #> 6065       2         5  41       62137   E5     5 #> 6066       2         5  41       62137   N1     2 #> 6067       2         5  41       62137   N2     3 #> 6068       2         5  41       62137   N3     2 #> 6069       2         5  41       62137   N4     1 #> 6070       2         5  41       62137   N5     1 #> 6071       2         5  41       62137   O1     5 #> 6072       2         5  41       62137   O2     4 #> 6073       2         5  41       62137   O3     4 #> 6074       2         5  41       62137   O4     4 #> 6075       2         5  41       62137   O5     1 #> 6076       1         3  40       62142   A1     1 #> 6077       1         3  40       62142   A2     6 #> 6078       1         3  40       62142   A3     4 #> 6079       1         3  40       62142   A4     6 #> 6080       1         3  40       62142   A5     6 #> 6081       1         3  40       62142   C1     5 #> 6082       1         3  40       62142   C2     4 #> 6083       1         3  40       62142   C3     5 #> 6084       1         3  40       62142   C4     1 #> 6085       1         3  40       62142   C5     2 #> 6086       1         3  40       62142   E1     5 #> 6087       1         3  40       62142   E2     1 #> 6088       1         3  40       62142   E3     5 #> 6089       1         3  40       62142   E4     4 #> 6090       1         3  40       62142   E5     6 #> 6091       1         3  40       62142   N1     1 #> 6092       1         3  40       62142   N2     2 #> 6093       1         3  40       62142   N3     2 #> 6094       1         3  40       62142   N4     1 #> 6095       1         3  40       62142   N5     1 #> 6096       1         3  40       62142   O1     4 #> 6097       1         3  40       62142   O2     2 #> 6098       1         3  40       62142   O3     5 #> 6099       1         3  40       62142   O4     5 #> 6100       1         3  40       62142   O5     3 #> 6101       2         5  44       62144   A1     4 #> 6102       2         5  44       62144   A2     3 #> 6103       2         5  44       62144   A3     4 #> 6104       2         5  44       62144   A4     2 #> 6105       2         5  44       62144   A5     5 #> 6106       2         5  44       62144   C1     5 #> 6107       2         5  44       62144   C2     5 #> 6108       2         5  44       62144   C3     5 #> 6109       2         5  44       62144   C4     2 #> 6110       2         5  44       62144   C5     2 #> 6111       2         5  44       62144   E1     2 #> 6112       2         5  44       62144   E2     2 #> 6113       2         5  44       62144   E3     3 #> 6114       2         5  44       62144   E4     3 #> 6115       2         5  44       62144   E5     6 #> 6116       2         5  44       62144   N1     4 #> 6117       2         5  44       62144   N2     4 #> 6118       2         5  44       62144   N3     3 #> 6119       2         5  44       62144   N4     3 #> 6120       2         5  44       62144   N5     2 #> 6121       2         5  44       62144   O1     6 #> 6122       2         5  44       62144   O2     1 #> 6123       2         5  44       62144   O3     4 #> 6124       2         5  44       62144   O4     6 #> 6125       2         5  44       62144   O5     1 #> 6126       1         3  25       62147   A1     3 #> 6127       1         3  25       62147   A2     5 #> 6128       1         3  25       62147   A3     5 #> 6129       1         3  25       62147   A4     5 #> 6130       1         3  25       62147   A5     5 #> 6131       1         3  25       62147   C1     4 #> 6132       1         3  25       62147   C2     3 #> 6133       1         3  25       62147   C3     3 #> 6134       1         3  25       62147   C4     3 #> 6135       1         3  25       62147   C5     5 #> 6136       1         3  25       62147   E1     3 #> 6137       1         3  25       62147   E2     4 #> 6138       1         3  25       62147   E3     3 #> 6139       1         3  25       62147   E4     5 #> 6140       1         3  25       62147   E5     5 #> 6141       1         3  25       62147   N1     4 #> 6142       1         3  25       62147   N2     5 #> 6143       1         3  25       62147   N3     2 #> 6144       1         3  25       62147   N4     2 #> 6145       1         3  25       62147   N5     2 #> 6146       1         3  25       62147   O1     5 #> 6147       1         3  25       62147   O2     4 #> 6148       1         3  25       62147   O3     4 #> 6149       1         3  25       62147   O4     5 #> 6150       1         3  25       62147   O5     5 #> 6151       2        NA  15       62151   A1     5 #> 6152       2        NA  15       62151   A2     2 #> 6153       2        NA  15       62151   A3     1 #> 6154       2        NA  15       62151   A4     1 #> 6155       2        NA  15       62151   A5     2 #> 6156       2        NA  15       62151   C1     2 #> 6157       2        NA  15       62151   C2     1 #> 6158       2        NA  15       62151   C3     2 #> 6159       2        NA  15       62151   C4     5 #> 6160       2        NA  15       62151   C5     6 #> 6161       2        NA  15       62151   E1     2 #> 6162       2        NA  15       62151   E2     5 #> 6163       2        NA  15       62151   E3     2 #> 6164       2        NA  15       62151   E4     4 #> 6165       2        NA  15       62151   E5     2 #> 6166       2        NA  15       62151   N1     3 #> 6167       2        NA  15       62151   N2     6 #> 6168       2        NA  15       62151   N3     4 #> 6169       2        NA  15       62151   N4     5 #> 6170       2        NA  15       62151   N5     3 #> 6171       2        NA  15       62151   O1     5 #> 6172       2        NA  15       62151   O2     6 #> 6173       2        NA  15       62151   O3     2 #> 6174       2        NA  15       62151   O4     6 #> 6175       2        NA  15       62151   O5     4 #> 6176       1         3  24       62156   A1     3 #> 6177       1         3  24       62156   A2     3 #> 6178       1         3  24       62156   A3     3 #> 6179       1         3  24       62156   A4     4 #> 6180       1         3  24       62156   A5     4 #> 6181       1         3  24       62156   C1     5 #> 6182       1         3  24       62156   C2     2 #> 6183       1         3  24       62156   C3     5 #> 6184       1         3  24       62156   C4     2 #> 6185       1         3  24       62156   C5     3 #> 6186       1         3  24       62156   E1     4 #> 6187       1         3  24       62156   E2     2 #> 6188       1         3  24       62156   E3     3 #> 6189       1         3  24       62156   E4     5 #> 6190       1         3  24       62156   E5     4 #> 6191       1         3  24       62156   N1     2 #> 6192       1         3  24       62156   N2     2 #> 6193       1         3  24       62156   N3     2 #> 6194       1         3  24       62156   N4     3 #> 6195       1         3  24       62156   N5     2 #> 6196       1         3  24       62156   O1     2 #> 6197       1         3  24       62156   O2     3 #> 6198       1         3  24       62156   O3     2 #> 6199       1         3  24       62156   O4     4 #> 6200       1         3  24       62156   O5     5 #> 6201       2         3  23       62160   A1     4 #> 6202       2         3  23       62160   A2     2 #> 6203       2         3  23       62160   A3     4 #> 6204       2         3  23       62160   A4     1 #> 6205       2         3  23       62160   A5     4 #> 6206       2         3  23       62160   C1     5 #> 6207       2         3  23       62160   C2     4 #> 6208       2         3  23       62160   C3     4 #> 6209       2         3  23       62160   C4     1 #> 6210       2         3  23       62160   C5     5 #> 6211       2         3  23       62160   E1     2 #> 6212       2         3  23       62160   E2     2 #> 6213       2         3  23       62160   E3     4 #> 6214       2         3  23       62160   E4     3 #> 6215       2         3  23       62160   E5     5 #> 6216       2         3  23       62160   N1     2 #> 6217       2         3  23       62160   N2     4 #> 6218       2         3  23       62160   N3     1 #> 6219       2         3  23       62160   N4     2 #> 6220       2         3  23       62160   N5     1 #> 6221       2         3  23       62160   O1     4 #> 6222       2         3  23       62160   O2     4 #> 6223       2         3  23       62160   O3    NA #> 6224       2         3  23       62160   O4     4 #> 6225       2         3  23       62160   O5     2 #> 6226       2         1  22       62161   A1     3 #> 6227       2         1  22       62161   A2     5 #> 6228       2         1  22       62161   A3     4 #> 6229       2         1  22       62161   A4     5 #> 6230       2         1  22       62161   A5     5 #> 6231       2         1  22       62161   C1     4 #> 6232       2         1  22       62161   C2     2 #> 6233       2         1  22       62161   C3     4 #> 6234       2         1  22       62161   C4     4 #> 6235       2         1  22       62161   C5     4 #> 6236       2         1  22       62161   E1     3 #> 6237       2         1  22       62161   E2     4 #> 6238       2         1  22       62161   E3     6 #> 6239       2         1  22       62161   E4     6 #> 6240       2         1  22       62161   E5     6 #> 6241       2         1  22       62161   N1     3 #> 6242       2         1  22       62161   N2     4 #> 6243       2         1  22       62161   N3     2 #> 6244       2         1  22       62161   N4     2 #> 6245       2         1  22       62161   N5     1 #> 6246       2         1  22       62161   O1     5 #> 6247       2         1  22       62161   O2     1 #> 6248       2         1  22       62161   O3     3 #> 6249       2         1  22       62161   O4     5 #> 6250       2         1  22       62161   O5     3 #> 6251       2         3  19       62162   A1     3 #> 6252       2         3  19       62162   A2     5 #> 6253       2         3  19       62162   A3     6 #> 6254       2         3  19       62162   A4     6 #> 6255       2         3  19       62162   A5     5 #> 6256       2         3  19       62162   C1     4 #> 6257       2         3  19       62162   C2     4 #> 6258       2         3  19       62162   C3     6 #> 6259       2         3  19       62162   C4     3 #> 6260       2         3  19       62162   C5     3 #> 6261       2         3  19       62162   E1     3 #> 6262       2         3  19       62162   E2     4 #> 6263       2         3  19       62162   E3     4 #> 6264       2         3  19       62162   E4     5 #> 6265       2         3  19       62162   E5     4 #> 6266       2         3  19       62162   N1     1 #> 6267       2         3  19       62162   N2     1 #> 6268       2         3  19       62162   N3     2 #> 6269       2         3  19       62162   N4     3 #> 6270       2         3  19       62162   N5     2 #> 6271       2         3  19       62162   O1     5 #> 6272       2         3  19       62162   O2     4 #> 6273       2         3  19       62162   O3     4 #> 6274       2         3  19       62162   O4     5 #> 6275       2         3  19       62162   O5     3 #> 6276       2         3  23       62163   A1     4 #> 6277       2         3  23       62163   A2     5 #> 6278       2         3  23       62163   A3     5 #> 6279       2         3  23       62163   A4     6 #> 6280       2         3  23       62163   A5     4 #> 6281       2         3  23       62163   C1     6 #> 6282       2         3  23       62163   C2     5 #> 6283       2         3  23       62163   C3     5 #> 6284       2         3  23       62163   C4     2 #> 6285       2         3  23       62163   C5     2 #> 6286       2         3  23       62163   E1     5 #> 6287       2         3  23       62163   E2     5 #> 6288       2         3  23       62163   E3     4 #> 6289       2         3  23       62163   E4     4 #> 6290       2         3  23       62163   E5     4 #> 6291       2         3  23       62163   N1     2 #> 6292       2         3  23       62163   N2     4 #> 6293       2         3  23       62163   N3     2 #> 6294       2         3  23       62163   N4     6 #> 6295       2         3  23       62163   N5     2 #> 6296       2         3  23       62163   O1     5 #> 6297       2         3  23       62163   O2     2 #> 6298       2         3  23       62163   O3     4 #> 6299       2         3  23       62163   O4     4 #> 6300       2         3  23       62163   O5     2 #> 6301       2         3  38       62164   A1     6 #> 6302       2         3  38       62164   A2     1 #> 6303       2         3  38       62164   A3     1 #> 6304       2         3  38       62164   A4     4 #> 6305       2         3  38       62164   A5     6 #> 6306       2         3  38       62164   C1     5 #> 6307       2         3  38       62164   C2     6 #> 6308       2         3  38       62164   C3     5 #> 6309       2         3  38       62164   C4     1 #> 6310       2         3  38       62164   C5     1 #> 6311       2         3  38       62164   E1     1 #> 6312       2         3  38       62164   E2     1 #> 6313       2         3  38       62164   E3     5 #> 6314       2         3  38       62164   E4     6 #> 6315       2         3  38       62164   E5     6 #> 6316       2         3  38       62164   N1     1 #> 6317       2         3  38       62164   N2     1 #> 6318       2         3  38       62164   N3     1 #> 6319       2         3  38       62164   N4     1 #> 6320       2         3  38       62164   N5     1 #> 6321       2         3  38       62164   O1     5 #> 6322       2         3  38       62164   O2     1 #> 6323       2         3  38       62164   O3     6 #> 6324       2         3  38       62164   O4     1 #> 6325       2         3  38       62164   O5     1 #> 6326       1         2  26       62165   A1     3 #> 6327       1         2  26       62165   A2     5 #> 6328       1         2  26       62165   A3     5 #> 6329       1         2  26       62165   A4     6 #> 6330       1         2  26       62165   A5     6 #> 6331       1         2  26       62165   C1     6 #> 6332       1         2  26       62165   C2     6 #> 6333       1         2  26       62165   C3     6 #> 6334       1         2  26       62165   C4     1 #> 6335       1         2  26       62165   C5     1 #> 6336       1         2  26       62165   E1     1 #> 6337       1         2  26       62165   E2     2 #> 6338       1         2  26       62165   E3     4 #> 6339       1         2  26       62165   E4     4 #> 6340       1         2  26       62165   E5     6 #> 6341       1         2  26       62165   N1     1 #> 6342       1         2  26       62165   N2     1 #> 6343       1         2  26       62165   N3     1 #> 6344       1         2  26       62165   N4     2 #> 6345       1         2  26       62165   N5     2 #> 6346       1         2  26       62165   O1     3 #> 6347       1         2  26       62165   O2     2 #> 6348       1         2  26       62165   O3     4 #> 6349       1         2  26       62165   O4     2 #> 6350       1         2  26       62165   O5     1 #> 6351       2         3  46       62166   A1     5 #> 6352       2         3  46       62166   A2     5 #> 6353       2         3  46       62166   A3     5 #> 6354       2         3  46       62166   A4     6 #> 6355       2         3  46       62166   A5     6 #> 6356       2         3  46       62166   C1     5 #> 6357       2         3  46       62166   C2     4 #> 6358       2         3  46       62166   C3     5 #> 6359       2         3  46       62166   C4     2 #> 6360       2         3  46       62166   C5     2 #> 6361       2         3  46       62166   E1     4 #> 6362       2         3  46       62166   E2     1 #> 6363       2         3  46       62166   E3     5 #> 6364       2         3  46       62166   E4     6 #> 6365       2         3  46       62166   E5     5 #> 6366       2         3  46       62166   N1     1 #> 6367       2         3  46       62166   N2     1 #> 6368       2         3  46       62166   N3     2 #> 6369       2         3  46       62166   N4     5 #> 6370       2         3  46       62166   N5     1 #> 6371       2         3  46       62166   O1     6 #> 6372       2         3  46       62166   O2     5 #> 6373       2         3  46       62166   O3     4 #> 6374       2         3  46       62166   O4     4 #> 6375       2         3  46       62166   O5     4 #> 6376       2         2  24       62168   A1     3 #> 6377       2         2  24       62168   A2     4 #> 6378       2         2  24       62168   A3     4 #> 6379       2         2  24       62168   A4     5 #> 6380       2         2  24       62168   A5     3 #> 6381       2         2  24       62168   C1     4 #> 6382       2         2  24       62168   C2     4 #> 6383       2         2  24       62168   C3     4 #> 6384       2         2  24       62168   C4     2 #> 6385       2         2  24       62168   C5     2 #> 6386       2         2  24       62168   E1     4 #> 6387       2         2  24       62168   E2     3 #> 6388       2         2  24       62168   E3     3 #> 6389       2         2  24       62168   E4     3 #> 6390       2         2  24       62168   E5     5 #> 6391       2         2  24       62168   N1     3 #> 6392       2         2  24       62168   N2     3 #> 6393       2         2  24       62168   N3     3 #> 6394       2         2  24       62168   N4     2 #> 6395       2         2  24       62168   N5     2 #> 6396       2         2  24       62168   O1     5 #> 6397       2         2  24       62168   O2     4 #> 6398       2         2  24       62168   O3     4 #> 6399       2         2  24       62168   O4     4 #> 6400       2         2  24       62168   O5     3 #> 6401       2         1  18       62170   A1     5 #> 6402       2         1  18       62170   A2     6 #> 6403       2         1  18       62170   A3     5 #> 6404       2         1  18       62170   A4     5 #> 6405       2         1  18       62170   A5     6 #> 6406       2         1  18       62170   C1     5 #> 6407       2         1  18       62170   C2     4 #> 6408       2         1  18       62170   C3     6 #> 6409       2         1  18       62170   C4     1 #> 6410       2         1  18       62170   C5     2 #> 6411       2         1  18       62170   E1     1 #> 6412       2         1  18       62170   E2     3 #> 6413       2         1  18       62170   E3     5 #> 6414       2         1  18       62170   E4     6 #> 6415       2         1  18       62170   E5     5 #> 6416       2         1  18       62170   N1     1 #> 6417       2         1  18       62170   N2     4 #> 6418       2         1  18       62170   N3     1 #> 6419       2         1  18       62170   N4     3 #> 6420       2         1  18       62170   N5     2 #> 6421       2         1  18       62170   O1     4 #> 6422       2         1  18       62170   O2     5 #> 6423       2         1  18       62170   O3     4 #> 6424       2         1  18       62170   O4     6 #> 6425       2         1  18       62170   O5     2 #> 6426       1         2  48       62171   A1     1 #> 6427       1         2  48       62171   A2     6 #> 6428       1         2  48       62171   A3     5 #> 6429       1         2  48       62171   A4     6 #> 6430       1         2  48       62171   A5     5 #> 6431       1         2  48       62171   C1     3 #> 6432       1         2  48       62171   C2     3 #> 6433       1         2  48       62171   C3     5 #> 6434       1         2  48       62171   C4     4 #> 6435       1         2  48       62171   C5     4 #> 6436       1         2  48       62171   E1     4 #> 6437       1         2  48       62171   E2     5 #> 6438       1         2  48       62171   E3     4 #> 6439       1         2  48       62171   E4     3 #> 6440       1         2  48       62171   E5     5 #> 6441       1         2  48       62171   N1     2 #> 6442       1         2  48       62171   N2     2 #> 6443       1         2  48       62171   N3     3 #> 6444       1         2  48       62171   N4     5 #> 6445       1         2  48       62171   N5     4 #> 6446       1         2  48       62171   O1     5 #> 6447       1         2  48       62171   O2     3 #> 6448       1         2  48       62171   O3     5 #> 6449       1         2  48       62171   O4     6 #> 6450       1         2  48       62171   O5     2 #> 6451       2         3  30       62173   A1     1 #> 6452       2         3  30       62173   A2     6 #> 6453       2         3  30       62173   A3     6 #> 6454       2         3  30       62173   A4     6 #> 6455       2         3  30       62173   A5     5 #> 6456       2         3  30       62173   C1     6 #> 6457       2         3  30       62173   C2     4 #> 6458       2         3  30       62173   C3     4 #> 6459       2         3  30       62173   C4     1 #> 6460       2         3  30       62173   C5     5 #> 6461       2         3  30       62173   E1     1 #> 6462       2         3  30       62173   E2     1 #> 6463       2         3  30       62173   E3     6 #> 6464       2         3  30       62173   E4     3 #> 6465       2         3  30       62173   E5     3 #> 6466       2         3  30       62173   N1     3 #> 6467       2         3  30       62173   N2     5 #> 6468       2         3  30       62173   N3     5 #> 6469       2         3  30       62173   N4     2 #> 6470       2         3  30       62173   N5     4 #> 6471       2         3  30       62173   O1     6 #> 6472       2         3  30       62173   O2     6 #> 6473       2         3  30       62173   O3     4 #> 6474       2         3  30       62173   O4     6 #> 6475       2         3  30       62173   O5     1 #> 6476       1         2  22       62176   A1     4 #> 6477       1         2  22       62176   A2     4 #> 6478       1         2  22       62176   A3     6 #> 6479       1         2  22       62176   A4     5 #> 6480       1         2  22       62176   A5     4 #> 6481       1         2  22       62176   C1     4 #> 6482       1         2  22       62176   C2     5 #> 6483       1         2  22       62176   C3     3 #> 6484       1         2  22       62176   C4     5 #> 6485       1         2  22       62176   C5     4 #> 6486       1         2  22       62176   E1     2 #> 6487       1         2  22       62176   E2     4 #> 6488       1         2  22       62176   E3     4 #> 6489       1         2  22       62176   E4     5 #> 6490       1         2  22       62176   E5     3 #> 6491       1         2  22       62176   N1     2 #> 6492       1         2  22       62176   N2     4 #> 6493       1         2  22       62176   N3     2 #> 6494       1         2  22       62176   N4     3 #> 6495       1         2  22       62176   N5     3 #> 6496       1         2  22       62176   O1     5 #> 6497       1         2  22       62176   O2     4 #> 6498       1         2  22       62176   O3     4 #> 6499       1         2  22       62176   O4     4 #> 6500       1         2  22       62176   O5     4 #> 6501       1         3  20       62179   A1     5 #> 6502       1         3  20       62179   A2     5 #> 6503       1         3  20       62179   A3     5 #> 6504       1         3  20       62179   A4     1 #> 6505       1         3  20       62179   A5     5 #> 6506       1         3  20       62179   C1     6 #> 6507       1         3  20       62179   C2     5 #> 6508       1         3  20       62179   C3     2 #> 6509       1         3  20       62179   C4     6 #> 6510       1         3  20       62179   C5     6 #> 6511       1         3  20       62179   E1     4 #> 6512       1         3  20       62179   E2     4 #> 6513       1         3  20       62179   E3     6 #> 6514       1         3  20       62179   E4     3 #> 6515       1         3  20       62179   E5     3 #> 6516       1         3  20       62179   N1     5 #> 6517       1         3  20       62179   N2     4 #> 6518       1         3  20       62179   N3     4 #> 6519       1         3  20       62179   N4     5 #> 6520       1         3  20       62179   N5     4 #> 6521       1         3  20       62179   O1     6 #> 6522       1         3  20       62179   O2     6 #> 6523       1         3  20       62179   O3     6 #> 6524       1         3  20       62179   O4     6 #> 6525       1         3  20       62179   O5     4 #> 6526       1         5  36       62180   A1     3 #> 6527       1         5  36       62180   A2     4 #> 6528       1         5  36       62180   A3     4 #> 6529       1         5  36       62180   A4     3 #> 6530       1         5  36       62180   A5     2 #> 6531       1         5  36       62180   C1     6 #> 6532       1         5  36       62180   C2     5 #> 6533       1         5  36       62180   C3     5 #> 6534       1         5  36       62180   C4     1 #> 6535       1         5  36       62180   C5     3 #> 6536       1         5  36       62180   E1     2 #> 6537       1         5  36       62180   E2     5 #> 6538       1         5  36       62180   E3     5 #> 6539       1         5  36       62180   E4     2 #> 6540       1         5  36       62180   E5     5 #> 6541       1         5  36       62180   N1     4 #> 6542       1         5  36       62180   N2     5 #> 6543       1         5  36       62180   N3     3 #> 6544       1         5  36       62180   N4     6 #> 6545       1         5  36       62180   N5     1 #> 6546       1         5  36       62180   O1     6 #> 6547       1         5  36       62180   O2     1 #> 6548       1         5  36       62180   O3     6 #> 6549       1         5  36       62180   O4     6 #> 6550       1         5  36       62180   O5     1 #> 6551       1         1  18       62181   A1     6 #> 6552       1         1  18       62181   A2     6 #> 6553       1         1  18       62181   A3     6 #> 6554       1         1  18       62181   A4     6 #> 6555       1         1  18       62181   A5     6 #> 6556       1         1  18       62181   C1     5 #> 6557       1         1  18       62181   C2     5 #> 6558       1         1  18       62181   C3     6 #> 6559       1         1  18       62181   C4     6 #> 6560       1         1  18       62181   C5     1 #> 6561       1         1  18       62181   E1     1 #> 6562       1         1  18       62181   E2     1 #> 6563       1         1  18       62181   E3     6 #> 6564       1         1  18       62181   E4     6 #> 6565       1         1  18       62181   E5     6 #> 6566       1         1  18       62181   N1     6 #> 6567       1         1  18       62181   N2     3 #> 6568       1         1  18       62181   N3     2 #> 6569       1         1  18       62181   N4     2 #> 6570       1         1  18       62181   N5     5 #> 6571       1         1  18       62181   O1     1 #> 6572       1         1  18       62181   O2     2 #> 6573       1         1  18       62181   O3     6 #> 6574       1         1  18       62181   O4     6 #> 6575       1         1  18       62181   O5     6 #> 6576       1         3  27       62182   A1     2 #> 6577       1         3  27       62182   A2     6 #> 6578       1         3  27       62182   A3     5 #> 6579       1         3  27       62182   A4     6 #> 6580       1         3  27       62182   A5     5 #> 6581       1         3  27       62182   C1     6 #> 6582       1         3  27       62182   C2     5 #> 6583       1         3  27       62182   C3     5 #> 6584       1         3  27       62182   C4     1 #> 6585       1         3  27       62182   C5     1 #> 6586       1         3  27       62182   E1     5 #> 6587       1         3  27       62182   E2     1 #> 6588       1         3  27       62182   E3     5 #> 6589       1         3  27       62182   E4     5 #> 6590       1         3  27       62182   E5     6 #> 6591       1         3  27       62182   N1     5 #> 6592       1         3  27       62182   N2     4 #> 6593       1         3  27       62182   N3     2 #> 6594       1         3  27       62182   N4     2 #> 6595       1         3  27       62182   N5     1 #> 6596       1         3  27       62182   O1     6 #> 6597       1         3  27       62182   O2     1 #> 6598       1         3  27       62182   O3     5 #> 6599       1         3  27       62182   O4     6 #> 6600       1         3  27       62182   O5     2 #> 6601       2         2  30       62183   A1     4 #> 6602       2         2  30       62183   A2     1 #> 6603       2         2  30       62183   A3     4 #> 6604       2         2  30       62183   A4     6 #> 6605       2         2  30       62183   A5     6 #> 6606       2         2  30       62183   C1     6 #> 6607       2         2  30       62183   C2     4 #> 6608       2         2  30       62183   C3     4 #> 6609       2         2  30       62183   C4     2 #> 6610       2         2  30       62183   C5     6 #> 6611       2         2  30       62183   E1     1 #> 6612       2         2  30       62183   E2     1 #> 6613       2         2  30       62183   E3     6 #> 6614       2         2  30       62183   E4     6 #> 6615       2         2  30       62183   E5     5 #> 6616       2         2  30       62183   N1     1 #> 6617       2         2  30       62183   N2     2 #> 6618       2         2  30       62183   N3     1 #> 6619       2         2  30       62183   N4     1 #> 6620       2         2  30       62183   N5     1 #> 6621       2         2  30       62183   O1     4 #> 6622       2         2  30       62183   O2     3 #> 6623       2         2  30       62183   O3     4 #> 6624       2         2  30       62183   O4     2 #> 6625       2         2  30       62183   O5     5 #> 6626       1         3  18       62189   A1     3 #> 6627       1         3  18       62189   A2     5 #> 6628       1         3  18       62189   A3     4 #> 6629       1         3  18       62189   A4     5 #> 6630       1         3  18       62189   A5     5 #> 6631       1         3  18       62189   C1     5 #> 6632       1         3  18       62189   C2     6 #> 6633       1         3  18       62189   C3     5 #> 6634       1         3  18       62189   C4     2 #> 6635       1         3  18       62189   C5     1 #> 6636       1         3  18       62189   E1     3 #> 6637       1         3  18       62189   E2     3 #> 6638       1         3  18       62189   E3     4 #> 6639       1         3  18       62189   E4     6 #> 6640       1         3  18       62189   E5     5 #> 6641       1         3  18       62189   N1     1 #> 6642       1         3  18       62189   N2     2 #> 6643       1         3  18       62189   N3     2 #> 6644       1         3  18       62189   N4     1 #> 6645       1         3  18       62189   N5     2 #> 6646       1         3  18       62189   O1     4 #> 6647       1         3  18       62189   O2     5 #> 6648       1         3  18       62189   O3     3 #> 6649       1         3  18       62189   O4     4 #> 6650       1         3  18       62189   O5     3 #> 6651       1         3  20       62192   A1     2 #> 6652       1         3  20       62192   A2     4 #> 6653       1         3  20       62192   A3     5 #> 6654       1         3  20       62192   A4     5 #> 6655       1         3  20       62192   A5     5 #> 6656       1         3  20       62192   C1     4 #> 6657       1         3  20       62192   C2     4 #> 6658       1         3  20       62192   C3     4 #> 6659       1         3  20       62192   C4     2 #> 6660       1         3  20       62192   C5     3 #> 6661       1         3  20       62192   E1     3 #> 6662       1         3  20       62192   E2     4 #> 6663       1         3  20       62192   E3     3 #> 6664       1         3  20       62192   E4     5 #> 6665       1         3  20       62192   E5     3 #> 6666       1         3  20       62192   N1     5 #> 6667       1         3  20       62192   N2     5 #> 6668       1         3  20       62192   N3     2 #> 6669       1         3  20       62192   N4     4 #> 6670       1         3  20       62192   N5     4 #> 6671       1         3  20       62192   O1     4 #> 6672       1         3  20       62192   O2     4 #> 6673       1         3  20       62192   O3     4 #> 6674       1         3  20       62192   O4     2 #> 6675       1         3  20       62192   O5     2 #> 6676       2         3  55       62197   A1     1 #> 6677       2         3  55       62197   A2     5 #> 6678       2         3  55       62197   A3     5 #> 6679       2         3  55       62197   A4     5 #> 6680       2         3  55       62197   A5     5 #> 6681       2         3  55       62197   C1     5 #> 6682       2         3  55       62197   C2     4 #> 6683       2         3  55       62197   C3     5 #> 6684       2         3  55       62197   C4     1 #> 6685       2         3  55       62197   C5     1 #> 6686       2         3  55       62197   E1     2 #> 6687       2         3  55       62197   E2     2 #> 6688       2         3  55       62197   E3     3 #> 6689       2         3  55       62197   E4     5 #> 6690       2         3  55       62197   E5     5 #> 6691       2         3  55       62197   N1     1 #> 6692       2         3  55       62197   N2     2 #> 6693       2         3  55       62197   N3     2 #> 6694       2         3  55       62197   N4     2 #> 6695       2         3  55       62197   N5     2 #> 6696       2         3  55       62197   O1     2 #> 6697       2         3  55       62197   O2     2 #> 6698       2         3  55       62197   O3     5 #> 6699       2         3  55       62197   O4     4 #> 6700       2         3  55       62197   O5     2 #> 6701       2         3  17       62198   A1     1 #> 6702       2         3  17       62198   A2     6 #> 6703       2         3  17       62198   A3     6 #> 6704       2         3  17       62198   A4     5 #> 6705       2         3  17       62198   A5     5 #> 6706       2         3  17       62198   C1     5 #> 6707       2         3  17       62198   C2     6 #> 6708       2         3  17       62198   C3     5 #> 6709       2         3  17       62198   C4     2 #> 6710       2         3  17       62198   C5     5 #> 6711       2         3  17       62198   E1     4 #> 6712       2         3  17       62198   E2     4 #> 6713       2         3  17       62198   E3     3 #> 6714       2         3  17       62198   E4     6 #> 6715       2         3  17       62198   E5     5 #> 6716       2         3  17       62198   N1     3 #> 6717       2         3  17       62198   N2     4 #> 6718       2         3  17       62198   N3     2 #> 6719       2         3  17       62198   N4     2 #> 6720       2         3  17       62198   N5     6 #> 6721       2         3  17       62198   O1     4 #> 6722       2         3  17       62198   O2     2 #> 6723       2         3  17       62198   O3     5 #> 6724       2         3  17       62198   O4     5 #> 6725       2         3  17       62198   O5     3 #> 6726       1         4  28       62199   A1     4 #> 6727       1         4  28       62199   A2     5 #> 6728       1         4  28       62199   A3     4 #> 6729       1         4  28       62199   A4     3 #> 6730       1         4  28       62199   A5     4 #> 6731       1         4  28       62199   C1     4 #> 6732       1         4  28       62199   C2     4 #> 6733       1         4  28       62199   C3     5 #> 6734       1         4  28       62199   C4     4 #> 6735       1         4  28       62199   C5     5 #> 6736       1         4  28       62199   E1     4 #> 6737       1         4  28       62199   E2     4 #> 6738       1         4  28       62199   E3     4 #> 6739       1         4  28       62199   E4     4 #> 6740       1         4  28       62199   E5     5 #> 6741       1         4  28       62199   N1     2 #> 6742       1         4  28       62199   N2     2 #> 6743       1         4  28       62199   N3     5 #> 6744       1         4  28       62199   N4     5 #> 6745       1         4  28       62199   N5     3 #> 6746       1         4  28       62199   O1     6 #> 6747       1         4  28       62199   O2     2 #> 6748       1         4  28       62199   O3     3 #> 6749       1         4  28       62199   O4     6 #> 6750       1         4  28       62199   O5     6 #> 6751       2         3  19       62201   A1     3 #> 6752       2         3  19       62201   A2     5 #> 6753       2         3  19       62201   A3     4 #> 6754       2         3  19       62201   A4     5 #> 6755       2         3  19       62201   A5     5 #> 6756       2         3  19       62201   C1     5 #> 6757       2         3  19       62201   C2     5 #> 6758       2         3  19       62201   C3     3 #> 6759       2         3  19       62201   C4     4 #> 6760       2         3  19       62201   C5     6 #> 6761       2         3  19       62201   E1     1 #> 6762       2         3  19       62201   E2     6 #> 6763       2         3  19       62201   E3     5 #> 6764       2         3  19       62201   E4     6 #> 6765       2         3  19       62201   E5     6 #> 6766       2         3  19       62201   N1     6 #> 6767       2         3  19       62201   N2     6 #> 6768       2         3  19       62201   N3     5 #> 6769       2         3  19       62201   N4     4 #> 6770       2         3  19       62201   N5     5 #> 6771       2         3  19       62201   O1     6 #> 6772       2         3  19       62201   O2     6 #> 6773       2         3  19       62201   O3     3 #> 6774       2         3  19       62201   O4     4 #> 6775       2         3  19       62201   O5     2 #> 6776       2         3  19       62202   A1     4 #> 6777       2         3  19       62202   A2     5 #> 6778       2         3  19       62202   A3     6 #> 6779       2         3  19       62202   A4     4 #> 6780       2         3  19       62202   A5     6 #> 6781       2         3  19       62202   C1     4 #> 6782       2         3  19       62202   C2     5 #> 6783       2         3  19       62202   C3     5 #> 6784       2         3  19       62202   C4     3 #> 6785       2         3  19       62202   C5     3 #> 6786       2         3  19       62202   E1     1 #> 6787       2         3  19       62202   E2     1 #> 6788       2         3  19       62202   E3     5 #> 6789       2         3  19       62202   E4     5 #> 6790       2         3  19       62202   E5     6 #> 6791       2         3  19       62202   N1     2 #> 6792       2         3  19       62202   N2     4 #> 6793       2         3  19       62202   N3     4 #> 6794       2         3  19       62202   N4    NA #> 6795       2         3  19       62202   N5     1 #> 6796       2         3  19       62202   O1     6 #> 6797       2         3  19       62202   O2     2 #> 6798       2         3  19       62202   O3     5 #> 6799       2         3  19       62202   O4     5 #> 6800       2         3  19       62202   O5     3 #> 6801       2        NA  16       62203   A1     1 #> 6802       2        NA  16       62203   A2     6 #> 6803       2        NA  16       62203   A3     6 #> 6804       2        NA  16       62203   A4     6 #> 6805       2        NA  16       62203   A5     6 #> 6806       2        NA  16       62203   C1     6 #> 6807       2        NA  16       62203   C2     5 #> 6808       2        NA  16       62203   C3     6 #> 6809       2        NA  16       62203   C4     1 #> 6810       2        NA  16       62203   C5     3 #> 6811       2        NA  16       62203   E1     1 #> 6812       2        NA  16       62203   E2     1 #> 6813       2        NA  16       62203   E3     6 #> 6814       2        NA  16       62203   E4     6 #> 6815       2        NA  16       62203   E5     6 #> 6816       2        NA  16       62203   N1     1 #> 6817       2        NA  16       62203   N2     1 #> 6818       2        NA  16       62203   N3     1 #> 6819       2        NA  16       62203   N4     3 #> 6820       2        NA  16       62203   N5     3 #> 6821       2        NA  16       62203   O1     6 #> 6822       2        NA  16       62203   O2     1 #> 6823       2        NA  16       62203   O3     6 #> 6824       2        NA  16       62203   O4     5 #> 6825       2        NA  16       62203   O5     3 #> 6826       1         1  31       62204   A1     1 #> 6827       1         1  31       62204   A2     5 #> 6828       1         1  31       62204   A3     6 #> 6829       1         1  31       62204   A4     6 #> 6830       1         1  31       62204   A5     6 #> 6831       1         1  31       62204   C1     5 #> 6832       1         1  31       62204   C2     6 #> 6833       1         1  31       62204   C3     6 #> 6834       1         1  31       62204   C4     5 #> 6835       1         1  31       62204   C5     1 #> 6836       1         1  31       62204   E1     4 #> 6837       1         1  31       62204   E2     1 #> 6838       1         1  31       62204   E3     6 #> 6839       1         1  31       62204   E4     6 #> 6840       1         1  31       62204   E5     5 #> 6841       1         1  31       62204   N1     5 #> 6842       1         1  31       62204   N2     1 #> 6843       1         1  31       62204   N3     5 #> 6844       1         1  31       62204   N4     5 #> 6845       1         1  31       62204   N5     2 #> 6846       1         1  31       62204   O1     6 #> 6847       1         1  31       62204   O2     5 #> 6848       1         1  31       62204   O3     5 #> 6849       1         1  31       62204   O4     5 #> 6850       1         1  31       62204   O5     2 #> 6851       2         3  50       62205   A1     2 #> 6852       2         3  50       62205   A2     4 #> 6853       2         3  50       62205   A3     4 #> 6854       2         3  50       62205   A4     4 #> 6855       2         3  50       62205   A5     5 #> 6856       2         3  50       62205   C1     4 #> 6857       2         3  50       62205   C2     5 #> 6858       2         3  50       62205   C3     4 #> 6859       2         3  50       62205   C4     2 #> 6860       2         3  50       62205   C5     4 #> 6861       2         3  50       62205   E1     1 #> 6862       2         3  50       62205   E2     1 #> 6863       2         3  50       62205   E3     5 #> 6864       2         3  50       62205   E4     6 #> 6865       2         3  50       62205   E5     6 #> 6866       2         3  50       62205   N1     4 #> 6867       2         3  50       62205   N2     5 #> 6868       2         3  50       62205   N3     6 #> 6869       2         3  50       62205   N4     4 #> 6870       2         3  50       62205   N5     4 #> 6871       2         3  50       62205   O1     6 #> 6872       2         3  50       62205   O2     3 #> 6873       2         3  50       62205   O3     5 #> 6874       2         3  50       62205   O4     4 #> 6875       2         3  50       62205   O5     1 #> 6876       2         3  31       62206   A1     3 #> 6877       2         3  31       62206   A2     5 #> 6878       2         3  31       62206   A3     6 #> 6879       2         3  31       62206   A4     6 #> 6880       2         3  31       62206   A5     6 #> 6881       2         3  31       62206   C1     4 #> 6882       2         3  31       62206   C2     5 #> 6883       2         3  31       62206   C3     6 #> 6884       2         3  31       62206   C4     3 #> 6885       2         3  31       62206   C5     1 #> 6886       2         3  31       62206   E1     2 #> 6887       2         3  31       62206   E2     1 #> 6888       2         3  31       62206   E3     5 #> 6889       2         3  31       62206   E4     2 #> 6890       2         3  31       62206   E5     6 #> 6891       2         3  31       62206   N1     5 #> 6892       2         3  31       62206   N2     4 #> 6893       2         3  31       62206   N3     1 #> 6894       2         3  31       62206   N4     2 #> 6895       2         3  31       62206   N5     3 #> 6896       2         3  31       62206   O1     4 #> 6897       2         3  31       62206   O2     5 #> 6898       2         3  31       62206   O3     5 #> 6899       2         3  31       62206   O4     6 #> 6900       2         3  31       62206   O5     4 #> 6901       2         5  27       62208   A1     1 #> 6902       2         5  27       62208   A2     5 #> 6903       2         5  27       62208   A3     6 #> 6904       2         5  27       62208   A4     5 #> 6905       2         5  27       62208   A5     6 #> 6906       2         5  27       62208   C1     6 #> 6907       2         5  27       62208   C2     5 #> 6908       2         5  27       62208   C3     5 #> 6909       2         5  27       62208   C4     1 #> 6910       2         5  27       62208   C5     2 #> 6911       2         5  27       62208   E1     2 #> 6912       2         5  27       62208   E2     1 #> 6913       2         5  27       62208   E3     5 #> 6914       2         5  27       62208   E4     5 #> 6915       2         5  27       62208   E5     6 #> 6916       2         5  27       62208   N1     2 #> 6917       2         5  27       62208   N2     2 #> 6918       2         5  27       62208   N3     2 #> 6919       2         5  27       62208   N4     2 #> 6920       2         5  27       62208   N5     1 #> 6921       2         5  27       62208   O1     5 #> 6922       2         5  27       62208   O2     1 #> 6923       2         5  27       62208   O3     6 #> 6924       2         5  27       62208   O4     4 #> 6925       2         5  27       62208   O5     2 #> 6926       1         3  16       62209   A1     1 #> 6927       1         3  16       62209   A2     4 #> 6928       1         3  16       62209   A3     4 #> 6929       1         3  16       62209   A4     2 #> 6930       1         3  16       62209   A5     5 #> 6931       1         3  16       62209   C1     5 #> 6932       1         3  16       62209   C2     4 #> 6933       1         3  16       62209   C3     3 #> 6934       1         3  16       62209   C4     2 #> 6935       1         3  16       62209   C5     4 #> 6936       1         3  16       62209   E1     5 #> 6937       1         3  16       62209   E2     5 #> 6938       1         3  16       62209   E3     4 #> 6939       1         3  16       62209   E4     2 #> 6940       1         3  16       62209   E5     3 #> 6941       1         3  16       62209   N1     4 #> 6942       1         3  16       62209   N2     4 #> 6943       1         3  16       62209   N3     4 #> 6944       1         3  16       62209   N4     5 #> 6945       1         3  16       62209   N5     4 #> 6946       1         3  16       62209   O1     4 #> 6947       1         3  16       62209   O2     2 #> 6948       1         3  16       62209   O3     2 #> 6949       1         3  16       62209   O4     5 #> 6950       1         3  16       62209   O5     4 #> 6951       1         3  18       62212   A1     4 #> 6952       1         3  18       62212   A2     6 #> 6953       1         3  18       62212   A3     6 #> 6954       1         3  18       62212   A4     6 #> 6955       1         3  18       62212   A5     5 #> 6956       1         3  18       62212   C1     4 #> 6957       1         3  18       62212   C2     4 #> 6958       1         3  18       62212   C3     5 #> 6959       1         3  18       62212   C4     2 #> 6960       1         3  18       62212   C5     5 #> 6961       1         3  18       62212   E1     1 #> 6962       1         3  18       62212   E2     1 #> 6963       1         3  18       62212   E3     4 #> 6964       1         3  18       62212   E4     5 #> 6965       1         3  18       62212   E5     5 #> 6966       1         3  18       62212   N1     4 #> 6967       1         3  18       62212   N2     5 #> 6968       1         3  18       62212   N3     4 #> 6969       1         3  18       62212   N4     4 #> 6970       1         3  18       62212   N5     5 #> 6971       1         3  18       62212   O1     5 #> 6972       1         3  18       62212   O2     1 #> 6973       1         3  18       62212   O3     6 #> 6974       1         3  18       62212   O4     6 #> 6975       1         3  18       62212   O5     1 #> 6976       2         3  20       62213   A1     2 #> 6977       2         3  20       62213   A2     6 #> 6978       2         3  20       62213   A3     5 #> 6979       2         3  20       62213   A4     6 #> 6980       2         3  20       62213   A5     3 #> 6981       2         3  20       62213   C1     3 #> 6982       2         3  20       62213   C2     3 #> 6983       2         3  20       62213   C3     5 #> 6984       2         3  20       62213   C4     3 #> 6985       2         3  20       62213   C5     5 #> 6986       2         3  20       62213   E1     1 #> 6987       2         3  20       62213   E2     4 #> 6988       2         3  20       62213   E3     3 #> 6989       2         3  20       62213   E4     5 #> 6990       2         3  20       62213   E5     2 #> 6991       2         3  20       62213   N1     4 #> 6992       2         3  20       62213   N2     5 #> 6993       2         3  20       62213   N3     6 #> 6994       2         3  20       62213   N4     4 #> 6995       2         3  20       62213   N5     4 #> 6996       2         3  20       62213   O1     2 #> 6997       2         3  20       62213   O2     4 #> 6998       2         3  20       62213   O3     3 #> 6999       2         3  20       62213   O4     6 #> 7000       2         3  20       62213   O5     3 #> 7001       1         3  21       62214   A1     3 #> 7002       1         3  21       62214   A2     4 #> 7003       1         3  21       62214   A3     5 #> 7004       1         3  21       62214   A4     4 #> 7005       1         3  21       62214   A5     4 #> 7006       1         3  21       62214   C1     4 #> 7007       1         3  21       62214   C2     4 #> 7008       1         3  21       62214   C3     3 #> 7009       1         3  21       62214   C4     4 #> 7010       1         3  21       62214   C5     5 #> 7011       1         3  21       62214   E1     5 #> 7012       1         3  21       62214   E2     4 #> 7013       1         3  21       62214   E3     3 #> 7014       1         3  21       62214   E4     3 #> 7015       1         3  21       62214   E5     4 #> 7016       1         3  21       62214   N1     5 #> 7017       1         3  21       62214   N2     5 #> 7018       1         3  21       62214   N3     3 #> 7019       1         3  21       62214   N4     3 #> 7020       1         3  21       62214   N5     3 #> 7021       1         3  21       62214   O1     4 #> 7022       1         3  21       62214   O2     3 #> 7023       1         3  21       62214   O3     5 #> 7024       1         3  21       62214   O4     5 #> 7025       1         3  21       62214   O5     3 #> 7026       2         5  32       62215   A1     2 #> 7027       2         5  32       62215   A2     6 #> 7028       2         5  32       62215   A3     6 #> 7029       2         5  32       62215   A4     6 #> 7030       2         5  32       62215   A5     6 #> 7031       2         5  32       62215   C1     5 #> 7032       2         5  32       62215   C2     4 #> 7033       2         5  32       62215   C3     5 #> 7034       2         5  32       62215   C4     1 #> 7035       2         5  32       62215   C5     1 #> 7036       2         5  32       62215   E1     1 #> 7037       2         5  32       62215   E2     1 #> 7038       2         5  32       62215   E3     5 #> 7039       2         5  32       62215   E4     5 #> 7040       2         5  32       62215   E5     5 #> 7041       2         5  32       62215   N1     4 #> 7042       2         5  32       62215   N2     3 #> 7043       2         5  32       62215   N3     2 #> 7044       2         5  32       62215   N4     2 #> 7045       2         5  32       62215   N5     3 #> 7046       2         5  32       62215   O1     5 #> 7047       2         5  32       62215   O2     1 #> 7048       2         5  32       62215   O3     5 #> 7049       2         5  32       62215   O4     4 #> 7050       2         5  32       62215   O5     2 #> 7051       2         3  30       62216   A1     2 #> 7052       2         3  30       62216   A2     2 #> 7053       2         3  30       62216   A3     2 #> 7054       2         3  30       62216   A4     4 #> 7055       2         3  30       62216   A5     3 #> 7056       2         3  30       62216   C1     3 #> 7057       2         3  30       62216   C2     2 #> 7058       2         3  30       62216   C3     4 #> 7059       2         3  30       62216   C4     4 #> 7060       2         3  30       62216   C5     6 #> 7061       2         3  30       62216   E1     4 #> 7062       2         3  30       62216   E2     4 #> 7063       2         3  30       62216   E3     3 #> 7064       2         3  30       62216   E4     3 #> 7065       2         3  30       62216   E5     3 #> 7066       2         3  30       62216   N1     1 #> 7067       2         3  30       62216   N2     3 #> 7068       2         3  30       62216   N3     3 #> 7069       2         3  30       62216   N4     5 #> 7070       2         3  30       62216   N5     1 #> 7071       2         3  30       62216   O1     4 #> 7072       2         3  30       62216   O2     6 #> 7073       2         3  30       62216   O3     3 #> 7074       2         3  30       62216   O4     4 #> 7075       2         3  30       62216   O5     4 #> 7076       2         3  24       62219   A1     2 #> 7077       2         3  24       62219   A2     5 #> 7078       2         3  24       62219   A3     4 #> 7079       2         3  24       62219   A4     6 #> 7080       2         3  24       62219   A5     4 #> 7081       2         3  24       62219   C1     4 #> 7082       2         3  24       62219   C2     5 #> 7083       2         3  24       62219   C3     4 #> 7084       2         3  24       62219   C4     3 #> 7085       2         3  24       62219   C5     4 #> 7086       2         3  24       62219   E1     1 #> 7087       2         3  24       62219   E2     3 #> 7088       2         3  24       62219   E3     3 #> 7089       2         3  24       62219   E4     4 #> 7090       2         3  24       62219   E5     4 #> 7091       2         3  24       62219   N1     4 #> 7092       2         3  24       62219   N2     4 #> 7093       2         3  24       62219   N3     5 #> 7094       2         3  24       62219   N4     3 #> 7095       2         3  24       62219   N5     5 #> 7096       2         3  24       62219   O1     4 #> 7097       2         3  24       62219   O2     4 #> 7098       2         3  24       62219   O3     4 #> 7099       2         3  24       62219   O4     4 #> 7100       2         3  24       62219   O5     2 #> 7101       2         1  35       62220   A1     3 #> 7102       2         1  35       62220   A2     4 #> 7103       2         1  35       62220   A3     6 #> 7104       2         1  35       62220   A4     6 #> 7105       2         1  35       62220   A5     4 #> 7106       2         1  35       62220   C1     4 #> 7107       2         1  35       62220   C2     4 #> 7108       2         1  35       62220   C3     5 #> 7109       2         1  35       62220   C4     5 #> 7110       2         1  35       62220   C5     4 #> 7111       2         1  35       62220   E1     2 #> 7112       2         1  35       62220   E2     4 #> 7113       2         1  35       62220   E3     4 #> 7114       2         1  35       62220   E4     3 #> 7115       2         1  35       62220   E5     3 #> 7116       2         1  35       62220   N1     6 #> 7117       2         1  35       62220   N2     6 #> 7118       2         1  35       62220   N3     6 #> 7119       2         1  35       62220   N4     4 #> 7120       2         1  35       62220   N5     5 #> 7121       2         1  35       62220   O1     3 #> 7122       2         1  35       62220   O2     4 #> 7123       2         1  35       62220   O3     5 #> 7124       2         1  35       62220   O4     4 #> 7125       2         1  35       62220   O5     4 #> 7126       2         3  19       62224   A1     1 #> 7127       2         3  19       62224   A2     5 #> 7128       2         3  19       62224   A3     5 #> 7129       2         3  19       62224   A4     6 #> 7130       2         3  19       62224   A5     5 #> 7131       2         3  19       62224   C1     5 #> 7132       2         3  19       62224   C2     4 #> 7133       2         3  19       62224   C3     5 #> 7134       2         3  19       62224   C4     2 #> 7135       2         3  19       62224   C5     1 #> 7136       2         3  19       62224   E1     1 #> 7137       2         3  19       62224   E2     3 #> 7138       2         3  19       62224   E3     5 #> 7139       2         3  19       62224   E4     6 #> 7140       2         3  19       62224   E5     5 #> 7141       2         3  19       62224   N1     3 #> 7142       2         3  19       62224   N2     4 #> 7143       2         3  19       62224   N3     4 #> 7144       2         3  19       62224   N4     3 #> 7145       2         3  19       62224   N5     4 #> 7146       2         3  19       62224   O1     5 #> 7147       2         3  19       62224   O2     3 #> 7148       2         3  19       62224   O3     4 #> 7149       2         3  19       62224   O4     4 #> 7150       2         3  19       62224   O5     2 #> 7151       2         3  23       62225   A1     2 #> 7152       2         3  23       62225   A2     5 #> 7153       2         3  23       62225   A3     6 #> 7154       2         3  23       62225   A4     5 #> 7155       2         3  23       62225   A5     4 #> 7156       2         3  23       62225   C1     3 #> 7157       2         3  23       62225   C2     5 #> 7158       2         3  23       62225   C3     5 #> 7159       2         3  23       62225   C4     2 #> 7160       2         3  23       62225   C5     1 #> 7161       2         3  23       62225   E1     4 #> 7162       2         3  23       62225   E2     5 #> 7163       2         3  23       62225   E3     4 #> 7164       2         3  23       62225   E4     1 #> 7165       2         3  23       62225   E5     6 #> 7166       2         3  23       62225   N1     6 #> 7167       2         3  23       62225   N2     6 #> 7168       2         3  23       62225   N3     6 #> 7169       2         3  23       62225   N4     6 #> 7170       2         3  23       62225   N5     4 #> 7171       2         3  23       62225   O1     4 #> 7172       2         3  23       62225   O2     2 #> 7173       2         3  23       62225   O3     6 #> 7174       2         3  23       62225   O4     6 #> 7175       2         3  23       62225   O5     2 #> 7176       2        NA  16       62226   A1     2 #> 7177       2        NA  16       62226   A2     4 #> 7178       2        NA  16       62226   A3     2 #> 7179       2        NA  16       62226   A4     4 #> 7180       2        NA  16       62226   A5     4 #> 7181       2        NA  16       62226   C1     5 #> 7182       2        NA  16       62226   C2     3 #> 7183       2        NA  16       62226   C3     5 #> 7184       2        NA  16       62226   C4     2 #> 7185       2        NA  16       62226   C5     3 #> 7186       2        NA  16       62226   E1     6 #> 7187       2        NA  16       62226   E2     4 #> 7188       2        NA  16       62226   E3     4 #> 7189       2        NA  16       62226   E4     5 #> 7190       2        NA  16       62226   E5     5 #> 7191       2        NA  16       62226   N1     4 #> 7192       2        NA  16       62226   N2     4 #> 7193       2        NA  16       62226   N3     3 #> 7194       2        NA  16       62226   N4     3 #> 7195       2        NA  16       62226   N5     2 #> 7196       2        NA  16       62226   O1     5 #> 7197       2        NA  16       62226   O2     2 #> 7198       2        NA  16       62226   O3     5 #> 7199       2        NA  16       62226   O4     5 #> 7200       2        NA  16       62226   O5     4 #> 7201       1         4  21       62227   A1     1 #> 7202       1         4  21       62227   A2     6 #> 7203       1         4  21       62227   A3     6 #> 7204       1         4  21       62227   A4     6 #> 7205       1         4  21       62227   A5     6 #> 7206       1         4  21       62227   C1     6 #> 7207       1         4  21       62227   C2     4 #> 7208       1         4  21       62227   C3     4 #> 7209       1         4  21       62227   C4     3 #> 7210       1         4  21       62227   C5     4 #> 7211       1         4  21       62227   E1     1 #> 7212       1         4  21       62227   E2     1 #> 7213       1         4  21       62227   E3     6 #> 7214       1         4  21       62227   E4     6 #> 7215       1         4  21       62227   E5     6 #> 7216       1         4  21       62227   N1     1 #> 7217       1         4  21       62227   N2     2 #> 7218       1         4  21       62227   N3     4 #> 7219       1         4  21       62227   N4     2 #> 7220       1         4  21       62227   N5     2 #> 7221       1         4  21       62227   O1     6 #> 7222       1         4  21       62227   O2     1 #> 7223       1         4  21       62227   O3     6 #> 7224       1         4  21       62227   O4     6 #> 7225       1         4  21       62227   O5     1 #> 7226       2         3  22       62228   A1     1 #> 7227       2         3  22       62228   A2     6 #> 7228       2         3  22       62228   A3     6 #> 7229       2         3  22       62228   A4     6 #> 7230       2         3  22       62228   A5     5 #> 7231       2         3  22       62228   C1     5 #> 7232       2         3  22       62228   C2     4 #> 7233       2         3  22       62228   C3     6 #> 7234       2         3  22       62228   C4     2 #> 7235       2         3  22       62228   C5     2 #> 7236       2         3  22       62228   E1     2 #> 7237       2         3  22       62228   E2     4 #> 7238       2         3  22       62228   E3     4 #> 7239       2         3  22       62228   E4     6 #> 7240       2         3  22       62228   E5     3 #> 7241       2         3  22       62228   N1     5 #> 7242       2         3  22       62228   N2     6 #> 7243       2         3  22       62228   N3     3 #> 7244       2         3  22       62228   N4     2 #> 7245       2         3  22       62228   N5     4 #> 7246       2         3  22       62228   O1     3 #> 7247       2         3  22       62228   O2     5 #> 7248       2         3  22       62228   O3     3 #> 7249       2         3  22       62228   O4     3 #> 7250       2         3  22       62228   O5     1 #> 7251       2         3  20       62231   A1     1 #> 7252       2         3  20       62231   A2     5 #> 7253       2         3  20       62231   A3     5 #> 7254       2         3  20       62231   A4     6 #> 7255       2         3  20       62231   A5     5 #> 7256       2         3  20       62231   C1     2 #> 7257       2         3  20       62231   C2     4 #> 7258       2         3  20       62231   C3     5 #> 7259       2         3  20       62231   C4     5 #> 7260       2         3  20       62231   C5     5 #> 7261       2         3  20       62231   E1     3 #> 7262       2         3  20       62231   E2     5 #> 7263       2         3  20       62231   E3     5 #> 7264       2         3  20       62231   E4     5 #> 7265       2         3  20       62231   E5     2 #> 7266       2         3  20       62231   N1     3 #> 7267       2         3  20       62231   N2     4 #> 7268       2         3  20       62231   N3     4 #> 7269       2         3  20       62231   N4     4 #> 7270       2         3  20       62231   N5     2 #> 7271       2         3  20       62231   O1     1 #> 7272       2         3  20       62231   O2     4 #> 7273       2         3  20       62231   O3     3 #> 7274       2         3  20       62231   O4     5 #> 7275       2         3  20       62231   O5     4 #> 7276       1         4  26       62233   A1     2 #> 7277       1         4  26       62233   A2     5 #> 7278       1         4  26       62233   A3     5 #> 7279       1         4  26       62233   A4     5 #> 7280       1         4  26       62233   A5     5 #> 7281       1         4  26       62233   C1     5 #> 7282       1         4  26       62233   C2     5 #> 7283       1         4  26       62233   C3     5 #> 7284       1         4  26       62233   C4     2 #> 7285       1         4  26       62233   C5     1 #> 7286       1         4  26       62233   E1     2 #> 7287       1         4  26       62233   E2     2 #> 7288       1         4  26       62233   E3     4 #> 7289       1         4  26       62233   E4     5 #> 7290       1         4  26       62233   E5     5 #> 7291       1         4  26       62233   N1     1 #> 7292       1         4  26       62233   N2     1 #> 7293       1         4  26       62233   N3     1 #> 7294       1         4  26       62233   N4     1 #> 7295       1         4  26       62233   N5     1 #> 7296       1         4  26       62233   O1     5 #> 7297       1         4  26       62233   O2     1 #> 7298       1         4  26       62233   O3     5 #> 7299       1         4  26       62233   O4     2 #> 7300       1         4  26       62233   O5     2 #> 7301       1         3  19       62237   A1     3 #> 7302       1         3  19       62237   A2     4 #> 7303       1         3  19       62237   A3     5 #> 7304       1         3  19       62237   A4     5 #> 7305       1         3  19       62237   A5     5 #> 7306       1         3  19       62237   C1     4 #> 7307       1         3  19       62237   C2     4 #> 7308       1         3  19       62237   C3     5 #> 7309       1         3  19       62237   C4     4 #> 7310       1         3  19       62237   C5     4 #> 7311       1         3  19       62237   E1     5 #> 7312       1         3  19       62237   E2     4 #> 7313       1         3  19       62237   E3     3 #> 7314       1         3  19       62237   E4    NA #> 7315       1         3  19       62237   E5     4 #> 7316       1         3  19       62237   N1     3 #> 7317       1         3  19       62237   N2     4 #> 7318       1         3  19       62237   N3     2 #> 7319       1         3  19       62237   N4     4 #> 7320       1         3  19       62237   N5     4 #> 7321       1         3  19       62237   O1     3 #> 7322       1         3  19       62237   O2     5 #> 7323       1         3  19       62237   O3     3 #> 7324       1         3  19       62237   O4     3 #> 7325       1         3  19       62237   O5     4 #> 7326       2         5  25       62239   A1     3 #> 7327       2         5  25       62239   A2     6 #> 7328       2         5  25       62239   A3     5 #> 7329       2         5  25       62239   A4     5 #> 7330       2         5  25       62239   A5     5 #> 7331       2         5  25       62239   C1     4 #> 7332       2         5  25       62239   C2     4 #> 7333       2         5  25       62239   C3     5 #> 7334       2         5  25       62239   C4     4 #> 7335       2         5  25       62239   C5     4 #> 7336       2         5  25       62239   E1     1 #> 7337       2         5  25       62239   E2     2 #> 7338       2         5  25       62239   E3     5 #> 7339       2         5  25       62239   E4     6 #> 7340       2         5  25       62239   E5     6 #> 7341       2         5  25       62239   N1     4 #> 7342       2         5  25       62239   N2     4 #> 7343       2         5  25       62239   N3     4 #> 7344       2         5  25       62239   N4     3 #> 7345       2         5  25       62239   N5     5 #> 7346       2         5  25       62239   O1     6 #> 7347       2         5  25       62239   O2     2 #> 7348       2         5  25       62239   O3     5 #> 7349       2         5  25       62239   O4     4 #> 7350       2         5  25       62239   O5     3 #> 7351       2         5  28       62240   A1     1 #> 7352       2         5  28       62240   A2     5 #> 7353       2         5  28       62240   A3     6 #> 7354       2         5  28       62240   A4     4 #> 7355       2         5  28       62240   A5     6 #> 7356       2         5  28       62240   C1     5 #> 7357       2         5  28       62240   C2     3 #> 7358       2         5  28       62240   C3     3 #> 7359       2         5  28       62240   C4     4 #> 7360       2         5  28       62240   C5     5 #> 7361       2         5  28       62240   E1     1 #> 7362       2         5  28       62240   E2     1 #> 7363       2         5  28       62240   E3     5 #> 7364       2         5  28       62240   E4     6 #> 7365       2         5  28       62240   E5     5 #> 7366       2         5  28       62240   N1     4 #> 7367       2         5  28       62240   N2     6 #> 7368       2         5  28       62240   N3     6 #> 7369       2         5  28       62240   N4     5 #> 7370       2         5  28       62240   N5     6 #> 7371       2         5  28       62240   O1     6 #> 7372       2         5  28       62240   O2     2 #> 7373       2         5  28       62240   O3     6 #> 7374       2         5  28       62240   O4     5 #> 7375       2         5  28       62240   O5     1 #> 7376       2        NA  17       62242   A1     5 #> 7377       2        NA  17       62242   A2     6 #> 7378       2        NA  17       62242   A3     6 #> 7379       2        NA  17       62242   A4     6 #> 7380       2        NA  17       62242   A5     6 #> 7381       2        NA  17       62242   C1     4 #> 7382       2        NA  17       62242   C2     5 #> 7383       2        NA  17       62242   C3     4 #> 7384       2        NA  17       62242   C4     3 #> 7385       2        NA  17       62242   C5     5 #> 7386       2        NA  17       62242   E1     1 #> 7387       2        NA  17       62242   E2     2 #> 7388       2        NA  17       62242   E3     4 #> 7389       2        NA  17       62242   E4     6 #> 7390       2        NA  17       62242   E5     6 #> 7391       2        NA  17       62242   N1     4 #> 7392       2        NA  17       62242   N2     5 #> 7393       2        NA  17       62242   N3     1 #> 7394       2        NA  17       62242   N4     2 #> 7395       2        NA  17       62242   N5     3 #> 7396       2        NA  17       62242   O1     6 #> 7397       2        NA  17       62242   O2     6 #> 7398       2        NA  17       62242   O3     5 #> 7399       2        NA  17       62242   O4     6 #> 7400       2        NA  17       62242   O5     2 #> 7401       2        NA  16       62244   A1     1 #> 7402       2        NA  16       62244   A2     6 #> 7403       2        NA  16       62244   A3     6 #> 7404       2        NA  16       62244   A4     1 #> 7405       2        NA  16       62244   A5     5 #> 7406       2        NA  16       62244   C1     5 #> 7407       2        NA  16       62244   C2     6 #> 7408       2        NA  16       62244   C3     6 #> 7409       2        NA  16       62244   C4     5 #> 7410       2        NA  16       62244   C5     6 #> 7411       2        NA  16       62244   E1     1 #> 7412       2        NA  16       62244   E2     3 #> 7413       2        NA  16       62244   E3     6 #> 7414       2        NA  16       62244   E4     5 #> 7415       2        NA  16       62244   E5     5 #> 7416       2        NA  16       62244   N1     4 #> 7417       2        NA  16       62244   N2     4 #> 7418       2        NA  16       62244   N3     1 #> 7419       2        NA  16       62244   N4     2 #> 7420       2        NA  16       62244   N5     2 #> 7421       2        NA  16       62244   O1     5 #> 7422       2        NA  16       62244   O2     3 #> 7423       2        NA  16       62244   O3     4 #> 7424       2        NA  16       62244   O4     5 #> 7425       2        NA  16       62244   O5     5 #> 7426       1        NA  17       62245   A1     3 #> 7427       1        NA  17       62245   A2     4 #> 7428       1        NA  17       62245   A3     4 #> 7429       1        NA  17       62245   A4     4 #> 7430       1        NA  17       62245   A5     3 #> 7431       1        NA  17       62245   C1     3 #> 7432       1        NA  17       62245   C2     2 #> 7433       1        NA  17       62245   C3     2 #> 7434       1        NA  17       62245   C4     4 #> 7435       1        NA  17       62245   C5     6 #> 7436       1        NA  17       62245   E1     5 #> 7437       1        NA  17       62245   E2     3 #> 7438       1        NA  17       62245   E3     4 #> 7439       1        NA  17       62245   E4     4 #> 7440       1        NA  17       62245   E5     5 #> 7441       1        NA  17       62245   N1     4 #> 7442       1        NA  17       62245   N2     5 #> 7443       1        NA  17       62245   N3     2 #> 7444       1        NA  17       62245   N4     3 #> 7445       1        NA  17       62245   N5     1 #> 7446       1        NA  17       62245   O1     4 #> 7447       1        NA  17       62245   O2     2 #> 7448       1        NA  17       62245   O3     5 #> 7449       1        NA  17       62245   O4     5 #> 7450       1        NA  17       62245   O5     3 #> 7451       1         1  29       62246   A1     4 #> 7452       1         1  29       62246   A2     5 #> 7453       1         1  29       62246   A3     5 #> 7454       1         1  29       62246   A4     6 #> 7455       1         1  29       62246   A5     6 #> 7456       1         1  29       62246   C1     3 #> 7457       1         1  29       62246   C2     5 #> 7458       1         1  29       62246   C3     6 #> 7459       1         1  29       62246   C4     2 #> 7460       1         1  29       62246   C5     1 #> 7461       1         1  29       62246   E1     6 #> 7462       1         1  29       62246   E2     6 #> 7463       1         1  29       62246   E3     1 #> 7464       1         1  29       62246   E4     5 #> 7465       1         1  29       62246   E5     6 #> 7466       1         1  29       62246   N1     2 #> 7467       1         1  29       62246   N2     3 #> 7468       1         1  29       62246   N3     3 #> 7469       1         1  29       62246   N4     2 #> 7470       1         1  29       62246   N5     1 #> 7471       1         1  29       62246   O1     5 #> 7472       1         1  29       62246   O2     3 #> 7473       1         1  29       62246   O3     1 #> 7474       1         1  29       62246   O4     3 #> 7475       1         1  29       62246   O5     4 #> 7476       2         2  19       62252   A1     3 #> 7477       2         2  19       62252   A2     4 #> 7478       2         2  19       62252   A3     4 #> 7479       2         2  19       62252   A4     3 #> 7480       2         2  19       62252   A5     4 #> 7481       2         2  19       62252   C1     4 #> 7482       2         2  19       62252   C2     4 #> 7483       2         2  19       62252   C3     4 #> 7484       2         2  19       62252   C4     4 #> 7485       2         2  19       62252   C5     3 #> 7486       2         2  19       62252   E1     3 #> 7487       2         2  19       62252   E2     4 #> 7488       2         2  19       62252   E3     3 #> 7489       2         2  19       62252   E4     4 #> 7490       2         2  19       62252   E5     3 #> 7491       2         2  19       62252   N1     4 #> 7492       2         2  19       62252   N2     4 #> 7493       2         2  19       62252   N3     3 #> 7494       2         2  19       62252   N4     3 #> 7495       2         2  19       62252   N5     3 #> 7496       2         2  19       62252   O1     4 #> 7497       2         2  19       62252   O2     3 #> 7498       2         2  19       62252   O3     4 #> 7499       2         2  19       62252   O4     4 #> 7500       2         2  19       62252   O5     3 #> 7501       2         4  47       62259   A1     1 #> 7502       2         4  47       62259   A2     5 #> 7503       2         4  47       62259   A3     5 #> 7504       2         4  47       62259   A4     6 #> 7505       2         4  47       62259   A5     5 #> 7506       2         4  47       62259   C1     5 #> 7507       2         4  47       62259   C2     6 #> 7508       2         4  47       62259   C3     2 #> 7509       2         4  47       62259   C4     1 #> 7510       2         4  47       62259   C5     5 #> 7511       2         4  47       62259   E1     3 #> 7512       2         4  47       62259   E2     3 #> 7513       2         4  47       62259   E3     5 #> 7514       2         4  47       62259   E4     6 #> 7515       2         4  47       62259   E5     4 #> 7516       2         4  47       62259   N1     2 #> 7517       2         4  47       62259   N2     4 #> 7518       2         4  47       62259   N3     5 #> 7519       2         4  47       62259   N4     4 #> 7520       2         4  47       62259   N5     4 #> 7521       2         4  47       62259   O1     6 #> 7522       2         4  47       62259   O2     1 #> 7523       2         4  47       62259   O3     6 #> 7524       2         4  47       62259   O4     6 #> 7525       2         4  47       62259   O5     1 #> 7526       2         4  52       62260   A1     1 #> 7527       2         4  52       62260   A2     5 #> 7528       2         4  52       62260   A3     5 #> 7529       2         4  52       62260   A4     5 #> 7530       2         4  52       62260   A5     4 #> 7531       2         4  52       62260   C1     4 #> 7532       2         4  52       62260   C2     4 #> 7533       2         4  52       62260   C3     4 #> 7534       2         4  52       62260   C4     2 #> 7535       2         4  52       62260   C5     2 #> 7536       2         4  52       62260   E1     4 #> 7537       2         4  52       62260   E2     4 #> 7538       2         4  52       62260   E3     4 #> 7539       2         4  52       62260   E4     2 #> 7540       2         4  52       62260   E5     5 #> 7541       2         4  52       62260   N1     1 #> 7542       2         4  52       62260   N2     2 #> 7543       2         4  52       62260   N3     1 #> 7544       2         4  52       62260   N4     1 #> 7545       2         4  52       62260   N5     2 #> 7546       2         4  52       62260   O1     4 #> 7547       2         4  52       62260   O2     2 #> 7548       2         4  52       62260   O3     5 #> 7549       2         4  52       62260   O4     6 #> 7550       2         4  52       62260   O5     2 #> 7551       2         4  22       62261   A1     2 #> 7552       2         4  22       62261   A2     6 #> 7553       2         4  22       62261   A3     6 #> 7554       2         4  22       62261   A4     5 #> 7555       2         4  22       62261   A5     5 #> 7556       2         4  22       62261   C1     5 #> 7557       2         4  22       62261   C2     2 #> 7558       2         4  22       62261   C3     6 #> 7559       2         4  22       62261   C4     5 #> 7560       2         4  22       62261   C5     5 #> 7561       2         4  22       62261   E1     6 #> 7562       2         4  22       62261   E2     5 #> 7563       2         4  22       62261   E3     3 #> 7564       2         4  22       62261   E4     4 #> 7565       2         4  22       62261   E5     2 #> 7566       2         4  22       62261   N1     2 #> 7567       2         4  22       62261   N2     2 #> 7568       2         4  22       62261   N3     2 #> 7569       2         4  22       62261   N4     4 #> 7570       2         4  22       62261   N5     2 #> 7571       2         4  22       62261   O1     4 #> 7572       2         4  22       62261   O2     5 #> 7573       2         4  22       62261   O3     3 #> 7574       2         4  22       62261   O4     4 #> 7575       2         4  22       62261   O5     3 #> 7576       1         3  18       62263   A1     3 #> 7577       1         3  18       62263   A2     5 #> 7578       1         3  18       62263   A3     6 #> 7579       1         3  18       62263   A4     4 #> 7580       1         3  18       62263   A5     5 #> 7581       1         3  18       62263   C1     4 #> 7582       1         3  18       62263   C2     3 #> 7583       1         3  18       62263   C3     5 #> 7584       1         3  18       62263   C4     3 #> 7585       1         3  18       62263   C5     4 #> 7586       1         3  18       62263   E1     1 #> 7587       1         3  18       62263   E2     2 #> 7588       1         3  18       62263   E3     4 #> 7589       1         3  18       62263   E4     5 #> 7590       1         3  18       62263   E5     4 #> 7591       1         3  18       62263   N1     2 #> 7592       1         3  18       62263   N2     3 #> 7593       1         3  18       62263   N3     5 #> 7594       1         3  18       62263   N4     4 #> 7595       1         3  18       62263   N5     3 #> 7596       1         3  18       62263   O1     4 #> 7597       1         3  18       62263   O2     3 #> 7598       1         3  18       62263   O3     5 #> 7599       1         3  18       62263   O4     6 #> 7600       1         3  18       62263   O5     5 #> 7601       2         3  30       62264   A1     4 #> 7602       2         3  30       62264   A2     5 #> 7603       2         3  30       62264   A3     4 #> 7604       2         3  30       62264   A4     5 #> 7605       2         3  30       62264   A5     4 #> 7606       2         3  30       62264   C1     5 #> 7607       2         3  30       62264   C2     5 #> 7608       2         3  30       62264   C3     4 #> 7609       2         3  30       62264   C4     1 #> 7610       2         3  30       62264   C5     1 #> 7611       2         3  30       62264   E1     2 #> 7612       2         3  30       62264   E2     2 #> 7613       2         3  30       62264   E3     4 #> 7614       2         3  30       62264   E4     5 #> 7615       2         3  30       62264   E5     5 #> 7616       2         3  30       62264   N1     3 #> 7617       2         3  30       62264   N2     4 #> 7618       2         3  30       62264   N3     4 #> 7619       2         3  30       62264   N4     4 #> 7620       2         3  30       62264   N5     4 #> 7621       2         3  30       62264   O1     5 #> 7622       2         3  30       62264   O2     1 #> 7623       2         3  30       62264   O3     5 #> 7624       2         3  30       62264   O4     4 #> 7625       2         3  30       62264   O5     1 #> 7626       2        NA  17       62265   A1     2 #> 7627       2        NA  17       62265   A2     6 #> 7628       2        NA  17       62265   A3     4 #> 7629       2        NA  17       62265   A4     2 #> 7630       2        NA  17       62265   A5     5 #> 7631       2        NA  17       62265   C1     5 #> 7632       2        NA  17       62265   C2     5 #> 7633       2        NA  17       62265   C3     4 #> 7634       2        NA  17       62265   C4     2 #> 7635       2        NA  17       62265   C5     4 #> 7636       2        NA  17       62265   E1     2 #> 7637       2        NA  17       62265   E2     4 #> 7638       2        NA  17       62265   E3     3 #> 7639       2        NA  17       62265   E4     2 #> 7640       2        NA  17       62265   E5     6 #> 7641       2        NA  17       62265   N1     2 #> 7642       2        NA  17       62265   N2     5 #> 7643       2        NA  17       62265   N3     6 #> 7644       2        NA  17       62265   N4     5 #> 7645       2        NA  17       62265   N5     2 #> 7646       2        NA  17       62265   O1     6 #> 7647       2        NA  17       62265   O2     1 #> 7648       2        NA  17       62265   O3     6 #> 7649       2        NA  17       62265   O4    NA #> 7650       2        NA  17       62265   O5     1 #> 7651       1         3  31       62266   A1     5 #> 7652       1         3  31       62266   A2     2 #> 7653       1         3  31       62266   A3     3 #> 7654       1         3  31       62266   A4     2 #> 7655       1         3  31       62266   A5     3 #> 7656       1         3  31       62266   C1     5 #> 7657       1         3  31       62266   C2     4 #> 7658       1         3  31       62266   C3     3 #> 7659       1         3  31       62266   C4     2 #> 7660       1         3  31       62266   C5     3 #> 7661       1         3  31       62266   E1     4 #> 7662       1         3  31       62266   E2     5 #> 7663       1         3  31       62266   E3     4 #> 7664       1         3  31       62266   E4     4 #> 7665       1         3  31       62266   E5     1 #> 7666       1         3  31       62266   N1     2 #> 7667       1         3  31       62266   N2     4 #> 7668       1         3  31       62266   N3     5 #> 7669       1         3  31       62266   N4     5 #> 7670       1         3  31       62266   N5     4 #> 7671       1         3  31       62266   O1     5 #> 7672       1         3  31       62266   O2     1 #> 7673       1         3  31       62266   O3     5 #> 7674       1         3  31       62266   O4     6 #> 7675       1         3  31       62266   O5     3 #> 7676       1         2  56       62267   A1     1 #> 7677       1         2  56       62267   A2     6 #> 7678       1         2  56       62267   A3     5 #> 7679       1         2  56       62267   A4     6 #> 7680       1         2  56       62267   A5     5 #> 7681       1         2  56       62267   C1     5 #> 7682       1         2  56       62267   C2     6 #> 7683       1         2  56       62267   C3     6 #> 7684       1         2  56       62267   C4     2 #> 7685       1         2  56       62267   C5     1 #> 7686       1         2  56       62267   E1     5 #> 7687       1         2  56       62267   E2     2 #> 7688       1         2  56       62267   E3     5 #> 7689       1         2  56       62267   E4     1 #> 7690       1         2  56       62267   E5     6 #> 7691       1         2  56       62267   N1     5 #> 7692       1         2  56       62267   N2     4 #> 7693       1         2  56       62267   N3     5 #> 7694       1         2  56       62267   N4     4 #> 7695       1         2  56       62267   N5     2 #> 7696       1         2  56       62267   O1     6 #> 7697       1         2  56       62267   O2     5 #> 7698       1         2  56       62267   O3     3 #> 7699       1         2  56       62267   O4     6 #> 7700       1         2  56       62267   O5     5 #> 7701       2         4  28       62272   A1     3 #> 7702       2         4  28       62272   A2     4 #> 7703       2         4  28       62272   A3     4 #> 7704       2         4  28       62272   A4     4 #> 7705       2         4  28       62272   A5     4 #> 7706       2         4  28       62272   C1     6 #> 7707       2         4  28       62272   C2     6 #> 7708       2         4  28       62272   C3     5 #> 7709       2         4  28       62272   C4     1 #> 7710       2         4  28       62272   C5     2 #> 7711       2         4  28       62272   E1     2 #> 7712       2         4  28       62272   E2     2 #> 7713       2         4  28       62272   E3     5 #> 7714       2         4  28       62272   E4     5 #> 7715       2         4  28       62272   E5     6 #> 7716       2         4  28       62272   N1     2 #> 7717       2         4  28       62272   N2     2 #> 7718       2         4  28       62272   N3     2 #> 7719       2         4  28       62272   N4     2 #> 7720       2         4  28       62272   N5     4 #> 7721       2         4  28       62272   O1     5 #> 7722       2         4  28       62272   O2     6 #> 7723       2         4  28       62272   O3     6 #> 7724       2         4  28       62272   O4     6 #> 7725       2         4  28       62272   O5     4 #> 7726       2         4  33       62276   A1     1 #> 7727       2         4  33       62276   A2     6 #> 7728       2         4  33       62276   A3     6 #> 7729       2         4  33       62276   A4     4 #> 7730       2         4  33       62276   A5     6 #> 7731       2         4  33       62276   C1     4 #> 7732       2         4  33       62276   C2     5 #> 7733       2         4  33       62276   C3     5 #> 7734       2         4  33       62276   C4     2 #> 7735       2         4  33       62276   C5     3 #> 7736       2         4  33       62276   E1     1 #> 7737       2         4  33       62276   E2     1 #> 7738       2         4  33       62276   E3     6 #> 7739       2         4  33       62276   E4     6 #> 7740       2         4  33       62276   E5     5 #> 7741       2         4  33       62276   N1     3 #> 7742       2         4  33       62276   N2     5 #> 7743       2         4  33       62276   N3     5 #> 7744       2         4  33       62276   N4     2 #> 7745       2         4  33       62276   N5     2 #> 7746       2         4  33       62276   O1     5 #> 7747       2         4  33       62276   O2     5 #> 7748       2         4  33       62276   O3     6 #> 7749       2         4  33       62276   O4     6 #> 7750       2         4  33       62276   O5    NA #> 7751       2         3  30       62278   A1     3 #> 7752       2         3  30       62278   A2     5 #> 7753       2         3  30       62278   A3     6 #> 7754       2         3  30       62278   A4     6 #> 7755       2         3  30       62278   A5     6 #> 7756       2         3  30       62278   C1     3 #> 7757       2         3  30       62278   C2     5 #> 7758       2         3  30       62278   C3     3 #> 7759       2         3  30       62278   C4     2 #> 7760       2         3  30       62278   C5     2 #> 7761       2         3  30       62278   E1     2 #> 7762       2         3  30       62278   E2     4 #> 7763       2         3  30       62278   E3     4 #> 7764       2         3  30       62278   E4     4 #> 7765       2         3  30       62278   E5     4 #> 7766       2         3  30       62278   N1     2 #> 7767       2         3  30       62278   N2     3 #> 7768       2         3  30       62278   N3     1 #> 7769       2         3  30       62278   N4     2 #> 7770       2         3  30       62278   N5     3 #> 7771       2         3  30       62278   O1     5 #> 7772       2         3  30       62278   O2     5 #> 7773       2         3  30       62278   O3     4 #> 7774       2         3  30       62278   O4     5 #> 7775       2         3  30       62278   O5    NA #> 7776       2         3  23       62279   A1     1 #> 7777       2         3  23       62279   A2     6 #> 7778       2         3  23       62279   A3     5 #> 7779       2         3  23       62279   A4     4 #> 7780       2         3  23       62279   A5     5 #> 7781       2         3  23       62279   C1     6 #> 7782       2         3  23       62279   C2     5 #> 7783       2         3  23       62279   C3     5 #> 7784       2         3  23       62279   C4     1 #> 7785       2         3  23       62279   C5     2 #> 7786       2         3  23       62279   E1     1 #> 7787       2         3  23       62279   E2     4 #> 7788       2         3  23       62279   E3     1 #> 7789       2         3  23       62279   E4     5 #> 7790       2         3  23       62279   E5     4 #> 7791       2         3  23       62279   N1     2 #> 7792       2         3  23       62279   N2     4 #> 7793       2         3  23       62279   N3     4 #> 7794       2         3  23       62279   N4     2 #> 7795       2         3  23       62279   N5     6 #> 7796       2         3  23       62279   O1     2 #> 7797       2         3  23       62279   O2     1 #> 7798       2         3  23       62279   O3     4 #> 7799       2         3  23       62279   O4     6 #> 7800       2         3  23       62279   O5     4 #> 7801       2         3  20       62280   A1     2 #> 7802       2         3  20       62280   A2     5 #> 7803       2         3  20       62280   A3     5 #> 7804       2         3  20       62280   A4     6 #> 7805       2         3  20       62280   A5     5 #> 7806       2         3  20       62280   C1     4 #> 7807       2         3  20       62280   C2     4 #> 7808       2         3  20       62280   C3     4 #> 7809       2         3  20       62280   C4     1 #> 7810       2         3  20       62280   C5     2 #> 7811       2         3  20       62280   E1     3 #> 7812       2         3  20       62280   E2     2 #> 7813       2         3  20       62280   E3     4 #> 7814       2         3  20       62280   E4     5 #> 7815       2         3  20       62280   E5     5 #> 7816       2         3  20       62280   N1     1 #> 7817       2         3  20       62280   N2     1 #> 7818       2         3  20       62280   N3     1 #> 7819       2         3  20       62280   N4     2 #> 7820       2         3  20       62280   N5     1 #> 7821       2         3  20       62280   O1     5 #> 7822       2         3  20       62280   O2     2 #> 7823       2         3  20       62280   O3     4 #> 7824       2         3  20       62280   O4     5 #> 7825       2         3  20       62280   O5     2 #> 7826       2         2  27       62281   A1     1 #> 7827       2         2  27       62281   A2     6 #> 7828       2         2  27       62281   A3     6 #> 7829       2         2  27       62281   A4     6 #> 7830       2         2  27       62281   A5     6 #> 7831       2         2  27       62281   C1     5 #> 7832       2         2  27       62281   C2     4 #> 7833       2         2  27       62281   C3     6 #> 7834       2         2  27       62281   C4     1 #> 7835       2         2  27       62281   C5     2 #> 7836       2         2  27       62281   E1     1 #> 7837       2         2  27       62281   E2     2 #> 7838       2         2  27       62281   E3     6 #> 7839       2         2  27       62281   E4     6 #> 7840       2         2  27       62281   E5     6 #> 7841       2         2  27       62281   N1     1 #> 7842       2         2  27       62281   N2     4 #> 7843       2         2  27       62281   N3     4 #> 7844       2         2  27       62281   N4     1 #> 7845       2         2  27       62281   N5     1 #> 7846       2         2  27       62281   O1     5 #> 7847       2         2  27       62281   O2     4 #> 7848       2         2  27       62281   O3     6 #> 7849       2         2  27       62281   O4     4 #> 7850       2         2  27       62281   O5     4 #> 7851       1         2  25       62282   A1     3 #> 7852       1         2  25       62282   A2     5 #> 7853       1         2  25       62282   A3     5 #> 7854       1         2  25       62282   A4     6 #> 7855       1         2  25       62282   A5     1 #> 7856       1         2  25       62282   C1     6 #> 7857       1         2  25       62282   C2     5 #> 7858       1         2  25       62282   C3     5 #> 7859       1         2  25       62282   C4     4 #> 7860       1         2  25       62282   C5     6 #> 7861       1         2  25       62282   E1     1 #> 7862       1         2  25       62282   E2     1 #> 7863       1         2  25       62282   E3     6 #> 7864       1         2  25       62282   E4     3 #> 7865       1         2  25       62282   E5     6 #> 7866       1         2  25       62282   N1     6 #> 7867       1         2  25       62282   N2     6 #> 7868       1         2  25       62282   N3     6 #> 7869       1         2  25       62282   N4     5 #> 7870       1         2  25       62282   N5     1 #> 7871       1         2  25       62282   O1     6 #> 7872       1         2  25       62282   O2     1 #> 7873       1         2  25       62282   O3     6 #> 7874       1         2  25       62282   O4     3 #> 7875       1         2  25       62282   O5     1 #> 7876       2         2  27       62287   A1     2 #> 7877       2         2  27       62287   A2     4 #> 7878       2         2  27       62287   A3     4 #> 7879       2         2  27       62287   A4     6 #> 7880       2         2  27       62287   A5     2 #> 7881       2         2  27       62287   C1     5 #> 7882       2         2  27       62287   C2     5 #> 7883       2         2  27       62287   C3     5 #> 7884       2         2  27       62287   C4     2 #> 7885       2         2  27       62287   C5     1 #> 7886       2         2  27       62287   E1     3 #> 7887       2         2  27       62287   E2     6 #> 7888       2         2  27       62287   E3     5 #> 7889       2         2  27       62287   E4     4 #> 7890       2         2  27       62287   E5     5 #> 7891       2         2  27       62287   N1     5 #> 7892       2         2  27       62287   N2     5 #> 7893       2         2  27       62287   N3     6 #> 7894       2         2  27       62287   N4     5 #> 7895       2         2  27       62287   N5     5 #> 7896       2         2  27       62287   O1     5 #> 7897       2         2  27       62287   O2     2 #> 7898       2         2  27       62287   O3     4 #> 7899       2         2  27       62287   O4     6 #> 7900       2         2  27       62287   O5     1 #> 7901       2        NA  56       62288   A1     3 #> 7902       2        NA  56       62288   A2     5 #> 7903       2        NA  56       62288   A3     6 #> 7904       2        NA  56       62288   A4     4 #> 7905       2        NA  56       62288   A5     6 #> 7906       2        NA  56       62288   C1     2 #> 7907       2        NA  56       62288   C2     1 #> 7908       2        NA  56       62288   C3     5 #> 7909       2        NA  56       62288   C4     6 #> 7910       2        NA  56       62288   C5     6 #> 7911       2        NA  56       62288   E1     1 #> 7912       2        NA  56       62288   E2     1 #> 7913       2        NA  56       62288   E3     5 #> 7914       2        NA  56       62288   E4     5 #> 7915       2        NA  56       62288   E5     1 #> 7916       2        NA  56       62288   N1     1 #> 7917       2        NA  56       62288   N2     1 #> 7918       2        NA  56       62288   N3     3 #> 7919       2        NA  56       62288   N4     6 #> 7920       2        NA  56       62288   N5     4 #> 7921       2        NA  56       62288   O1     6 #> 7922       2        NA  56       62288   O2     2 #> 7923       2        NA  56       62288   O3     2 #> 7924       2        NA  56       62288   O4     4 #> 7925       2        NA  56       62288   O5     1 #> 7926       2         3  18       62289   A1     3 #> 7927       2         3  18       62289   A2     5 #> 7928       2         3  18       62289   A3     4 #> 7929       2         3  18       62289   A4     2 #> 7930       2         3  18       62289   A5     2 #> 7931       2         3  18       62289   C1     4 #> 7932       2         3  18       62289   C2     4 #> 7933       2         3  18       62289   C3     4 #> 7934       2         3  18       62289   C4     2 #> 7935       2         3  18       62289   C5     2 #> 7936       2         3  18       62289   E1     4 #> 7937       2         3  18       62289   E2     4 #> 7938       2         3  18       62289   E3     2 #> 7939       2         3  18       62289   E4     4 #> 7940       2         3  18       62289   E5     4 #> 7941       2         3  18       62289   N1     5 #> 7942       2         3  18       62289   N2     6 #> 7943       2         3  18       62289   N3     6 #> 7944       2         3  18       62289   N4     6 #> 7945       2         3  18       62289   N5     6 #> 7946       2         3  18       62289   O1     5 #> 7947       2         3  18       62289   O2     1 #> 7948       2         3  18       62289   O3     3 #> 7949       2         3  18       62289   O4     6 #> 7950       2         3  18       62289   O5     2 #> 7951       1         1  18       62290   A1     2 #> 7952       1         1  18       62290   A2     5 #> 7953       1         1  18       62290   A3     3 #> 7954       1         1  18       62290   A4     2 #> 7955       1         1  18       62290   A5     2 #> 7956       1         1  18       62290   C1     5 #> 7957       1         1  18       62290   C2     3 #> 7958       1         1  18       62290   C3     4 #> 7959       1         1  18       62290   C4     4 #> 7960       1         1  18       62290   C5     4 #> 7961       1         1  18       62290   E1     2 #> 7962       1         1  18       62290   E2     2 #> 7963       1         1  18       62290   E3     4 #> 7964       1         1  18       62290   E4     4 #> 7965       1         1  18       62290   E5     5 #> 7966       1         1  18       62290   N1     2 #> 7967       1         1  18       62290   N2     2 #> 7968       1         1  18       62290   N3     4 #> 7969       1         1  18       62290   N4     2 #> 7970       1         1  18       62290   N5     2 #> 7971       1         1  18       62290   O1     4 #> 7972       1         1  18       62290   O2     2 #> 7973       1         1  18       62290   O3     5 #> 7974       1         1  18       62290   O4     6 #> 7975       1         1  18       62290   O5     1 #> 7976       1         3  21       62293   A1     2 #> 7977       1         3  21       62293   A2     6 #> 7978       1         3  21       62293   A3     6 #> 7979       1         3  21       62293   A4     6 #> 7980       1         3  21       62293   A5     6 #> 7981       1         3  21       62293   C1     4 #> 7982       1         3  21       62293   C2     5 #> 7983       1         3  21       62293   C3     5 #> 7984       1         3  21       62293   C4     1 #> 7985       1         3  21       62293   C5     2 #> 7986       1         3  21       62293   E1     2 #> 7987       1         3  21       62293   E2     2 #> 7988       1         3  21       62293   E3     3 #> 7989       1         3  21       62293   E4     5 #> 7990       1         3  21       62293   E5     6 #> 7991       1         3  21       62293   N1     2 #> 7992       1         3  21       62293   N2     3 #> 7993       1         3  21       62293   N3     3 #> 7994       1         3  21       62293   N4     1 #> 7995       1         3  21       62293   N5     1 #> 7996       1         3  21       62293   O1     5 #> 7997       1         3  21       62293   O2     2 #> 7998       1         3  21       62293   O3     3 #> 7999       1         3  21       62293   O4     2 #> 8000       1         3  21       62293   O5     1 #> 8001       1         2  19       62295   A1     2 #> 8002       1         2  19       62295   A2     2 #> 8003       1         2  19       62295   A3     6 #> 8004       1         2  19       62295   A4     2 #> 8005       1         2  19       62295   A5     2 #> 8006       1         2  19       62295   C1     6 #> 8007       1         2  19       62295   C2     6 #> 8008       1         2  19       62295   C3     6 #> 8009       1         2  19       62295   C4     1 #> 8010       1         2  19       62295   C5     2 #> 8011       1         2  19       62295   E1     6 #> 8012       1         2  19       62295   E2     2 #> 8013       1         2  19       62295   E3     6 #> 8014       1         2  19       62295   E4     1 #> 8015       1         2  19       62295   E5     6 #> 8016       1         2  19       62295   N1     5 #> 8017       1         2  19       62295   N2     5 #> 8018       1         2  19       62295   N3     5 #> 8019       1         2  19       62295   N4     5 #> 8020       1         2  19       62295   N5     1 #> 8021       1         2  19       62295   O1     6 #> 8022       1         2  19       62295   O2     1 #> 8023       1         2  19       62295   O3     6 #> 8024       1         2  19       62295   O4     6 #> 8025       1         2  19       62295   O5     1 #> 8026       2         2  18       62296   A1     2 #> 8027       2         2  18       62296   A2     6 #> 8028       2         2  18       62296   A3     6 #> 8029       2         2  18       62296   A4     6 #> 8030       2         2  18       62296   A5     6 #> 8031       2         2  18       62296   C1     4 #> 8032       2         2  18       62296   C2     5 #> 8033       2         2  18       62296   C3     4 #> 8034       2         2  18       62296   C4     2 #> 8035       2         2  18       62296   C5     2 #> 8036       2         2  18       62296   E1     1 #> 8037       2         2  18       62296   E2     1 #> 8038       2         2  18       62296   E3     5 #> 8039       2         2  18       62296   E4     6 #> 8040       2         2  18       62296   E5     5 #> 8041       2         2  18       62296   N1     2 #> 8042       2         2  18       62296   N2     2 #> 8043       2         2  18       62296   N3     2 #> 8044       2         2  18       62296   N4     1 #> 8045       2         2  18       62296   N5     2 #> 8046       2         2  18       62296   O1     4 #> 8047       2         2  18       62296   O2     1 #> 8048       2         2  18       62296   O3     5 #> 8049       2         2  18       62296   O4     5 #> 8050       2         2  18       62296   O5     4 #> 8051       1         4  29       62298   A1     5 #> 8052       1         4  29       62298   A2     2 #> 8053       1         4  29       62298   A3     2 #> 8054       1         4  29       62298   A4     2 #> 8055       1         4  29       62298   A5     5 #> 8056       1         4  29       62298   C1     5 #> 8057       1         4  29       62298   C2     5 #> 8058       1         4  29       62298   C3     6 #> 8059       1         4  29       62298   C4     2 #> 8060       1         4  29       62298   C5     5 #> 8061       1         4  29       62298   E1     4 #> 8062       1         4  29       62298   E2     5 #> 8063       1         4  29       62298   E3     3 #> 8064       1         4  29       62298   E4     3 #> 8065       1         4  29       62298   E5     3 #> 8066       1         4  29       62298   N1     5 #> 8067       1         4  29       62298   N2     5 #> 8068       1         4  29       62298   N3     5 #> 8069       1         4  29       62298   N4     6 #> 8070       1         4  29       62298   N5     5 #> 8071       1         4  29       62298   O1     5 #> 8072       1         4  29       62298   O2     1 #> 8073       1         4  29       62298   O3     5 #> 8074       1         4  29       62298   O4     6 #> 8075       1         4  29       62298   O5     1 #> 8076       1         4  33       62299   A1     4 #> 8077       1         4  33       62299   A2     4 #> 8078       1         4  33       62299   A3     4 #> 8079       1         4  33       62299   A4     4 #> 8080       1         4  33       62299   A5     4 #> 8081       1         4  33       62299   C1     5 #> 8082       1         4  33       62299   C2     5 #> 8083       1         4  33       62299   C3     5 #> 8084       1         4  33       62299   C4     5 #> 8085       1         4  33       62299   C5     5 #> 8086       1         4  33       62299   E1     3 #> 8087       1         4  33       62299   E2     3 #> 8088       1         4  33       62299   E3     3 #> 8089       1         4  33       62299   E4     3 #> 8090       1         4  33       62299   E5     3 #> 8091       1         4  33       62299   N1     4 #> 8092       1         4  33       62299   N2     4 #> 8093       1         4  33       62299   N3     4 #> 8094       1         4  33       62299   N4     4 #> 8095       1         4  33       62299   N5     4 #> 8096       1         4  33       62299   O1     3 #> 8097       1         4  33       62299   O2     3 #> 8098       1         4  33       62299   O3     3 #> 8099       1         4  33       62299   O4     3 #> 8100       1         4  33       62299   O5     3 #> 8101       1         3  31       62300   A1     3 #> 8102       1         3  31       62300   A2     3 #> 8103       1         3  31       62300   A3     4 #> 8104       1         3  31       62300   A4     6 #> 8105       1         3  31       62300   A5     3 #> 8106       1         3  31       62300   C1     4 #> 8107       1         3  31       62300   C2     4 #> 8108       1         3  31       62300   C3     3 #> 8109       1         3  31       62300   C4     4 #> 8110       1         3  31       62300   C5     4 #> 8111       1         3  31       62300   E1     5 #> 8112       1         3  31       62300   E2     5 #> 8113       1         3  31       62300   E3     2 #> 8114       1         3  31       62300   E4     3 #> 8115       1         3  31       62300   E5     5 #> 8116       1         3  31       62300   N1     4 #> 8117       1         3  31       62300   N2     5 #> 8118       1         3  31       62300   N3     4 #> 8119       1         3  31       62300   N4     4 #> 8120       1         3  31       62300   N5     2 #> 8121       1         3  31       62300   O1     5 #> 8122       1         3  31       62300   O2     2 #> 8123       1         3  31       62300   O3     3 #> 8124       1         3  31       62300   O4     4 #> 8125       1         3  31       62300   O5     3 #> 8126       2         5  33       62301   A1     1 #> 8127       2         5  33       62301   A2     6 #> 8128       2         5  33       62301   A3     5 #> 8129       2         5  33       62301   A4     6 #> 8130       2         5  33       62301   A5     5 #> 8131       2         5  33       62301   C1     5 #> 8132       2         5  33       62301   C2     5 #> 8133       2         5  33       62301   C3     5 #> 8134       2         5  33       62301   C4     2 #> 8135       2         5  33       62301   C5     4 #> 8136       2         5  33       62301   E1     2 #> 8137       2         5  33       62301   E2     2 #> 8138       2         5  33       62301   E3     5 #> 8139       2         5  33       62301   E4     5 #> 8140       2         5  33       62301   E5     5 #> 8141       2         5  33       62301   N1     2 #> 8142       2         5  33       62301   N2     4 #> 8143       2         5  33       62301   N3     2 #> 8144       2         5  33       62301   N4     4 #> 8145       2         5  33       62301   N5     1 #> 8146       2         5  33       62301   O1     5 #> 8147       2         5  33       62301   O2     1 #> 8148       2         5  33       62301   O3     6 #> 8149       2         5  33       62301   O4     6 #> 8150       2         5  33       62301   O5     1 #> 8151       2         5  28       62303   A1     2 #> 8152       2         5  28       62303   A2    NA #> 8153       2         5  28       62303   A3     5 #> 8154       2         5  28       62303   A4     5 #> 8155       2         5  28       62303   A5     2 #> 8156       2         5  28       62303   C1     5 #> 8157       2         5  28       62303   C2     5 #> 8158       2         5  28       62303   C3     6 #> 8159       2         5  28       62303   C4     1 #> 8160       2         5  28       62303   C5     2 #> 8161       2         5  28       62303   E1     6 #> 8162       2         5  28       62303   E2     5 #> 8163       2         5  28       62303   E3     2 #> 8164       2         5  28       62303   E4     4 #> 8165       2         5  28       62303   E5     5 #> 8166       2         5  28       62303   N1     2 #> 8167       2         5  28       62303   N2     2 #> 8168       2         5  28       62303   N3     4 #> 8169       2         5  28       62303   N4     2 #> 8170       2         5  28       62303   N5     4 #> 8171       2         5  28       62303   O1     4 #> 8172       2         5  28       62303   O2     1 #> 8173       2         5  28       62303   O3    NA #> 8174       2         5  28       62303   O4     4 #> 8175       2         5  28       62303   O5     4 #> 8176       2         3  21       62305   A1     4 #> 8177       2         3  21       62305   A2     5 #> 8178       2         3  21       62305   A3     5 #> 8179       2         3  21       62305   A4     6 #> 8180       2         3  21       62305   A5     4 #> 8181       2         3  21       62305   C1     4 #> 8182       2         3  21       62305   C2     4 #> 8183       2         3  21       62305   C3     3 #> 8184       2         3  21       62305   C4     3 #> 8185       2         3  21       62305   C5     4 #> 8186       2         3  21       62305   E1     6 #> 8187       2         3  21       62305   E2     6 #> 8188       2         3  21       62305   E3     3 #> 8189       2         3  21       62305   E4     2 #> 8190       2         3  21       62305   E5     3 #> 8191       2         3  21       62305   N1     4 #> 8192       2         3  21       62305   N2     4 #> 8193       2         3  21       62305   N3     3 #> 8194       2         3  21       62305   N4     3 #> 8195       2         3  21       62305   N5     5 #> 8196       2         3  21       62305   O1    NA #> 8197       2         3  21       62305   O2     4 #> 8198       2         3  21       62305   O3     3 #> 8199       2         3  21       62305   O4     6 #> 8200       2         3  21       62305   O5     2 #> 8201       2         4  45       62307   A1     1 #> 8202       2         4  45       62307   A2     5 #> 8203       2         4  45       62307   A3     4 #> 8204       2         4  45       62307   A4     5 #> 8205       2         4  45       62307   A5     6 #> 8206       2         4  45       62307   C1     6 #> 8207       2         4  45       62307   C2     6 #> 8208       2         4  45       62307   C3     2 #> 8209       2         4  45       62307   C4     6 #> 8210       2         4  45       62307   C5     6 #> 8211       2         4  45       62307   E1     1 #> 8212       2         4  45       62307   E2     1 #> 8213       2         4  45       62307   E3     6 #> 8214       2         4  45       62307   E4     6 #> 8215       2         4  45       62307   E5     6 #> 8216       2         4  45       62307   N1     6 #> 8217       2         4  45       62307   N2     6 #> 8218       2         4  45       62307   N3     6 #> 8219       2         4  45       62307   N4     6 #> 8220       2         4  45       62307   N5     5 #> 8221       2         4  45       62307   O1     6 #> 8222       2         4  45       62307   O2     5 #> 8223       2         4  45       62307   O3     6 #> 8224       2         4  45       62307   O4     6 #> 8225       2         4  45       62307   O5     1 #> 8226       2         5  44       62312   A1     2 #> 8227       2         5  44       62312   A2     2 #> 8228       2         5  44       62312   A3     5 #> 8229       2         5  44       62312   A4     4 #> 8230       2         5  44       62312   A5     4 #> 8231       2         5  44       62312   C1     5 #> 8232       2         5  44       62312   C2     5 #> 8233       2         5  44       62312   C3     4 #> 8234       2         5  44       62312   C4     3 #> 8235       2         5  44       62312   C5     5 #> 8236       2         5  44       62312   E1     5 #> 8237       2         5  44       62312   E2     5 #> 8238       2         5  44       62312   E3     4 #> 8239       2         5  44       62312   E4     5 #> 8240       2         5  44       62312   E5     4 #> 8241       2         5  44       62312   N1     3 #> 8242       2         5  44       62312   N2     2 #> 8243       2         5  44       62312   N3     4 #> 8244       2         5  44       62312   N4     4 #> 8245       2         5  44       62312   N5     2 #> 8246       2         5  44       62312   O1     6 #> 8247       2         5  44       62312   O2     2 #> 8248       2         5  44       62312   O3     6 #> 8249       2         5  44       62312   O4     6 #> 8250       2         5  44       62312   O5     2 #> 8251       2         3  18       62313   A1     1 #> 8252       2         3  18       62313   A2     6 #> 8253       2         3  18       62313   A3     6 #> 8254       2         3  18       62313   A4     6 #> 8255       2         3  18       62313   A5     4 #> 8256       2         3  18       62313   C1     5 #> 8257       2         3  18       62313   C2     5 #> 8258       2         3  18       62313   C3     3 #> 8259       2         3  18       62313   C4     4 #> 8260       2         3  18       62313   C5     5 #> 8261       2         3  18       62313   E1     4 #> 8262       2         3  18       62313   E2     4 #> 8263       2         3  18       62313   E3     4 #> 8264       2         3  18       62313   E4     5 #> 8265       2         3  18       62313   E5     3 #> 8266       2         3  18       62313   N1     4 #> 8267       2         3  18       62313   N2     4 #> 8268       2         3  18       62313   N3     5 #> 8269       2         3  18       62313   N4     6 #> 8270       2         3  18       62313   N5     4 #> 8271       2         3  18       62313   O1     6 #> 8272       2         3  18       62313   O2     1 #> 8273       2         3  18       62313   O3     6 #> 8274       2         3  18       62313   O4     6 #> 8275       2         3  18       62313   O5     1 #> 8276       2         5  27       62316   A1     2 #> 8277       2         5  27       62316   A2     6 #> 8278       2         5  27       62316   A3     5 #> 8279       2         5  27       62316   A4     5 #> 8280       2         5  27       62316   A5     6 #> 8281       2         5  27       62316   C1     4 #> 8282       2         5  27       62316   C2     6 #> 8283       2         5  27       62316   C3     5 #> 8284       2         5  27       62316   C4     1 #> 8285       2         5  27       62316   C5     2 #> 8286       2         5  27       62316   E1     3 #> 8287       2         5  27       62316   E2     2 #> 8288       2         5  27       62316   E3     5 #> 8289       2         5  27       62316   E4     5 #> 8290       2         5  27       62316   E5     5 #> 8291       2         5  27       62316   N1     1 #> 8292       2         5  27       62316   N2     2 #> 8293       2         5  27       62316   N3     3 #> 8294       2         5  27       62316   N4    NA #> 8295       2         5  27       62316   N5     2 #> 8296       2         5  27       62316   O1     5 #> 8297       2         5  27       62316   O2     2 #> 8298       2         5  27       62316   O3     6 #> 8299       2         5  27       62316   O4     4 #> 8300       2         5  27       62316   O5     2 #> 8301       2         4  45       62317   A1     4 #> 8302       2         4  45       62317   A2     4 #> 8303       2         4  45       62317   A3     3 #> 8304       2         4  45       62317   A4     2 #> 8305       2         4  45       62317   A5     3 #> 8306       2         4  45       62317   C1     4 #> 8307       2         4  45       62317   C2     4 #> 8308       2         4  45       62317   C3     5 #> 8309       2         4  45       62317   C4     2 #> 8310       2         4  45       62317   C5     3 #> 8311       2         4  45       62317   E1     2 #> 8312       2         4  45       62317   E2     4 #> 8313       2         4  45       62317   E3     4 #> 8314       2         4  45       62317   E4     1 #> 8315       2         4  45       62317   E5     5 #> 8316       2         4  45       62317   N1     6 #> 8317       2         4  45       62317   N2     6 #> 8318       2         4  45       62317   N3     5 #> 8319       2         4  45       62317   N4     5 #> 8320       2         4  45       62317   N5     5 #> 8321       2         4  45       62317   O1     4 #> 8322       2         4  45       62317   O2     1 #> 8323       2         4  45       62317   O3     5 #> 8324       2         4  45       62317   O4     5 #> 8325       2         4  45       62317   O5     4 #> 8326       2         2  18       62325   A1     1 #> 8327       2         2  18       62325   A2     4 #> 8328       2         2  18       62325   A3     6 #> 8329       2         2  18       62325   A4     6 #> 8330       2         2  18       62325   A5     6 #> 8331       2         2  18       62325   C1     5 #> 8332       2         2  18       62325   C2     6 #> 8333       2         2  18       62325   C3     4 #> 8334       2         2  18       62325   C4     1 #> 8335       2         2  18       62325   C5     1 #> 8336       2         2  18       62325   E1     3 #> 8337       2         2  18       62325   E2     4 #> 8338       2         2  18       62325   E3     6 #> 8339       2         2  18       62325   E4     6 #> 8340       2         2  18       62325   E5     6 #> 8341       2         2  18       62325   N1     1 #> 8342       2         2  18       62325   N2     3 #> 8343       2         2  18       62325   N3     3 #> 8344       2         2  18       62325   N4     4 #> 8345       2         2  18       62325   N5     2 #> 8346       2         2  18       62325   O1     6 #> 8347       2         2  18       62325   O2     1 #> 8348       2         2  18       62325   O3     6 #> 8349       2         2  18       62325   O4     5 #> 8350       2         2  18       62325   O5     1 #> 8351       2         3  24       62327   A1     2 #> 8352       2         3  24       62327   A2     6 #> 8353       2         3  24       62327   A3     6 #> 8354       2         3  24       62327   A4     6 #> 8355       2         3  24       62327   A5     5 #> 8356       2         3  24       62327   C1     4 #> 8357       2         3  24       62327   C2     6 #> 8358       2         3  24       62327   C3     6 #> 8359       2         3  24       62327   C4     2 #> 8360       2         3  24       62327   C5     5 #> 8361       2         3  24       62327   E1     6 #> 8362       2         3  24       62327   E2     6 #> 8363       2         3  24       62327   E3     2 #> 8364       2         3  24       62327   E4     3 #> 8365       2         3  24       62327   E5     4 #> 8366       2         3  24       62327   N1     5 #> 8367       2         3  24       62327   N2    NA #> 8368       2         3  24       62327   N3     5 #> 8369       2         3  24       62327   N4     5 #> 8370       2         3  24       62327   N5     6 #> 8371       2         3  24       62327   O1     6 #> 8372       2         3  24       62327   O2     4 #> 8373       2         3  24       62327   O3     1 #> 8374       2         3  24       62327   O4     6 #> 8375       2         3  24       62327   O5     4 #> 8376       2         2  48       62328   A1     3 #> 8377       2         2  48       62328   A2     2 #> 8378       2         2  48       62328   A3     6 #> 8379       2         2  48       62328   A4     1 #> 8380       2         2  48       62328   A5     6 #> 8381       2         2  48       62328   C1     3 #> 8382       2         2  48       62328   C2     1 #> 8383       2         2  48       62328   C3     5 #> 8384       2         2  48       62328   C4     2 #> 8385       2         2  48       62328   C5     5 #> 8386       2         2  48       62328   E1     3 #> 8387       2         2  48       62328   E2     5 #> 8388       2         2  48       62328   E3     2 #> 8389       2         2  48       62328   E4     4 #> 8390       2         2  48       62328   E5     5 #> 8391       2         2  48       62328   N1     5 #> 8392       2         2  48       62328   N2     6 #> 8393       2         2  48       62328   N3     6 #> 8394       2         2  48       62328   N4     6 #> 8395       2         2  48       62328   N5     5 #> 8396       2         2  48       62328   O1    NA #> 8397       2         2  48       62328   O2     2 #> 8398       2         2  48       62328   O3     1 #> 8399       2         2  48       62328   O4     6 #> 8400       2         2  48       62328   O5     1 #> 8401       2         3  29       62330   A1     3 #> 8402       2         3  29       62330   A2     5 #> 8403       2         3  29       62330   A3     5 #> 8404       2         3  29       62330   A4     5 #> 8405       2         3  29       62330   A5     5 #> 8406       2         3  29       62330   C1     3 #> 8407       2         3  29       62330   C2     5 #> 8408       2         3  29       62330   C3     5 #> 8409       2         3  29       62330   C4     2 #> 8410       2         3  29       62330   C5     1 #> 8411       2         3  29       62330   E1     1 #> 8412       2         3  29       62330   E2     1 #> 8413       2         3  29       62330   E3     5 #> 8414       2         3  29       62330   E4     6 #> 8415       2         3  29       62330   E5     5 #> 8416       2         3  29       62330   N1     6 #> 8417       2         3  29       62330   N2     6 #> 8418       2         3  29       62330   N3     5 #> 8419       2         3  29       62330   N4     5 #> 8420       2         3  29       62330   N5     3 #> 8421       2         3  29       62330   O1     5 #> 8422       2         3  29       62330   O2     5 #> 8423       2         3  29       62330   O3     6 #> 8424       2         3  29       62330   O4     6 #> 8425       2         3  29       62330   O5     4 #> 8426       2         1  51       62333   A1     1 #> 8427       2         1  51       62333   A2     6 #> 8428       2         1  51       62333   A3     6 #> 8429       2         1  51       62333   A4     6 #> 8430       2         1  51       62333   A5     6 #> 8431       2         1  51       62333   C1     6 #> 8432       2         1  51       62333   C2     6 #> 8433       2         1  51       62333   C3     6 #> 8434       2         1  51       62333   C4     1 #> 8435       2         1  51       62333   C5     1 #> 8436       2         1  51       62333   E1     5 #> 8437       2         1  51       62333   E2     1 #> 8438       2         1  51       62333   E3     6 #> 8439       2         1  51       62333   E4     6 #> 8440       2         1  51       62333   E5     6 #> 8441       2         1  51       62333   N1     1 #> 8442       2         1  51       62333   N2     6 #> 8443       2         1  51       62333   N3     1 #> 8444       2         1  51       62333   N4     1 #> 8445       2         1  51       62333   N5     1 #> 8446       2         1  51       62333   O1     6 #> 8447       2         1  51       62333   O2     1 #> 8448       2         1  51       62333   O3     3 #> 8449       2         1  51       62333   O4     6 #> 8450       2         1  51       62333   O5     1 #> 8451       2         2  50       62335   A1     2 #> 8452       2         2  50       62335   A2     5 #> 8453       2         2  50       62335   A3     5 #> 8454       2         2  50       62335   A4     6 #> 8455       2         2  50       62335   A5     4 #> 8456       2         2  50       62335   C1     3 #> 8457       2         2  50       62335   C2     3 #> 8458       2         2  50       62335   C3     4 #> 8459       2         2  50       62335   C4     4 #> 8460       2         2  50       62335   C5     2 #> 8461       2         2  50       62335   E1     5 #> 8462       2         2  50       62335   E2     5 #> 8463       2         2  50       62335   E3     4 #> 8464       2         2  50       62335   E4     3 #> 8465       2         2  50       62335   E5     3 #> 8466       2         2  50       62335   N1     2 #> 8467       2         2  50       62335   N2     4 #> 8468       2         2  50       62335   N3     5 #> 8469       2         2  50       62335   N4     6 #> 8470       2         2  50       62335   N5     4 #> 8471       2         2  50       62335   O1     4 #> 8472       2         2  50       62335   O2     5 #> 8473       2         2  50       62335   O3     4 #> 8474       2         2  50       62335   O4     5 #> 8475       2         2  50       62335   O5     4 #> 8476       2         2  36       62336   A1     1 #> 8477       2         2  36       62336   A2     5 #> 8478       2         2  36       62336   A3     4 #> 8479       2         2  36       62336   A4     3 #> 8480       2         2  36       62336   A5     2 #> 8481       2         2  36       62336   C1     6 #> 8482       2         2  36       62336   C2     5 #> 8483       2         2  36       62336   C3     2 #> 8484       2         2  36       62336   C4     1 #> 8485       2         2  36       62336   C5     5 #> 8486       2         2  36       62336   E1     2 #> 8487       2         2  36       62336   E2     2 #> 8488       2         2  36       62336   E3     1 #> 8489       2         2  36       62336   E4     2 #> 8490       2         2  36       62336   E5     2 #> 8491       2         2  36       62336   N1     4 #> 8492       2         2  36       62336   N2     4 #> 8493       2         2  36       62336   N3     5 #> 8494       2         2  36       62336   N4     5 #> 8495       2         2  36       62336   N5     2 #> 8496       2         2  36       62336   O1     4 #> 8497       2         2  36       62336   O2     3 #> 8498       2         2  36       62336   O3     4 #> 8499       2         2  36       62336   O4     5 #> 8500       2         2  36       62336   O5     3 #> 8501       2        NA  18       62339   A1     3 #> 8502       2        NA  18       62339   A2     5 #> 8503       2        NA  18       62339   A3     3 #> 8504       2        NA  18       62339   A4     4 #> 8505       2        NA  18       62339   A5     4 #> 8506       2        NA  18       62339   C1     3 #> 8507       2        NA  18       62339   C2     2 #> 8508       2        NA  18       62339   C3     5 #> 8509       2        NA  18       62339   C4     4 #> 8510       2        NA  18       62339   C5     5 #> 8511       2        NA  18       62339   E1     2 #> 8512       2        NA  18       62339   E2     5 #> 8513       2        NA  18       62339   E3     3 #> 8514       2        NA  18       62339   E4     3 #> 8515       2        NA  18       62339   E5     4 #> 8516       2        NA  18       62339   N1     4 #> 8517       2        NA  18       62339   N2     5 #> 8518       2        NA  18       62339   N3     4 #> 8519       2        NA  18       62339   N4     2 #> 8520       2        NA  18       62339   N5     2 #> 8521       2        NA  18       62339   O1     4 #> 8522       2        NA  18       62339   O2     5 #> 8523       2        NA  18       62339   O3     3 #> 8524       2        NA  18       62339   O4     5 #> 8525       2        NA  18       62339   O5     4 #> 8526       2         4  52       62342   A1     1 #> 8527       2         4  52       62342   A2     5 #> 8528       2         4  52       62342   A3     5 #> 8529       2         4  52       62342   A4     5 #> 8530       2         4  52       62342   A5     5 #> 8531       2         4  52       62342   C1     4 #> 8532       2         4  52       62342   C2     1 #> 8533       2         4  52       62342   C3     2 #> 8534       2         4  52       62342   C4     6 #> 8535       2         4  52       62342   C5     3 #> 8536       2         4  52       62342   E1     2 #> 8537       2         4  52       62342   E2     1 #> 8538       2         4  52       62342   E3     5 #> 8539       2         4  52       62342   E4     3 #> 8540       2         4  52       62342   E5     6 #> 8541       2         4  52       62342   N1     6 #> 8542       2         4  52       62342   N2     5 #> 8543       2         4  52       62342   N3     2 #> 8544       2         4  52       62342   N4     4 #> 8545       2         4  52       62342   N5    NA #> 8546       2         4  52       62342   O1     6 #> 8547       2         4  52       62342   O2     1 #> 8548       2         4  52       62342   O3     6 #> 8549       2         4  52       62342   O4     6 #> 8550       2         4  52       62342   O5     1 #> 8551       2         5  38       62343   A1     2 #> 8552       2         5  38       62343   A2     4 #> 8553       2         5  38       62343   A3     2 #> 8554       2         5  38       62343   A4     6 #> 8555       2         5  38       62343   A5     5 #> 8556       2         5  38       62343   C1     5 #> 8557       2         5  38       62343   C2     4 #> 8558       2         5  38       62343   C3     4 #> 8559       2         5  38       62343   C4     6 #> 8560       2         5  38       62343   C5     5 #> 8561       2         5  38       62343   E1     5 #> 8562       2         5  38       62343   E2     6 #> 8563       2         5  38       62343   E3     3 #> 8564       2         5  38       62343   E4     3 #> 8565       2         5  38       62343   E5     5 #> 8566       2         5  38       62343   N1     4 #> 8567       2         5  38       62343   N2     4 #> 8568       2         5  38       62343   N3     4 #> 8569       2         5  38       62343   N4     3 #> 8570       2         5  38       62343   N5     4 #> 8571       2         5  38       62343   O1     3 #> 8572       2         5  38       62343   O2     2 #> 8573       2         5  38       62343   O3     2 #> 8574       2         5  38       62343   O4     2 #> 8575       2         5  38       62343   O5     4 #> 8576       2         3  17       62344   A1     1 #> 8577       2         3  17       62344   A2     6 #> 8578       2         3  17       62344   A3     5 #> 8579       2         3  17       62344   A4     6 #> 8580       2         3  17       62344   A5     5 #> 8581       2         3  17       62344   C1     5 #> 8582       2         3  17       62344   C2     6 #> 8583       2         3  17       62344   C3     4 #> 8584       2         3  17       62344   C4     2 #> 8585       2         3  17       62344   C5     4 #> 8586       2         3  17       62344   E1     1 #> 8587       2         3  17       62344   E2     1 #> 8588       2         3  17       62344   E3     5 #> 8589       2         3  17       62344   E4     6 #> 8590       2         3  17       62344   E5     6 #> 8591       2         3  17       62344   N1     2 #> 8592       2         3  17       62344   N2     2 #> 8593       2         3  17       62344   N3     4 #> 8594       2         3  17       62344   N4     2 #> 8595       2         3  17       62344   N5     2 #> 8596       2         3  17       62344   O1     4 #> 8597       2         3  17       62344   O2     5 #> 8598       2         3  17       62344   O3     6 #> 8599       2         3  17       62344   O4     5 #> 8600       2         3  17       62344   O5     1 #> 8601       2         5  53       62345   A1     2 #> 8602       2         5  53       62345   A2     4 #> 8603       2         5  53       62345   A3     4 #> 8604       2         5  53       62345   A4     6 #> 8605       2         5  53       62345   A5     5 #> 8606       2         5  53       62345   C1     4 #> 8607       2         5  53       62345   C2     4 #> 8608       2         5  53       62345   C3     4 #> 8609       2         5  53       62345   C4     3 #> 8610       2         5  53       62345   C5     4 #> 8611       2         5  53       62345   E1     1 #> 8612       2         5  53       62345   E2     1 #> 8613       2         5  53       62345   E3     5 #> 8614       2         5  53       62345   E4     5 #> 8615       2         5  53       62345   E5     6 #> 8616       2         5  53       62345   N1     2 #> 8617       2         5  53       62345   N2     4 #> 8618       2         5  53       62345   N3     3 #> 8619       2         5  53       62345   N4     3 #> 8620       2         5  53       62345   N5     1 #> 8621       2         5  53       62345   O1     6 #> 8622       2         5  53       62345   O2     1 #> 8623       2         5  53       62345   O3     6 #> 8624       2         5  53       62345   O4     4 #> 8625       2         5  53       62345   O5     2 #> 8626       2         5  35       62346   A1     1 #> 8627       2         5  35       62346   A2     5 #> 8628       2         5  35       62346   A3     5 #> 8629       2         5  35       62346   A4     5 #> 8630       2         5  35       62346   A5     5 #> 8631       2         5  35       62346   C1     5 #> 8632       2         5  35       62346   C2     4 #> 8633       2         5  35       62346   C3     5 #> 8634       2         5  35       62346   C4     3 #> 8635       2         5  35       62346   C5     3 #> 8636       2         5  35       62346   E1     3 #> 8637       2         5  35       62346   E2     2 #> 8638       2         5  35       62346   E3     5 #> 8639       2         5  35       62346   E4     5 #> 8640       2         5  35       62346   E5     5 #> 8641       2         5  35       62346   N1     5 #> 8642       2         5  35       62346   N2     5 #> 8643       2         5  35       62346   N3     4 #> 8644       2         5  35       62346   N4     4 #> 8645       2         5  35       62346   N5     3 #> 8646       2         5  35       62346   O1     6 #> 8647       2         5  35       62346   O2     4 #> 8648       2         5  35       62346   O3     6 #> 8649       2         5  35       62346   O4     6 #> 8650       2         5  35       62346   O5     2 #> 8651       2         3  21       62347   A1     2 #> 8652       2         3  21       62347   A2     4 #> 8653       2         3  21       62347   A3     5 #> 8654       2         3  21       62347   A4     6 #> 8655       2         3  21       62347   A5     5 #> 8656       2         3  21       62347   C1     4 #> 8657       2         3  21       62347   C2     3 #> 8658       2         3  21       62347   C3     3 #> 8659       2         3  21       62347   C4     5 #> 8660       2         3  21       62347   C5     5 #> 8661       2         3  21       62347   E1     5 #> 8662       2         3  21       62347   E2     4 #> 8663       2         3  21       62347   E3     5 #> 8664       2         3  21       62347   E4     3 #> 8665       2         3  21       62347   E5     1 #> 8666       2         3  21       62347   N1     3 #> 8667       2         3  21       62347   N2     4 #> 8668       2         3  21       62347   N3     4 #> 8669       2         3  21       62347   N4     5 #> 8670       2         3  21       62347   N5     5 #> 8671       2         3  21       62347   O1     4 #> 8672       2         3  21       62347   O2     2 #> 8673       2         3  21       62347   O3     4 #> 8674       2         3  21       62347   O4     5 #> 8675       2         3  21       62347   O5     2 #> 8676       1         3  29       62348   A1     5 #> 8677       1         3  29       62348   A2     5 #> 8678       1         3  29       62348   A3     5 #> 8679       1         3  29       62348   A4     6 #> 8680       1         3  29       62348   A5     5 #> 8681       1         3  29       62348   C1     4 #> 8682       1         3  29       62348   C2     5 #> 8683       1         3  29       62348   C3     6 #> 8684       1         3  29       62348   C4     4 #> 8685       1         3  29       62348   C5     3 #> 8686       1         3  29       62348   E1     5 #> 8687       1         3  29       62348   E2     4 #> 8688       1         3  29       62348   E3     4 #> 8689       1         3  29       62348   E4     4 #> 8690       1         3  29       62348   E5     6 #> 8691       1         3  29       62348   N1     6 #> 8692       1         3  29       62348   N2     6 #> 8693       1         3  29       62348   N3     6 #> 8694       1         3  29       62348   N4     5 #> 8695       1         3  29       62348   N5     4 #> 8696       1         3  29       62348   O1     3 #> 8697       1         3  29       62348   O2     6 #> 8698       1         3  29       62348   O3     3 #> 8699       1         3  29       62348   O4     5 #> 8700       1         3  29       62348   O5     4 #> 8701       2         5  36       62349   A1     2 #> 8702       2         5  36       62349   A2     5 #> 8703       2         5  36       62349   A3     5 #> 8704       2         5  36       62349   A4     6 #> 8705       2         5  36       62349   A5     5 #> 8706       2         5  36       62349   C1     6 #> 8707       2         5  36       62349   C2     6 #> 8708       2         5  36       62349   C3     5 #> 8709       2         5  36       62349   C4     1 #> 8710       2         5  36       62349   C5     3 #> 8711       2         5  36       62349   E1     1 #> 8712       2         5  36       62349   E2     1 #> 8713       2         5  36       62349   E3     4 #> 8714       2         5  36       62349   E4     4 #> 8715       2         5  36       62349   E5     6 #> 8716       2         5  36       62349   N1     4 #> 8717       2         5  36       62349   N2     4 #> 8718       2         5  36       62349   N3     1 #> 8719       2         5  36       62349   N4     5 #> 8720       2         5  36       62349   N5     3 #> 8721       2         5  36       62349   O1     5 #> 8722       2         5  36       62349   O2     2 #> 8723       2         5  36       62349   O3     4 #> 8724       2         5  36       62349   O4     6 #> 8725       2         5  36       62349   O5     1 #> 8726       2         3  19       62350   A1     2 #> 8727       2         3  19       62350   A2     5 #> 8728       2         3  19       62350   A3     1 #> 8729       2         3  19       62350   A4     6 #> 8730       2         3  19       62350   A5     4 #> 8731       2         3  19       62350   C1     4 #> 8732       2         3  19       62350   C2     4 #> 8733       2         3  19       62350   C3     6 #> 8734       2         3  19       62350   C4     4 #> 8735       2         3  19       62350   C5     2 #> 8736       2         3  19       62350   E1     4 #> 8737       2         3  19       62350   E2     3 #> 8738       2         3  19       62350   E3     4 #> 8739       2         3  19       62350   E4     5 #> 8740       2         3  19       62350   E5     4 #> 8741       2         3  19       62350   N1     3 #> 8742       2         3  19       62350   N2     5 #> 8743       2         3  19       62350   N3     4 #> 8744       2         3  19       62350   N4     4 #> 8745       2         3  19       62350   N5     4 #> 8746       2         3  19       62350   O1     4 #> 8747       2         3  19       62350   O2     4 #> 8748       2         3  19       62350   O3     5 #> 8749       2         3  19       62350   O4     5 #> 8750       2         3  19       62350   O5     4 #> 8751       2         3  23       62351   A1     4 #> 8752       2         3  23       62351   A2     6 #> 8753       2         3  23       62351   A3     6 #> 8754       2         3  23       62351   A4     4 #> 8755       2         3  23       62351   A5     4 #> 8756       2         3  23       62351   C1     6 #> 8757       2         3  23       62351   C2     4 #> 8758       2         3  23       62351   C3     6 #> 8759       2         3  23       62351   C4     1 #> 8760       2         3  23       62351   C5     4 #> 8761       2         3  23       62351   E1     1 #> 8762       2         3  23       62351   E2     5 #> 8763       2         3  23       62351   E3     5 #> 8764       2         3  23       62351   E4     2 #> 8765       2         3  23       62351   E5     5 #> 8766       2         3  23       62351   N1     4 #> 8767       2         3  23       62351   N2     6 #> 8768       2         3  23       62351   N3     4 #> 8769       2         3  23       62351   N4     2 #> 8770       2         3  23       62351   N5     5 #> 8771       2         3  23       62351   O1     6 #> 8772       2         3  23       62351   O2     3 #> 8773       2         3  23       62351   O3     4 #> 8774       2         3  23       62351   O4     6 #> 8775       2         3  23       62351   O5     2 #> 8776       2         2  39       62352   A1     2 #> 8777       2         2  39       62352   A2     5 #> 8778       2         2  39       62352   A3     5 #> 8779       2         2  39       62352   A4     6 #> 8780       2         2  39       62352   A5     5 #> 8781       2         2  39       62352   C1     5 #> 8782       2         2  39       62352   C2     5 #> 8783       2         2  39       62352   C3     5 #> 8784       2         2  39       62352   C4     1 #> 8785       2         2  39       62352   C5     1 #> 8786       2         2  39       62352   E1     2 #> 8787       2         2  39       62352   E2     2 #> 8788       2         2  39       62352   E3     4 #> 8789       2         2  39       62352   E4     5 #> 8790       2         2  39       62352   E5     4 #> 8791       2         2  39       62352   N1     3 #> 8792       2         2  39       62352   N2     4 #> 8793       2         2  39       62352   N3     4 #> 8794       2         2  39       62352   N4     4 #> 8795       2         2  39       62352   N5     4 #> 8796       2         2  39       62352   O1     4 #> 8797       2         2  39       62352   O2     3 #> 8798       2         2  39       62352   O3     4 #> 8799       2         2  39       62352   O4     5 #> 8800       2         2  39       62352   O5     3 #> 8801       2         3  19       62353   A1     3 #> 8802       2         3  19       62353   A2     5 #> 8803       2         3  19       62353   A3     1 #> 8804       2         3  19       62353   A4     4 #> 8805       2         3  19       62353   A5     4 #> 8806       2         3  19       62353   C1     2 #> 8807       2         3  19       62353   C2     4 #> 8808       2         3  19       62353   C3     4 #> 8809       2         3  19       62353   C4     4 #> 8810       2         3  19       62353   C5     4 #> 8811       2         3  19       62353   E1     4 #> 8812       2         3  19       62353   E2     2 #> 8813       2         3  19       62353   E3     5 #> 8814       2         3  19       62353   E4     4 #> 8815       2         3  19       62353   E5     5 #> 8816       2         3  19       62353   N1     4 #> 8817       2         3  19       62353   N2     4 #> 8818       2         3  19       62353   N3     5 #> 8819       2         3  19       62353   N4     2 #> 8820       2         3  19       62353   N5     1 #> 8821       2         3  19       62353   O1     5 #> 8822       2         3  19       62353   O2     1 #> 8823       2         3  19       62353   O3     6 #> 8824       2         3  19       62353   O4     5 #> 8825       2         3  19       62353   O5     1 #> 8826       2        NA  17       62354   A1     2 #> 8827       2        NA  17       62354   A2     5 #> 8828       2        NA  17       62354   A3     5 #> 8829       2        NA  17       62354   A4     4 #> 8830       2        NA  17       62354   A5     5 #> 8831       2        NA  17       62354   C1     4 #> 8832       2        NA  17       62354   C2     4 #> 8833       2        NA  17       62354   C3     5 #> 8834       2        NA  17       62354   C4     3 #> 8835       2        NA  17       62354   C5     2 #> 8836       2        NA  17       62354   E1     4 #> 8837       2        NA  17       62354   E2     5 #> 8838       2        NA  17       62354   E3     5 #> 8839       2        NA  17       62354   E4     6 #> 8840       2        NA  17       62354   E5     3 #> 8841       2        NA  17       62354   N1     5 #> 8842       2        NA  17       62354   N2     6 #> 8843       2        NA  17       62354   N3     2 #> 8844       2        NA  17       62354   N4     1 #> 8845       2        NA  17       62354   N5     4 #> 8846       2        NA  17       62354   O1     4 #> 8847       2        NA  17       62354   O2     5 #> 8848       2        NA  17       62354   O3     4 #> 8849       2        NA  17       62354   O4     3 #> 8850       2        NA  17       62354   O5     3 #> 8851       2         3  18       62358   A1     2 #> 8852       2         3  18       62358   A2     3 #> 8853       2         3  18       62358   A3     6 #> 8854       2         3  18       62358   A4     4 #> 8855       2         3  18       62358   A5     5 #> 8856       2         3  18       62358   C1     3 #> 8857       2         3  18       62358   C2     3 #> 8858       2         3  18       62358   C3     5 #> 8859       2         3  18       62358   C4     3 #> 8860       2         3  18       62358   C5     4 #> 8861       2         3  18       62358   E1     3 #> 8862       2         3  18       62358   E2     1 #> 8863       2         3  18       62358   E3     6 #> 8864       2         3  18       62358   E4     5 #> 8865       2         3  18       62358   E5     6 #> 8866       2         3  18       62358   N1     1 #> 8867       2         3  18       62358   N2     2 #> 8868       2         3  18       62358   N3     4 #> 8869       2         3  18       62358   N4     4 #> 8870       2         3  18       62358   N5     1 #> 8871       2         3  18       62358   O1     6 #> 8872       2         3  18       62358   O2     5 #> 8873       2         3  18       62358   O3     6 #> 8874       2         3  18       62358   O4     6 #> 8875       2         3  18       62358   O5     4 #> 8876       2         3  25       62359   A1     1 #> 8877       2         3  25       62359   A2     5 #> 8878       2         3  25       62359   A3     5 #> 8879       2         3  25       62359   A4     6 #> 8880       2         3  25       62359   A5     5 #> 8881       2         3  25       62359   C1     5 #> 8882       2         3  25       62359   C2     5 #> 8883       2         3  25       62359   C3     5 #> 8884       2         3  25       62359   C4     2 #> 8885       2         3  25       62359   C5     4 #> 8886       2         3  25       62359   E1     4 #> 8887       2         3  25       62359   E2    NA #> 8888       2         3  25       62359   E3     4 #> 8889       2         3  25       62359   E4     5 #> 8890       2         3  25       62359   E5     3 #> 8891       2         3  25       62359   N1     3 #> 8892       2         3  25       62359   N2     5 #> 8893       2         3  25       62359   N3     4 #> 8894       2         3  25       62359   N4     4 #> 8895       2         3  25       62359   N5     4 #> 8896       2         3  25       62359   O1     5 #> 8897       2         3  25       62359   O2     5 #> 8898       2         3  25       62359   O3     4 #> 8899       2         3  25       62359   O4     5 #> 8900       2         3  25       62359   O5     3 #> 8901       2        NA  15       62360   A1     4 #> 8902       2        NA  15       62360   A2     2 #> 8903       2        NA  15       62360   A3     5 #> 8904       2        NA  15       62360   A4     5 #> 8905       2        NA  15       62360   A5     5 #> 8906       2        NA  15       62360   C1     4 #> 8907       2        NA  15       62360   C2     4 #> 8908       2        NA  15       62360   C3     3 #> 8909       2        NA  15       62360   C4     4 #> 8910       2        NA  15       62360   C5     6 #> 8911       2        NA  15       62360   E1     4 #> 8912       2        NA  15       62360   E2     2 #> 8913       2        NA  15       62360   E3     3 #> 8914       2        NA  15       62360   E4     3 #> 8915       2        NA  15       62360   E5     4 #> 8916       2        NA  15       62360   N1     2 #> 8917       2        NA  15       62360   N2     2 #> 8918       2        NA  15       62360   N3     4 #> 8919       2        NA  15       62360   N4     3 #> 8920       2        NA  15       62360   N5     2 #> 8921       2        NA  15       62360   O1     4 #> 8922       2        NA  15       62360   O2     3 #> 8923       2        NA  15       62360   O3     3 #> 8924       2        NA  15       62360   O4     5 #> 8925       2        NA  15       62360   O5     4 #> 8926       1         3  20       62362   A1     1 #> 8927       1         3  20       62362   A2     5 #> 8928       1         3  20       62362   A3     5 #> 8929       1         3  20       62362   A4     5 #> 8930       1         3  20       62362   A5     4 #> 8931       1         3  20       62362   C1     5 #> 8932       1         3  20       62362   C2     4 #> 8933       1         3  20       62362   C3     5 #> 8934       1         3  20       62362   C4     4 #> 8935       1         3  20       62362   C5     5 #> 8936       1         3  20       62362   E1     5 #> 8937       1         3  20       62362   E2     5 #> 8938       1         3  20       62362   E3     1 #> 8939       1         3  20       62362   E4     2 #> 8940       1         3  20       62362   E5     4 #> 8941       1         3  20       62362   N1     5 #> 8942       1         3  20       62362   N2     5 #> 8943       1         3  20       62362   N3     2 #> 8944       1         3  20       62362   N4     3 #> 8945       1         3  20       62362   N5     3 #> 8946       1         3  20       62362   O1     4 #> 8947       1         3  20       62362   O2     2 #> 8948       1         3  20       62362   O3     5 #> 8949       1         3  20       62362   O4     6 #> 8950       1         3  20       62362   O5     1 #> 8951       2        NA  14       62363   A1     2 #> 8952       2        NA  14       62363   A2     5 #> 8953       2        NA  14       62363   A3     5 #> 8954       2        NA  14       62363   A4     6 #> 8955       2        NA  14       62363   A5     6 #> 8956       2        NA  14       62363   C1     4 #> 8957       2        NA  14       62363   C2     5 #> 8958       2        NA  14       62363   C3     2 #> 8959       2        NA  14       62363   C4     4 #> 8960       2        NA  14       62363   C5     4 #> 8961       2        NA  14       62363   E1     6 #> 8962       2        NA  14       62363   E2     5 #> 8963       2        NA  14       62363   E3     5 #> 8964       2        NA  14       62363   E4     4 #> 8965       2        NA  14       62363   E5     4 #> 8966       2        NA  14       62363   N1     1 #> 8967       2        NA  14       62363   N2     3 #> 8968       2        NA  14       62363   N3     4 #> 8969       2        NA  14       62363   N4     4 #> 8970       2        NA  14       62363   N5     6 #> 8971       2        NA  14       62363   O1     5 #> 8972       2        NA  14       62363   O2     2 #> 8973       2        NA  14       62363   O3     5 #> 8974       2        NA  14       62363   O4     5 #> 8975       2        NA  14       62363   O5     3 #> 8976       2         3  20       62366   A1     4 #> 8977       2         3  20       62366   A2     3 #> 8978       2         3  20       62366   A3     6 #> 8979       2         3  20       62366   A4     6 #> 8980       2         3  20       62366   A5     6 #> 8981       2         3  20       62366   C1     5 #> 8982       2         3  20       62366   C2     6 #> 8983       2         3  20       62366   C3     5 #> 8984       2         3  20       62366   C4     1 #> 8985       2         3  20       62366   C5     3 #> 8986       2         3  20       62366   E1     1 #> 8987       2         3  20       62366   E2     1 #> 8988       2         3  20       62366   E3     3 #> 8989       2         3  20       62366   E4     5 #> 8990       2         3  20       62366   E5     6 #> 8991       2         3  20       62366   N1     1 #> 8992       2         3  20       62366   N2     6 #> 8993       2         3  20       62366   N3     4 #> 8994       2         3  20       62366   N4     2 #> 8995       2         3  20       62366   N5     1 #> 8996       2         3  20       62366   O1     5 #> 8997       2         3  20       62366   O2     1 #> 8998       2         3  20       62366   O3     5 #> 8999       2         3  20       62366   O4     1 #> 9000       2         3  20       62366   O5     2 #> 9001       1         1  25       62367   A1     3 #> 9002       1         1  25       62367   A2     4 #> 9003       1         1  25       62367   A3     6 #> 9004       1         1  25       62367   A4     6 #> 9005       1         1  25       62367   A5     6 #> 9006       1         1  25       62367   C1     6 #> 9007       1         1  25       62367   C2     4 #> 9008       1         1  25       62367   C3     5 #> 9009       1         1  25       62367   C4     1 #> 9010       1         1  25       62367   C5     2 #> 9011       1         1  25       62367   E1     4 #> 9012       1         1  25       62367   E2     1 #> 9013       1         1  25       62367   E3     5 #> 9014       1         1  25       62367   E4     4 #> 9015       1         1  25       62367   E5     5 #> 9016       1         1  25       62367   N1     5 #> 9017       1         1  25       62367   N2     5 #> 9018       1         1  25       62367   N3     4 #> 9019       1         1  25       62367   N4     4 #> 9020       1         1  25       62367   N5     6 #> 9021       1         1  25       62367   O1     4 #> 9022       1         1  25       62367   O2     4 #> 9023       1         1  25       62367   O3     5 #> 9024       1         1  25       62367   O4     5 #> 9025       1         1  25       62367   O5     2 #> 9026       2         5  64       62368   A1     1 #> 9027       2         5  64       62368   A2     6 #> 9028       2         5  64       62368   A3     1 #> 9029       2         5  64       62368   A4     6 #> 9030       2         5  64       62368   A5     5 #> 9031       2         5  64       62368   C1     4 #> 9032       2         5  64       62368   C2     1 #> 9033       2         5  64       62368   C3     2 #> 9034       2         5  64       62368   C4     5 #> 9035       2         5  64       62368   C5     6 #> 9036       2         5  64       62368   E1     1 #> 9037       2         5  64       62368   E2     1 #> 9038       2         5  64       62368   E3     5 #> 9039       2         5  64       62368   E4     5 #> 9040       2         5  64       62368   E5     6 #> 9041       2         5  64       62368   N1     2 #> 9042       2         5  64       62368   N2     4 #> 9043       2         5  64       62368   N3     6 #> 9044       2         5  64       62368   N4     6 #> 9045       2         5  64       62368   N5     5 #> 9046       2         5  64       62368   O1     6 #> 9047       2         5  64       62368   O2     1 #> 9048       2         5  64       62368   O3     6 #> 9049       2         5  64       62368   O4     6 #> 9050       2         5  64       62368   O5     1 #> 9051       2         2  18       62369   A1     1 #> 9052       2         2  18       62369   A2     5 #> 9053       2         2  18       62369   A3     3 #> 9054       2         2  18       62369   A4     6 #> 9055       2         2  18       62369   A5     5 #> 9056       2         2  18       62369   C1     2 #> 9057       2         2  18       62369   C2     5 #> 9058       2         2  18       62369   C3     5 #> 9059       2         2  18       62369   C4     1 #> 9060       2         2  18       62369   C5     2 #> 9061       2         2  18       62369   E1     2 #> 9062       2         2  18       62369   E2     4 #> 9063       2         2  18       62369   E3     6 #> 9064       2         2  18       62369   E4     5 #> 9065       2         2  18       62369   E5     5 #> 9066       2         2  18       62369   N1     1 #> 9067       2         2  18       62369   N2     2 #> 9068       2         2  18       62369   N3     2 #> 9069       2         2  18       62369   N4     3 #> 9070       2         2  18       62369   N5     3 #> 9071       2         2  18       62369   O1     6 #> 9072       2         2  18       62369   O2     1 #> 9073       2         2  18       62369   O3     5 #> 9074       2         2  18       62369   O4     6 #> 9075       2         2  18       62369   O5     1 #> 9076       2         3  20       62370   A1     1 #> 9077       2         3  20       62370   A2     6 #> 9078       2         3  20       62370   A3     6 #> 9079       2         3  20       62370   A4     4 #> 9080       2         3  20       62370   A5     5 #> 9081       2         3  20       62370   C1     4 #> 9082       2         3  20       62370   C2     5 #> 9083       2         3  20       62370   C3     2 #> 9084       2         3  20       62370   C4     3 #> 9085       2         3  20       62370   C5     6 #> 9086       2         3  20       62370   E1     3 #> 9087       2         3  20       62370   E2     4 #> 9088       2         3  20       62370   E3     4 #> 9089       2         3  20       62370   E4     6 #> 9090       2         3  20       62370   E5     4 #> 9091       2         3  20       62370   N1     2 #> 9092       2         3  20       62370   N2     4 #> 9093       2         3  20       62370   N3     4 #> 9094       2         3  20       62370   N4     2 #> 9095       2         3  20       62370   N5     5 #> 9096       2         3  20       62370   O1     5 #> 9097       2         3  20       62370   O2     1 #> 9098       2         3  20       62370   O3     4 #> 9099       2         3  20       62370   O4     5 #> 9100       2         3  20       62370   O5     2 #> 9101       2         1  19       62371   A1     4 #> 9102       2         1  19       62371   A2     5 #> 9103       2         1  19       62371   A3     2 #> 9104       2         1  19       62371   A4     6 #> 9105       2         1  19       62371   A5     5 #> 9106       2         1  19       62371   C1     3 #> 9107       2         1  19       62371   C2     2 #> 9108       2         1  19       62371   C3     4 #> 9109       2         1  19       62371   C4     3 #> 9110       2         1  19       62371   C5     6 #> 9111       2         1  19       62371   E1     1 #> 9112       2         1  19       62371   E2     5 #> 9113       2         1  19       62371   E3     4 #> 9114       2         1  19       62371   E4     3 #> 9115       2         1  19       62371   E5     2 #> 9116       2         1  19       62371   N1     5 #> 9117       2         1  19       62371   N2     6 #> 9118       2         1  19       62371   N3     2 #> 9119       2         1  19       62371   N4     2 #> 9120       2         1  19       62371   N5     5 #> 9121       2         1  19       62371   O1     4 #> 9122       2         1  19       62371   O2     2 #> 9123       2         1  19       62371   O3     4 #> 9124       2         1  19       62371   O4     5 #> 9125       2         1  19       62371   O5     3 #> 9126       2         5  32       62375   A1     1 #> 9127       2         5  32       62375   A2     6 #> 9128       2         5  32       62375   A3     5 #> 9129       2         5  32       62375   A4     3 #> 9130       2         5  32       62375   A5     6 #> 9131       2         5  32       62375   C1     5 #> 9132       2         5  32       62375   C2     6 #> 9133       2         5  32       62375   C3     6 #> 9134       2         5  32       62375   C4     1 #> 9135       2         5  32       62375   C5     2 #> 9136       2         5  32       62375   E1     4 #> 9137       2         5  32       62375   E2     4 #> 9138       2         5  32       62375   E3     4 #> 9139       2         5  32       62375   E4     6 #> 9140       2         5  32       62375   E5     5 #> 9141       2         5  32       62375   N1     4 #> 9142       2         5  32       62375   N2     4 #> 9143       2         5  32       62375   N3     3 #> 9144       2         5  32       62375   N4     2 #> 9145       2         5  32       62375   N5     1 #> 9146       2         5  32       62375   O1     6 #> 9147       2         5  32       62375   O2     1 #> 9148       2         5  32       62375   O3     5 #> 9149       2         5  32       62375   O4     6 #> 9150       2         5  32       62375   O5     1 #> 9151       1         2  49       62376   A1    NA #> 9152       1         2  49       62376   A2     5 #> 9153       1         2  49       62376   A3     4 #> 9154       1         2  49       62376   A4     3 #> 9155       1         2  49       62376   A5     4 #> 9156       1         2  49       62376   C1     4 #> 9157       1         2  49       62376   C2     3 #> 9158       1         2  49       62376   C3     3 #> 9159       1         2  49       62376   C4     3 #> 9160       1         2  49       62376   C5     4 #> 9161       1         2  49       62376   E1     4 #> 9162       1         2  49       62376   E2     2 #> 9163       1         2  49       62376   E3     4 #> 9164       1         2  49       62376   E4     4 #> 9165       1         2  49       62376   E5     4 #> 9166       1         2  49       62376   N1     3 #> 9167       1         2  49       62376   N2     3 #> 9168       1         2  49       62376   N3     4 #> 9169       1         2  49       62376   N4     3 #> 9170       1         2  49       62376   N5     2 #> 9171       1         2  49       62376   O1     5 #> 9172       1         2  49       62376   O2     2 #> 9173       1         2  49       62376   O3     4 #> 9174       1         2  49       62376   O4     4 #> 9175       1         2  49       62376   O5     3 #> 9176       1         2  45       62377   A1     4 #> 9177       1         2  45       62377   A2     6 #> 9178       1         2  45       62377   A3     5 #> 9179       1         2  45       62377   A4     6 #> 9180       1         2  45       62377   A5     6 #> 9181       1         2  45       62377   C1     5 #> 9182       1         2  45       62377   C2     5 #> 9183       1         2  45       62377   C3     5 #> 9184       1         2  45       62377   C4     5 #> 9185       1         2  45       62377   C5     4 #> 9186       1         2  45       62377   E1     1 #> 9187       1         2  45       62377   E2     1 #> 9188       1         2  45       62377   E3     6 #> 9189       1         2  45       62377   E4     6 #> 9190       1         2  45       62377   E5     6 #> 9191       1         2  45       62377   N1     4 #> 9192       1         2  45       62377   N2     4 #> 9193       1         2  45       62377   N3     6 #> 9194       1         2  45       62377   N4     4 #> 9195       1         2  45       62377   N5     1 #> 9196       1         2  45       62377   O1     6 #> 9197       1         2  45       62377   O2     2 #> 9198       1         2  45       62377   O3     6 #> 9199       1         2  45       62377   O4     6 #> 9200       1         2  45       62377   O5     2 #> 9201       1         1  18       62380   A1     1 #> 9202       1         1  18       62380   A2     6 #> 9203       1         1  18       62380   A3     5 #> 9204       1         1  18       62380   A4     2 #> 9205       1         1  18       62380   A5     2 #> 9206       1         1  18       62380   C1     4 #> 9207       1         1  18       62380   C2     1 #> 9208       1         1  18       62380   C3     5 #> 9209       1         1  18       62380   C4     6 #> 9210       1         1  18       62380   C5     6 #> 9211       1         1  18       62380   E1     6 #> 9212       1         1  18       62380   E2     6 #> 9213       1         1  18       62380   E3     3 #> 9214       1         1  18       62380   E4     2 #> 9215       1         1  18       62380   E5     1 #> 9216       1         1  18       62380   N1     4 #> 9217       1         1  18       62380   N2     5 #> 9218       1         1  18       62380   N3     5 #> 9219       1         1  18       62380   N4     5 #> 9220       1         1  18       62380   N5     2 #> 9221       1         1  18       62380   O1     5 #> 9222       1         1  18       62380   O2     6 #> 9223       1         1  18       62380   O3     5 #> 9224       1         1  18       62380   O4     6 #> 9225       1         1  18       62380   O5     2 #> 9226       2         5  28       62382   A1     6 #> 9227       2         5  28       62382   A2     6 #> 9228       2         5  28       62382   A3     6 #> 9229       2         5  28       62382   A4     6 #> 9230       2         5  28       62382   A5     6 #> 9231       2         5  28       62382   C1     3 #> 9232       2         5  28       62382   C2     6 #> 9233       2         5  28       62382   C3     2 #> 9234       2         5  28       62382   C4     5 #> 9235       2         5  28       62382   C5     5 #> 9236       2         5  28       62382   E1     2 #> 9237       2         5  28       62382   E2     2 #> 9238       2         5  28       62382   E3     6 #> 9239       2         5  28       62382   E4     6 #> 9240       2         5  28       62382   E5     6 #> 9241       2         5  28       62382   N1     6 #> 9242       2         5  28       62382   N2     6 #> 9243       2         5  28       62382   N3     6 #> 9244       2         5  28       62382   N4     6 #> 9245       2         5  28       62382   N5     6 #> 9246       2         5  28       62382   O1     6 #> 9247       2         5  28       62382   O2     5 #> 9248       2         5  28       62382   O3     5 #> 9249       2         5  28       62382   O4     6 #> 9250       2         5  28       62382   O5     2 #> 9251       2         5  22       62384   A1     1 #> 9252       2         5  22       62384   A2     4 #> 9253       2         5  22       62384   A3     2 #> 9254       2         5  22       62384   A4     5 #> 9255       2         5  22       62384   A5     3 #> 9256       2         5  22       62384   C1     5 #> 9257       2         5  22       62384   C2     5 #> 9258       2         5  22       62384   C3     5 #> 9259       2         5  22       62384   C4     3 #> 9260       2         5  22       62384   C5     4 #> 9261       2         5  22       62384   E1     4 #> 9262       2         5  22       62384   E2     5 #> 9263       2         5  22       62384   E3     3 #> 9264       2         5  22       62384   E4     3 #> 9265       2         5  22       62384   E5     2 #> 9266       2         5  22       62384   N1     2 #> 9267       2         5  22       62384   N2     2 #> 9268       2         5  22       62384   N3     3 #> 9269       2         5  22       62384   N4     4 #> 9270       2         5  22       62384   N5     5 #> 9271       2         5  22       62384   O1     4 #> 9272       2         5  22       62384   O2     2 #> 9273       2         5  22       62384   O3     4 #> 9274       2         5  22       62384   O4     5 #> 9275       2         5  22       62384   O5     2 #> 9276       2         4  25       62387   A1     3 #> 9277       2         4  25       62387   A2     5 #> 9278       2         4  25       62387   A3     4 #> 9279       2         4  25       62387   A4     4 #> 9280       2         4  25       62387   A5     3 #> 9281       2         4  25       62387   C1     5 #> 9282       2         4  25       62387   C2     5 #> 9283       2         4  25       62387   C3     4 #> 9284       2         4  25       62387   C4     2 #> 9285       2         4  25       62387   C5     4 #> 9286       2         4  25       62387   E1     5 #> 9287       2         4  25       62387   E2     2 #> 9288       2         4  25       62387   E3     3 #> 9289       2         4  25       62387   E4     4 #> 9290       2         4  25       62387   E5     4 #> 9291       2         4  25       62387   N1     4 #> 9292       2         4  25       62387   N2     4 #> 9293       2         4  25       62387   N3     4 #> 9294       2         4  25       62387   N4     3 #> 9295       2         4  25       62387   N5     2 #> 9296       2         4  25       62387   O1     6 #> 9297       2         4  25       62387   O2     1 #> 9298       2         4  25       62387   O3     5 #> 9299       2         4  25       62387   O4     5 #> 9300       2         4  25       62387   O5     2 #> 9301       2         5  34       62390   A1     1 #> 9302       2         5  34       62390   A2     6 #> 9303       2         5  34       62390   A3     6 #> 9304       2         5  34       62390   A4     6 #> 9305       2         5  34       62390   A5     6 #> 9306       2         5  34       62390   C1     6 #> 9307       2         5  34       62390   C2     5 #> 9308       2         5  34       62390   C3     4 #> 9309       2         5  34       62390   C4     1 #> 9310       2         5  34       62390   C5     1 #> 9311       2         5  34       62390   E1     1 #> 9312       2         5  34       62390   E2     1 #> 9313       2         5  34       62390   E3     6 #> 9314       2         5  34       62390   E4     6 #> 9315       2         5  34       62390   E5     6 #> 9316       2         5  34       62390   N1     1 #> 9317       2         5  34       62390   N2     1 #> 9318       2         5  34       62390   N3     1 #> 9319       2         5  34       62390   N4     1 #> 9320       2         5  34       62390   N5     1 #> 9321       2         5  34       62390   O1     6 #> 9322       2         5  34       62390   O2     1 #> 9323       2         5  34       62390   O3     6 #> 9324       2         5  34       62390   O4     6 #> 9325       2         5  34       62390   O5     1 #> 9326       2         5  42       62391   A1     2 #> 9327       2         5  42       62391   A2     5 #> 9328       2         5  42       62391   A3     5 #> 9329       2         5  42       62391   A4     5 #> 9330       2         5  42       62391   A5     3 #> 9331       2         5  42       62391   C1     5 #> 9332       2         5  42       62391   C2     5 #> 9333       2         5  42       62391   C3     5 #> 9334       2         5  42       62391   C4     1 #> 9335       2         5  42       62391   C5     4 #> 9336       2         5  42       62391   E1     4 #> 9337       2         5  42       62391   E2     4 #> 9338       2         5  42       62391   E3     3 #> 9339       2         5  42       62391   E4     3 #> 9340       2         5  42       62391   E5     5 #> 9341       2         5  42       62391   N1     4 #> 9342       2         5  42       62391   N2     4 #> 9343       2         5  42       62391   N3     4 #> 9344       2         5  42       62391   N4     4 #> 9345       2         5  42       62391   N5     2 #> 9346       2         5  42       62391   O1     6 #> 9347       2         5  42       62391   O2     1 #> 9348       2         5  42       62391   O3     5 #> 9349       2         5  42       62391   O4     4 #> 9350       2         5  42       62391   O5     1 #> 9351       2         3  37       62394   A1     1 #> 9352       2         3  37       62394   A2     5 #> 9353       2         3  37       62394   A3     3 #> 9354       2         3  37       62394   A4     5 #> 9355       2         3  37       62394   A5     4 #> 9356       2         3  37       62394   C1     5 #> 9357       2         3  37       62394   C2     4 #> 9358       2         3  37       62394   C3     3 #> 9359       2         3  37       62394   C4     1 #> 9360       2         3  37       62394   C5     4 #> 9361       2         3  37       62394   E1     3 #> 9362       2         3  37       62394   E2     2 #> 9363       2         3  37       62394   E3     4 #> 9364       2         3  37       62394   E4     5 #> 9365       2         3  37       62394   E5     5 #> 9366       2         3  37       62394   N1     3 #> 9367       2         3  37       62394   N2     4 #> 9368       2         3  37       62394   N3     2 #> 9369       2         3  37       62394   N4     3 #> 9370       2         3  37       62394   N5     2 #> 9371       2         3  37       62394   O1     5 #> 9372       2         3  37       62394   O2     4 #> 9373       2         3  37       62394   O3     4 #> 9374       2         3  37       62394   O4     5 #> 9375       2         3  37       62394   O5     1 #> 9376       2         3  42       62397   A1     1 #> 9377       2         3  42       62397   A2     6 #> 9378       2         3  42       62397   A3     5 #> 9379       2         3  42       62397   A4     6 #> 9380       2         3  42       62397   A5     6 #> 9381       2         3  42       62397   C1     6 #> 9382       2         3  42       62397   C2     6 #> 9383       2         3  42       62397   C3     6 #> 9384       2         3  42       62397   C4     1 #> 9385       2         3  42       62397   C5     1 #> 9386       2         3  42       62397   E1     1 #> 9387       2         3  42       62397   E2     1 #> 9388       2         3  42       62397   E3     4 #> 9389       2         3  42       62397   E4     6 #> 9390       2         3  42       62397   E5     5 #> 9391       2         3  42       62397   N1     1 #> 9392       2         3  42       62397   N2     1 #> 9393       2         3  42       62397   N3     1 #> 9394       2         3  42       62397   N4     1 #> 9395       2         3  42       62397   N5     1 #> 9396       2         3  42       62397   O1     6 #> 9397       2         3  42       62397   O2     1 #> 9398       2         3  42       62397   O3     6 #> 9399       2         3  42       62397   O4     6 #> 9400       2         3  42       62397   O5     1 #> 9401       2         5  23       62401   A1     1 #> 9402       2         5  23       62401   A2     5 #> 9403       2         5  23       62401   A3     5 #> 9404       2         5  23       62401   A4     6 #> 9405       2         5  23       62401   A5     4 #> 9406       2         5  23       62401   C1     5 #> 9407       2         5  23       62401   C2     5 #> 9408       2         5  23       62401   C3     4 #> 9409       2         5  23       62401   C4     1 #> 9410       2         5  23       62401   C5     1 #> 9411       2         5  23       62401   E1     3 #> 9412       2         5  23       62401   E2     3 #> 9413       2         5  23       62401   E3     3 #> 9414       2         5  23       62401   E4     4 #> 9415       2         5  23       62401   E5     5 #> 9416       2         5  23       62401   N1     2 #> 9417       2         5  23       62401   N2     2 #> 9418       2         5  23       62401   N3     2 #> 9419       2         5  23       62401   N4     2 #> 9420       2         5  23       62401   N5     4 #> 9421       2         5  23       62401   O1     5 #> 9422       2         5  23       62401   O2     1 #> 9423       2         5  23       62401   O3     5 #> 9424       2         5  23       62401   O4     5 #> 9425       2         5  23       62401   O5     1 #> 9426       2         4  29       62408   A1     4 #> 9427       2         4  29       62408   A2     4 #> 9428       2         4  29       62408   A3     5 #> 9429       2         4  29       62408   A4     1 #> 9430       2         4  29       62408   A5     5 #> 9431       2         4  29       62408   C1     5 #> 9432       2         4  29       62408   C2     4 #> 9433       2         4  29       62408   C3     4 #> 9434       2         4  29       62408   C4     2 #> 9435       2         4  29       62408   C5     5 #> 9436       2         4  29       62408   E1     1 #> 9437       2         4  29       62408   E2     4 #> 9438       2         4  29       62408   E3     6 #> 9439       2         4  29       62408   E4     6 #> 9440       2         4  29       62408   E5     5 #> 9441       2         4  29       62408   N1     3 #> 9442       2         4  29       62408   N2     4 #> 9443       2         4  29       62408   N3     4 #> 9444       2         4  29       62408   N4     2 #> 9445       2         4  29       62408   N5     5 #> 9446       2         4  29       62408   O1     3 #> 9447       2         4  29       62408   O2     3 #> 9448       2         4  29       62408   O3     5 #> 9449       2         4  29       62408   O4     6 #> 9450       2         4  29       62408   O5     5 #> 9451       1         2  18       62412   A1     2 #> 9452       1         2  18       62412   A2     3 #> 9453       1         2  18       62412   A3     4 #> 9454       1         2  18       62412   A4     1 #> 9455       1         2  18       62412   A5     2 #> 9456       1         2  18       62412   C1     3 #> 9457       1         2  18       62412   C2     5 #> 9458       1         2  18       62412   C3     3 #> 9459       1         2  18       62412   C4     1 #> 9460       1         2  18       62412   C5     1 #> 9461       1         2  18       62412   E1     6 #> 9462       1         2  18       62412   E2     6 #> 9463       1         2  18       62412   E3     1 #> 9464       1         2  18       62412   E4     2 #> 9465       1         2  18       62412   E5     3 #> 9466       1         2  18       62412   N1     1 #> 9467       1         2  18       62412   N2     1 #> 9468       1         2  18       62412   N3     2 #> 9469       1         2  18       62412   N4     2 #> 9470       1         2  18       62412   N5     2 #> 9471       1         2  18       62412   O1     5 #> 9472       1         2  18       62412   O2     2 #> 9473       1         2  18       62412   O3     3 #> 9474       1         2  18       62412   O4     5 #> 9475       1         2  18       62412   O5     3 #> 9476       2         3  22       62416   A1     2 #> 9477       2         3  22       62416   A2     6 #> 9478       2         3  22       62416   A3     6 #> 9479       2         3  22       62416   A4     4 #> 9480       2         3  22       62416   A5     6 #> 9481       2         3  22       62416   C1     6 #> 9482       2         3  22       62416   C2     5 #> 9483       2         3  22       62416   C3     5 #> 9484       2         3  22       62416   C4     3 #> 9485       2         3  22       62416   C5     2 #> 9486       2         3  22       62416   E1     1 #> 9487       2         3  22       62416   E2     1 #> 9488       2         3  22       62416   E3     3 #> 9489       2         3  22       62416   E4     6 #> 9490       2         3  22       62416   E5     5 #> 9491       2         3  22       62416   N1     1 #> 9492       2         3  22       62416   N2     1 #> 9493       2         3  22       62416   N3     1 #> 9494       2         3  22       62416   N4     1 #> 9495       2         3  22       62416   N5     1 #> 9496       2         3  22       62416   O1     5 #> 9497       2         3  22       62416   O2     1 #> 9498       2         3  22       62416   O3     5 #> 9499       2         3  22       62416   O4     1 #> 9500       2         3  22       62416   O5     6 #> 9501       2         3  30       62419   A1     4 #> 9502       2         3  30       62419   A2     6 #> 9503       2         3  30       62419   A3     6 #> 9504       2         3  30       62419   A4     6 #> 9505       2         3  30       62419   A5     5 #> 9506       2         3  30       62419   C1     4 #> 9507       2         3  30       62419   C2     5 #> 9508       2         3  30       62419   C3     6 #> 9509       2         3  30       62419   C4    NA #> 9510       2         3  30       62419   C5     1 #> 9511       2         3  30       62419   E1     1 #> 9512       2         3  30       62419   E2     3 #> 9513       2         3  30       62419   E3     6 #> 9514       2         3  30       62419   E4     6 #> 9515       2         3  30       62419   E5     6 #> 9516       2         3  30       62419   N1     1 #> 9517       2         3  30       62419   N2     6 #> 9518       2         3  30       62419   N3     6 #> 9519       2         3  30       62419   N4     3 #> 9520       2         3  30       62419   N5     4 #> 9521       2         3  30       62419   O1     6 #> 9522       2         3  30       62419   O2     1 #> 9523       2         3  30       62419   O3     6 #> 9524       2         3  30       62419   O4     5 #> 9525       2         3  30       62419   O5     1 #> 9526       2         3  38       62421   A1     4 #> 9527       2         3  38       62421   A2     4 #> 9528       2         3  38       62421   A3     4 #> 9529       2         3  38       62421   A4     4 #> 9530       2         3  38       62421   A5     5 #> 9531       2         3  38       62421   C1     5 #> 9532       2         3  38       62421   C2     5 #> 9533       2         3  38       62421   C3     4 #> 9534       2         3  38       62421   C4     2 #> 9535       2         3  38       62421   C5     3 #> 9536       2         3  38       62421   E1     4 #> 9537       2         3  38       62421   E2     2 #> 9538       2         3  38       62421   E3     4 #> 9539       2         3  38       62421   E4     4 #> 9540       2         3  38       62421   E5     5 #> 9541       2         3  38       62421   N1     2 #> 9542       2         3  38       62421   N2     4 #> 9543       2         3  38       62421   N3     5 #> 9544       2         3  38       62421   N4     4 #> 9545       2         3  38       62421   N5     5 #> 9546       2         3  38       62421   O1     4 #> 9547       2         3  38       62421   O2     1 #> 9548       2         3  38       62421   O3     4 #> 9549       2         3  38       62421   O4     5 #> 9550       2         3  38       62421   O5     3 #> 9551       2         2  53       62423   A1     2 #> 9552       2         2  53       62423   A2     6 #> 9553       2         2  53       62423   A3     5 #> 9554       2         2  53       62423   A4     6 #> 9555       2         2  53       62423   A5     3 #> 9556       2         2  53       62423   C1     5 #> 9557       2         2  53       62423   C2     6 #> 9558       2         2  53       62423   C3     5 #> 9559       2         2  53       62423   C4     2 #> 9560       2         2  53       62423   C5     3 #> 9561       2         2  53       62423   E1     3 #> 9562       2         2  53       62423   E2     2 #> 9563       2         2  53       62423   E3     5 #> 9564       2         2  53       62423   E4     5 #> 9565       2         2  53       62423   E5     5 #> 9566       2         2  53       62423   N1     3 #> 9567       2         2  53       62423   N2     5 #> 9568       2         2  53       62423   N3     5 #> 9569       2         2  53       62423   N4     1 #> 9570       2         2  53       62423   N5     4 #> 9571       2         2  53       62423   O1     4 #> 9572       2         2  53       62423   O2     2 #> 9573       2         2  53       62423   O3     4 #> 9574       2         2  53       62423   O4     6 #> 9575       2         2  53       62423   O5     2 #> 9576       1         3  28       62426   A1     2 #> 9577       1         3  28       62426   A2     4 #> 9578       1         3  28       62426   A3     4 #> 9579       1         3  28       62426   A4     6 #> 9580       1         3  28       62426   A5    NA #> 9581       1         3  28       62426   C1     5 #> 9582       1         3  28       62426   C2     4 #> 9583       1         3  28       62426   C3     5 #> 9584       1         3  28       62426   C4     4 #> 9585       1         3  28       62426   C5     4 #> 9586       1         3  28       62426   E1     6 #> 9587       1         3  28       62426   E2     4 #> 9588       1         3  28       62426   E3     4 #> 9589       1         3  28       62426   E4     5 #> 9590       1         3  28       62426   E5     2 #> 9591       1         3  28       62426   N1     1 #> 9592       1         3  28       62426   N2     2 #> 9593       1         3  28       62426   N3     3 #> 9594       1         3  28       62426   N4     4 #> 9595       1         3  28       62426   N5     2 #> 9596       1         3  28       62426   O1     3 #> 9597       1         3  28       62426   O2     1 #> 9598       1         3  28       62426   O3     2 #> 9599       1         3  28       62426   O4     4 #> 9600       1         3  28       62426   O5     5 #> 9601       1         3  18       62433   A1     5 #> 9602       1         3  18       62433   A2     6 #> 9603       1         3  18       62433   A3     6 #> 9604       1         3  18       62433   A4     5 #> 9605       1         3  18       62433   A5     6 #> 9606       1         3  18       62433   C1     5 #> 9607       1         3  18       62433   C2     6 #> 9608       1         3  18       62433   C3     2 #> 9609       1         3  18       62433   C4     2 #> 9610       1         3  18       62433   C5     4 #> 9611       1         3  18       62433   E1     3 #> 9612       1         3  18       62433   E2     4 #> 9613       1         3  18       62433   E3     5 #> 9614       1         3  18       62433   E4     5 #> 9615       1         3  18       62433   E5     5 #> 9616       1         3  18       62433   N1     3 #> 9617       1         3  18       62433   N2     3 #> 9618       1         3  18       62433   N3     2 #> 9619       1         3  18       62433   N4     4 #> 9620       1         3  18       62433   N5     3 #> 9621       1         3  18       62433   O1     6 #> 9622       1         3  18       62433   O2     4 #> 9623       1         3  18       62433   O3     5 #> 9624       1         3  18       62433   O4     6 #> 9625       1         3  18       62433   O5     4 #> 9626       2         4  31       62434   A1     1 #> 9627       2         4  31       62434   A2     5 #> 9628       2         4  31       62434   A3     6 #> 9629       2         4  31       62434   A4     6 #> 9630       2         4  31       62434   A5     6 #> 9631       2         4  31       62434   C1     6 #> 9632       2         4  31       62434   C2     5 #> 9633       2         4  31       62434   C3     5 #> 9634       2         4  31       62434   C4     1 #> 9635       2         4  31       62434   C5     1 #> 9636       2         4  31       62434   E1     2 #> 9637       2         4  31       62434   E2     4 #> 9638       2         4  31       62434   E3     5 #> 9639       2         4  31       62434   E4     6 #> 9640       2         4  31       62434   E5     5 #> 9641       2         4  31       62434   N1     2 #> 9642       2         4  31       62434   N2     3 #> 9643       2         4  31       62434   N3     2 #> 9644       2         4  31       62434   N4     2 #> 9645       2         4  31       62434   N5     4 #> 9646       2         4  31       62434   O1     5 #> 9647       2         4  31       62434   O2     1 #> 9648       2         4  31       62434   O3     4 #> 9649       2         4  31       62434   O4     5 #> 9650       2         4  31       62434   O5     2 #> 9651       2         1  32       62435   A1     2 #> 9652       2         1  32       62435   A2     5 #> 9653       2         1  32       62435   A3     5 #> 9654       2         1  32       62435   A4     4 #> 9655       2         1  32       62435   A5     6 #> 9656       2         1  32       62435   C1     5 #> 9657       2         1  32       62435   C2     5 #> 9658       2         1  32       62435   C3     5 #> 9659       2         1  32       62435   C4     2 #> 9660       2         1  32       62435   C5     2 #> 9661       2         1  32       62435   E1     5 #> 9662       2         1  32       62435   E2     4 #> 9663       2         1  32       62435   E3     5 #> 9664       2         1  32       62435   E4     4 #> 9665       2         1  32       62435   E5     5 #> 9666       2         1  32       62435   N1     1 #> 9667       2         1  32       62435   N2     1 #> 9668       2         1  32       62435   N3     1 #> 9669       2         1  32       62435   N4     1 #> 9670       2         1  32       62435   N5     3 #> 9671       2         1  32       62435   O1     5 #> 9672       2         1  32       62435   O2     4 #> 9673       2         1  32       62435   O3     4 #> 9674       2         1  32       62435   O4     4 #> 9675       2         1  32       62435   O5     2 #> 9676       2         5  40       62438   A1     4 #> 9677       2         5  40       62438   A2     4 #> 9678       2         5  40       62438   A3     4 #> 9679       2         5  40       62438   A4    NA #> 9680       2         5  40       62438   A5     5 #> 9681       2         5  40       62438   C1     4 #> 9682       2         5  40       62438   C2     4 #> 9683       2         5  40       62438   C3     4 #> 9684       2         5  40       62438   C4    NA #> 9685       2         5  40       62438   C5     3 #> 9686       2         5  40       62438   E1     4 #> 9687       2         5  40       62438   E2     2 #> 9688       2         5  40       62438   E3     4 #> 9689       2         5  40       62438   E4    NA #> 9690       2         5  40       62438   E5     5 #> 9691       2         5  40       62438   N1     4 #> 9692       2         5  40       62438   N2     4 #> 9693       2         5  40       62438   N3     4 #> 9694       2         5  40       62438   N4     4 #> 9695       2         5  40       62438   N5     4 #> 9696       2         5  40       62438   O1     5 #> 9697       2         5  40       62438   O2     4 #> 9698       2         5  40       62438   O3    NA #> 9699       2         5  40       62438   O4     5 #> 9700       2         5  40       62438   O5     2 #> 9701       2        NA  15       62440   A1     1 #> 9702       2        NA  15       62440   A2     6 #> 9703       2        NA  15       62440   A3     5 #> 9704       2        NA  15       62440   A4     5 #> 9705       2        NA  15       62440   A5     6 #> 9706       2        NA  15       62440   C1     3 #> 9707       2        NA  15       62440   C2     4 #> 9708       2        NA  15       62440   C3     5 #> 9709       2        NA  15       62440   C4     5 #> 9710       2        NA  15       62440   C5     6 #> 9711       2        NA  15       62440   E1     4 #> 9712       2        NA  15       62440   E2     5 #> 9713       2        NA  15       62440   E3     6 #> 9714       2        NA  15       62440   E4     6 #> 9715       2        NA  15       62440   E5     4 #> 9716       2        NA  15       62440   N1     4 #> 9717       2        NA  15       62440   N2     4 #> 9718       2        NA  15       62440   N3     4 #> 9719       2        NA  15       62440   N4     6 #> 9720       2        NA  15       62440   N5     4 #> 9721       2        NA  15       62440   O1     6 #> 9722       2        NA  15       62440   O2     1 #> 9723       2        NA  15       62440   O3     6 #> 9724       2        NA  15       62440   O4     6 #> 9725       2        NA  15       62440   O5     3 #> 9726       2         2  23       62443   A1     4 #> 9727       2         2  23       62443   A2     2 #> 9728       2         2  23       62443   A3     4 #> 9729       2         2  23       62443   A4     6 #> 9730       2         2  23       62443   A5     5 #> 9731       2         2  23       62443   C1     5 #> 9732       2         2  23       62443   C2     5 #> 9733       2         2  23       62443   C3     4 #> 9734       2         2  23       62443   C4     1 #> 9735       2         2  23       62443   C5     2 #> 9736       2         2  23       62443   E1     3 #> 9737       2         2  23       62443   E2     2 #> 9738       2         2  23       62443   E3     3 #> 9739       2         2  23       62443   E4     5 #> 9740       2         2  23       62443   E5     5 #> 9741       2         2  23       62443   N1     3 #> 9742       2         2  23       62443   N2     4 #> 9743       2         2  23       62443   N3     2 #> 9744       2         2  23       62443   N4     1 #> 9745       2         2  23       62443   N5     1 #> 9746       2         2  23       62443   O1     3 #> 9747       2         2  23       62443   O2     4 #> 9748       2         2  23       62443   O3     1 #> 9749       2         2  23       62443   O4     4 #> 9750       2         2  23       62443   O5     2 #> 9751       2         4  34       62444   A1     2 #> 9752       2         4  34       62444   A2     5 #> 9753       2         4  34       62444   A3     5 #> 9754       2         4  34       62444   A4     4 #> 9755       2         4  34       62444   A5     4 #> 9756       2         4  34       62444   C1     2 #> 9757       2         4  34       62444   C2     3 #> 9758       2         4  34       62444   C3     3 #> 9759       2         4  34       62444   C4     4 #> 9760       2         4  34       62444   C5     3 #> 9761       2         4  34       62444   E1     1 #> 9762       2         4  34       62444   E2     6 #> 9763       2         4  34       62444   E3     4 #> 9764       2         4  34       62444   E4     3 #> 9765       2         4  34       62444   E5     6 #> 9766       2         4  34       62444   N1     4 #> 9767       2         4  34       62444   N2     5 #> 9768       2         4  34       62444   N3     4 #> 9769       2         4  34       62444   N4     4 #> 9770       2         4  34       62444   N5     2 #> 9771       2         4  34       62444   O1     6 #> 9772       2         4  34       62444   O2     2 #> 9773       2         4  34       62444   O3     5 #> 9774       2         4  34       62444   O4     6 #> 9775       2         4  34       62444   O5     2 #> 9776       1         5  29       62447   A1     2 #> 9777       1         5  29       62447   A2     6 #> 9778       1         5  29       62447   A3     6 #> 9779       1         5  29       62447   A4     6 #> 9780       1         5  29       62447   A5     6 #> 9781       1         5  29       62447   C1     5 #> 9782       1         5  29       62447   C2     5 #> 9783       1         5  29       62447   C3     5 #> 9784       1         5  29       62447   C4     1 #> 9785       1         5  29       62447   C5     5 #> 9786       1         5  29       62447   E1     5 #> 9787       1         5  29       62447   E2     1 #> 9788       1         5  29       62447   E3     5 #> 9789       1         5  29       62447   E4     5 #> 9790       1         5  29       62447   E5     6 #> 9791       1         5  29       62447   N1     5 #> 9792       1         5  29       62447   N2     5 #> 9793       1         5  29       62447   N3     5 #> 9794       1         5  29       62447   N4     4 #> 9795       1         5  29       62447   N5     2 #> 9796       1         5  29       62447   O1     6 #> 9797       1         5  29       62447   O2     2 #> 9798       1         5  29       62447   O3     6 #> 9799       1         5  29       62447   O4     5 #> 9800       1         5  29       62447   O5     1 #> 9801       2         3  51       62448   A1     1 #> 9802       2         3  51       62448   A2     6 #> 9803       2         3  51       62448   A3     2 #> 9804       2         3  51       62448   A4     6 #> 9805       2         3  51       62448   A5     5 #> 9806       2         3  51       62448   C1     5 #> 9807       2         3  51       62448   C2     5 #> 9808       2         3  51       62448   C3     5 #> 9809       2         3  51       62448   C4     4 #> 9810       2         3  51       62448   C5     2 #> 9811       2         3  51       62448   E1     2 #> 9812       2         3  51       62448   E2     6 #> 9813       2         3  51       62448   E3     1 #> 9814       2         3  51       62448   E4     4 #> 9815       2         3  51       62448   E5     5 #> 9816       2         3  51       62448   N1     4 #> 9817       2         3  51       62448   N2     2 #> 9818       2         3  51       62448   N3     1 #> 9819       2         3  51       62448   N4     2 #> 9820       2         3  51       62448   N5     3 #> 9821       2         3  51       62448   O1     6 #> 9822       2         3  51       62448   O2     5 #> 9823       2         3  51       62448   O3     3 #> 9824       2         3  51       62448   O4     6 #> 9825       2         3  51       62448   O5     5 #> 9826       2         3  22       62450   A1     1 #> 9827       2         3  22       62450   A2     6 #> 9828       2         3  22       62450   A3     6 #> 9829       2         3  22       62450   A4     6 #> 9830       2         3  22       62450   A5     6 #> 9831       2         3  22       62450   C1     6 #> 9832       2         3  22       62450   C2     5 #> 9833       2         3  22       62450   C3     3 #> 9834       2         3  22       62450   C4     3 #> 9835       2         3  22       62450   C5     4 #> 9836       2         3  22       62450   E1     2 #> 9837       2         3  22       62450   E2     1 #> 9838       2         3  22       62450   E3     5 #> 9839       2         3  22       62450   E4     5 #> 9840       2         3  22       62450   E5     6 #> 9841       2         3  22       62450   N1     2 #> 9842       2         3  22       62450   N2     4 #> 9843       2         3  22       62450   N3     4 #> 9844       2         3  22       62450   N4     3 #> 9845       2         3  22       62450   N5     1 #> 9846       2         3  22       62450   O1     6 #> 9847       2         3  22       62450   O2     1 #> 9848       2         3  22       62450   O3     6 #> 9849       2         3  22       62450   O4     6 #> 9850       2         3  22       62450   O5     2 #> 9851       2         3  38       62453   A1     2 #> 9852       2         3  38       62453   A2     5 #> 9853       2         3  38       62453   A3     5 #> 9854       2         3  38       62453   A4     6 #> 9855       2         3  38       62453   A5     4 #> 9856       2         3  38       62453   C1     4 #> 9857       2         3  38       62453   C2     5 #> 9858       2         3  38       62453   C3     4 #> 9859       2         3  38       62453   C4     2 #> 9860       2         3  38       62453   C5     3 #> 9861       2         3  38       62453   E1     2 #> 9862       2         3  38       62453   E2     3 #> 9863       2         3  38       62453   E3     4 #> 9864       2         3  38       62453   E4     4 #> 9865       2         3  38       62453   E5     5 #> 9866       2         3  38       62453   N1     3 #> 9867       2         3  38       62453   N2     3 #> 9868       2         3  38       62453   N3     1 #> 9869       2         3  38       62453   N4     1 #> 9870       2         3  38       62453   N5     2 #> 9871       2         3  38       62453   O1     5 #> 9872       2         3  38       62453   O2     1 #> 9873       2         3  38       62453   O3     4 #> 9874       2         3  38       62453   O4     4 #> 9875       2         3  38       62453   O5     2 #> 9876       2         5  26       62454   A1     3 #> 9877       2         5  26       62454   A2     3 #> 9878       2         5  26       62454   A3     6 #> 9879       2         5  26       62454   A4     2 #> 9880       2         5  26       62454   A5     6 #> 9881       2         5  26       62454   C1     3 #> 9882       2         5  26       62454   C2     4 #> 9883       2         5  26       62454   C3     3 #> 9884       2         5  26       62454   C4     4 #> 9885       2         5  26       62454   C5     4 #> 9886       2         5  26       62454   E1     1 #> 9887       2         5  26       62454   E2     1 #> 9888       2         5  26       62454   E3     6 #> 9889       2         5  26       62454   E4     6 #> 9890       2         5  26       62454   E5     5 #> 9891       2         5  26       62454   N1     3 #> 9892       2         5  26       62454   N2     3 #> 9893       2         5  26       62454   N3     4 #> 9894       2         5  26       62454   N4     1 #> 9895       2         5  26       62454   N5     2 #> 9896       2         5  26       62454   O1    NA #> 9897       2         5  26       62454   O2     4 #> 9898       2         5  26       62454   O3     6 #> 9899       2         5  26       62454   O4     5 #> 9900       2         5  26       62454   O5     2 #> 9901       2         2  36       62457   A1     4 #> 9902       2         2  36       62457   A2     4 #> 9903       2         2  36       62457   A3    NA #> 9904       2         2  36       62457   A4     3 #> 9905       2         2  36       62457   A5     4 #> 9906       2         2  36       62457   C1     5 #> 9907       2         2  36       62457   C2     3 #> 9908       2         2  36       62457   C3     4 #> 9909       2         2  36       62457   C4     3 #> 9910       2         2  36       62457   C5     3 #> 9911       2         2  36       62457   E1     2 #> 9912       2         2  36       62457   E2     2 #> 9913       2         2  36       62457   E3     3 #> 9914       2         2  36       62457   E4     3 #> 9915       2         2  36       62457   E5     5 #> 9916       2         2  36       62457   N1     3 #> 9917       2         2  36       62457   N2     3 #> 9918       2         2  36       62457   N3     2 #> 9919       2         2  36       62457   N4     2 #> 9920       2         2  36       62457   N5     2 #> 9921       2         2  36       62457   O1     5 #> 9922       2         2  36       62457   O2     2 #> 9923       2         2  36       62457   O3     3 #> 9924       2         2  36       62457   O4     4 #> 9925       2         2  36       62457   O5     5 #> 9926       2         3  19       62462   A1     1 #> 9927       2         3  19       62462   A2     5 #> 9928       2         3  19       62462   A3     3 #> 9929       2         3  19       62462   A4     4 #> 9930       2         3  19       62462   A5     4 #> 9931       2         3  19       62462   C1     6 #> 9932       2         3  19       62462   C2     4 #> 9933       2         3  19       62462   C3     4 #> 9934       2         3  19       62462   C4     1 #> 9935       2         3  19       62462   C5     1 #> 9936       2         3  19       62462   E1     3 #> 9937       2         3  19       62462   E2     4 #> 9938       2         3  19       62462   E3     3 #> 9939       2         3  19       62462   E4     5 #> 9940       2         3  19       62462   E5     5 #> 9941       2         3  19       62462   N1     3 #> 9942       2         3  19       62462   N2     3 #> 9943       2         3  19       62462   N3     3 #> 9944       2         3  19       62462   N4     1 #> 9945       2         3  19       62462   N5     3 #> 9946       2         3  19       62462   O1     4 #> 9947       2         3  19       62462   O2     4 #> 9948       2         3  19       62462   O3     3 #> 9949       2         3  19       62462   O4     4 #> 9950       2         3  19       62462   O5     3 #> 9951       2         3  33       62463   A1     2 #> 9952       2         3  33       62463   A2     6 #> 9953       2         3  33       62463   A3     5 #> 9954       2         3  33       62463   A4     4 #> 9955       2         3  33       62463   A5     5 #> 9956       2         3  33       62463   C1     5 #> 9957       2         3  33       62463   C2     2 #> 9958       2         3  33       62463   C3     5 #> 9959       2         3  33       62463   C4     3 #> 9960       2         3  33       62463   C5     2 #> 9961       2         3  33       62463   E1     2 #> 9962       2         3  33       62463   E2     4 #> 9963       2         3  33       62463   E3     4 #> 9964       2         3  33       62463   E4     6 #> 9965       2         3  33       62463   E5     4 #> 9966       2         3  33       62463   N1     5 #> 9967       2         3  33       62463   N2     5 #> 9968       2         3  33       62463   N3     4 #> 9969       2         3  33       62463   N4     4 #> 9970       2         3  33       62463   N5     5 #> 9971       2         3  33       62463   O1     2 #> 9972       2         3  33       62463   O2     4 #> 9973       2         3  33       62463   O3     4 #> 9974       2         3  33       62463   O4     5 #> 9975       2         3  33       62463   O5     3 #> 9976       2         1  40       62464   A1     1 #> 9977       2         1  40       62464   A2     5 #> 9978       2         1  40       62464   A3     5 #> 9979       2         1  40       62464   A4     6 #> 9980       2         1  40       62464   A5     5 #> 9981       2         1  40       62464   C1     5 #> 9982       2         1  40       62464   C2     4 #> 9983       2         1  40       62464   C3     4 #> 9984       2         1  40       62464   C4     4 #> 9985       2         1  40       62464   C5     4 #> 9986       2         1  40       62464   E1     1 #> 9987       2         1  40       62464   E2     2 #> 9988       2         1  40       62464   E3     4 #> 9989       2         1  40       62464   E4     6 #> 9990       2         1  40       62464   E5     4 #> 9991       2         1  40       62464   N1     4 #> 9992       2         1  40       62464   N2     5 #> 9993       2         1  40       62464   N3     5 #> 9994       2         1  40       62464   N4     3 #> 9995       2         1  40       62464   N5     5 #> 9996       2         1  40       62464   O1     2 #> 9997       2         1  40       62464   O2     2 #> 9998       2         1  40       62464   O3     6 #> 9999       2         1  40       62464   O4     5 #> 10000      2         1  40       62464   O5     2 #> 10001      2         5  26       62467   A1     3 #> 10002      2         5  26       62467   A2     4 #> 10003      2         5  26       62467   A3     3 #> 10004      2         5  26       62467   A4     5 #> 10005      2         5  26       62467   A5     5 #> 10006      2         5  26       62467   C1     3 #> 10007      2         5  26       62467   C2     2 #> 10008      2         5  26       62467   C3     2 #> 10009      2         5  26       62467   C4     3 #> 10010      2         5  26       62467   C5     2 #> 10011      2         5  26       62467   E1     4 #> 10012      2         5  26       62467   E2     2 #> 10013      2         5  26       62467   E3     3 #> 10014      2         5  26       62467   E4     4 #> 10015      2         5  26       62467   E5     3 #> 10016      2         5  26       62467   N1     2 #> 10017      2         5  26       62467   N2     3 #> 10018      2         5  26       62467   N3     2 #> 10019      2         5  26       62467   N4     3 #> 10020      2         5  26       62467   N5     1 #> 10021      2         5  26       62467   O1     5 #> 10022      2         5  26       62467   O2     3 #> 10023      2         5  26       62467   O3     4 #> 10024      2         5  26       62467   O4     4 #> 10025      2         5  26       62467   O5     4 #> 10026      2        NA  12       62468   A1     1 #> 10027      2        NA  12       62468   A2     6 #> 10028      2        NA  12       62468   A3     6 #> 10029      2        NA  12       62468   A4     5 #> 10030      2        NA  12       62468   A5     2 #> 10031      2        NA  12       62468   C1     4 #> 10032      2        NA  12       62468   C2     2 #> 10033      2        NA  12       62468   C3     2 #> 10034      2        NA  12       62468   C4     5 #> 10035      2        NA  12       62468   C5     4 #> 10036      2        NA  12       62468   E1     6 #> 10037      2        NA  12       62468   E2     1 #> 10038      2        NA  12       62468   E3     5 #> 10039      2        NA  12       62468   E4     6 #> 10040      2        NA  12       62468   E5     1 #> 10041      2        NA  12       62468   N1     3 #> 10042      2        NA  12       62468   N2     5 #> 10043      2        NA  12       62468   N3     4 #> 10044      2        NA  12       62468   N4     4 #> 10045      2        NA  12       62468   N5     2 #> 10046      2        NA  12       62468   O1     6 #> 10047      2        NA  12       62468   O2     1 #> 10048      2        NA  12       62468   O3     4 #> 10049      2        NA  12       62468   O4     6 #> 10050      2        NA  12       62468   O5     3 #> 10051      2         3  30       62469   A1     3 #> 10052      2         3  30       62469   A2     5 #> 10053      2         3  30       62469   A3     5 #> 10054      2         3  30       62469   A4     4 #> 10055      2         3  30       62469   A5     4 #> 10056      2         3  30       62469   C1     5 #> 10057      2         3  30       62469   C2     5 #> 10058      2         3  30       62469   C3     6 #> 10059      2         3  30       62469   C4     1 #> 10060      2         3  30       62469   C5     1 #> 10061      2         3  30       62469   E1     2 #> 10062      2         3  30       62469   E2     1 #> 10063      2         3  30       62469   E3     4 #> 10064      2         3  30       62469   E4     5 #> 10065      2         3  30       62469   E5     5 #> 10066      2         3  30       62469   N1     4 #> 10067      2         3  30       62469   N2     4 #> 10068      2         3  30       62469   N3     2 #> 10069      2         3  30       62469   N4     1 #> 10070      2         3  30       62469   N5     2 #> 10071      2         3  30       62469   O1     5 #> 10072      2         3  30       62469   O2     1 #> 10073      2         3  30       62469   O3     5 #> 10074      2         3  30       62469   O4     5 #> 10075      2         3  30       62469   O5     1 #> 10076      2         1  59       62470   A1     2 #> 10077      2         1  59       62470   A2     6 #> 10078      2         1  59       62470   A3     5 #> 10079      2         1  59       62470   A4     5 #> 10080      2         1  59       62470   A5     6 #> 10081      2         1  59       62470   C1     5 #> 10082      2         1  59       62470   C2     4 #> 10083      2         1  59       62470   C3     2 #> 10084      2         1  59       62470   C4     2 #> 10085      2         1  59       62470   C5     4 #> 10086      2         1  59       62470   E1     2 #> 10087      2         1  59       62470   E2     4 #> 10088      2         1  59       62470   E3     5 #> 10089      2         1  59       62470   E4     5 #> 10090      2         1  59       62470   E5     3 #> 10091      2         1  59       62470   N1     1 #> 10092      2         1  59       62470   N2    NA #> 10093      2         1  59       62470   N3     1 #> 10094      2         1  59       62470   N4     1 #> 10095      2         1  59       62470   N5     2 #> 10096      2         1  59       62470   O1     4 #> 10097      2         1  59       62470   O2     2 #> 10098      2         1  59       62470   O3     4 #> 10099      2         1  59       62470   O4     5 #> 10100      2         1  59       62470   O5     3 #> 10101      2         3  32       62474   A1     4 #> 10102      2         3  32       62474   A2     5 #> 10103      2         3  32       62474   A3     5 #> 10104      2         3  32       62474   A4     4 #> 10105      2         3  32       62474   A5     5 #> 10106      2         3  32       62474   C1     2 #> 10107      2         3  32       62474   C2     6 #> 10108      2         3  32       62474   C3     6 #> 10109      2         3  32       62474   C4     1 #> 10110      2         3  32       62474   C5     1 #> 10111      2         3  32       62474   E1     6 #> 10112      2         3  32       62474   E2     1 #> 10113      2         3  32       62474   E3     5 #> 10114      2         3  32       62474   E4     5 #> 10115      2         3  32       62474   E5     5 #> 10116      2         3  32       62474   N1     4 #> 10117      2         3  32       62474   N2     5 #> 10118      2         3  32       62474   N3     3 #> 10119      2         3  32       62474   N4     4 #> 10120      2         3  32       62474   N5     5 #> 10121      2         3  32       62474   O1     6 #> 10122      2         3  32       62474   O2     1 #> 10123      2         3  32       62474   O3     6 #> 10124      2         3  32       62474   O4     5 #> 10125      2         3  32       62474   O5     1 #> 10126      1         2  30       62476   A1     4 #> 10127      1         2  30       62476   A2     3 #> 10128      1         2  30       62476   A3     2 #> 10129      1         2  30       62476   A4     5 #> 10130      1         2  30       62476   A5     3 #> 10131      1         2  30       62476   C1     4 #> 10132      1         2  30       62476   C2     2 #> 10133      1         2  30       62476   C3     4 #> 10134      1         2  30       62476   C4     4 #> 10135      1         2  30       62476   C5     5 #> 10136      1         2  30       62476   E1     5 #> 10137      1         2  30       62476   E2     4 #> 10138      1         2  30       62476   E3     4 #> 10139      1         2  30       62476   E4     4 #> 10140      1         2  30       62476   E5     2 #> 10141      1         2  30       62476   N1     5 #> 10142      1         2  30       62476   N2     4 #> 10143      1         2  30       62476   N3     5 #> 10144      1         2  30       62476   N4     5 #> 10145      1         2  30       62476   N5     3 #> 10146      1         2  30       62476   O1     6 #> 10147      1         2  30       62476   O2     1 #> 10148      1         2  30       62476   O3     6 #> 10149      1         2  30       62476   O4     6 #> 10150      1         2  30       62476   O5     3 #> 10151      2         4  32       62479   A1     1 #> 10152      2         4  32       62479   A2     4 #> 10153      2         4  32       62479   A3     6 #> 10154      2         4  32       62479   A4     5 #> 10155      2         4  32       62479   A5     6 #> 10156      2         4  32       62479   C1     5 #> 10157      2         4  32       62479   C2     6 #> 10158      2         4  32       62479   C3     5 #> 10159      2         4  32       62479   C4     1 #> 10160      2         4  32       62479   C5     1 #> 10161      2         4  32       62479   E1     2 #> 10162      2         4  32       62479   E2     2 #> 10163      2         4  32       62479   E3     6 #> 10164      2         4  32       62479   E4     6 #> 10165      2         4  32       62479   E5     4 #> 10166      2         4  32       62479   N1     4 #> 10167      2         4  32       62479   N2     4 #> 10168      2         4  32       62479   N3     2 #> 10169      2         4  32       62479   N4     1 #> 10170      2         4  32       62479   N5     1 #> 10171      2         4  32       62479   O1     5 #> 10172      2         4  32       62479   O2     1 #> 10173      2         4  32       62479   O3     6 #> 10174      2         4  32       62479   O4     5 #> 10175      2         4  32       62479   O5     2 #> 10176      2         5  29       62480   A1     1 #> 10177      2         5  29       62480   A2     5 #> 10178      2         5  29       62480   A3     6 #> 10179      2         5  29       62480   A4     6 #> 10180      2         5  29       62480   A5     3 #> 10181      2         5  29       62480   C1     5 #> 10182      2         5  29       62480   C2     5 #> 10183      2         5  29       62480   C3     4 #> 10184      2         5  29       62480   C4     2 #> 10185      2         5  29       62480   C5     4 #> 10186      2         5  29       62480   E1     1 #> 10187      2         5  29       62480   E2     3 #> 10188      2         5  29       62480   E3     4 #> 10189      2         5  29       62480   E4     5 #> 10190      2         5  29       62480   E5     5 #> 10191      2         5  29       62480   N1     5 #> 10192      2         5  29       62480   N2     5 #> 10193      2         5  29       62480   N3     4 #> 10194      2         5  29       62480   N4     5 #> 10195      2         5  29       62480   N5     5 #> 10196      2         5  29       62480   O1     5 #> 10197      2         5  29       62480   O2     2 #> 10198      2         5  29       62480   O3     5 #> 10199      2         5  29       62480   O4     6 #> 10200      2         5  29       62480   O5     1 #> 10201      1         3  27       62481   A1     3 #> 10202      1         3  27       62481   A2     4 #> 10203      1         3  27       62481   A3     4 #> 10204      1         3  27       62481   A4     5 #> 10205      1         3  27       62481   A5     4 #> 10206      1         3  27       62481   C1     5 #> 10207      1         3  27       62481   C2     5 #> 10208      1         3  27       62481   C3     6 #> 10209      1         3  27       62481   C4     1 #> 10210      1         3  27       62481   C5     2 #> 10211      1         3  27       62481   E1     3 #> 10212      1         3  27       62481   E2     3 #> 10213      1         3  27       62481   E3     2 #> 10214      1         3  27       62481   E4     4 #> 10215      1         3  27       62481   E5     5 #> 10216      1         3  27       62481   N1     4 #> 10217      1         3  27       62481   N2     4 #> 10218      1         3  27       62481   N3     4 #> 10219      1         3  27       62481   N4     2 #> 10220      1         3  27       62481   N5     1 #> 10221      1         3  27       62481   O1     5 #> 10222      1         3  27       62481   O2     3 #> 10223      1         3  27       62481   O3     2 #> 10224      1         3  27       62481   O4     3 #> 10225      1         3  27       62481   O5     4 #> 10226      2         3  48       62486   A1     3 #> 10227      2         3  48       62486   A2     4 #> 10228      2         3  48       62486   A3     5 #> 10229      2         3  48       62486   A4     2 #> 10230      2         3  48       62486   A5     3 #> 10231      2         3  48       62486   C1     5 #> 10232      2         3  48       62486   C2     3 #> 10233      2         3  48       62486   C3     4 #> 10234      2         3  48       62486   C4     1 #> 10235      2         3  48       62486   C5     1 #> 10236      2         3  48       62486   E1     6 #> 10237      2         3  48       62486   E2     5 #> 10238      2         3  48       62486   E3     2 #> 10239      2         3  48       62486   E4     1 #> 10240      2         3  48       62486   E5     3 #> 10241      2         3  48       62486   N1     3 #> 10242      2         3  48       62486   N2     3 #> 10243      2         3  48       62486   N3     2 #> 10244      2         3  48       62486   N4     3 #> 10245      2         3  48       62486   N5     2 #> 10246      2         3  48       62486   O1     5 #> 10247      2         3  48       62486   O2     5 #> 10248      2         3  48       62486   O3     2 #> 10249      2         3  48       62486   O4     2 #> 10250      2         3  48       62486   O5     5 #> 10251      2         4  39       62489   A1     1 #> 10252      2         4  39       62489   A2     6 #> 10253      2         4  39       62489   A3     5 #> 10254      2         4  39       62489   A4     6 #> 10255      2         4  39       62489   A5     5 #> 10256      2         4  39       62489   C1     4 #> 10257      2         4  39       62489   C2     4 #> 10258      2         4  39       62489   C3     5 #> 10259      2         4  39       62489   C4     2 #> 10260      2         4  39       62489   C5     5 #> 10261      2         4  39       62489   E1     3 #> 10262      2         4  39       62489   E2     2 #> 10263      2         4  39       62489   E3     5 #> 10264      2         4  39       62489   E4     5 #> 10265      2         4  39       62489   E5     4 #> 10266      2         4  39       62489   N1     1 #> 10267      2         4  39       62489   N2     1 #> 10268      2         4  39       62489   N3     1 #> 10269      2         4  39       62489   N4     1 #> 10270      2         4  39       62489   N5     1 #> 10271      2         4  39       62489   O1     6 #> 10272      2         4  39       62489   O2     1 #> 10273      2         4  39       62489   O3     4 #> 10274      2         4  39       62489   O4     5 #> 10275      2         4  39       62489   O5     2 #> 10276      2         3  22       62491   A1     3 #> 10277      2         3  22       62491   A2     6 #> 10278      2         3  22       62491   A3     6 #> 10279      2         3  22       62491   A4     6 #> 10280      2         3  22       62491   A5     4 #> 10281      2         3  22       62491   C1     5 #> 10282      2         3  22       62491   C2     2 #> 10283      2         3  22       62491   C3     5 #> 10284      2         3  22       62491   C4     4 #> 10285      2         3  22       62491   C5     3 #> 10286      2         3  22       62491   E1     4 #> 10287      2         3  22       62491   E2     2 #> 10288      2         3  22       62491   E3     3 #> 10289      2         3  22       62491   E4     4 #> 10290      2         3  22       62491   E5     5 #> 10291      2         3  22       62491   N1     4 #> 10292      2         3  22       62491   N2     4 #> 10293      2         3  22       62491   N3     6 #> 10294      2         3  22       62491   N4     5 #> 10295      2         3  22       62491   N5     4 #> 10296      2         3  22       62491   O1     3 #> 10297      2         3  22       62491   O2     6 #> 10298      2         3  22       62491   O3     1 #> 10299      2         3  22       62491   O4     5 #> 10300      2         3  22       62491   O5     4 #> 10301      2         3  26       62493   A1     1 #> 10302      2         3  26       62493   A2     6 #> 10303      2         3  26       62493   A3     3 #> 10304      2         3  26       62493   A4     5 #> 10305      2         3  26       62493   A5     3 #> 10306      2         3  26       62493   C1     2 #> 10307      2         3  26       62493   C2     3 #> 10308      2         3  26       62493   C3     2 #> 10309      2         3  26       62493   C4     3 #> 10310      2         3  26       62493   C5     4 #> 10311      2         3  26       62493   E1     4 #> 10312      2         3  26       62493   E2     2 #> 10313      2         3  26       62493   E3     3 #> 10314      2         3  26       62493   E4     3 #> 10315      2         3  26       62493   E5     4 #> 10316      2         3  26       62493   N1     4 #> 10317      2         3  26       62493   N2     6 #> 10318      2         3  26       62493   N3     6 #> 10319      2         3  26       62493   N4     6 #> 10320      2         3  26       62493   N5     6 #> 10321      2         3  26       62493   O1     6 #> 10322      2         3  26       62493   O2     1 #> 10323      2         3  26       62493   O3     4 #> 10324      2         3  26       62493   O4     6 #> 10325      2         3  26       62493   O5     1 #> 10326      2         3  26       62494   A1     1 #> 10327      2         3  26       62494   A2     2 #> 10328      2         3  26       62494   A3     1 #> 10329      2         3  26       62494   A4     4 #> 10330      2         3  26       62494   A5     2 #> 10331      2         3  26       62494   C1     6 #> 10332      2         3  26       62494   C2     5 #> 10333      2         3  26       62494   C3     5 #> 10334      2         3  26       62494   C4     1 #> 10335      2         3  26       62494   C5     1 #> 10336      2         3  26       62494   E1     6 #> 10337      2         3  26       62494   E2     6 #> 10338      2         3  26       62494   E3     4 #> 10339      2         3  26       62494   E4     1 #> 10340      2         3  26       62494   E5     3 #> 10341      2         3  26       62494   N1     2 #> 10342      2         3  26       62494   N2     5 #> 10343      2         3  26       62494   N3     1 #> 10344      2         3  26       62494   N4     5 #> 10345      2         3  26       62494   N5     5 #> 10346      2         3  26       62494   O1     6 #> 10347      2         3  26       62494   O2     1 #> 10348      2         3  26       62494   O3     5 #> 10349      2         3  26       62494   O4     6 #> 10350      2         3  26       62494   O5     1 #> 10351      2         2  39       62496   A1     3 #> 10352      2         2  39       62496   A2     5 #> 10353      2         2  39       62496   A3     5 #> 10354      2         2  39       62496   A4     6 #> 10355      2         2  39       62496   A5     3 #> 10356      2         2  39       62496   C1     5 #> 10357      2         2  39       62496   C2     2 #> 10358      2         2  39       62496   C3     4 #> 10359      2         2  39       62496   C4     1 #> 10360      2         2  39       62496   C5     5 #> 10361      2         2  39       62496   E1     1 #> 10362      2         2  39       62496   E2     1 #> 10363      2         2  39       62496   E3     3 #> 10364      2         2  39       62496   E4     3 #> 10365      2         2  39       62496   E5     5 #> 10366      2         2  39       62496   N1     5 #> 10367      2         2  39       62496   N2     5 #> 10368      2         2  39       62496   N3     2 #> 10369      2         2  39       62496   N4     2 #> 10370      2         2  39       62496   N5     2 #> 10371      2         2  39       62496   O1     6 #> 10372      2         2  39       62496   O2     1 #> 10373      2         2  39       62496   O3     6 #> 10374      2         2  39       62496   O4     4 #> 10375      2         2  39       62496   O5     1 #> 10376      2         3  32       62497   A1     4 #> 10377      2         3  32       62497   A2     6 #> 10378      2         3  32       62497   A3     5 #> 10379      2         3  32       62497   A4     6 #> 10380      2         3  32       62497   A5     6 #> 10381      2         3  32       62497   C1     5 #> 10382      2         3  32       62497   C2     3 #> 10383      2         3  32       62497   C3     2 #> 10384      2         3  32       62497   C4     3 #> 10385      2         3  32       62497   C5     5 #> 10386      2         3  32       62497   E1     1 #> 10387      2         3  32       62497   E2     2 #> 10388      2         3  32       62497   E3     3 #> 10389      2         3  32       62497   E4     4 #> 10390      2         3  32       62497   E5     5 #> 10391      2         3  32       62497   N1     4 #> 10392      2         3  32       62497   N2     4 #> 10393      2         3  32       62497   N3     5 #> 10394      2         3  32       62497   N4     1 #> 10395      2         3  32       62497   N5     5 #> 10396      2         3  32       62497   O1     6 #> 10397      2         3  32       62497   O2     3 #> 10398      2         3  32       62497   O3     4 #> 10399      2         3  32       62497   O4     4 #> 10400      2         3  32       62497   O5     5 #> 10401      2         3  52       62498   A1     1 #> 10402      2         3  52       62498   A2     5 #> 10403      2         3  52       62498   A3     6 #> 10404      2         3  52       62498   A4     6 #> 10405      2         3  52       62498   A5     6 #> 10406      2         3  52       62498   C1     1 #> 10407      2         3  52       62498   C2     5 #> 10408      2         3  52       62498   C3     6 #> 10409      2         3  52       62498   C4     4 #> 10410      2         3  52       62498   C5     1 #> 10411      2         3  52       62498   E1     6 #> 10412      2         3  52       62498   E2     6 #> 10413      2         3  52       62498   E3     3 #> 10414      2         3  52       62498   E4     2 #> 10415      2         3  52       62498   E5     5 #> 10416      2         3  52       62498   N1     4 #> 10417      2         3  52       62498   N2     1 #> 10418      2         3  52       62498   N3     2 #> 10419      2         3  52       62498   N4     6 #> 10420      2         3  52       62498   N5     6 #> 10421      2         3  52       62498   O1     6 #> 10422      2         3  52       62498   O2     5 #> 10423      2         3  52       62498   O3     2 #> 10424      2         3  52       62498   O4     6 #> 10425      2         3  52       62498   O5     5 #> 10426      2         3  40       62499   A1     3 #> 10427      2         3  40       62499   A2     5 #> 10428      2         3  40       62499   A3     5 #> 10429      2         3  40       62499   A4     6 #> 10430      2         3  40       62499   A5     5 #> 10431      2         3  40       62499   C1     5 #> 10432      2         3  40       62499   C2     4 #> 10433      2         3  40       62499   C3     5 #> 10434      2         3  40       62499   C4     2 #> 10435      2         3  40       62499   C5     3 #> 10436      2         3  40       62499   E1     2 #> 10437      2         3  40       62499   E2     4 #> 10438      2         3  40       62499   E3     4 #> 10439      2         3  40       62499   E4     5 #> 10440      2         3  40       62499   E5     4 #> 10441      2         3  40       62499   N1     4 #> 10442      2         3  40       62499   N2     5 #> 10443      2         3  40       62499   N3     5 #> 10444      2         3  40       62499   N4     5 #> 10445      2         3  40       62499   N5     4 #> 10446      2         3  40       62499   O1     5 #> 10447      2         3  40       62499   O2     2 #> 10448      2         3  40       62499   O3     3 #> 10449      2         3  40       62499   O4     5 #> 10450      2         3  40       62499   O5     4 #> 10451      2         2  40       62500   A1     3 #> 10452      2         2  40       62500   A2     6 #> 10453      2         2  40       62500   A3     5 #> 10454      2         2  40       62500   A4     6 #> 10455      2         2  40       62500   A5     6 #> 10456      2         2  40       62500   C1     5 #> 10457      2         2  40       62500   C2     6 #> 10458      2         2  40       62500   C3     5 #> 10459      2         2  40       62500   C4     1 #> 10460      2         2  40       62500   C5     3 #> 10461      2         2  40       62500   E1     1 #> 10462      2         2  40       62500   E2     2 #> 10463      2         2  40       62500   E3     5 #> 10464      2         2  40       62500   E4     6 #> 10465      2         2  40       62500   E5     6 #> 10466      2         2  40       62500   N1     2 #> 10467      2         2  40       62500   N2     4 #> 10468      2         2  40       62500   N3     3 #> 10469      2         2  40       62500   N4     2 #> 10470      2         2  40       62500   N5     2 #> 10471      2         2  40       62500   O1     5 #> 10472      2         2  40       62500   O2     3 #> 10473      2         2  40       62500   O3     5 #> 10474      2         2  40       62500   O4     4 #> 10475      2         2  40       62500   O5     3 #> 10476      2         3  40       62502   A1     1 #> 10477      2         3  40       62502   A2     6 #> 10478      2         3  40       62502   A3     6 #> 10479      2         3  40       62502   A4     6 #> 10480      2         3  40       62502   A5     6 #> 10481      2         3  40       62502   C1     1 #> 10482      2         3  40       62502   C2     5 #> 10483      2         3  40       62502   C3     6 #> 10484      2         3  40       62502   C4     1 #> 10485      2         3  40       62502   C5     1 #> 10486      2         3  40       62502   E1     1 #> 10487      2         3  40       62502   E2     6 #> 10488      2         3  40       62502   E3     5 #> 10489      2         3  40       62502   E4     5 #> 10490      2         3  40       62502   E5     1 #> 10491      2         3  40       62502   N1     1 #> 10492      2         3  40       62502   N2     3 #> 10493      2         3  40       62502   N3     1 #> 10494      2         3  40       62502   N4     2 #> 10495      2         3  40       62502   N5     1 #> 10496      2         3  40       62502   O1     6 #> 10497      2         3  40       62502   O2     1 #> 10498      2         3  40       62502   O3     5 #> 10499      2         3  40       62502   O4     6 #> 10500      2         3  40       62502   O5     1 #> 10501      2         3  40       62505   A1     1 #> 10502      2         3  40       62505   A2     6 #> 10503      2         3  40       62505   A3     4 #> 10504      2         3  40       62505   A4     6 #> 10505      2         3  40       62505   A5     6 #> 10506      2         3  40       62505   C1     5 #> 10507      2         3  40       62505   C2     6 #> 10508      2         3  40       62505   C3     4 #> 10509      2         3  40       62505   C4     3 #> 10510      2         3  40       62505   C5     4 #> 10511      2         3  40       62505   E1     3 #> 10512      2         3  40       62505   E2     2 #> 10513      2         3  40       62505   E3     5 #> 10514      2         3  40       62505   E4     6 #> 10515      2         3  40       62505   E5     2 #> 10516      2         3  40       62505   N1     5 #> 10517      2         3  40       62505   N2     6 #> 10518      2         3  40       62505   N3     6 #> 10519      2         3  40       62505   N4     6 #> 10520      2         3  40       62505   N5     6 #> 10521      2         3  40       62505   O1     6 #> 10522      2         3  40       62505   O2     6 #> 10523      2         3  40       62505   O3     6 #> 10524      2         3  40       62505   O4     6 #> 10525      2         3  40       62505   O5     1 #> 10526      2         2  24       62508   A1     3 #> 10527      2         2  24       62508   A2     6 #> 10528      2         2  24       62508   A3     5 #> 10529      2         2  24       62508   A4     6 #> 10530      2         2  24       62508   A5     5 #> 10531      2         2  24       62508   C1     5 #> 10532      2         2  24       62508   C2     6 #> 10533      2         2  24       62508   C3     6 #> 10534      2         2  24       62508   C4     1 #> 10535      2         2  24       62508   C5     2 #> 10536      2         2  24       62508   E1     1 #> 10537      2         2  24       62508   E2     2 #> 10538      2         2  24       62508   E3     5 #> 10539      2         2  24       62508   E4     6 #> 10540      2         2  24       62508   E5     6 #> 10541      2         2  24       62508   N1     5 #> 10542      2         2  24       62508   N2     2 #> 10543      2         2  24       62508   N3     1 #> 10544      2         2  24       62508   N4    NA #> 10545      2         2  24       62508   N5     4 #> 10546      2         2  24       62508   O1     4 #> 10547      2         2  24       62508   O2     5 #> 10548      2         2  24       62508   O3     6 #> 10549      2         2  24       62508   O4     5 #> 10550      2         2  24       62508   O5     4 #> 10551      2         3  23       62509   A1     1 #> 10552      2         3  23       62509   A2     6 #> 10553      2         3  23       62509   A3     6 #> 10554      2         3  23       62509   A4     5 #> 10555      2         3  23       62509   A5     6 #> 10556      2         3  23       62509   C1     6 #> 10557      2         3  23       62509   C2     5 #> 10558      2         3  23       62509   C3     6 #> 10559      2         3  23       62509   C4     2 #> 10560      2         3  23       62509   C5     2 #> 10561      2         3  23       62509   E1     5 #> 10562      2         3  23       62509   E2     4 #> 10563      2         3  23       62509   E3     5 #> 10564      2         3  23       62509   E4     3 #> 10565      2         3  23       62509   E5     3 #> 10566      2         3  23       62509   N1     1 #> 10567      2         3  23       62509   N2     5 #> 10568      2         3  23       62509   N3     5 #> 10569      2         3  23       62509   N4     5 #> 10570      2         3  23       62509   N5     6 #> 10571      2         3  23       62509   O1     6 #> 10572      2         3  23       62509   O2     5 #> 10573      2         3  23       62509   O3     3 #> 10574      2         3  23       62509   O4     6 #> 10575      2         3  23       62509   O5     1 #> 10576      2         3  26       62512   A1     5 #> 10577      2         3  26       62512   A2    NA #> 10578      2         3  26       62512   A3     5 #> 10579      2         3  26       62512   A4     6 #> 10580      2         3  26       62512   A5     5 #> 10581      2         3  26       62512   C1     6 #> 10582      2         3  26       62512   C2     6 #> 10583      2         3  26       62512   C3     5 #> 10584      2         3  26       62512   C4    NA #> 10585      2         3  26       62512   C5     2 #> 10586      2         3  26       62512   E1     5 #> 10587      2         3  26       62512   E2     2 #> 10588      2         3  26       62512   E3     6 #> 10589      2         3  26       62512   E4     3 #> 10590      2         3  26       62512   E5     6 #> 10591      2         3  26       62512   N1     4 #> 10592      2         3  26       62512   N2    NA #> 10593      2         3  26       62512   N3    NA #> 10594      2         3  26       62512   N4     4 #> 10595      2         3  26       62512   N5     1 #> 10596      2         3  26       62512   O1     6 #> 10597      2         3  26       62512   O2     1 #> 10598      2         3  26       62512   O3     3 #> 10599      2         3  26       62512   O4     2 #> 10600      2         3  26       62512   O5     1 #> 10601      2         3  26       62514   A1     5 #> 10602      2         3  26       62514   A2    NA #> 10603      2         3  26       62514   A3     5 #> 10604      2         3  26       62514   A4     6 #> 10605      2         3  26       62514   A5     5 #> 10606      2         3  26       62514   C1     6 #> 10607      2         3  26       62514   C2     6 #> 10608      2         3  26       62514   C3     5 #> 10609      2         3  26       62514   C4     1 #> 10610      2         3  26       62514   C5     2 #> 10611      2         3  26       62514   E1     5 #> 10612      2         3  26       62514   E2     2 #> 10613      2         3  26       62514   E3     6 #> 10614      2         3  26       62514   E4     3 #> 10615      2         3  26       62514   E5     6 #> 10616      2         3  26       62514   N1     4 #> 10617      2         3  26       62514   N2     3 #> 10618      2         3  26       62514   N3     2 #> 10619      2         3  26       62514   N4     4 #> 10620      2         3  26       62514   N5     1 #> 10621      2         3  26       62514   O1     6 #> 10622      2         3  26       62514   O2     1 #> 10623      2         3  26       62514   O3     3 #> 10624      2         3  26       62514   O4     2 #> 10625      2         3  26       62514   O5     1 #> 10626      2         2  51       62518   A1     2 #> 10627      2         2  51       62518   A2     6 #> 10628      2         2  51       62518   A3     6 #> 10629      2         2  51       62518   A4     6 #> 10630      2         2  51       62518   A5     6 #> 10631      2         2  51       62518   C1     6 #> 10632      2         2  51       62518   C2     6 #> 10633      2         2  51       62518   C3     5 #> 10634      2         2  51       62518   C4     1 #> 10635      2         2  51       62518   C5     1 #> 10636      2         2  51       62518   E1     1 #> 10637      2         2  51       62518   E2     1 #> 10638      2         2  51       62518   E3     6 #> 10639      2         2  51       62518   E4     5 #> 10640      2         2  51       62518   E5     6 #> 10641      2         2  51       62518   N1     1 #> 10642      2         2  51       62518   N2     1 #> 10643      2         2  51       62518   N3     2 #> 10644      2         2  51       62518   N4     2 #> 10645      2         2  51       62518   N5     1 #> 10646      2         2  51       62518   O1     6 #> 10647      2         2  51       62518   O2     1 #> 10648      2         2  51       62518   O3     6 #> 10649      2         2  51       62518   O4     5 #> 10650      2         2  51       62518   O5     1 #> 10651      2         1  39       62520   A1    NA #> 10652      2         1  39       62520   A2     6 #> 10653      2         1  39       62520   A3     6 #> 10654      2         1  39       62520   A4     6 #> 10655      2         1  39       62520   A5     6 #> 10656      2         1  39       62520   C1     4 #> 10657      2         1  39       62520   C2     5 #> 10658      2         1  39       62520   C3     4 #> 10659      2         1  39       62520   C4     1 #> 10660      2         1  39       62520   C5     1 #> 10661      2         1  39       62520   E1     4 #> 10662      2         1  39       62520   E2     1 #> 10663      2         1  39       62520   E3     6 #> 10664      2         1  39       62520   E4     6 #> 10665      2         1  39       62520   E5     6 #> 10666      2         1  39       62520   N1     1 #> 10667      2         1  39       62520   N2     3 #> 10668      2         1  39       62520   N3     3 #> 10669      2         1  39       62520   N4     2 #> 10670      2         1  39       62520   N5    NA #> 10671      2         1  39       62520   O1     6 #> 10672      2         1  39       62520   O2     1 #> 10673      2         1  39       62520   O3     3 #> 10674      2         1  39       62520   O4     4 #> 10675      2         1  39       62520   O5     1 #> 10676      2         3  20       62522   A1     1 #> 10677      2         3  20       62522   A2     6 #> 10678      2         3  20       62522   A3     6 #> 10679      2         3  20       62522   A4     6 #> 10680      2         3  20       62522   A5     6 #> 10681      2         3  20       62522   C1     6 #> 10682      2         3  20       62522   C2     5 #> 10683      2         3  20       62522   C3     4 #> 10684      2         3  20       62522   C4     2 #> 10685      2         3  20       62522   C5     3 #> 10686      2         3  20       62522   E1     1 #> 10687      2         3  20       62522   E2     1 #> 10688      2         3  20       62522   E3     6 #> 10689      2         3  20       62522   E4     6 #> 10690      2         3  20       62522   E5     5 #> 10691      2         3  20       62522   N1     1 #> 10692      2         3  20       62522   N2     2 #> 10693      2         3  20       62522   N3     2 #> 10694      2         3  20       62522   N4     1 #> 10695      2         3  20       62522   N5     2 #> 10696      2         3  20       62522   O1     5 #> 10697      2         3  20       62522   O2     1 #> 10698      2         3  20       62522   O3     6 #> 10699      2         3  20       62522   O4     5 #> 10700      2         3  20       62522   O5     3 #> 10701      2         1  19       62526   A1     2 #> 10702      2         1  19       62526   A2     6 #> 10703      2         1  19       62526   A3     5 #> 10704      2         1  19       62526   A4     6 #> 10705      2         1  19       62526   A5     5 #> 10706      2         1  19       62526   C1     5 #> 10707      2         1  19       62526   C2     4 #> 10708      2         1  19       62526   C3     1 #> 10709      2         1  19       62526   C4     3 #> 10710      2         1  19       62526   C5     1 #> 10711      2         1  19       62526   E1     4 #> 10712      2         1  19       62526   E2     1 #> 10713      2         1  19       62526   E3     6 #> 10714      2         1  19       62526   E4     6 #> 10715      2         1  19       62526   E5     5 #> 10716      2         1  19       62526   N1     5 #> 10717      2         1  19       62526   N2     5 #> 10718      2         1  19       62526   N3     5 #> 10719      2         1  19       62526   N4     5 #> 10720      2         1  19       62526   N5     5 #> 10721      2         1  19       62526   O1     4 #> 10722      2         1  19       62526   O2     1 #> 10723      2         1  19       62526   O3     6 #> 10724      2         1  19       62526   O4     5 #> 10725      2         1  19       62526   O5     5 #> 10726      1         5  28       62527   A1     2 #> 10727      1         5  28       62527   A2     5 #> 10728      1         5  28       62527   A3     2 #> 10729      1         5  28       62527   A4     4 #> 10730      1         5  28       62527   A5     2 #> 10731      1         5  28       62527   C1     2 #> 10732      1         5  28       62527   C2     3 #> 10733      1         5  28       62527   C3     2 #> 10734      1         5  28       62527   C4     3 #> 10735      1         5  28       62527   C5     5 #> 10736      1         5  28       62527   E1     5 #> 10737      1         5  28       62527   E2     5 #> 10738      1         5  28       62527   E3     4 #> 10739      1         5  28       62527   E4     2 #> 10740      1         5  28       62527   E5     5 #> 10741      1         5  28       62527   N1     3 #> 10742      1         5  28       62527   N2     4 #> 10743      1         5  28       62527   N3     4 #> 10744      1         5  28       62527   N4     4 #> 10745      1         5  28       62527   N5     4 #> 10746      1         5  28       62527   O1     5 #> 10747      1         5  28       62527   O2     3 #> 10748      1         5  28       62527   O3     5 #> 10749      1         5  28       62527   O4     5 #> 10750      1         5  28       62527   O5     2 #> 10751      1         1  35       62528   A1     2 #> 10752      1         1  35       62528   A2     5 #> 10753      1         1  35       62528   A3     2 #> 10754      1         1  35       62528   A4     5 #> 10755      1         1  35       62528   A5     5 #> 10756      1         1  35       62528   C1     5 #> 10757      1         1  35       62528   C2     6 #> 10758      1         1  35       62528   C3     5 #> 10759      1         1  35       62528   C4     1 #> 10760      1         1  35       62528   C5     3 #> 10761      1         1  35       62528   E1     2 #> 10762      1         1  35       62528   E2     2 #> 10763      1         1  35       62528   E3     5 #> 10764      1         1  35       62528   E4     2 #> 10765      1         1  35       62528   E5     4 #> 10766      1         1  35       62528   N1     2 #> 10767      1         1  35       62528   N2     5 #> 10768      1         1  35       62528   N3     3 #> 10769      1         1  35       62528   N4     5 #> 10770      1         1  35       62528   N5     2 #> 10771      1         1  35       62528   O1     5 #> 10772      1         1  35       62528   O2     2 #> 10773      1         1  35       62528   O3     5 #> 10774      1         1  35       62528   O4     6 #> 10775      1         1  35       62528   O5     1 #> 10776      1         1  35       62529   A1     2 #> 10777      1         1  35       62529   A2     5 #> 10778      1         1  35       62529   A3     2 #> 10779      1         1  35       62529   A4     5 #> 10780      1         1  35       62529   A5     5 #> 10781      1         1  35       62529   C1     5 #> 10782      1         1  35       62529   C2     6 #> 10783      1         1  35       62529   C3     5 #> 10784      1         1  35       62529   C4     1 #> 10785      1         1  35       62529   C5     3 #> 10786      1         1  35       62529   E1     2 #> 10787      1         1  35       62529   E2     2 #> 10788      1         1  35       62529   E3     5 #> 10789      1         1  35       62529   E4     2 #> 10790      1         1  35       62529   E5     4 #> 10791      1         1  35       62529   N1     1 #> 10792      1         1  35       62529   N2     4 #> 10793      1         1  35       62529   N3     2 #> 10794      1         1  35       62529   N4     5 #> 10795      1         1  35       62529   N5     2 #> 10796      1         1  35       62529   O1     5 #> 10797      1         1  35       62529   O2     2 #> 10798      1         1  35       62529   O3     5 #> 10799      1         1  35       62529   O4     6 #> 10800      1         1  35       62529   O5     1 #> 10801      1         1  35       62530   A1     2 #> 10802      1         1  35       62530   A2     5 #> 10803      1         1  35       62530   A3     2 #> 10804      1         1  35       62530   A4     5 #> 10805      1         1  35       62530   A5     5 #> 10806      1         1  35       62530   C1     5 #> 10807      1         1  35       62530   C2     6 #> 10808      1         1  35       62530   C3     5 #> 10809      1         1  35       62530   C4     1 #> 10810      1         1  35       62530   C5     3 #> 10811      1         1  35       62530   E1     2 #> 10812      1         1  35       62530   E2     2 #> 10813      1         1  35       62530   E3     5 #> 10814      1         1  35       62530   E4     2 #> 10815      1         1  35       62530   E5     4 #> 10816      1         1  35       62530   N1     1 #> 10817      1         1  35       62530   N2     4 #> 10818      1         1  35       62530   N3     3 #> 10819      1         1  35       62530   N4     4 #> 10820      1         1  35       62530   N5     2 #> 10821      1         1  35       62530   O1     5 #> 10822      1         1  35       62530   O2     2 #> 10823      1         1  35       62530   O3     5 #> 10824      1         1  35       62530   O4     6 #> 10825      1         1  35       62530   O5     1 #> 10826      1         1  35       62531   A1     2 #> 10827      1         1  35       62531   A2     5 #> 10828      1         1  35       62531   A3     2 #> 10829      1         1  35       62531   A4     5 #> 10830      1         1  35       62531   A5     5 #> 10831      1         1  35       62531   C1     5 #> 10832      1         1  35       62531   C2     6 #> 10833      1         1  35       62531   C3     5 #> 10834      1         1  35       62531   C4     1 #> 10835      1         1  35       62531   C5     3 #> 10836      1         1  35       62531   E1     2 #> 10837      1         1  35       62531   E2     2 #> 10838      1         1  35       62531   E3     5 #> 10839      1         1  35       62531   E4     2 #> 10840      1         1  35       62531   E5     4 #> 10841      1         1  35       62531   N1     1 #> 10842      1         1  35       62531   N2     5 #> 10843      1         1  35       62531   N3     4 #> 10844      1         1  35       62531   N4     4 #> 10845      1         1  35       62531   N5     2 #> 10846      1         1  35       62531   O1     5 #> 10847      1         1  35       62531   O2     2 #> 10848      1         1  35       62531   O3     5 #> 10849      1         1  35       62531   O4     6 #> 10850      1         1  35       62531   O5     1 #> 10851      1         1  35       62532   A1     2 #> 10852      1         1  35       62532   A2     5 #> 10853      1         1  35       62532   A3     5 #> 10854      1         1  35       62532   A4     5 #> 10855      1         1  35       62532   A5     5 #> 10856      1         1  35       62532   C1     5 #> 10857      1         1  35       62532   C2     6 #> 10858      1         1  35       62532   C3     4 #> 10859      1         1  35       62532   C4     2 #> 10860      1         1  35       62532   C5     4 #> 10861      1         1  35       62532   E1     2 #> 10862      1         1  35       62532   E2     2 #> 10863      1         1  35       62532   E3     4 #> 10864      1         1  35       62532   E4     2 #> 10865      1         1  35       62532   E5     4 #> 10866      1         1  35       62532   N1     1 #> 10867      1         1  35       62532   N2     5 #> 10868      1         1  35       62532   N3     4 #> 10869      1         1  35       62532   N4     4 #> 10870      1         1  35       62532   N5     2 #> 10871      1         1  35       62532   O1     5 #> 10872      1         1  35       62532   O2     2 #> 10873      1         1  35       62532   O3     5 #> 10874      1         1  35       62532   O4     6 #> 10875      1         1  35       62532   O5     1 #> 10876      1         1  35       62533   A1     2 #> 10877      1         1  35       62533   A2     5 #> 10878      1         1  35       62533   A3     5 #> 10879      1         1  35       62533   A4     5 #> 10880      1         1  35       62533   A5     5 #> 10881      1         1  35       62533   C1     5 #> 10882      1         1  35       62533   C2     6 #> 10883      1         1  35       62533   C3     4 #> 10884      1         1  35       62533   C4     2 #> 10885      1         1  35       62533   C5     4 #> 10886      1         1  35       62533   E1     2 #> 10887      1         1  35       62533   E2     2 #> 10888      1         1  35       62533   E3     4 #> 10889      1         1  35       62533   E4     3 #> 10890      1         1  35       62533   E5     5 #> 10891      1         1  35       62533   N1     1 #> 10892      1         1  35       62533   N2     5 #> 10893      1         1  35       62533   N3     4 #> 10894      1         1  35       62533   N4     4 #> 10895      1         1  35       62533   N5     2 #> 10896      1         1  35       62533   O1     5 #> 10897      1         1  35       62533   O2     2 #> 10898      1         1  35       62533   O3     5 #> 10899      1         1  35       62533   O4     6 #> 10900      1         1  35       62533   O5     1 #> 10901      1         1  35       62535   A1     2 #> 10902      1         1  35       62535   A2     5 #> 10903      1         1  35       62535   A3     5 #> 10904      1         1  35       62535   A4     5 #> 10905      1         1  35       62535   A5     5 #> 10906      1         1  35       62535   C1     5 #> 10907      1         1  35       62535   C2     6 #> 10908      1         1  35       62535   C3     4 #> 10909      1         1  35       62535   C4     2 #> 10910      1         1  35       62535   C5     4 #> 10911      1         1  35       62535   E1     2 #> 10912      1         1  35       62535   E2     2 #> 10913      1         1  35       62535   E3     4 #> 10914      1         1  35       62535   E4     3 #> 10915      1         1  35       62535   E5     5 #> 10916      1         1  35       62535   N1     1 #> 10917      1         1  35       62535   N2     5 #> 10918      1         1  35       62535   N3     4 #> 10919      1         1  35       62535   N4     4 #> 10920      1         1  35       62535   N5     2 #> 10921      1         1  35       62535   O1     5 #> 10922      1         1  35       62535   O2     3 #> 10923      1         1  35       62535   O3     5 #> 10924      1         1  35       62535   O4     6 #> 10925      1         1  35       62535   O5     2 #> 10926      2         4  32       62537   A1     2 #> 10927      2         4  32       62537   A2     5 #> 10928      2         4  32       62537   A3     2 #> 10929      2         4  32       62537   A4     6 #> 10930      2         4  32       62537   A5     5 #> 10931      2         4  32       62537   C1     2 #> 10932      2         4  32       62537   C2     2 #> 10933      2         4  32       62537   C3     2 #> 10934      2         4  32       62537   C4     4 #> 10935      2         4  32       62537   C5     5 #> 10936      2         4  32       62537   E1     2 #> 10937      2         4  32       62537   E2     1 #> 10938      2         4  32       62537   E3     4 #> 10939      2         4  32       62537   E4     5 #> 10940      2         4  32       62537   E5     2 #> 10941      2         4  32       62537   N1     2 #> 10942      2         4  32       62537   N2     2 #> 10943      2         4  32       62537   N3     2 #> 10944      2         4  32       62537   N4     2 #> 10945      2         4  32       62537   N5     1 #> 10946      2         4  32       62537   O1     6 #> 10947      2         4  32       62537   O2     5 #> 10948      2         4  32       62537   O3     2 #> 10949      2         4  32       62537   O4     3 #> 10950      2         4  32       62537   O5     4 #> 10951      2         3  24       62538   A1     4 #> 10952      2         3  24       62538   A2     5 #> 10953      2         3  24       62538   A3     6 #> 10954      2         3  24       62538   A4     6 #> 10955      2         3  24       62538   A5     3 #> 10956      2         3  24       62538   C1     6 #> 10957      2         3  24       62538   C2     6 #> 10958      2         3  24       62538   C3     6 #> 10959      2         3  24       62538   C4     1 #> 10960      2         3  24       62538   C5     1 #> 10961      2         3  24       62538   E1     2 #> 10962      2         3  24       62538   E2     4 #> 10963      2         3  24       62538   E3     4 #> 10964      2         3  24       62538   E4     5 #> 10965      2         3  24       62538   E5     6 #> 10966      2         3  24       62538   N1     6 #> 10967      2         3  24       62538   N2     6 #> 10968      2         3  24       62538   N3     6 #> 10969      2         3  24       62538   N4     3 #> 10970      2         3  24       62538   N5     2 #> 10971      2         3  24       62538   O1     6 #> 10972      2         3  24       62538   O2     1 #> 10973      2         3  24       62538   O3     6 #> 10974      2         3  24       62538   O4     6 #> 10975      2         3  24       62538   O5     1 #> 10976      2         1  23       62541   A1     2 #> 10977      2         1  23       62541   A2     6 #> 10978      2         1  23       62541   A3     6 #> 10979      2         1  23       62541   A4     6 #> 10980      2         1  23       62541   A5     5 #> 10981      2         1  23       62541   C1     6 #> 10982      2         1  23       62541   C2     5 #> 10983      2         1  23       62541   C3     6 #> 10984      2         1  23       62541   C4     4 #> 10985      2         1  23       62541   C5     4 #> 10986      2         1  23       62541   E1     4 #> 10987      2         1  23       62541   E2     1 #> 10988      2         1  23       62541   E3     4 #> 10989      2         1  23       62541   E4     6 #> 10990      2         1  23       62541   E5     5 #> 10991      2         1  23       62541   N1     1 #> 10992      2         1  23       62541   N2     1 #> 10993      2         1  23       62541   N3     1 #> 10994      2         1  23       62541   N4     5 #> 10995      2         1  23       62541   N5     1 #> 10996      2         1  23       62541   O1     6 #> 10997      2         1  23       62541   O2     1 #> 10998      2         1  23       62541   O3     6 #> 10999      2         1  23       62541   O4     6 #> 11000      2         1  23       62541   O5     1 #> 11001      1         3  39       62542   A1     5 #> 11002      1         3  39       62542   A2     4 #> 11003      1         3  39       62542   A3     5 #> 11004      1         3  39       62542   A4     6 #> 11005      1         3  39       62542   A5     4 #> 11006      1         3  39       62542   C1     6 #> 11007      1         3  39       62542   C2     5 #> 11008      1         3  39       62542   C3     6 #> 11009      1         3  39       62542   C4     1 #> 11010      1         3  39       62542   C5     3 #> 11011      1         3  39       62542   E1     5 #> 11012      1         3  39       62542   E2     5 #> 11013      1         3  39       62542   E3     5 #> 11014      1         3  39       62542   E4     5 #> 11015      1         3  39       62542   E5     6 #> 11016      1         3  39       62542   N1     6 #> 11017      1         3  39       62542   N2     6 #> 11018      1         3  39       62542   N3     4 #> 11019      1         3  39       62542   N4     6 #> 11020      1         3  39       62542   N5     5 #> 11021      1         3  39       62542   O1     5 #> 11022      1         3  39       62542   O2     6 #> 11023      1         3  39       62542   O3     4 #> 11024      1         3  39       62542   O4     6 #> 11025      1         3  39       62542   O5     1 #> 11026      1         4  48       62543   A1     2 #> 11027      1         4  48       62543   A2     5 #> 11028      1         4  48       62543   A3     6 #> 11029      1         4  48       62543   A4     5 #> 11030      1         4  48       62543   A5     5 #> 11031      1         4  48       62543   C1     4 #> 11032      1         4  48       62543   C2     5 #> 11033      1         4  48       62543   C3     5 #> 11034      1         4  48       62543   C4     1 #> 11035      1         4  48       62543   C5    NA #> 11036      1         4  48       62543   E1     6 #> 11037      1         4  48       62543   E2     5 #> 11038      1         4  48       62543   E3     3 #> 11039      1         4  48       62543   E4     2 #> 11040      1         4  48       62543   E5     5 #> 11041      1         4  48       62543   N1     4 #> 11042      1         4  48       62543   N2     4 #> 11043      1         4  48       62543   N3     1 #> 11044      1         4  48       62543   N4     1 #> 11045      1         4  48       62543   N5     1 #> 11046      1         4  48       62543   O1     6 #> 11047      1         4  48       62543   O2     1 #> 11048      1         4  48       62543   O3     4 #> 11049      1         4  48       62543   O4     6 #> 11050      1         4  48       62543   O5     1 #> 11051      2         1  26       62545   A1     1 #> 11052      2         1  26       62545   A2     4 #> 11053      2         1  26       62545   A3     4 #> 11054      2         1  26       62545   A4     5 #> 11055      2         1  26       62545   A5     4 #> 11056      2         1  26       62545   C1     2 #> 11057      2         1  26       62545   C2     5 #> 11058      2         1  26       62545   C3     2 #> 11059      2         1  26       62545   C4     4 #> 11060      2         1  26       62545   C5     3 #> 11061      2         1  26       62545   E1     6 #> 11062      2         1  26       62545   E2     5 #> 11063      2         1  26       62545   E3     3 #> 11064      2         1  26       62545   E4     5 #> 11065      2         1  26       62545   E5     5 #> 11066      2         1  26       62545   N1     6 #> 11067      2         1  26       62545   N2     6 #> 11068      2         1  26       62545   N3     6 #> 11069      2         1  26       62545   N4     5 #> 11070      2         1  26       62545   N5     6 #> 11071      2         1  26       62545   O1     3 #> 11072      2         1  26       62545   O2     6 #> 11073      2         1  26       62545   O3     4 #> 11074      2         1  26       62545   O4     5 #> 11075      2         1  26       62545   O5     2 #> 11076      1         3  28       62546   A1     4 #> 11077      1         3  28       62546   A2     4 #> 11078      1         3  28       62546   A3     4 #> 11079      1         3  28       62546   A4     5 #> 11080      1         3  28       62546   A5     5 #> 11081      1         3  28       62546   C1     4 #> 11082      1         3  28       62546   C2     4 #> 11083      1         3  28       62546   C3     4 #> 11084      1         3  28       62546   C4     2 #> 11085      1         3  28       62546   C5     4 #> 11086      1         3  28       62546   E1     4 #> 11087      1         3  28       62546   E2     5 #> 11088      1         3  28       62546   E3     4 #> 11089      1         3  28       62546   E4     5 #> 11090      1         3  28       62546   E5     5 #> 11091      1         3  28       62546   N1     4 #> 11092      1         3  28       62546   N2     4 #> 11093      1         3  28       62546   N3     5 #> 11094      1         3  28       62546   N4     3 #> 11095      1         3  28       62546   N5     2 #> 11096      1         3  28       62546   O1     4 #> 11097      1         3  28       62546   O2     5 #> 11098      1         3  28       62546   O3     5 #> 11099      1         3  28       62546   O4     4 #> 11100      1         3  28       62546   O5     2 #> 11101      2         1  23       62547   A1     1 #> 11102      2         1  23       62547   A2     6 #> 11103      2         1  23       62547   A3     6 #> 11104      2         1  23       62547   A4     6 #> 11105      2         1  23       62547   A5     5 #> 11106      2         1  23       62547   C1     6 #> 11107      2         1  23       62547   C2     5 #> 11108      2         1  23       62547   C3     6 #> 11109      2         1  23       62547   C4     5 #> 11110      2         1  23       62547   C5     4 #> 11111      2         1  23       62547   E1     5 #> 11112      2         1  23       62547   E2     1 #> 11113      2         1  23       62547   E3     5 #> 11114      2         1  23       62547   E4     6 #> 11115      2         1  23       62547   E5     5 #> 11116      2         1  23       62547   N1     1 #> 11117      2         1  23       62547   N2     1 #> 11118      2         1  23       62547   N3     2 #> 11119      2         1  23       62547   N4     5 #> 11120      2         1  23       62547   N5     1 #> 11121      2         1  23       62547   O1     6 #> 11122      2         1  23       62547   O2     4 #> 11123      2         1  23       62547   O3     6 #> 11124      2         1  23       62547   O4     6 #> 11125      2         1  23       62547   O5     2 #> 11126      1         3  24       62548   A1     2 #> 11127      1         3  24       62548   A2     6 #> 11128      1         3  24       62548   A3     6 #> 11129      1         3  24       62548   A4     5 #> 11130      1         3  24       62548   A5     4 #> 11131      1         3  24       62548   C1     4 #> 11132      1         3  24       62548   C2     3 #> 11133      1         3  24       62548   C3     5 #> 11134      1         3  24       62548   C4     1 #> 11135      1         3  24       62548   C5     2 #> 11136      1         3  24       62548   E1     5 #> 11137      1         3  24       62548   E2     2 #> 11138      1         3  24       62548   E3     4 #> 11139      1         3  24       62548   E4     2 #> 11140      1         3  24       62548   E5     5 #> 11141      1         3  24       62548   N1     1 #> 11142      1         3  24       62548   N2     1 #> 11143      1         3  24       62548   N3     1 #> 11144      1         3  24       62548   N4     5 #> 11145      1         3  24       62548   N5     1 #> 11146      1         3  24       62548   O1     5 #> 11147      1         3  24       62548   O2     6 #> 11148      1         3  24       62548   O3     2 #> 11149      1         3  24       62548   O4     5 #> 11150      1         3  24       62548   O5     2 #> 11151      2         3  29       62550   A1     2 #> 11152      2         3  29       62550   A2     5 #> 11153      2         3  29       62550   A3     6 #> 11154      2         3  29       62550   A4     6 #> 11155      2         3  29       62550   A5     5 #> 11156      2         3  29       62550   C1     4 #> 11157      2         3  29       62550   C2     4 #> 11158      2         3  29       62550   C3     4 #> 11159      2         3  29       62550   C4     2 #> 11160      2         3  29       62550   C5     4 #> 11161      2         3  29       62550   E1     3 #> 11162      2         3  29       62550   E2     3 #> 11163      2         3  29       62550   E3     5 #> 11164      2         3  29       62550   E4     5 #> 11165      2         3  29       62550   E5     5 #> 11166      2         3  29       62550   N1     2 #> 11167      2         3  29       62550   N2     3 #> 11168      2         3  29       62550   N3     2 #> 11169      2         3  29       62550   N4     2 #> 11170      2         3  29       62550   N5     1 #> 11171      2         3  29       62550   O1     4 #> 11172      2         3  29       62550   O2     2 #> 11173      2         3  29       62550   O3     5 #> 11174      2         3  29       62550   O4     5 #> 11175      2         3  29       62550   O5     3 #> 11176      2        NA  19       62551   A1     5 #> 11177      2        NA  19       62551   A2     4 #> 11178      2        NA  19       62551   A3     2 #> 11179      2        NA  19       62551   A4     2 #> 11180      2        NA  19       62551   A5     1 #> 11181      2        NA  19       62551   C1     4 #> 11182      2        NA  19       62551   C2     4 #> 11183      2        NA  19       62551   C3     3 #> 11184      2        NA  19       62551   C4     6 #> 11185      2        NA  19       62551   C5     6 #> 11186      2        NA  19       62551   E1     4 #> 11187      2        NA  19       62551   E2     1 #> 11188      2        NA  19       62551   E3     4 #> 11189      2        NA  19       62551   E4     2 #> 11190      2        NA  19       62551   E5     6 #> 11191      2        NA  19       62551   N1     6 #> 11192      2        NA  19       62551   N2     6 #> 11193      2        NA  19       62551   N3     6 #> 11194      2        NA  19       62551   N4     6 #> 11195      2        NA  19       62551   N5     6 #> 11196      2        NA  19       62551   O1     6 #> 11197      2        NA  19       62551   O2     3 #> 11198      2        NA  19       62551   O3     1 #> 11199      2        NA  19       62551   O4     6 #> 11200      2        NA  19       62551   O5     1 #> 11201      1         3  29       62552   A1     6 #> 11202      1         3  29       62552   A2     1 #> 11203      1         3  29       62552   A3     1 #> 11204      1         3  29       62552   A4     4 #> 11205      1         3  29       62552   A5     1 #> 11206      1         3  29       62552   C1     6 #> 11207      1         3  29       62552   C2     6 #> 11208      1         3  29       62552   C3     6 #> 11209      1         3  29       62552   C4     1 #> 11210      1         3  29       62552   C5     1 #> 11211      1         3  29       62552   E1     6 #> 11212      1         3  29       62552   E2     6 #> 11213      1         3  29       62552   E3     1 #> 11214      1         3  29       62552   E4     1 #> 11215      1         3  29       62552   E5     2 #> 11216      1         3  29       62552   N1     1 #> 11217      1         3  29       62552   N2     1 #> 11218      1         3  29       62552   N3     1 #> 11219      1         3  29       62552   N4     1 #> 11220      1         3  29       62552   N5     1 #> 11221      1         3  29       62552   O1     6 #> 11222      1         3  29       62552   O2     5 #> 11223      1         3  29       62552   O3     6 #> 11224      1         3  29       62552   O4     6 #> 11225      1         3  29       62552   O5     4 #> 11226      2         3  26       62553   A1     1 #> 11227      2         3  26       62553   A2     6 #> 11228      2         3  26       62553   A3     6 #> 11229      2         3  26       62553   A4     6 #> 11230      2         3  26       62553   A5     5 #> 11231      2         3  26       62553   C1     6 #> 11232      2         3  26       62553   C2     6 #> 11233      2         3  26       62553   C3     6 #> 11234      2         3  26       62553   C4     1 #> 11235      2         3  26       62553   C5     1 #> 11236      2         3  26       62553   E1     2 #> 11237      2         3  26       62553   E2     3 #> 11238      2         3  26       62553   E3     6 #> 11239      2         3  26       62553   E4     5 #> 11240      2         3  26       62553   E5     6 #> 11241      2         3  26       62553   N1     3 #> 11242      2         3  26       62553   N2     3 #> 11243      2         3  26       62553   N3     3 #> 11244      2         3  26       62553   N4     1 #> 11245      2         3  26       62553   N5     2 #> 11246      2         3  26       62553   O1     6 #> 11247      2         3  26       62553   O2     1 #> 11248      2         3  26       62553   O3     6 #> 11249      2         3  26       62553   O4     5 #> 11250      2         3  26       62553   O5     1 #> 11251      2        NA  15       62555   A1     4 #> 11252      2        NA  15       62555   A2     4 #> 11253      2        NA  15       62555   A3     3 #> 11254      2        NA  15       62555   A4     4 #> 11255      2        NA  15       62555   A5     2 #> 11256      2        NA  15       62555   C1     3 #> 11257      2        NA  15       62555   C2     4 #> 11258      2        NA  15       62555   C3     4 #> 11259      2        NA  15       62555   C4     5 #> 11260      2        NA  15       62555   C5     5 #> 11261      2        NA  15       62555   E1     6 #> 11262      2        NA  15       62555   E2     5 #> 11263      2        NA  15       62555   E3     2 #> 11264      2        NA  15       62555   E4     2 #> 11265      2        NA  15       62555   E5     2 #> 11266      2        NA  15       62555   N1     5 #> 11267      2        NA  15       62555   N2     4 #> 11268      2        NA  15       62555   N3     4 #> 11269      2        NA  15       62555   N4     5 #> 11270      2        NA  15       62555   N5     5 #> 11271      2        NA  15       62555   O1     5 #> 11272      2        NA  15       62555   O2     5 #> 11273      2        NA  15       62555   O3     4 #> 11274      2        NA  15       62555   O4     4 #> 11275      2        NA  15       62555   O5     3 #> 11276      2         3  37       62556   A1     4 #> 11277      2         3  37       62556   A2     5 #> 11278      2         3  37       62556   A3     5 #> 11279      2         3  37       62556   A4     5 #> 11280      2         3  37       62556   A5     4 #> 11281      2         3  37       62556   C1     5 #> 11282      2         3  37       62556   C2     5 #> 11283      2         3  37       62556   C3     4 #> 11284      2         3  37       62556   C4     3 #> 11285      2         3  37       62556   C5     3 #> 11286      2         3  37       62556   E1     3 #> 11287      2         3  37       62556   E2     2 #> 11288      2         3  37       62556   E3     5 #> 11289      2         3  37       62556   E4     5 #> 11290      2         3  37       62556   E5     6 #> 11291      2         3  37       62556   N1     4 #> 11292      2         3  37       62556   N2     2 #> 11293      2         3  37       62556   N3     3 #> 11294      2         3  37       62556   N4     1 #> 11295      2         3  37       62556   N5     3 #> 11296      2         3  37       62556   O1     4 #> 11297      2         3  37       62556   O2     1 #> 11298      2         3  37       62556   O3     5 #> 11299      2         3  37       62556   O4    NA #> 11300      2         3  37       62556   O5     2 #> 11301      1         3  30       62557   A1     3 #> 11302      1         3  30       62557   A2     6 #> 11303      1         3  30       62557   A3     6 #> 11304      1         3  30       62557   A4     6 #> 11305      1         3  30       62557   A5     5 #> 11306      1         3  30       62557   C1     5 #> 11307      1         3  30       62557   C2     5 #> 11308      1         3  30       62557   C3     5 #> 11309      1         3  30       62557   C4     1 #> 11310      1         3  30       62557   C5     2 #> 11311      1         3  30       62557   E1     1 #> 11312      1         3  30       62557   E2     1 #> 11313      1         3  30       62557   E3     5 #> 11314      1         3  30       62557   E4     5 #> 11315      1         3  30       62557   E5     6 #> 11316      1         3  30       62557   N1     4 #> 11317      1         3  30       62557   N2     4 #> 11318      1         3  30       62557   N3     3 #> 11319      1         3  30       62557   N4     2 #> 11320      1         3  30       62557   N5     1 #> 11321      1         3  30       62557   O1     6 #> 11322      1         3  30       62557   O2     1 #> 11323      1         3  30       62557   O3     5 #> 11324      1         3  30       62557   O4     5 #> 11325      1         3  30       62557   O5     1 #> 11326      2         3  36       62559   A1     5 #> 11327      2         3  36       62559   A2     6 #> 11328      2         3  36       62559   A3     5 #> 11329      2         3  36       62559   A4     6 #> 11330      2         3  36       62559   A5     5 #> 11331      2         3  36       62559   C1     5 #> 11332      2         3  36       62559   C2     5 #> 11333      2         3  36       62559   C3     5 #> 11334      2         3  36       62559   C4     4 #> 11335      2         3  36       62559   C5     3 #> 11336      2         3  36       62559   E1     3 #> 11337      2         3  36       62559   E2     2 #> 11338      2         3  36       62559   E3     5 #> 11339      2         3  36       62559   E4     6 #> 11340      2         3  36       62559   E5     5 #> 11341      2         3  36       62559   N1     2 #> 11342      2         3  36       62559   N2     3 #> 11343      2         3  36       62559   N3     2 #> 11344      2         3  36       62559   N4     5 #> 11345      2         3  36       62559   N5     4 #> 11346      2         3  36       62559   O1     6 #> 11347      2         3  36       62559   O2     1 #> 11348      2         3  36       62559   O3     5 #> 11349      2         3  36       62559   O4     2 #> 11350      2         3  36       62559   O5     1 #> 11351      2         3  33       62561   A1     1 #> 11352      2         3  33       62561   A2     6 #> 11353      2         3  33       62561   A3     5 #> 11354      2         3  33       62561   A4     6 #> 11355      2         3  33       62561   A5     5 #> 11356      2         3  33       62561   C1     6 #> 11357      2         3  33       62561   C2     4 #> 11358      2         3  33       62561   C3     5 #> 11359      2         3  33       62561   C4     1 #> 11360      2         3  33       62561   C5     1 #> 11361      2         3  33       62561   E1     1 #> 11362      2         3  33       62561   E2     1 #> 11363      2         3  33       62561   E3     5 #> 11364      2         3  33       62561   E4     5 #> 11365      2         3  33       62561   E5     6 #> 11366      2         3  33       62561   N1     4 #> 11367      2         3  33       62561   N2     5 #> 11368      2         3  33       62561   N3     4 #> 11369      2         3  33       62561   N4     1 #> 11370      2         3  33       62561   N5     5 #> 11371      2         3  33       62561   O1     5 #> 11372      2         3  33       62561   O2     6 #> 11373      2         3  33       62561   O3     5 #> 11374      2         3  33       62561   O4     5 #> 11375      2         3  33       62561   O5     2 #> 11376      1         3  23       62562   A1     2 #> 11377      1         3  23       62562   A2     5 #> 11378      1         3  23       62562   A3     4 #> 11379      1         3  23       62562   A4     6 #> 11380      1         3  23       62562   A5     5 #> 11381      1         3  23       62562   C1     3 #> 11382      1         3  23       62562   C2     5 #> 11383      1         3  23       62562   C3     5 #> 11384      1         3  23       62562   C4     1 #> 11385      1         3  23       62562   C5     2 #> 11386      1         3  23       62562   E1     4 #> 11387      1         3  23       62562   E2     3 #> 11388      1         3  23       62562   E3     2 #> 11389      1         3  23       62562   E4     5 #> 11390      1         3  23       62562   E5     6 #> 11391      1         3  23       62562   N1     2 #> 11392      1         3  23       62562   N2     2 #> 11393      1         3  23       62562   N3     1 #> 11394      1         3  23       62562   N4     1 #> 11395      1         3  23       62562   N5     2 #> 11396      1         3  23       62562   O1     5 #> 11397      1         3  23       62562   O2     2 #> 11398      1         3  23       62562   O3     4 #> 11399      1         3  23       62562   O4     3 #> 11400      1         3  23       62562   O5     4 #> 11401      2         2  22       62565   A1     1 #> 11402      2         2  22       62565   A2     6 #> 11403      2         2  22       62565   A3     6 #> 11404      2         2  22       62565   A4     6 #> 11405      2         2  22       62565   A5     6 #> 11406      2         2  22       62565   C1     1 #> 11407      2         2  22       62565   C2     6 #> 11408      2         2  22       62565   C3     6 #> 11409      2         2  22       62565   C4     1 #> 11410      2         2  22       62565   C5     1 #> 11411      2         2  22       62565   E1     1 #> 11412      2         2  22       62565   E2     1 #> 11413      2         2  22       62565   E3     6 #> 11414      2         2  22       62565   E4     6 #> 11415      2         2  22       62565   E5     6 #> 11416      2         2  22       62565   N1     1 #> 11417      2         2  22       62565   N2     4 #> 11418      2         2  22       62565   N3     5 #> 11419      2         2  22       62565   N4     1 #> 11420      2         2  22       62565   N5     1 #> 11421      2         2  22       62565   O1     6 #> 11422      2         2  22       62565   O2     1 #> 11423      2         2  22       62565   O3     6 #> 11424      2         2  22       62565   O4     6 #> 11425      2         2  22       62565   O5     1 #> 11426      2         3  17       62567   A1     2 #> 11427      2         3  17       62567   A2     5 #> 11428      2         3  17       62567   A3     6 #> 11429      2         3  17       62567   A4     5 #> 11430      2         3  17       62567   A5     4 #> 11431      2         3  17       62567   C1     6 #> 11432      2         3  17       62567   C2     6 #> 11433      2         3  17       62567   C3     4 #> 11434      2         3  17       62567   C4     1 #> 11435      2         3  17       62567   C5     5 #> 11436      2         3  17       62567   E1     1 #> 11437      2         3  17       62567   E2     2 #> 11438      2         3  17       62567   E3     4 #> 11439      2         3  17       62567   E4     5 #> 11440      2         3  17       62567   E5     5 #> 11441      2         3  17       62567   N1     5 #> 11442      2         3  17       62567   N2     5 #> 11443      2         3  17       62567   N3     5 #> 11444      2         3  17       62567   N4     2 #> 11445      2         3  17       62567   N5     2 #> 11446      2         3  17       62567   O1     6 #> 11447      2         3  17       62567   O2     1 #> 11448      2         3  17       62567   O3     6 #> 11449      2         3  17       62567   O4     6 #> 11450      2         3  17       62567   O5     1 #> 11451      1         2  28       62570   A1     2 #> 11452      1         2  28       62570   A2     2 #> 11453      1         2  28       62570   A3     2 #> 11454      1         2  28       62570   A4     1 #> 11455      1         2  28       62570   A5     3 #> 11456      1         2  28       62570   C1     3 #> 11457      1         2  28       62570   C2     4 #> 11458      1         2  28       62570   C3     2 #> 11459      1         2  28       62570   C4     5 #> 11460      1         2  28       62570   C5     5 #> 11461      1         2  28       62570   E1     5 #> 11462      1         2  28       62570   E2     6 #> 11463      1         2  28       62570   E3     2 #> 11464      1         2  28       62570   E4     1 #> 11465      1         2  28       62570   E5     1 #> 11466      1         2  28       62570   N1     2 #> 11467      1         2  28       62570   N2     3 #> 11468      1         2  28       62570   N3     1 #> 11469      1         2  28       62570   N4     2 #> 11470      1         2  28       62570   N5     5 #> 11471      1         2  28       62570   O1     6 #> 11472      1         2  28       62570   O2     2 #> 11473      1         2  28       62570   O3     6 #> 11474      1         2  28       62570   O4     6 #> 11475      1         2  28       62570   O5     1 #> 11476      2         3  36       62573   A1     5 #> 11477      2         3  36       62573   A2     4 #> 11478      2         3  36       62573   A3     4 #> 11479      2         3  36       62573   A4     4 #> 11480      2         3  36       62573   A5     3 #> 11481      2         3  36       62573   C1     6 #> 11482      2         3  36       62573   C2     6 #> 11483      2         3  36       62573   C3     6 #> 11484      2         3  36       62573   C4     1 #> 11485      2         3  36       62573   C5     6 #> 11486      2         3  36       62573   E1     3 #> 11487      2         3  36       62573   E2     2 #> 11488      2         3  36       62573   E3     4 #> 11489      2         3  36       62573   E4     4 #> 11490      2         3  36       62573   E5     5 #> 11491      2         3  36       62573   N1     6 #> 11492      2         3  36       62573   N2     6 #> 11493      2         3  36       62573   N3     4 #> 11494      2         3  36       62573   N4     4 #> 11495      2         3  36       62573   N5     3 #> 11496      2         3  36       62573   O1     6 #> 11497      2         3  36       62573   O2     3 #> 11498      2         3  36       62573   O3     5 #> 11499      2         3  36       62573   O4     4 #> 11500      2         3  36       62573   O5     3 #> 11501      2         3  29       62574   A1     2 #> 11502      2         3  29       62574   A2     5 #> 11503      2         3  29       62574   A3     4 #> 11504      2         3  29       62574   A4     4 #> 11505      2         3  29       62574   A5     6 #> 11506      2         3  29       62574   C1     4 #> 11507      2         3  29       62574   C2     3 #> 11508      2         3  29       62574   C3     3 #> 11509      2         3  29       62574   C4     2 #> 11510      2         3  29       62574   C5     4 #> 11511      2         3  29       62574   E1     3 #> 11512      2         3  29       62574   E2     2 #> 11513      2         3  29       62574   E3     4 #> 11514      2         3  29       62574   E4     5 #> 11515      2         3  29       62574   E5     3 #> 11516      2         3  29       62574   N1     1 #> 11517      2         3  29       62574   N2     1 #> 11518      2         3  29       62574   N3     2 #> 11519      2         3  29       62574   N4     4 #> 11520      2         3  29       62574   N5     2 #> 11521      2         3  29       62574   O1     3 #> 11522      2         3  29       62574   O2     4 #> 11523      2         3  29       62574   O3     5 #> 11524      2         3  29       62574   O4     5 #> 11525      2         3  29       62574   O5     3 #> 11526      2         5  59       62577   A1     2 #> 11527      2         5  59       62577   A2     5 #> 11528      2         5  59       62577   A3     5 #> 11529      2         5  59       62577   A4     5 #> 11530      2         5  59       62577   A5     5 #> 11531      2         5  59       62577   C1     2 #> 11532      2         5  59       62577   C2     1 #> 11533      2         5  59       62577   C3     2 #> 11534      2         5  59       62577   C4     5 #> 11535      2         5  59       62577   C5     4 #> 11536      2         5  59       62577   E1     5 #> 11537      2         5  59       62577   E2     5 #> 11538      2         5  59       62577   E3     5 #> 11539      2         5  59       62577   E4     4 #> 11540      2         5  59       62577   E5     2 #> 11541      2         5  59       62577   N1     5 #> 11542      2         5  59       62577   N2     5 #> 11543      2         5  59       62577   N3     5 #> 11544      2         5  59       62577   N4     6 #> 11545      2         5  59       62577   N5     4 #> 11546      2         5  59       62577   O1     5 #> 11547      2         5  59       62577   O2     6 #> 11548      2         5  59       62577   O3     4 #> 11549      2         5  59       62577   O4     6 #> 11550      2         5  59       62577   O5     2 #> 11551      2         3  19       62578   A1     1 #> 11552      2         3  19       62578   A2     5 #> 11553      2         3  19       62578   A3     6 #> 11554      2         3  19       62578   A4     6 #> 11555      2         3  19       62578   A5     5 #> 11556      2         3  19       62578   C1     5 #> 11557      2         3  19       62578   C2     5 #> 11558      2         3  19       62578   C3     6 #> 11559      2         3  19       62578   C4     1 #> 11560      2         3  19       62578   C5     3 #> 11561      2         3  19       62578   E1     3 #> 11562      2         3  19       62578   E2     3 #> 11563      2         3  19       62578   E3     5 #> 11564      2         3  19       62578   E4     6 #> 11565      2         3  19       62578   E5     5 #> 11566      2         3  19       62578   N1     1 #> 11567      2         3  19       62578   N2     2 #> 11568      2         3  19       62578   N3     1 #> 11569      2         3  19       62578   N4     2 #> 11570      2         3  19       62578   N5     2 #> 11571      2         3  19       62578   O1     6 #> 11572      2         3  19       62578   O2     2 #> 11573      2         3  19       62578   O3     5 #> 11574      2         3  19       62578   O4     6 #> 11575      2         3  19       62578   O5     2 #> 11576      1         3  27       62582   A1     3 #> 11577      1         3  27       62582   A2     6 #> 11578      1         3  27       62582   A3     5 #> 11579      1         3  27       62582   A4     6 #> 11580      1         3  27       62582   A5     6 #> 11581      1         3  27       62582   C1     5 #> 11582      1         3  27       62582   C2     3 #> 11583      1         3  27       62582   C3     3 #> 11584      1         3  27       62582   C4     2 #> 11585      1         3  27       62582   C5     2 #> 11586      1         3  27       62582   E1     1 #> 11587      1         3  27       62582   E2     3 #> 11588      1         3  27       62582   E3     6 #> 11589      1         3  27       62582   E4     4 #> 11590      1         3  27       62582   E5     6 #> 11591      1         3  27       62582   N1     5 #> 11592      1         3  27       62582   N2     6 #> 11593      1         3  27       62582   N3     4 #> 11594      1         3  27       62582   N4     5 #> 11595      1         3  27       62582   N5     5 #> 11596      1         3  27       62582   O1     6 #> 11597      1         3  27       62582   O2     1 #> 11598      1         3  27       62582   O3     5 #> 11599      1         3  27       62582   O4     6 #> 11600      1         3  27       62582   O5     1 #> 11601      2         4  41       62589   A1     1 #> 11602      2         4  41       62589   A2     5 #> 11603      2         4  41       62589   A3     1 #> 11604      2         4  41       62589   A4     5 #> 11605      2         4  41       62589   A5     5 #> 11606      2         4  41       62589   C1     5 #> 11607      2         4  41       62589   C2     4 #> 11608      2         4  41       62589   C3     3 #> 11609      2         4  41       62589   C4     2 #> 11610      2         4  41       62589   C5     2 #> 11611      2         4  41       62589   E1     2 #> 11612      2         4  41       62589   E2     4 #> 11613      2         4  41       62589   E3     3 #> 11614      2         4  41       62589   E4     5 #> 11615      2         4  41       62589   E5     4 #> 11616      2         4  41       62589   N1     1 #> 11617      2         4  41       62589   N2     3 #> 11618      2         4  41       62589   N3     5 #> 11619      2         4  41       62589   N4     2 #> 11620      2         4  41       62589   N5     4 #> 11621      2         4  41       62589   O1     4 #> 11622      2         4  41       62589   O2     2 #> 11623      2         4  41       62589   O3     4 #> 11624      2         4  41       62589   O4     5 #> 11625      2         4  41       62589   O5     2 #> 11626      2         4  27       62590   A1     1 #> 11627      2         4  27       62590   A2     5 #> 11628      2         4  27       62590   A3     6 #> 11629      2         4  27       62590   A4     5 #> 11630      2         4  27       62590   A5     5 #> 11631      2         4  27       62590   C1     5 #> 11632      2         4  27       62590   C2     4 #> 11633      2         4  27       62590   C3     4 #> 11634      2         4  27       62590   C4     1 #> 11635      2         4  27       62590   C5     4 #> 11636      2         4  27       62590   E1     1 #> 11637      2         4  27       62590   E2     2 #> 11638      2         4  27       62590   E3     3 #> 11639      2         4  27       62590   E4     6 #> 11640      2         4  27       62590   E5     4 #> 11641      2         4  27       62590   N1     1 #> 11642      2         4  27       62590   N2     1 #> 11643      2         4  27       62590   N3     1 #> 11644      2         4  27       62590   N4     1 #> 11645      2         4  27       62590   N5     2 #> 11646      2         4  27       62590   O1     4 #> 11647      2         4  27       62590   O2     1 #> 11648      2         4  27       62590   O3     4 #> 11649      2         4  27       62590   O4     5 #> 11650      2         4  27       62590   O5     2 #> 11651      2         2  24       62594   A1     2 #> 11652      2         2  24       62594   A2     6 #> 11653      2         2  24       62594   A3    NA #> 11654      2         2  24       62594   A4     5 #> 11655      2         2  24       62594   A5     5 #> 11656      2         2  24       62594   C1     6 #> 11657      2         2  24       62594   C2     5 #> 11658      2         2  24       62594   C3     6 #> 11659      2         2  24       62594   C4     2 #> 11660      2         2  24       62594   C5     3 #> 11661      2         2  24       62594   E1     1 #> 11662      2         2  24       62594   E2     1 #> 11663      2         2  24       62594   E3     6 #> 11664      2         2  24       62594   E4     6 #> 11665      2         2  24       62594   E5     6 #> 11666      2         2  24       62594   N1     2 #> 11667      2         2  24       62594   N2     3 #> 11668      2         2  24       62594   N3     4 #> 11669      2         2  24       62594   N4     1 #> 11670      2         2  24       62594   N5     2 #> 11671      2         2  24       62594   O1     6 #> 11672      2         2  24       62594   O2     6 #> 11673      2         2  24       62594   O3     5 #> 11674      2         2  24       62594   O4     4 #> 11675      2         2  24       62594   O5     2 #> 11676      2         5  60       62597   A1     2 #> 11677      2         5  60       62597   A2     5 #> 11678      2         5  60       62597   A3     5 #> 11679      2         5  60       62597   A4     5 #> 11680      2         5  60       62597   A5     4 #> 11681      2         5  60       62597   C1     6 #> 11682      2         5  60       62597   C2     2 #> 11683      2         5  60       62597   C3     4 #> 11684      2         5  60       62597   C4     2 #> 11685      2         5  60       62597   C5     5 #> 11686      2         5  60       62597   E1     1 #> 11687      2         5  60       62597   E2     2 #> 11688      2         5  60       62597   E3     5 #> 11689      2         5  60       62597   E4     5 #> 11690      2         5  60       62597   E5     5 #> 11691      2         5  60       62597   N1     4 #> 11692      2         5  60       62597   N2     5 #> 11693      2         5  60       62597   N3     5 #> 11694      2         5  60       62597   N4     5 #> 11695      2         5  60       62597   N5     4 #> 11696      2         5  60       62597   O1     6 #> 11697      2         5  60       62597   O2     2 #> 11698      2         5  60       62597   O3     5 #> 11699      2         5  60       62597   O4     6 #> 11700      2         5  60       62597   O5     1 #> 11701      2         3  52       62599   A1     1 #> 11702      2         3  52       62599   A2     5 #> 11703      2         3  52       62599   A3     5 #> 11704      2         3  52       62599   A4     6 #> 11705      2         3  52       62599   A5     6 #> 11706      2         3  52       62599   C1     6 #> 11707      2         3  52       62599   C2     1 #> 11708      2         3  52       62599   C3     5 #> 11709      2         3  52       62599   C4     3 #> 11710      2         3  52       62599   C5     1 #> 11711      2         3  52       62599   E1     6 #> 11712      2         3  52       62599   E2     6 #> 11713      2         3  52       62599   E3     1 #> 11714      2         3  52       62599   E4     5 #> 11715      2         3  52       62599   E5     1 #> 11716      2         3  52       62599   N1     4 #> 11717      2         3  52       62599   N2     5 #> 11718      2         3  52       62599   N3     5 #> 11719      2         3  52       62599   N4     6 #> 11720      2         3  52       62599   N5     1 #> 11721      2         3  52       62599   O1     6 #> 11722      2         3  52       62599   O2     5 #> 11723      2         3  52       62599   O3     1 #> 11724      2         3  52       62599   O4     6 #> 11725      2         3  52       62599   O5     4 #> 11726      2         3  21       62604   A1     1 #> 11727      2         3  21       62604   A2     5 #> 11728      2         3  21       62604   A3     6 #> 11729      2         3  21       62604   A4     6 #> 11730      2         3  21       62604   A5     6 #> 11731      2         3  21       62604   C1     6 #> 11732      2         3  21       62604   C2     5 #> 11733      2         3  21       62604   C3     6 #> 11734      2         3  21       62604   C4     1 #> 11735      2         3  21       62604   C5     2 #> 11736      2         3  21       62604   E1     1 #> 11737      2         3  21       62604   E2     1 #> 11738      2         3  21       62604   E3     5 #> 11739      2         3  21       62604   E4     6 #> 11740      2         3  21       62604   E5    NA #> 11741      2         3  21       62604   N1     2 #> 11742      2         3  21       62604   N2     4 #> 11743      2         3  21       62604   N3     2 #> 11744      2         3  21       62604   N4     3 #> 11745      2         3  21       62604   N5     5 #> 11746      2         3  21       62604   O1     6 #> 11747      2         3  21       62604   O2     4 #> 11748      2         3  21       62604   O3     6 #> 11749      2         3  21       62604   O4     6 #> 11750      2         3  21       62604   O5     1 #> 11751      2         3  20       62605   A1     3 #> 11752      2         3  20       62605   A2     5 #> 11753      2         3  20       62605   A3     5 #> 11754      2         3  20       62605   A4     6 #> 11755      2         3  20       62605   A5     5 #> 11756      2         3  20       62605   C1     5 #> 11757      2         3  20       62605   C2     5 #> 11758      2         3  20       62605   C3     3 #> 11759      2         3  20       62605   C4     4 #> 11760      2         3  20       62605   C5     1 #> 11761      2         3  20       62605   E1     4 #> 11762      2         3  20       62605   E2     3 #> 11763      2         3  20       62605   E3     4 #> 11764      2         3  20       62605   E4     4 #> 11765      2         3  20       62605   E5     5 #> 11766      2         3  20       62605   N1     5 #> 11767      2         3  20       62605   N2     6 #> 11768      2         3  20       62605   N3     6 #> 11769      2         3  20       62605   N4     2 #> 11770      2         3  20       62605   N5     2 #> 11771      2         3  20       62605   O1     5 #> 11772      2         3  20       62605   O2     2 #> 11773      2         3  20       62605   O3     5 #> 11774      2         3  20       62605   O4     6 #> 11775      2         3  20       62605   O5     3 #> 11776      2         3  25       62606   A1     2 #> 11777      2         3  25       62606   A2     6 #> 11778      2         3  25       62606   A3     6 #> 11779      2         3  25       62606   A4     6 #> 11780      2         3  25       62606   A5     6 #> 11781      2         3  25       62606   C1     5 #> 11782      2         3  25       62606   C2     5 #> 11783      2         3  25       62606   C3     3 #> 11784      2         3  25       62606   C4     1 #> 11785      2         3  25       62606   C5     1 #> 11786      2         3  25       62606   E1     1 #> 11787      2         3  25       62606   E2     4 #> 11788      2         3  25       62606   E3     5 #> 11789      2         3  25       62606   E4     6 #> 11790      2         3  25       62606   E5     4 #> 11791      2         3  25       62606   N1     1 #> 11792      2         3  25       62606   N2     1 #> 11793      2         3  25       62606   N3     1 #> 11794      2         3  25       62606   N4     1 #> 11795      2         3  25       62606   N5     1 #> 11796      2         3  25       62606   O1     5 #> 11797      2         3  25       62606   O2     1 #> 11798      2         3  25       62606   O3     5 #> 11799      2         3  25       62606   O4     3 #> 11800      2         3  25       62606   O5     4 #> 11801      2         3  24       62610   A1     1 #> 11802      2         3  24       62610   A2     6 #> 11803      2         3  24       62610   A3     5 #> 11804      2         3  24       62610   A4     6 #> 11805      2         3  24       62610   A5     6 #> 11806      2         3  24       62610   C1     5 #> 11807      2         3  24       62610   C2     6 #> 11808      2         3  24       62610   C3     5 #> 11809      2         3  24       62610   C4     1 #> 11810      2         3  24       62610   C5     1 #> 11811      2         3  24       62610   E1     3 #> 11812      2         3  24       62610   E2     4 #> 11813      2         3  24       62610   E3     3 #> 11814      2         3  24       62610   E4     6 #> 11815      2         3  24       62610   E5     4 #> 11816      2         3  24       62610   N1     3 #> 11817      2         3  24       62610   N2     4 #> 11818      2         3  24       62610   N3     4 #> 11819      2         3  24       62610   N4     3 #> 11820      2         3  24       62610   N5     5 #> 11821      2         3  24       62610   O1     3 #> 11822      2         3  24       62610   O2     3 #> 11823      2         3  24       62610   O3     4 #> 11824      2         3  24       62610   O4     5 #> 11825      2         3  24       62610   O5     4 #> 11826      2         2  25       62611   A1     2 #> 11827      2         2  25       62611   A2     2 #> 11828      2         2  25       62611   A3     4 #> 11829      2         2  25       62611   A4     3 #> 11830      2         2  25       62611   A5     2 #> 11831      2         2  25       62611   C1     4 #> 11832      2         2  25       62611   C2     3 #> 11833      2         2  25       62611   C3     4 #> 11834      2         2  25       62611   C4     3 #> 11835      2         2  25       62611   C5     4 #> 11836      2         2  25       62611   E1     4 #> 11837      2         2  25       62611   E2     2 #> 11838      2         2  25       62611   E3     1 #> 11839      2         2  25       62611   E4     5 #> 11840      2         2  25       62611   E5     4 #> 11841      2         2  25       62611   N1     4 #> 11842      2         2  25       62611   N2     4 #> 11843      2         2  25       62611   N3     4 #> 11844      2         2  25       62611   N4     5 #> 11845      2         2  25       62611   N5     1 #> 11846      2         2  25       62611   O1     3 #> 11847      2         2  25       62611   O2     2 #> 11848      2         2  25       62611   O3     2 #> 11849      2         2  25       62611   O4     4 #> 11850      2         2  25       62611   O5     4 #> 11851      2         2  30       62612   A1     2 #> 11852      2         2  30       62612   A2     4 #> 11853      2         2  30       62612   A3     4 #> 11854      2         2  30       62612   A4     3 #> 11855      2         2  30       62612   A5     3 #> 11856      2         2  30       62612   C1     4 #> 11857      2         2  30       62612   C2     2 #> 11858      2         2  30       62612   C3     4 #> 11859      2         2  30       62612   C4     4 #> 11860      2         2  30       62612   C5     2 #> 11861      2         2  30       62612   E1     5 #> 11862      2         2  30       62612   E2     3 #> 11863      2         2  30       62612   E3     3 #> 11864      2         2  30       62612   E4     4 #> 11865      2         2  30       62612   E5     4 #> 11866      2         2  30       62612   N1     2 #> 11867      2         2  30       62612   N2     3 #> 11868      2         2  30       62612   N3     3 #> 11869      2         2  30       62612   N4     3 #> 11870      2         2  30       62612   N5     3 #> 11871      2         2  30       62612   O1     4 #> 11872      2         2  30       62612   O2     2 #> 11873      2         2  30       62612   O3     3 #> 11874      2         2  30       62612   O4     3 #> 11875      2         2  30       62612   O5     3 #> 11876      2         3  19       62613   A1     4 #> 11877      2         3  19       62613   A2     2 #> 11878      2         3  19       62613   A3     1 #> 11879      2         3  19       62613   A4     3 #> 11880      2         3  19       62613   A5     1 #> 11881      2         3  19       62613   C1     3 #> 11882      2         3  19       62613   C2     1 #> 11883      2         3  19       62613   C3     2 #> 11884      2         3  19       62613   C4     4 #> 11885      2         3  19       62613   C5     2 #> 11886      2         3  19       62613   E1     3 #> 11887      2         3  19       62613   E2     4 #> 11888      2         3  19       62613   E3     1 #> 11889      2         3  19       62613   E4     3 #> 11890      2         3  19       62613   E5     2 #> 11891      2         3  19       62613   N1     4 #> 11892      2         3  19       62613   N2     5 #> 11893      2         3  19       62613   N3     4 #> 11894      2         3  19       62613   N4     3 #> 11895      2         3  19       62613   N5     3 #> 11896      2         3  19       62613   O1     2 #> 11897      2         3  19       62613   O2     4 #> 11898      2         3  19       62613   O3     2 #> 11899      2         3  19       62613   O4     6 #> 11900      2         3  19       62613   O5     2 #> 11901      1         3  17       62615   A1     6 #> 11902      1         3  17       62615   A2     5 #> 11903      1         3  17       62615   A3     5 #> 11904      1         3  17       62615   A4     6 #> 11905      1         3  17       62615   A5     6 #> 11906      1         3  17       62615   C1     3 #> 11907      1         3  17       62615   C2     1 #> 11908      1         3  17       62615   C3     4 #> 11909      1         3  17       62615   C4     5 #> 11910      1         3  17       62615   C5     2 #> 11911      1         3  17       62615   E1     3 #> 11912      1         3  17       62615   E2     1 #> 11913      1         3  17       62615   E3     6 #> 11914      1         3  17       62615   E4     5 #> 11915      1         3  17       62615   E5     3 #> 11916      1         3  17       62615   N1     1 #> 11917      1         3  17       62615   N2     1 #> 11918      1         3  17       62615   N3     4 #> 11919      1         3  17       62615   N4     3 #> 11920      1         3  17       62615   N5     1 #> 11921      1         3  17       62615   O1     6 #> 11922      1         3  17       62615   O2     6 #> 11923      1         3  17       62615   O3     6 #> 11924      1         3  17       62615   O4     6 #> 11925      1         3  17       62615   O5     3 #> 11926      2         3  52       62617   A1     2 #> 11927      2         3  52       62617   A2     6 #> 11928      2         3  52       62617   A3     6 #> 11929      2         3  52       62617   A4     6 #> 11930      2         3  52       62617   A5     6 #> 11931      2         3  52       62617   C1     6 #> 11932      2         3  52       62617   C2     6 #> 11933      2         3  52       62617   C3     6 #> 11934      2         3  52       62617   C4     1 #> 11935      2         3  52       62617   C5     1 #> 11936      2         3  52       62617   E1     3 #> 11937      2         3  52       62617   E2     6 #> 11938      2         3  52       62617   E3     6 #> 11939      2         3  52       62617   E4     1 #> 11940      2         3  52       62617   E5     6 #> 11941      2         3  52       62617   N1     4 #> 11942      2         3  52       62617   N2     4 #> 11943      2         3  52       62617   N3     1 #> 11944      2         3  52       62617   N4     1 #> 11945      2         3  52       62617   N5     3 #> 11946      2         3  52       62617   O1     6 #> 11947      2         3  52       62617   O2     1 #> 11948      2         3  52       62617   O3     6 #> 11949      2         3  52       62617   O4     2 #> 11950      2         3  52       62617   O5     1 #> 11951      2         3  27       62618   A1     2 #> 11952      2         3  27       62618   A2     5 #> 11953      2         3  27       62618   A3     6 #> 11954      2         3  27       62618   A4     5 #> 11955      2         3  27       62618   A5     4 #> 11956      2         3  27       62618   C1     4 #> 11957      2         3  27       62618   C2     3 #> 11958      2         3  27       62618   C3     4 #> 11959      2         3  27       62618   C4     5 #> 11960      2         3  27       62618   C5     4 #> 11961      2         3  27       62618   E1     1 #> 11962      2         3  27       62618   E2     2 #> 11963      2         3  27       62618   E3     4 #> 11964      2         3  27       62618   E4     6 #> 11965      2         3  27       62618   E5     5 #> 11966      2         3  27       62618   N1     6 #> 11967      2         3  27       62618   N2     6 #> 11968      2         3  27       62618   N3     6 #> 11969      2         3  27       62618   N4     3 #> 11970      2         3  27       62618   N5     2 #> 11971      2         3  27       62618   O1     4 #> 11972      2         3  27       62618   O2     3 #> 11973      2         3  27       62618   O3     2 #> 11974      2         3  27       62618   O4     3 #> 11975      2         3  27       62618   O5     5 #> 11976      2         3  55       62622   A1     1 #> 11977      2         3  55       62622   A2     6 #> 11978      2         3  55       62622   A3     6 #> 11979      2         3  55       62622   A4     6 #> 11980      2         3  55       62622   A5     6 #> 11981      2         3  55       62622   C1     6 #> 11982      2         3  55       62622   C2     6 #> 11983      2         3  55       62622   C3     5 #> 11984      2         3  55       62622   C4     1 #> 11985      2         3  55       62622   C5     4 #> 11986      2         3  55       62622   E1     1 #> 11987      2         3  55       62622   E2     1 #> 11988      2         3  55       62622   E3     6 #> 11989      2         3  55       62622   E4     6 #> 11990      2         3  55       62622   E5     6 #> 11991      2         3  55       62622   N1     4 #> 11992      2         3  55       62622   N2     6 #> 11993      2         3  55       62622   N3     5 #> 11994      2         3  55       62622   N4     2 #> 11995      2         3  55       62622   N5     4 #> 11996      2         3  55       62622   O1     6 #> 11997      2         3  55       62622   O2     5 #> 11998      2         3  55       62622   O3     6 #> 11999      2         3  55       62622   O4     6 #> 12000      2         3  55       62622   O5     1 #> 12001      2         5  39       62623   A1     1 #> 12002      2         5  39       62623   A2     6 #> 12003      2         5  39       62623   A3     2 #> 12004      2         5  39       62623   A4     6 #> 12005      2         5  39       62623   A5     6 #> 12006      2         5  39       62623   C1     5 #> 12007      2         5  39       62623   C2     4 #> 12008      2         5  39       62623   C3     2 #> 12009      2         5  39       62623   C4     1 #> 12010      2         5  39       62623   C5     2 #> 12011      2         5  39       62623   E1     1 #> 12012      2         5  39       62623   E2     1 #> 12013      2         5  39       62623   E3     5 #> 12014      2         5  39       62623   E4     6 #> 12015      2         5  39       62623   E5     5 #> 12016      2         5  39       62623   N1     5 #> 12017      2         5  39       62623   N2     4 #> 12018      2         5  39       62623   N3     4 #> 12019      2         5  39       62623   N4     4 #> 12020      2         5  39       62623   N5     5 #> 12021      2         5  39       62623   O1     6 #> 12022      2         5  39       62623   O2     1 #> 12023      2         5  39       62623   O3     6 #> 12024      2         5  39       62623   O4     5 #> 12025      2         5  39       62623   O5     1 #> 12026      2         3  39       62625   A1     1 #> 12027      2         3  39       62625   A2     6 #> 12028      2         3  39       62625   A3     6 #> 12029      2         3  39       62625   A4     6 #> 12030      2         3  39       62625   A5     6 #> 12031      2         3  39       62625   C1     6 #> 12032      2         3  39       62625   C2     5 #> 12033      2         3  39       62625   C3     5 #> 12034      2         3  39       62625   C4     1 #> 12035      2         3  39       62625   C5     2 #> 12036      2         3  39       62625   E1     2 #> 12037      2         3  39       62625   E2     1 #> 12038      2         3  39       62625   E3     5 #> 12039      2         3  39       62625   E4     5 #> 12040      2         3  39       62625   E5     6 #> 12041      2         3  39       62625   N1     2 #> 12042      2         3  39       62625   N2     4 #> 12043      2         3  39       62625   N3     2 #> 12044      2         3  39       62625   N4     2 #> 12045      2         3  39       62625   N5     4 #> 12046      2         3  39       62625   O1     6 #> 12047      2         3  39       62625   O2     2 #> 12048      2         3  39       62625   O3     6 #> 12049      2         3  39       62625   O4     6 #> 12050      2         3  39       62625   O5     2 #> 12051      2         3  39       62627   A1     1 #> 12052      2         3  39       62627   A2     3 #> 12053      2         3  39       62627   A3     2 #> 12054      2         3  39       62627   A4     6 #> 12055      2         3  39       62627   A5     3 #> 12056      2         3  39       62627   C1     5 #> 12057      2         3  39       62627   C2     5 #> 12058      2         3  39       62627   C3     5 #> 12059      2         3  39       62627   C4     2 #> 12060      2         3  39       62627   C5     4 #> 12061      2         3  39       62627   E1     5 #> 12062      2         3  39       62627   E2     6 #> 12063      2         3  39       62627   E3     1 #> 12064      2         3  39       62627   E4     3 #> 12065      2         3  39       62627   E5     3 #> 12066      2         3  39       62627   N1     3 #> 12067      2         3  39       62627   N2     4 #> 12068      2         3  39       62627   N3     1 #> 12069      2         3  39       62627   N4     4 #> 12070      2         3  39       62627   N5     1 #> 12071      2         3  39       62627   O1     2 #> 12072      2         3  39       62627   O2     6 #> 12073      2         3  39       62627   O3     1 #> 12074      2         3  39       62627   O4     5 #> 12075      2         3  39       62627   O5     3 #> 12076      1         3  18       62635   A1     3 #> 12077      1         3  18       62635   A2     5 #> 12078      1         3  18       62635   A3     2 #> 12079      1         3  18       62635   A4     5 #> 12080      1         3  18       62635   A5     5 #> 12081      1         3  18       62635   C1     4 #> 12082      1         3  18       62635   C2     4 #> 12083      1         3  18       62635   C3    NA #> 12084      1         3  18       62635   C4     4 #> 12085      1         3  18       62635   C5     5 #> 12086      1         3  18       62635   E1     4 #> 12087      1         3  18       62635   E2     2 #> 12088      1         3  18       62635   E3     5 #> 12089      1         3  18       62635   E4     3 #> 12090      1         3  18       62635   E5     4 #> 12091      1         3  18       62635   N1     2 #> 12092      1         3  18       62635   N2     4 #> 12093      1         3  18       62635   N3     5 #> 12094      1         3  18       62635   N4     4 #> 12095      1         3  18       62635   N5     4 #> 12096      1         3  18       62635   O1     4 #> 12097      1         3  18       62635   O2     3 #> 12098      1         3  18       62635   O3     5 #> 12099      1         3  18       62635   O4     5 #> 12100      1         3  18       62635   O5     2 #> 12101      2         3  51       62638   A1     2 #> 12102      2         3  51       62638   A2     6 #> 12103      2         3  51       62638   A3     6 #> 12104      2         3  51       62638   A4     2 #> 12105      2         3  51       62638   A5     6 #> 12106      2         3  51       62638   C1     6 #> 12107      2         3  51       62638   C2     3 #> 12108      2         3  51       62638   C3     5 #> 12109      2         3  51       62638   C4     1 #> 12110      2         3  51       62638   C5     2 #> 12111      2         3  51       62638   E1     1 #> 12112      2         3  51       62638   E2     1 #> 12113      2         3  51       62638   E3     5 #> 12114      2         3  51       62638   E4     5 #> 12115      2         3  51       62638   E5     6 #> 12116      2         3  51       62638   N1     1 #> 12117      2         3  51       62638   N2     2 #> 12118      2         3  51       62638   N3     2 #> 12119      2         3  51       62638   N4     1 #> 12120      2         3  51       62638   N5     1 #> 12121      2         3  51       62638   O1     5 #> 12122      2         3  51       62638   O2     1 #> 12123      2         3  51       62638   O3     6 #> 12124      2         3  51       62638   O4     4 #> 12125      2         3  51       62638   O5     1 #> 12126      2         3  25       62640   A1     1 #> 12127      2         3  25       62640   A2     5 #> 12128      2         3  25       62640   A3     6 #> 12129      2         3  25       62640   A4     6 #> 12130      2         3  25       62640   A5     6 #> 12131      2         3  25       62640   C1     6 #> 12132      2         3  25       62640   C2     5 #> 12133      2         3  25       62640   C3     5 #> 12134      2         3  25       62640   C4     5 #> 12135      2         3  25       62640   C5     1 #> 12136      2         3  25       62640   E1     6 #> 12137      2         3  25       62640   E2     5 #> 12138      2         3  25       62640   E3     5 #> 12139      2         3  25       62640   E4     5 #> 12140      2         3  25       62640   E5     2 #> 12141      2         3  25       62640   N1     1 #> 12142      2         3  25       62640   N2     1 #> 12143      2         3  25       62640   N3     4 #> 12144      2         3  25       62640   N4     1 #> 12145      2         3  25       62640   N5     5 #> 12146      2         3  25       62640   O1     6 #> 12147      2         3  25       62640   O2     6 #> 12148      2         3  25       62640   O3     6 #> 12149      2         3  25       62640   O4     6 #> 12150      2         3  25       62640   O5     5 #> 12151      2         1  15       62642   A1     2 #> 12152      2         1  15       62642   A2     5 #> 12153      2         1  15       62642   A3     5 #> 12154      2         1  15       62642   A4     6 #> 12155      2         1  15       62642   A5     5 #> 12156      2         1  15       62642   C1     6 #> 12157      2         1  15       62642   C2     5 #> 12158      2         1  15       62642   C3     5 #> 12159      2         1  15       62642   C4     3 #> 12160      2         1  15       62642   C5     3 #> 12161      2         1  15       62642   E1     2 #> 12162      2         1  15       62642   E2     3 #> 12163      2         1  15       62642   E3     4 #> 12164      2         1  15       62642   E4     5 #> 12165      2         1  15       62642   E5     5 #> 12166      2         1  15       62642   N1     1 #> 12167      2         1  15       62642   N2     2 #> 12168      2         1  15       62642   N3     2 #> 12169      2         1  15       62642   N4     2 #> 12170      2         1  15       62642   N5     2 #> 12171      2         1  15       62642   O1     6 #> 12172      2         1  15       62642   O2     5 #> 12173      2         1  15       62642   O3     5 #> 12174      2         1  15       62642   O4     4 #> 12175      2         1  15       62642   O5     1 #> 12176      2         2  42       62643   A1     1 #> 12177      2         2  42       62643   A2     4 #> 12178      2         2  42       62643   A3     4 #> 12179      2         2  42       62643   A4     6 #> 12180      2         2  42       62643   A5     4 #> 12181      2         2  42       62643   C1     3 #> 12182      2         2  42       62643   C2     4 #> 12183      2         2  42       62643   C3     2 #> 12184      2         2  42       62643   C4     2 #> 12185      2         2  42       62643   C5     2 #> 12186      2         2  42       62643   E1     2 #> 12187      2         2  42       62643   E2     4 #> 12188      2         2  42       62643   E3     4 #> 12189      2         2  42       62643   E4     4 #> 12190      2         2  42       62643   E5     4 #> 12191      2         2  42       62643   N1     4 #> 12192      2         2  42       62643   N2     4 #> 12193      2         2  42       62643   N3     5 #> 12194      2         2  42       62643   N4     4 #> 12195      2         2  42       62643   N5     4 #> 12196      2         2  42       62643   O1     3 #> 12197      2         2  42       62643   O2     4 #> 12198      2         2  42       62643   O3     3 #> 12199      2         2  42       62643   O4     5 #> 12200      2         2  42       62643   O5     3 #> 12201      2         4  24       62644   A1     2 #> 12202      2         4  24       62644   A2     6 #> 12203      2         4  24       62644   A3     5 #> 12204      2         4  24       62644   A4     6 #> 12205      2         4  24       62644   A5     4 #> 12206      2         4  24       62644   C1     5 #> 12207      2         4  24       62644   C2     6 #> 12208      2         4  24       62644   C3     5 #> 12209      2         4  24       62644   C4     1 #> 12210      2         4  24       62644   C5     2 #> 12211      2         4  24       62644   E1     1 #> 12212      2         4  24       62644   E2     4 #> 12213      2         4  24       62644   E3     5 #> 12214      2         4  24       62644   E4     5 #> 12215      2         4  24       62644   E5     5 #> 12216      2         4  24       62644   N1     4 #> 12217      2         4  24       62644   N2     4 #> 12218      2         4  24       62644   N3     5 #> 12219      2         4  24       62644   N4     5 #> 12220      2         4  24       62644   N5     6 #> 12221      2         4  24       62644   O1     4 #> 12222      2         4  24       62644   O2     1 #> 12223      2         4  24       62644   O3     6 #> 12224      2         4  24       62644   O4     6 #> 12225      2         4  24       62644   O5     1 #> 12226      2         3  46       62645   A1     1 #> 12227      2         3  46       62645   A2     5 #> 12228      2         3  46       62645   A3     5 #> 12229      2         3  46       62645   A4     6 #> 12230      2         3  46       62645   A5     5 #> 12231      2         3  46       62645   C1     5 #> 12232      2         3  46       62645   C2     5 #> 12233      2         3  46       62645   C3     5 #> 12234      2         3  46       62645   C4     1 #> 12235      2         3  46       62645   C5     3 #> 12236      2         3  46       62645   E1     6 #> 12237      2         3  46       62645   E2     4 #> 12238      2         3  46       62645   E3     2 #> 12239      2         3  46       62645   E4     5 #> 12240      2         3  46       62645   E5     5 #> 12241      2         3  46       62645   N1     2 #> 12242      2         3  46       62645   N2     2 #> 12243      2         3  46       62645   N3     4 #> 12244      2         3  46       62645   N4     2 #> 12245      2         3  46       62645   N5     2 #> 12246      2         3  46       62645   O1     5 #> 12247      2         3  46       62645   O2     2 #> 12248      2         3  46       62645   O3     5 #> 12249      2         3  46       62645   O4     3 #> 12250      2         3  46       62645   O5     1 #> 12251      2         3  37       62646   A1     1 #> 12252      2         3  37       62646   A2     6 #> 12253      2         3  37       62646   A3     6 #> 12254      2         3  37       62646   A4     3 #> 12255      2         3  37       62646   A5     6 #> 12256      2         3  37       62646   C1     3 #> 12257      2         3  37       62646   C2     4 #> 12258      2         3  37       62646   C3     6 #> 12259      2         3  37       62646   C4     5 #> 12260      2         3  37       62646   C5     2 #> 12261      2         3  37       62646   E1     6 #> 12262      2         3  37       62646   E2     1 #> 12263      2         3  37       62646   E3     6 #> 12264      2         3  37       62646   E4     6 #> 12265      2         3  37       62646   E5     6 #> 12266      2         3  37       62646   N1     2 #> 12267      2         3  37       62646   N2     5 #> 12268      2         3  37       62646   N3     2 #> 12269      2         3  37       62646   N4     3 #> 12270      2         3  37       62646   N5     4 #> 12271      2         3  37       62646   O1     4 #> 12272      2         3  37       62646   O2     2 #> 12273      2         3  37       62646   O3     6 #> 12274      2         3  37       62646   O4     3 #> 12275      2         3  37       62646   O5     1 #> 12276      2         3  24       62647   A1     4 #> 12277      2         3  24       62647   A2     5 #> 12278      2         3  24       62647   A3     5 #> 12279      2         3  24       62647   A4     6 #> 12280      2         3  24       62647   A5     5 #> 12281      2         3  24       62647   C1     5 #> 12282      2         3  24       62647   C2     5 #> 12283      2         3  24       62647   C3     5 #> 12284      2         3  24       62647   C4     2 #> 12285      2         3  24       62647   C5     2 #> 12286      2         3  24       62647   E1     4 #> 12287      2         3  24       62647   E2     2 #> 12288      2         3  24       62647   E3     2 #> 12289      2         3  24       62647   E4     6 #> 12290      2         3  24       62647   E5     2 #> 12291      2         3  24       62647   N1     1 #> 12292      2         3  24       62647   N2     2 #> 12293      2         3  24       62647   N3     5 #> 12294      2         3  24       62647   N4     2 #> 12295      2         3  24       62647   N5     4 #> 12296      2         3  24       62647   O1     2 #> 12297      2         3  24       62647   O2     6 #> 12298      2         3  24       62647   O3     3 #> 12299      2         3  24       62647   O4     3 #> 12300      2         3  24       62647   O5     3 #> 12301      1         3  20       62648   A1     1 #> 12302      1         3  20       62648   A2     6 #> 12303      1         3  20       62648   A3     5 #> 12304      1         3  20       62648   A4     5 #> 12305      1         3  20       62648   A5     2 #> 12306      1         3  20       62648   C1     1 #> 12307      1         3  20       62648   C2     4 #> 12308      1         3  20       62648   C3     6 #> 12309      1         3  20       62648   C4     4 #> 12310      1         3  20       62648   C5    NA #> 12311      1         3  20       62648   E1     3 #> 12312      1         3  20       62648   E2     1 #> 12313      1         3  20       62648   E3     1 #> 12314      1         3  20       62648   E4     3 #> 12315      1         3  20       62648   E5     5 #> 12316      1         3  20       62648   N1     4 #> 12317      1         3  20       62648   N2     4 #> 12318      1         3  20       62648   N3     4 #> 12319      1         3  20       62648   N4     1 #> 12320      1         3  20       62648   N5     4 #> 12321      1         3  20       62648   O1     6 #> 12322      1         3  20       62648   O2     2 #> 12323      1         3  20       62648   O3     4 #> 12324      1         3  20       62648   O4     3 #> 12325      1         3  20       62648   O5     2 #> 12326      2         4  50       62650   A1     2 #> 12327      2         4  50       62650   A2     5 #> 12328      2         4  50       62650   A3     1 #> 12329      2         4  50       62650   A4     6 #> 12330      2         4  50       62650   A5     6 #> 12331      2         4  50       62650   C1     6 #> 12332      2         4  50       62650   C2     6 #> 12333      2         4  50       62650   C3     5 #> 12334      2         4  50       62650   C4     1 #> 12335      2         4  50       62650   C5     1 #> 12336      2         4  50       62650   E1     2 #> 12337      2         4  50       62650   E2     1 #> 12338      2         4  50       62650   E3     6 #> 12339      2         4  50       62650   E4     5 #> 12340      2         4  50       62650   E5     6 #> 12341      2         4  50       62650   N1     2 #> 12342      2         4  50       62650   N2     4 #> 12343      2         4  50       62650   N3     2 #> 12344      2         4  50       62650   N4     2 #> 12345      2         4  50       62650   N5     5 #> 12346      2         4  50       62650   O1     6 #> 12347      2         4  50       62650   O2     1 #> 12348      2         4  50       62650   O3     5 #> 12349      2         4  50       62650   O4     4 #> 12350      2         4  50       62650   O5     1 #> 12351      2         3  46       62652   A1     1 #> 12352      2         3  46       62652   A2     6 #> 12353      2         3  46       62652   A3     5 #> 12354      2         3  46       62652   A4     3 #> 12355      2         3  46       62652   A5     4 #> 12356      2         3  46       62652   C1     4 #> 12357      2         3  46       62652   C2     3 #> 12358      2         3  46       62652   C3     3 #> 12359      2         3  46       62652   C4     1 #> 12360      2         3  46       62652   C5     1 #> 12361      2         3  46       62652   E1     6 #> 12362      2         3  46       62652   E2     3 #> 12363      2         3  46       62652   E3     3 #> 12364      2         3  46       62652   E4     3 #> 12365      2         3  46       62652   E5     5 #> 12366      2         3  46       62652   N1     1 #> 12367      2         3  46       62652   N2     3 #> 12368      2         3  46       62652   N3    NA #> 12369      2         3  46       62652   N4     5 #> 12370      2         3  46       62652   N5     3 #> 12371      2         3  46       62652   O1     6 #> 12372      2         3  46       62652   O2     3 #> 12373      2         3  46       62652   O3     3 #> 12374      2         3  46       62652   O4     3 #> 12375      2         3  46       62652   O5     4 #> 12376      1         2  23       62653   A1     4 #> 12377      1         2  23       62653   A2     3 #> 12378      1         2  23       62653   A3     5 #> 12379      1         2  23       62653   A4     5 #> 12380      1         2  23       62653   A5     5 #> 12381      1         2  23       62653   C1     4 #> 12382      1         2  23       62653   C2     6 #> 12383      1         2  23       62653   C3     5 #> 12384      1         2  23       62653   C4     2 #> 12385      1         2  23       62653   C5     3 #> 12386      1         2  23       62653   E1     4 #> 12387      1         2  23       62653   E2     1 #> 12388      1         2  23       62653   E3     5 #> 12389      1         2  23       62653   E4     6 #> 12390      1         2  23       62653   E5     6 #> 12391      1         2  23       62653   N1     5 #> 12392      1         2  23       62653   N2     6 #> 12393      1         2  23       62653   N3     5 #> 12394      1         2  23       62653   N4     4 #> 12395      1         2  23       62653   N5     2 #> 12396      1         2  23       62653   O1     6 #> 12397      1         2  23       62653   O2     6 #> 12398      1         2  23       62653   O3     5 #> 12399      1         2  23       62653   O4     5 #> 12400      1         2  23       62653   O5     1 #> 12401      2         3  18       62654   A1     4 #> 12402      2         3  18       62654   A2     4 #> 12403      2         3  18       62654   A3     5 #> 12404      2         3  18       62654   A4     6 #> 12405      2         3  18       62654   A5     5 #> 12406      2         3  18       62654   C1     5 #> 12407      2         3  18       62654   C2     6 #> 12408      2         3  18       62654   C3     6 #> 12409      2         3  18       62654   C4     1 #> 12410      2         3  18       62654   C5     2 #> 12411      2         3  18       62654   E1     3 #> 12412      2         3  18       62654   E2     4 #> 12413      2         3  18       62654   E3     5 #> 12414      2         3  18       62654   E4     6 #> 12415      2         3  18       62654   E5     6 #> 12416      2         3  18       62654   N1     3 #> 12417      2         3  18       62654   N2     3 #> 12418      2         3  18       62654   N3     1 #> 12419      2         3  18       62654   N4     4 #> 12420      2         3  18       62654   N5     1 #> 12421      2         3  18       62654   O1     5 #> 12422      2         3  18       62654   O2     1 #> 12423      2         3  18       62654   O3     5 #> 12424      2         3  18       62654   O4     5 #> 12425      2         3  18       62654   O5     2 #> 12426      1         2  21       62657   A1     2 #> 12427      1         2  21       62657   A2     6 #> 12428      1         2  21       62657   A3     4 #> 12429      1         2  21       62657   A4     2 #> 12430      1         2  21       62657   A5     5 #> 12431      1         2  21       62657   C1     6 #> 12432      1         2  21       62657   C2     3 #> 12433      1         2  21       62657   C3     5 #> 12434      1         2  21       62657   C4     2 #> 12435      1         2  21       62657   C5     1 #> 12436      1         2  21       62657   E1     3 #> 12437      1         2  21       62657   E2     2 #> 12438      1         2  21       62657   E3     4 #> 12439      1         2  21       62657   E4     5 #> 12440      1         2  21       62657   E5     5 #> 12441      1         2  21       62657   N1     3 #> 12442      1         2  21       62657   N2     5 #> 12443      1         2  21       62657   N3     3 #> 12444      1         2  21       62657   N4     1 #> 12445      1         2  21       62657   N5     1 #> 12446      1         2  21       62657   O1     5 #> 12447      1         2  21       62657   O2     2 #> 12448      1         2  21       62657   O3     4 #> 12449      1         2  21       62657   O4     4 #> 12450      1         2  21       62657   O5     2 #> 12451      2         3  26       62662   A1     4 #> 12452      2         3  26       62662   A2     5 #> 12453      2         3  26       62662   A3     4 #> 12454      2         3  26       62662   A4     5 #> 12455      2         3  26       62662   A5     4 #> 12456      2         3  26       62662   C1     5 #> 12457      2         3  26       62662   C2     4 #> 12458      2         3  26       62662   C3     4 #> 12459      2         3  26       62662   C4     5 #> 12460      2         3  26       62662   C5     5 #> 12461      2         3  26       62662   E1     1 #> 12462      2         3  26       62662   E2     4 #> 12463      2         3  26       62662   E3     6 #> 12464      2         3  26       62662   E4     5 #> 12465      2         3  26       62662   E5     6 #> 12466      2         3  26       62662   N1     6 #> 12467      2         3  26       62662   N2     6 #> 12468      2         3  26       62662   N3     6 #> 12469      2         3  26       62662   N4     5 #> 12470      2         3  26       62662   N5     5 #> 12471      2         3  26       62662   O1     5 #> 12472      2         3  26       62662   O2     2 #> 12473      2         3  26       62662   O3     5 #> 12474      2         3  26       62662   O4     5 #> 12475      2         3  26       62662   O5     5 #> 12476      2         3  53       62664   A1     1 #> 12477      2         3  53       62664   A2     5 #> 12478      2         3  53       62664   A3     6 #> 12479      2         3  53       62664   A4     5 #> 12480      2         3  53       62664   A5     6 #> 12481      2         3  53       62664   C1     6 #> 12482      2         3  53       62664   C2     5 #> 12483      2         3  53       62664   C3     5 #> 12484      2         3  53       62664   C4     1 #> 12485      2         3  53       62664   C5     2 #> 12486      2         3  53       62664   E1     2 #> 12487      2         3  53       62664   E2     1 #> 12488      2         3  53       62664   E3     5 #> 12489      2         3  53       62664   E4     1 #> 12490      2         3  53       62664   E5     5 #> 12491      2         3  53       62664   N1     4 #> 12492      2         3  53       62664   N2     5 #> 12493      2         3  53       62664   N3     4 #> 12494      2         3  53       62664   N4     5 #> 12495      2         3  53       62664   N5     4 #> 12496      2         3  53       62664   O1     5 #> 12497      2         3  53       62664   O2     4 #> 12498      2         3  53       62664   O3     4 #> 12499      2         3  53       62664   O4     6 #> 12500      2         3  53       62664   O5     1 #> 12501      2         2  26       62665   A1     3 #> 12502      2         2  26       62665   A2     6 #> 12503      2         2  26       62665   A3     6 #> 12504      2         2  26       62665   A4     6 #> 12505      2         2  26       62665   A5     6 #> 12506      2         2  26       62665   C1     4 #> 12507      2         2  26       62665   C2     5 #> 12508      2         2  26       62665   C3     3 #> 12509      2         2  26       62665   C4     1 #> 12510      2         2  26       62665   C5     1 #> 12511      2         2  26       62665   E1     1 #> 12512      2         2  26       62665   E2     1 #> 12513      2         2  26       62665   E3     5 #> 12514      2         2  26       62665   E4     6 #> 12515      2         2  26       62665   E5     3 #> 12516      2         2  26       62665   N1     1 #> 12517      2         2  26       62665   N2     1 #> 12518      2         2  26       62665   N3     1 #> 12519      2         2  26       62665   N4     2 #> 12520      2         2  26       62665   N5     1 #> 12521      2         2  26       62665   O1     5 #> 12522      2         2  26       62665   O2     3 #> 12523      2         2  26       62665   O3     5 #> 12524      2         2  26       62665   O4     1 #> 12525      2         2  26       62665   O5     1 #> 12526      1         3  30       62667   A1     1 #> 12527      1         3  30       62667   A2     6 #> 12528      1         3  30       62667   A3     6 #> 12529      1         3  30       62667   A4     6 #> 12530      1         3  30       62667   A5     6 #> 12531      1         3  30       62667   C1     5 #> 12532      1         3  30       62667   C2     6 #> 12533      1         3  30       62667   C3     5 #> 12534      1         3  30       62667   C4     1 #> 12535      1         3  30       62667   C5     1 #> 12536      1         3  30       62667   E1     5 #> 12537      1         3  30       62667   E2     2 #> 12538      1         3  30       62667   E3     6 #> 12539      1         3  30       62667   E4     6 #> 12540      1         3  30       62667   E5     6 #> 12541      1         3  30       62667   N1     1 #> 12542      1         3  30       62667   N2     1 #> 12543      1         3  30       62667   N3     1 #> 12544      1         3  30       62667   N4     2 #> 12545      1         3  30       62667   N5     2 #> 12546      1         3  30       62667   O1     5 #> 12547      1         3  30       62667   O2     6 #> 12548      1         3  30       62667   O3     6 #> 12549      1         3  30       62667   O4     6 #> 12550      1         3  30       62667   O5     2 #> 12551      1         2  51       62668   A1     5 #> 12552      1         2  51       62668   A2     6 #> 12553      1         2  51       62668   A3     5 #> 12554      1         2  51       62668   A4     4 #> 12555      1         2  51       62668   A5     5 #> 12556      1         2  51       62668   C1     1 #> 12557      1         2  51       62668   C2     6 #> 12558      1         2  51       62668   C3     5 #> 12559      1         2  51       62668   C4     3 #> 12560      1         2  51       62668   C5     4 #> 12561      1         2  51       62668   E1     4 #> 12562      1         2  51       62668   E2     1 #> 12563      1         2  51       62668   E3     6 #> 12564      1         2  51       62668   E4     5 #> 12565      1         2  51       62668   E5     6 #> 12566      1         2  51       62668   N1     4 #> 12567      1         2  51       62668   N2     4 #> 12568      1         2  51       62668   N3     4 #> 12569      1         2  51       62668   N4     4 #> 12570      1         2  51       62668   N5    NA #> 12571      1         2  51       62668   O1     6 #> 12572      1         2  51       62668   O2     4 #> 12573      1         2  51       62668   O3     5 #> 12574      1         2  51       62668   O4     6 #> 12575      1         2  51       62668   O5     5 #> 12576      2         3  34       62669   A1     5 #> 12577      2         3  34       62669   A2     6 #> 12578      2         3  34       62669   A3     6 #> 12579      2         3  34       62669   A4     5 #> 12580      2         3  34       62669   A5     6 #> 12581      2         3  34       62669   C1     5 #> 12582      2         3  34       62669   C2     4 #> 12583      2         3  34       62669   C3     6 #> 12584      2         3  34       62669   C4     2 #> 12585      2         3  34       62669   C5     4 #> 12586      2         3  34       62669   E1     3 #> 12587      2         3  34       62669   E2     3 #> 12588      2         3  34       62669   E3     4 #> 12589      2         3  34       62669   E4     4 #> 12590      2         3  34       62669   E5     5 #> 12591      2         3  34       62669   N1     5 #> 12592      2         3  34       62669   N2     4 #> 12593      2         3  34       62669   N3     4 #> 12594      2         3  34       62669   N4     3 #> 12595      2         3  34       62669   N5     2 #> 12596      2         3  34       62669   O1     5 #> 12597      2         3  34       62669   O2     2 #> 12598      2         3  34       62669   O3     5 #> 12599      2         3  34       62669   O4     4 #> 12600      2         3  34       62669   O5     3 #> 12601      2         3  28       62670   A1     2 #> 12602      2         3  28       62670   A2     4 #> 12603      2         3  28       62670   A3     2 #> 12604      2         3  28       62670   A4     6 #> 12605      2         3  28       62670   A5     4 #> 12606      2         3  28       62670   C1     5 #> 12607      2         3  28       62670   C2     5 #> 12608      2         3  28       62670   C3     5 #> 12609      2         3  28       62670   C4     2 #> 12610      2         3  28       62670   C5     2 #> 12611      2         3  28       62670   E1     3 #> 12612      2         3  28       62670   E2     3 #> 12613      2         3  28       62670   E3     5 #> 12614      2         3  28       62670   E4     5 #> 12615      2         3  28       62670   E5     5 #> 12616      2         3  28       62670   N1     3 #> 12617      2         3  28       62670   N2     4 #> 12618      2         3  28       62670   N3     4 #> 12619      2         3  28       62670   N4     2 #> 12620      2         3  28       62670   N5     3 #> 12621      2         3  28       62670   O1     5 #> 12622      2         3  28       62670   O2     2 #> 12623      2         3  28       62670   O3     4 #> 12624      2         3  28       62670   O4     4 #> 12625      2         3  28       62670   O5     2 #> 12626      2         2  42       62673   A1     2 #> 12627      2         2  42       62673   A2     6 #> 12628      2         2  42       62673   A3     5 #> 12629      2         2  42       62673   A4     5 #> 12630      2         2  42       62673   A5     4 #> 12631      2         2  42       62673   C1     5 #> 12632      2         2  42       62673   C2     5 #> 12633      2         2  42       62673   C3     5 #> 12634      2         2  42       62673   C4    NA #> 12635      2         2  42       62673   C5     2 #> 12636      2         2  42       62673   E1     1 #> 12637      2         2  42       62673   E2     4 #> 12638      2         2  42       62673   E3     4 #> 12639      2         2  42       62673   E4     6 #> 12640      2         2  42       62673   E5     5 #> 12641      2         2  42       62673   N1     1 #> 12642      2         2  42       62673   N2     4 #> 12643      2         2  42       62673   N3     5 #> 12644      2         2  42       62673   N4     2 #> 12645      2         2  42       62673   N5     6 #> 12646      2         2  42       62673   O1     4 #> 12647      2         2  42       62673   O2     4 #> 12648      2         2  42       62673   O3     4 #> 12649      2         2  42       62673   O4     5 #> 12650      2         2  42       62673   O5     2 #> 12651      1         3  21       62675   A1     1 #> 12652      1         3  21       62675   A2     6 #> 12653      1         3  21       62675   A3     5 #> 12654      1         3  21       62675   A4     5 #> 12655      1         3  21       62675   A5     6 #> 12656      1         3  21       62675   C1     6 #> 12657      1         3  21       62675   C2     6 #> 12658      1         3  21       62675   C3     4 #> 12659      1         3  21       62675   C4     1 #> 12660      1         3  21       62675   C5     2 #> 12661      1         3  21       62675   E1     2 #> 12662      1         3  21       62675   E2     4 #> 12663      1         3  21       62675   E3     4 #> 12664      1         3  21       62675   E4     5 #> 12665      1         3  21       62675   E5     5 #> 12666      1         3  21       62675   N1     3 #> 12667      1         3  21       62675   N2     3 #> 12668      1         3  21       62675   N3     4 #> 12669      1         3  21       62675   N4     3 #> 12670      1         3  21       62675   N5     4 #> 12671      1         3  21       62675   O1     5 #> 12672      1         3  21       62675   O2     4 #> 12673      1         3  21       62675   O3     4 #> 12674      1         3  21       62675   O4     5 #> 12675      1         3  21       62675   O5     2 #> 12676      2         3  20       62677   A1     1 #> 12677      2         3  20       62677   A2     6 #> 12678      2         3  20       62677   A3     6 #> 12679      2         3  20       62677   A4     6 #> 12680      2         3  20       62677   A5     6 #> 12681      2         3  20       62677   C1     1 #> 12682      2         3  20       62677   C2     4 #> 12683      2         3  20       62677   C3     4 #> 12684      2         3  20       62677   C4     4 #> 12685      2         3  20       62677   C5     1 #> 12686      2         3  20       62677   E1     5 #> 12687      2         3  20       62677   E2     1 #> 12688      2         3  20       62677   E3     6 #> 12689      2         3  20       62677   E4     6 #> 12690      2         3  20       62677   E5     6 #> 12691      2         3  20       62677   N1     6 #> 12692      2         3  20       62677   N2     4 #> 12693      2         3  20       62677   N3     6 #> 12694      2         3  20       62677   N4     5 #> 12695      2         3  20       62677   N5     5 #> 12696      2         3  20       62677   O1     6 #> 12697      2         3  20       62677   O2     6 #> 12698      2         3  20       62677   O3     6 #> 12699      2         3  20       62677   O4     3 #> 12700      2         3  20       62677   O5     6 #> 12701      1         1  26       62679   A1     2 #> 12702      1         1  26       62679   A2     4 #> 12703      1         1  26       62679   A3     6 #> 12704      1         1  26       62679   A4     5 #> 12705      1         1  26       62679   A5     6 #> 12706      1         1  26       62679   C1     5 #> 12707      1         1  26       62679   C2     4 #> 12708      1         1  26       62679   C3     4 #> 12709      1         1  26       62679   C4     1 #> 12710      1         1  26       62679   C5     2 #> 12711      1         1  26       62679   E1     4 #> 12712      1         1  26       62679   E2     3 #> 12713      1         1  26       62679   E3     6 #> 12714      1         1  26       62679   E4     5 #> 12715      1         1  26       62679   E5     4 #> 12716      1         1  26       62679   N1     2 #> 12717      1         1  26       62679   N2     5 #> 12718      1         1  26       62679   N3     4 #> 12719      1         1  26       62679   N4     3 #> 12720      1         1  26       62679   N5     4 #> 12721      1         1  26       62679   O1     5 #> 12722      1         1  26       62679   O2     1 #> 12723      1         1  26       62679   O3     5 #> 12724      1         1  26       62679   O4     6 #> 12725      1         1  26       62679   O5     1 #> 12726      2         3  18       62681   A1     1 #> 12727      2         3  18       62681   A2     5 #> 12728      2         3  18       62681   A3     6 #> 12729      2         3  18       62681   A4     6 #> 12730      2         3  18       62681   A5     5 #> 12731      2         3  18       62681   C1     5 #> 12732      2         3  18       62681   C2     4 #> 12733      2         3  18       62681   C3     4 #> 12734      2         3  18       62681   C4     1 #> 12735      2         3  18       62681   C5     2 #> 12736      2         3  18       62681   E1     1 #> 12737      2         3  18       62681   E2     4 #> 12738      2         3  18       62681   E3     4 #> 12739      2         3  18       62681   E4     4 #> 12740      2         3  18       62681   E5     5 #> 12741      2         3  18       62681   N1     4 #> 12742      2         3  18       62681   N2     4 #> 12743      2         3  18       62681   N3     4 #> 12744      2         3  18       62681   N4     2 #> 12745      2         3  18       62681   N5     1 #> 12746      2         3  18       62681   O1     2 #> 12747      2         3  18       62681   O2     6 #> 12748      2         3  18       62681   O3     4 #> 12749      2         3  18       62681   O4     5 #> 12750      2         3  18       62681   O5     1 #> 12751      2         3  39       62682   A1     3 #> 12752      2         3  39       62682   A2     5 #> 12753      2         3  39       62682   A3     5 #> 12754      2         3  39       62682   A4     6 #> 12755      2         3  39       62682   A5     5 #> 12756      2         3  39       62682   C1     3 #> 12757      2         3  39       62682   C2     6 #> 12758      2         3  39       62682   C3     3 #> 12759      2         3  39       62682   C4     3 #> 12760      2         3  39       62682   C5     1 #> 12761      2         3  39       62682   E1     4 #> 12762      2         3  39       62682   E2     2 #> 12763      2         3  39       62682   E3     4 #> 12764      2         3  39       62682   E4     5 #> 12765      2         3  39       62682   E5     6 #> 12766      2         3  39       62682   N1     3 #> 12767      2         3  39       62682   N2     3 #> 12768      2         3  39       62682   N3     4 #> 12769      2         3  39       62682   N4     2 #> 12770      2         3  39       62682   N5     4 #> 12771      2         3  39       62682   O1     5 #> 12772      2         3  39       62682   O2     3 #> 12773      2         3  39       62682   O3     5 #> 12774      2         3  39       62682   O4     4 #> 12775      2         3  39       62682   O5     4 #> 12776      2         3  45       62683   A1     6 #> 12777      2         3  45       62683   A2     1 #> 12778      2         3  45       62683   A3     6 #> 12779      2         3  45       62683   A4     6 #> 12780      2         3  45       62683   A5     6 #> 12781      2         3  45       62683   C1     6 #> 12782      2         3  45       62683   C2     5 #> 12783      2         3  45       62683   C3     6 #> 12784      2         3  45       62683   C4     1 #> 12785      2         3  45       62683   C5     1 #> 12786      2         3  45       62683   E1     4 #> 12787      2         3  45       62683   E2     1 #> 12788      2         3  45       62683   E3     6 #> 12789      2         3  45       62683   E4     6 #> 12790      2         3  45       62683   E5     6 #> 12791      2         3  45       62683   N1     1 #> 12792      2         3  45       62683   N2     1 #> 12793      2         3  45       62683   N3     1 #> 12794      2         3  45       62683   N4     1 #> 12795      2         3  45       62683   N5     1 #> 12796      2         3  45       62683   O1     6 #> 12797      2         3  45       62683   O2     4 #> 12798      2         3  45       62683   O3     5 #> 12799      2         3  45       62683   O4     5 #> 12800      2         3  45       62683   O5     5 #> 12801      2         3  20       62684   A1     2 #> 12802      2         3  20       62684   A2     5 #> 12803      2         3  20       62684   A3     4 #> 12804      2         3  20       62684   A4     6 #> 12805      2         3  20       62684   A5     4 #> 12806      2         3  20       62684   C1     5 #> 12807      2         3  20       62684   C2     5 #> 12808      2         3  20       62684   C3     5 #> 12809      2         3  20       62684   C4     1 #> 12810      2         3  20       62684   C5     2 #> 12811      2         3  20       62684   E1     1 #> 12812      2         3  20       62684   E2     1 #> 12813      2         3  20       62684   E3     5 #> 12814      2         3  20       62684   E4     6 #> 12815      2         3  20       62684   E5     6 #> 12816      2         3  20       62684   N1     5 #> 12817      2         3  20       62684   N2     4 #> 12818      2         3  20       62684   N3     6 #> 12819      2         3  20       62684   N4     3 #> 12820      2         3  20       62684   N5     4 #> 12821      2         3  20       62684   O1     5 #> 12822      2         3  20       62684   O2     1 #> 12823      2         3  20       62684   O3     4 #> 12824      2         3  20       62684   O4     6 #> 12825      2         3  20       62684   O5     2 #> 12826      1         3  43       62685   A1     3 #> 12827      1         3  43       62685   A2     2 #> 12828      1         3  43       62685   A3     4 #> 12829      1         3  43       62685   A4     6 #> 12830      1         3  43       62685   A5     6 #> 12831      1         3  43       62685   C1     6 #> 12832      1         3  43       62685   C2     2 #> 12833      1         3  43       62685   C3     5 #> 12834      1         3  43       62685   C4     1 #> 12835      1         3  43       62685   C5     2 #> 12836      1         3  43       62685   E1     3 #> 12837      1         3  43       62685   E2     2 #> 12838      1         3  43       62685   E3     5 #> 12839      1         3  43       62685   E4     6 #> 12840      1         3  43       62685   E5     4 #> 12841      1         3  43       62685   N1     1 #> 12842      1         3  43       62685   N2     1 #> 12843      1         3  43       62685   N3     2 #> 12844      1         3  43       62685   N4     3 #> 12845      1         3  43       62685   N5     1 #> 12846      1         3  43       62685   O1     6 #> 12847      1         3  43       62685   O2     2 #> 12848      1         3  43       62685   O3     6 #> 12849      1         3  43       62685   O4     5 #> 12850      1         3  43       62685   O5     3 #> 12851      2         3  23       62686   A1     2 #> 12852      2         3  23       62686   A2     4 #> 12853      2         3  23       62686   A3     4 #> 12854      2         3  23       62686   A4     6 #> 12855      2         3  23       62686   A5     4 #> 12856      2         3  23       62686   C1     6 #> 12857      2         3  23       62686   C2     5 #> 12858      2         3  23       62686   C3     3 #> 12859      2         3  23       62686   C4     2 #> 12860      2         3  23       62686   C5     2 #> 12861      2         3  23       62686   E1     5 #> 12862      2         3  23       62686   E2     5 #> 12863      2         3  23       62686   E3     2 #> 12864      2         3  23       62686   E4     3 #> 12865      2         3  23       62686   E5     4 #> 12866      2         3  23       62686   N1     3 #> 12867      2         3  23       62686   N2     3 #> 12868      2         3  23       62686   N3     2 #> 12869      2         3  23       62686   N4     4 #> 12870      2         3  23       62686   N5     2 #> 12871      2         3  23       62686   O1     3 #> 12872      2         3  23       62686   O2     5 #> 12873      2         3  23       62686   O3     5 #> 12874      2         3  23       62686   O4     5 #> 12875      2         3  23       62686   O5     2 #> 12876      2         3  25       62687   A1     2 #> 12877      2         3  25       62687   A2     4 #> 12878      2         3  25       62687   A3     6 #> 12879      2         3  25       62687   A4     5 #> 12880      2         3  25       62687   A5     5 #> 12881      2         3  25       62687   C1     2 #> 12882      2         3  25       62687   C2     6 #> 12883      2         3  25       62687   C3     5 #> 12884      2         3  25       62687   C4     3 #> 12885      2         3  25       62687   C5     2 #> 12886      2         3  25       62687   E1     4 #> 12887      2         3  25       62687   E2     2 #> 12888      2         3  25       62687   E3     4 #> 12889      2         3  25       62687   E4     5 #> 12890      2         3  25       62687   E5     6 #> 12891      2         3  25       62687   N1     4 #> 12892      2         3  25       62687   N2     4 #> 12893      2         3  25       62687   N3     4 #> 12894      2         3  25       62687   N4     4 #> 12895      2         3  25       62687   N5     2 #> 12896      2         3  25       62687   O1     5 #> 12897      2         3  25       62687   O2     2 #> 12898      2         3  25       62687   O3     5 #> 12899      2         3  25       62687   O4     5 #> 12900      2         3  25       62687   O5     4 #> 12901      2         3  51       62688   A1     1 #> 12902      2         3  51       62688   A2     6 #> 12903      2         3  51       62688   A3     5 #> 12904      2         3  51       62688   A4     6 #> 12905      2         3  51       62688   A5     4 #> 12906      2         3  51       62688   C1     6 #> 12907      2         3  51       62688   C2     6 #> 12908      2         3  51       62688   C3     5 #> 12909      2         3  51       62688   C4     1 #> 12910      2         3  51       62688   C5     2 #> 12911      2         3  51       62688   E1     1 #> 12912      2         3  51       62688   E2     5 #> 12913      2         3  51       62688   E3     2 #> 12914      2         3  51       62688   E4     5 #> 12915      2         3  51       62688   E5     6 #> 12916      2         3  51       62688   N1     5 #> 12917      2         3  51       62688   N2     6 #> 12918      2         3  51       62688   N3     4 #> 12919      2         3  51       62688   N4     4 #> 12920      2         3  51       62688   N5     3 #> 12921      2         3  51       62688   O1     4 #> 12922      2         3  51       62688   O2     5 #> 12923      2         3  51       62688   O3     5 #> 12924      2         3  51       62688   O4     5 #> 12925      2         3  51       62688   O5     2 #> 12926      2         3  26       62690   A1     1 #> 12927      2         3  26       62690   A2     5 #> 12928      2         3  26       62690   A3     4 #> 12929      2         3  26       62690   A4     6 #> 12930      2         3  26       62690   A5     4 #> 12931      2         3  26       62690   C1     4 #> 12932      2         3  26       62690   C2     6 #> 12933      2         3  26       62690   C3     5 #> 12934      2         3  26       62690   C4     4 #> 12935      2         3  26       62690   C5     1 #> 12936      2         3  26       62690   E1     6 #> 12937      2         3  26       62690   E2     4 #> 12938      2         3  26       62690   E3     5 #> 12939      2         3  26       62690   E4     4 #> 12940      2         3  26       62690   E5     6 #> 12941      2         3  26       62690   N1     4 #> 12942      2         3  26       62690   N2     6 #> 12943      2         3  26       62690   N3     5 #> 12944      2         3  26       62690   N4     4 #> 12945      2         3  26       62690   N5     4 #> 12946      2         3  26       62690   O1     5 #> 12947      2         3  26       62690   O2     1 #> 12948      2         3  26       62690   O3     5 #> 12949      2         3  26       62690   O4     4 #> 12950      2         3  26       62690   O5     1 #> 12951      1         3  28       62692   A1     1 #> 12952      1         3  28       62692   A2     6 #> 12953      1         3  28       62692   A3     6 #> 12954      1         3  28       62692   A4     6 #> 12955      1         3  28       62692   A5     6 #> 12956      1         3  28       62692   C1     6 #> 12957      1         3  28       62692   C2     6 #> 12958      1         3  28       62692   C3     5 #> 12959      1         3  28       62692   C4     1 #> 12960      1         3  28       62692   C5     1 #> 12961      1         3  28       62692   E1     1 #> 12962      1         3  28       62692   E2     1 #> 12963      1         3  28       62692   E3     5 #> 12964      1         3  28       62692   E4     6 #> 12965      1         3  28       62692   E5     6 #> 12966      1         3  28       62692   N1     1 #> 12967      1         3  28       62692   N2     2 #> 12968      1         3  28       62692   N3     1 #> 12969      1         3  28       62692   N4     1 #> 12970      1         3  28       62692   N5     1 #> 12971      1         3  28       62692   O1     6 #> 12972      1         3  28       62692   O2     1 #> 12973      1         3  28       62692   O3     6 #> 12974      1         3  28       62692   O4     5 #> 12975      1         3  28       62692   O5     1 #> 12976      2         3  23       62694   A1     3 #> 12977      2         3  23       62694   A2     5 #> 12978      2         3  23       62694   A3     5 #> 12979      2         3  23       62694   A4     6 #> 12980      2         3  23       62694   A5     5 #> 12981      2         3  23       62694   C1     6 #> 12982      2         3  23       62694   C2     6 #> 12983      2         3  23       62694   C3     5 #> 12984      2         3  23       62694   C4     1 #> 12985      2         3  23       62694   C5     1 #> 12986      2         3  23       62694   E1     3 #> 12987      2         3  23       62694   E2     2 #> 12988      2         3  23       62694   E3     6 #> 12989      2         3  23       62694   E4     6 #> 12990      2         3  23       62694   E5     6 #> 12991      2         3  23       62694   N1     4 #> 12992      2         3  23       62694   N2     4 #> 12993      2         3  23       62694   N3     4 #> 12994      2         3  23       62694   N4     2 #> 12995      2         3  23       62694   N5     1 #> 12996      2         3  23       62694   O1     6 #> 12997      2         3  23       62694   O2     4 #> 12998      2         3  23       62694   O3     5 #> 12999      2         3  23       62694   O4     2 #> 13000      2         3  23       62694   O5     2 #> 13001      2         3  34       62698   A1     4 #> 13002      2         3  34       62698   A2     6 #> 13003      2         3  34       62698   A3     6 #> 13004      2         3  34       62698   A4     6 #> 13005      2         3  34       62698   A5     5 #> 13006      2         3  34       62698   C1     4 #> 13007      2         3  34       62698   C2     3 #> 13008      2         3  34       62698   C3     4 #> 13009      2         3  34       62698   C4     4 #> 13010      2         3  34       62698   C5     3 #> 13011      2         3  34       62698   E1     2 #> 13012      2         3  34       62698   E2     4 #> 13013      2         3  34       62698   E3     4 #> 13014      2         3  34       62698   E4     5 #> 13015      2         3  34       62698   E5     4 #> 13016      2         3  34       62698   N1     4 #> 13017      2         3  34       62698   N2     5 #> 13018      2         3  34       62698   N3     5 #> 13019      2         3  34       62698   N4     3 #> 13020      2         3  34       62698   N5     2 #> 13021      2         3  34       62698   O1     4 #> 13022      2         3  34       62698   O2     5 #> 13023      2         3  34       62698   O3     4 #> 13024      2         3  34       62698   O4     6 #> 13025      2         3  34       62698   O5     2 #> 13026      1         3  23       62700   A1     1 #> 13027      1         3  23       62700   A2     4 #> 13028      1         3  23       62700   A3     4 #> 13029      1         3  23       62700   A4     4 #> 13030      1         3  23       62700   A5     6 #> 13031      1         3  23       62700   C1     4 #> 13032      1         3  23       62700   C2     4 #> 13033      1         3  23       62700   C3     6 #> 13034      1         3  23       62700   C4     3 #> 13035      1         3  23       62700   C5     3 #> 13036      1         3  23       62700   E1     4 #> 13037      1         3  23       62700   E2     2 #> 13038      1         3  23       62700   E3     4 #> 13039      1         3  23       62700   E4     6 #> 13040      1         3  23       62700   E5     2 #> 13041      1         3  23       62700   N1     2 #> 13042      1         3  23       62700   N2     3 #> 13043      1         3  23       62700   N3     2 #> 13044      1         3  23       62700   N4     1 #> 13045      1         3  23       62700   N5     1 #> 13046      1         3  23       62700   O1     6 #> 13047      1         3  23       62700   O2     2 #> 13048      1         3  23       62700   O3     5 #> 13049      1         3  23       62700   O4     4 #> 13050      1         3  23       62700   O5     2 #> 13051      2         3  29       62703   A1     2 #> 13052      2         3  29       62703   A2     5 #> 13053      2         3  29       62703   A3     5 #> 13054      2         3  29       62703   A4     4 #> 13055      2         3  29       62703   A5     6 #> 13056      2         3  29       62703   C1     4 #> 13057      2         3  29       62703   C2     3 #> 13058      2         3  29       62703   C3     5 #> 13059      2         3  29       62703   C4     2 #> 13060      2         3  29       62703   C5     3 #> 13061      2         3  29       62703   E1     3 #> 13062      2         3  29       62703   E2     2 #> 13063      2         3  29       62703   E3     5 #> 13064      2         3  29       62703   E4    NA #> 13065      2         3  29       62703   E5     3 #> 13066      2         3  29       62703   N1     1 #> 13067      2         3  29       62703   N2     2 #> 13068      2         3  29       62703   N3     1 #> 13069      2         3  29       62703   N4     3 #> 13070      2         3  29       62703   N5     2 #> 13071      2         3  29       62703   O1     4 #> 13072      2         3  29       62703   O2     4 #> 13073      2         3  29       62703   O3     5 #> 13074      2         3  29       62703   O4     4 #> 13075      2         3  29       62703   O5     3 #> 13076      2         3  35       62706   A1     1 #> 13077      2         3  35       62706   A2     6 #> 13078      2         3  35       62706   A3     5 #> 13079      2         3  35       62706   A4     6 #> 13080      2         3  35       62706   A5     5 #> 13081      2         3  35       62706   C1     4 #> 13082      2         3  35       62706   C2     5 #> 13083      2         3  35       62706   C3     6 #> 13084      2         3  35       62706   C4     1 #> 13085      2         3  35       62706   C5     1 #> 13086      2         3  35       62706   E1     1 #> 13087      2         3  35       62706   E2     1 #> 13088      2         3  35       62706   E3     5 #> 13089      2         3  35       62706   E4     5 #> 13090      2         3  35       62706   E5     6 #> 13091      2         3  35       62706   N1     5 #> 13092      2         3  35       62706   N2     5 #> 13093      2         3  35       62706   N3     6 #> 13094      2         3  35       62706   N4     1 #> 13095      2         3  35       62706   N5     5 #> 13096      2         3  35       62706   O1     3 #> 13097      2         3  35       62706   O2     6 #> 13098      2         3  35       62706   O3     5 #> 13099      2         3  35       62706   O4     6 #> 13100      2         3  35       62706   O5     5 #> 13101      2         3  40       62707   A1     2 #> 13102      2         3  40       62707   A2     4 #> 13103      2         3  40       62707   A3     2 #> 13104      2         3  40       62707   A4     6 #> 13105      2         3  40       62707   A5     5 #> 13106      2         3  40       62707   C1     3 #> 13107      2         3  40       62707   C2     5 #> 13108      2         3  40       62707   C3     4 #> 13109      2         3  40       62707   C4     2 #> 13110      2         3  40       62707   C5     2 #> 13111      2         3  40       62707   E1    NA #> 13112      2         3  40       62707   E2     4 #> 13113      2         3  40       62707   E3     3 #> 13114      2         3  40       62707   E4     4 #> 13115      2         3  40       62707   E5     4 #> 13116      2         3  40       62707   N1     4 #> 13117      2         3  40       62707   N2     4 #> 13118      2         3  40       62707   N3     4 #> 13119      2         3  40       62707   N4     5 #> 13120      2         3  40       62707   N5     6 #> 13121      2         3  40       62707   O1     5 #> 13122      2         3  40       62707   O2     4 #> 13123      2         3  40       62707   O3     3 #> 13124      2         3  40       62707   O4     4 #> 13125      2         3  40       62707   O5     4 #> 13126      2         2  27       62708   A1     6 #> 13127      2         2  27       62708   A2     6 #> 13128      2         2  27       62708   A3     6 #> 13129      2         2  27       62708   A4     6 #> 13130      2         2  27       62708   A5     6 #> 13131      2         2  27       62708   C1     5 #> 13132      2         2  27       62708   C2     6 #> 13133      2         2  27       62708   C3     6 #> 13134      2         2  27       62708   C4     1 #> 13135      2         2  27       62708   C5     1 #> 13136      2         2  27       62708   E1     1 #> 13137      2         2  27       62708   E2     1 #> 13138      2         2  27       62708   E3     6 #> 13139      2         2  27       62708   E4     6 #> 13140      2         2  27       62708   E5     6 #> 13141      2         2  27       62708   N1     6 #> 13142      2         2  27       62708   N2     6 #> 13143      2         2  27       62708   N3     6 #> 13144      2         2  27       62708   N4     1 #> 13145      2         2  27       62708   N5     6 #> 13146      2         2  27       62708   O1     6 #> 13147      2         2  27       62708   O2     6 #> 13148      2         2  27       62708   O3     6 #> 13149      2         2  27       62708   O4     6 #> 13150      2         2  27       62708   O5     1 #> 13151      1         4  45       62710   A1     1 #> 13152      1         4  45       62710   A2     6 #> 13153      1         4  45       62710   A3     4 #> 13154      1         4  45       62710   A4     3 #> 13155      1         4  45       62710   A5     5 #> 13156      1         4  45       62710   C1     5 #> 13157      1         4  45       62710   C2     4 #> 13158      1         4  45       62710   C3     4 #> 13159      1         4  45       62710   C4     3 #> 13160      1         4  45       62710   C5     4 #> 13161      1         4  45       62710   E1     1 #> 13162      1         4  45       62710   E2     2 #> 13163      1         4  45       62710   E3     4 #> 13164      1         4  45       62710   E4     5 #> 13165      1         4  45       62710   E5     3 #> 13166      1         4  45       62710   N1     3 #> 13167      1         4  45       62710   N2     3 #> 13168      1         4  45       62710   N3     4 #> 13169      1         4  45       62710   N4     3 #> 13170      1         4  45       62710   N5     2 #> 13171      1         4  45       62710   O1     5 #> 13172      1         4  45       62710   O2     2 #> 13173      1         4  45       62710   O3     4 #> 13174      1         4  45       62710   O4     6 #> 13175      1         4  45       62710   O5     2 #> 13176      1         2  60       62712   A1     1 #> 13177      1         2  60       62712   A2     6 #> 13178      1         2  60       62712   A3     5 #> 13179      1         2  60       62712   A4     5 #> 13180      1         2  60       62712   A5     6 #> 13181      1         2  60       62712   C1     6 #> 13182      1         2  60       62712   C2     5 #> 13183      1         2  60       62712   C3     5 #> 13184      1         2  60       62712   C4     1 #> 13185      1         2  60       62712   C5     3 #> 13186      1         2  60       62712   E1     1 #> 13187      1         2  60       62712   E2     1 #> 13188      1         2  60       62712   E3     5 #> 13189      1         2  60       62712   E4     6 #> 13190      1         2  60       62712   E5     6 #> 13191      1         2  60       62712   N1     1 #> 13192      1         2  60       62712   N2     2 #> 13193      1         2  60       62712   N3     1 #> 13194      1         2  60       62712   N4     1 #> 13195      1         2  60       62712   N5     1 #> 13196      1         2  60       62712   O1     6 #> 13197      1         2  60       62712   O2     1 #> 13198      1         2  60       62712   O3     6 #> 13199      1         2  60       62712   O4     6 #> 13200      1         2  60       62712   O5     1 #> 13201      2         3  27       62715   A1     2 #> 13202      2         3  27       62715   A2     4 #> 13203      2         3  27       62715   A3     5 #> 13204      2         3  27       62715   A4     6 #> 13205      2         3  27       62715   A5     5 #> 13206      2         3  27       62715   C1     5 #> 13207      2         3  27       62715   C2     5 #> 13208      2         3  27       62715   C3     5 #> 13209      2         3  27       62715   C4     2 #> 13210      2         3  27       62715   C5     4 #> 13211      2         3  27       62715   E1     3 #> 13212      2         3  27       62715   E2     4 #> 13213      2         3  27       62715   E3     4 #> 13214      2         3  27       62715   E4     5 #> 13215      2         3  27       62715   E5     1 #> 13216      2         3  27       62715   N1     2 #> 13217      2         3  27       62715   N2     4 #> 13218      2         3  27       62715   N3     3 #> 13219      2         3  27       62715   N4     5 #> 13220      2         3  27       62715   N5     4 #> 13221      2         3  27       62715   O1     4 #> 13222      2         3  27       62715   O2     5 #> 13223      2         3  27       62715   O3     4 #> 13224      2         3  27       62715   O4     4 #> 13225      2         3  27       62715   O5     3 #> 13226      1         3  20       62716   A1     4 #> 13227      1         3  20       62716   A2     5 #> 13228      1         3  20       62716   A3     4 #> 13229      1         3  20       62716   A4     5 #> 13230      1         3  20       62716   A5     4 #> 13231      1         3  20       62716   C1     3 #> 13232      1         3  20       62716   C2     1 #> 13233      1         3  20       62716   C3     1 #> 13234      1         3  20       62716   C4     3 #> 13235      1         3  20       62716   C5     6 #> 13236      1         3  20       62716   E1     2 #> 13237      1         3  20       62716   E2     3 #> 13238      1         3  20       62716   E3     4 #> 13239      1         3  20       62716   E4     5 #> 13240      1         3  20       62716   E5     3 #> 13241      1         3  20       62716   N1     2 #> 13242      1         3  20       62716   N2     4 #> 13243      1         3  20       62716   N3     3 #> 13244      1         3  20       62716   N4    NA #> 13245      1         3  20       62716   N5     1 #> 13246      1         3  20       62716   O1     6 #> 13247      1         3  20       62716   O2     2 #> 13248      1         3  20       62716   O3     5 #> 13249      1         3  20       62716   O4     4 #> 13250      1         3  20       62716   O5     2 #> 13251      2         3  21       62717   A1     1 #> 13252      2         3  21       62717   A2     5 #> 13253      2         3  21       62717   A3     5 #> 13254      2         3  21       62717   A4     6 #> 13255      2         3  21       62717   A5     4 #> 13256      2         3  21       62717   C1     5 #> 13257      2         3  21       62717   C2     4 #> 13258      2         3  21       62717   C3     5 #> 13259      2         3  21       62717   C4     4 #> 13260      2         3  21       62717   C5     2 #> 13261      2         3  21       62717   E1     4 #> 13262      2         3  21       62717   E2     4 #> 13263      2         3  21       62717   E3     4 #> 13264      2         3  21       62717   E4     5 #> 13265      2         3  21       62717   E5     3 #> 13266      2         3  21       62717   N1     3 #> 13267      2         3  21       62717   N2     4 #> 13268      2         3  21       62717   N3     4 #> 13269      2         3  21       62717   N4     3 #> 13270      2         3  21       62717   N5     4 #> 13271      2         3  21       62717   O1     5 #> 13272      2         3  21       62717   O2     2 #> 13273      2         3  21       62717   O3     4 #> 13274      2         3  21       62717   O4     6 #> 13275      2         3  21       62717   O5     2 #> 13276      2         3  25       62718   A1     3 #> 13277      2         3  25       62718   A2     4 #> 13278      2         3  25       62718   A3     4 #> 13279      2         3  25       62718   A4     5 #> 13280      2         3  25       62718   A5     5 #> 13281      2         3  25       62718   C1     5 #> 13282      2         3  25       62718   C2     4 #> 13283      2         3  25       62718   C3     3 #> 13284      2         3  25       62718   C4     3 #> 13285      2         3  25       62718   C5     4 #> 13286      2         3  25       62718   E1     2 #> 13287      2         3  25       62718   E2     2 #> 13288      2         3  25       62718   E3     5 #> 13289      2         3  25       62718   E4     6 #> 13290      2         3  25       62718   E5     4 #> 13291      2         3  25       62718   N1     5 #> 13292      2         3  25       62718   N2     5 #> 13293      2         3  25       62718   N3     6 #> 13294      2         3  25       62718   N4     4 #> 13295      2         3  25       62718   N5     4 #> 13296      2         3  25       62718   O1     4 #> 13297      2         3  25       62718   O2     2 #> 13298      2         3  25       62718   O3     4 #> 13299      2         3  25       62718   O4     4 #> 13300      2         3  25       62718   O5     4 #> 13301      2         2  51       62719   A1     1 #> 13302      2         2  51       62719   A2     4 #> 13303      2         2  51       62719   A3     6 #> 13304      2         2  51       62719   A4     5 #> 13305      2         2  51       62719   A5     6 #> 13306      2         2  51       62719   C1     5 #> 13307      2         2  51       62719   C2     2 #> 13308      2         2  51       62719   C3     6 #> 13309      2         2  51       62719   C4     1 #> 13310      2         2  51       62719   C5     1 #> 13311      2         2  51       62719   E1     1 #> 13312      2         2  51       62719   E2     3 #> 13313      2         2  51       62719   E3     5 #> 13314      2         2  51       62719   E4     1 #> 13315      2         2  51       62719   E5     5 #> 13316      2         2  51       62719   N1     6 #> 13317      2         2  51       62719   N2     6 #> 13318      2         2  51       62719   N3     6 #> 13319      2         2  51       62719   N4     6 #> 13320      2         2  51       62719   N5     6 #> 13321      2         2  51       62719   O1     4 #> 13322      2         2  51       62719   O2     1 #> 13323      2         2  51       62719   O3     5 #> 13324      2         2  51       62719   O4     6 #> 13325      2         2  51       62719   O5     1 #> 13326      2         3  27       62720   A1     2 #> 13327      2         3  27       62720   A2     5 #> 13328      2         3  27       62720   A3     5 #> 13329      2         3  27       62720   A4     6 #> 13330      2         3  27       62720   A5     5 #> 13331      2         3  27       62720   C1     1 #> 13332      2         3  27       62720   C2     5 #> 13333      2         3  27       62720   C3     2 #> 13334      2         3  27       62720   C4     1 #> 13335      2         3  27       62720   C5     2 #> 13336      2         3  27       62720   E1     1 #> 13337      2         3  27       62720   E2     5 #> 13338      2         3  27       62720   E3     5 #> 13339      2         3  27       62720   E4     5 #> 13340      2         3  27       62720   E5     3 #> 13341      2         3  27       62720   N1     3 #> 13342      2         3  27       62720   N2     5 #> 13343      2         3  27       62720   N3     1 #> 13344      2         3  27       62720   N4     1 #> 13345      2         3  27       62720   N5     3 #> 13346      2         3  27       62720   O1     3 #> 13347      2         3  27       62720   O2     1 #> 13348      2         3  27       62720   O3     3 #> 13349      2         3  27       62720   O4     4 #> 13350      2         3  27       62720   O5     5 #> 13351      1         3  24       62722   A1     1 #> 13352      1         3  24       62722   A2     5 #> 13353      1         3  24       62722   A3     4 #> 13354      1         3  24       62722   A4     1 #> 13355      1         3  24       62722   A5     5 #> 13356      1         3  24       62722   C1     5 #> 13357      1         3  24       62722   C2     4 #> 13358      1         3  24       62722   C3     5 #> 13359      1         3  24       62722   C4     2 #> 13360      1         3  24       62722   C5     5 #> 13361      1         3  24       62722   E1     6 #> 13362      1         3  24       62722   E2     6 #> 13363      1         3  24       62722   E3     3 #> 13364      1         3  24       62722   E4     1 #> 13365      1         3  24       62722   E5     2 #> 13366      1         3  24       62722   N1     4 #> 13367      1         3  24       62722   N2     4 #> 13368      1         3  24       62722   N3     6 #> 13369      1         3  24       62722   N4     6 #> 13370      1         3  24       62722   N5     5 #> 13371      1         3  24       62722   O1     5 #> 13372      1         3  24       62722   O2     2 #> 13373      1         3  24       62722   O3     5 #> 13374      1         3  24       62722   O4     6 #> 13375      1         3  24       62722   O5     2 #> 13376      2         1  21       62726   A1     1 #> 13377      2         1  21       62726   A2     5 #> 13378      2         1  21       62726   A3     6 #> 13379      2         1  21       62726   A4     6 #> 13380      2         1  21       62726   A5     5 #> 13381      2         1  21       62726   C1     2 #> 13382      2         1  21       62726   C2     5 #> 13383      2         1  21       62726   C3     4 #> 13384      2         1  21       62726   C4     2 #> 13385      2         1  21       62726   C5     1 #> 13386      2         1  21       62726   E1     1 #> 13387      2         1  21       62726   E2     1 #> 13388      2         1  21       62726   E3     4 #> 13389      2         1  21       62726   E4     6 #> 13390      2         1  21       62726   E5     4 #> 13391      2         1  21       62726   N1     3 #> 13392      2         1  21       62726   N2     4 #> 13393      2         1  21       62726   N3     1 #> 13394      2         1  21       62726   N4     1 #> 13395      2         1  21       62726   N5     1 #> 13396      2         1  21       62726   O1     5 #> 13397      2         1  21       62726   O2     6 #> 13398      2         1  21       62726   O3     4 #> 13399      2         1  21       62726   O4     2 #> 13400      2         1  21       62726   O5     3 #> 13401      2         2  55       62728   A1     3 #> 13402      2         2  55       62728   A2     4 #> 13403      2         2  55       62728   A3    NA #> 13404      2         2  55       62728   A4     5 #> 13405      2         2  55       62728   A5     3 #> 13406      2         2  55       62728   C1     5 #> 13407      2         2  55       62728   C2     5 #> 13408      2         2  55       62728   C3     4 #> 13409      2         2  55       62728   C4     2 #> 13410      2         2  55       62728   C5     5 #> 13411      2         2  55       62728   E1     3 #> 13412      2         2  55       62728   E2     5 #> 13413      2         2  55       62728   E3     1 #> 13414      2         2  55       62728   E4     2 #> 13415      2         2  55       62728   E5     3 #> 13416      2         2  55       62728   N1     2 #> 13417      2         2  55       62728   N2     5 #> 13418      2         2  55       62728   N3     3 #> 13419      2         2  55       62728   N4     3 #> 13420      2         2  55       62728   N5     2 #> 13421      2         2  55       62728   O1     4 #> 13422      2         2  55       62728   O2     2 #> 13423      2         2  55       62728   O3     6 #> 13424      2         2  55       62728   O4     5 #> 13425      2         2  55       62728   O5     1 #> 13426      1         2  30       62729   A1     5 #> 13427      1         2  30       62729   A2     4 #> 13428      1         2  30       62729   A3     3 #> 13429      1         2  30       62729   A4     4 #> 13430      1         2  30       62729   A5     3 #> 13431      1         2  30       62729   C1     3 #> 13432      1         2  30       62729   C2     2 #> 13433      1         2  30       62729   C3     3 #> 13434      1         2  30       62729   C4     4 #> 13435      1         2  30       62729   C5     4 #> 13436      1         2  30       62729   E1     3 #> 13437      1         2  30       62729   E2     3 #> 13438      1         2  30       62729   E3     5 #> 13439      1         2  30       62729   E4     4 #> 13440      1         2  30       62729   E5     3 #> 13441      1         2  30       62729   N1     5 #> 13442      1         2  30       62729   N2     5 #> 13443      1         2  30       62729   N3     4 #> 13444      1         2  30       62729   N4     5 #> 13445      1         2  30       62729   N5     4 #> 13446      1         2  30       62729   O1     5 #> 13447      1         2  30       62729   O2     1 #> 13448      1         2  30       62729   O3     5 #> 13449      1         2  30       62729   O4     6 #> 13450      1         2  30       62729   O5     3 #> 13451      1         3  31       62731   A1     5 #> 13452      1         3  31       62731   A2     4 #> 13453      1         3  31       62731   A3     4 #> 13454      1         3  31       62731   A4     4 #> 13455      1         3  31       62731   A5     2 #> 13456      1         3  31       62731   C1     5 #> 13457      1         3  31       62731   C2     2 #> 13458      1         3  31       62731   C3     2 #> 13459      1         3  31       62731   C4     4 #> 13460      1         3  31       62731   C5     2 #> 13461      1         3  31       62731   E1     3 #> 13462      1         3  31       62731   E2     4 #> 13463      1         3  31       62731   E3    NA #> 13464      1         3  31       62731   E4     4 #> 13465      1         3  31       62731   E5     3 #> 13466      1         3  31       62731   N1     4 #> 13467      1         3  31       62731   N2     4 #> 13468      1         3  31       62731   N3     5 #> 13469      1         3  31       62731   N4     5 #> 13470      1         3  31       62731   N5     2 #> 13471      1         3  31       62731   O1     2 #> 13472      1         3  31       62731   O2     2 #> 13473      1         3  31       62731   O3     2 #> 13474      1         3  31       62731   O4     5 #> 13475      1         3  31       62731   O5     4 #> 13476      2         2  22       62740   A1     3 #> 13477      2         2  22       62740   A2     5 #> 13478      2         2  22       62740   A3     5 #> 13479      2         2  22       62740   A4     5 #> 13480      2         2  22       62740   A5     5 #> 13481      2         2  22       62740   C1     5 #> 13482      2         2  22       62740   C2     4 #> 13483      2         2  22       62740   C3     3 #> 13484      2         2  22       62740   C4     2 #> 13485      2         2  22       62740   C5     4 #> 13486      2         2  22       62740   E1     2 #> 13487      2         2  22       62740   E2     2 #> 13488      2         2  22       62740   E3     4 #> 13489      2         2  22       62740   E4     4 #> 13490      2         2  22       62740   E5     4 #> 13491      2         2  22       62740   N1     5 #> 13492      2         2  22       62740   N2     6 #> 13493      2         2  22       62740   N3     4 #> 13494      2         2  22       62740   N4     3 #> 13495      2         2  22       62740   N5     4 #> 13496      2         2  22       62740   O1     5 #> 13497      2         2  22       62740   O2     4 #> 13498      2         2  22       62740   O3     4 #> 13499      2         2  22       62740   O4     5 #> 13500      2         2  22       62740   O5     2 #> 13501      2         3  28       62741   A1     3 #> 13502      2         3  28       62741   A2     4 #> 13503      2         3  28       62741   A3     4 #> 13504      2         3  28       62741   A4     6 #> 13505      2         3  28       62741   A5     5 #> 13506      2         3  28       62741   C1     5 #> 13507      2         3  28       62741   C2     4 #> 13508      2         3  28       62741   C3     5 #> 13509      2         3  28       62741   C4     2 #> 13510      2         3  28       62741   C5     1 #> 13511      2         3  28       62741   E1     5 #> 13512      2         3  28       62741   E2     3 #> 13513      2         3  28       62741   E3     4 #> 13514      2         3  28       62741   E4     4 #> 13515      2         3  28       62741   E5     4 #> 13516      2         3  28       62741   N1     2 #> 13517      2         3  28       62741   N2     3 #> 13518      2         3  28       62741   N3     3 #> 13519      2         3  28       62741   N4     2 #> 13520      2         3  28       62741   N5     2 #> 13521      2         3  28       62741   O1     4 #> 13522      2         3  28       62741   O2     4 #> 13523      2         3  28       62741   O3     4 #> 13524      2         3  28       62741   O4     5 #> 13525      2         3  28       62741   O5     3 #> 13526      2         3  24       62744   A1     4 #> 13527      2         3  24       62744   A2     5 #> 13528      2         3  24       62744   A3     4 #> 13529      2         3  24       62744   A4     5 #> 13530      2         3  24       62744   A5     2 #> 13531      2         3  24       62744   C1     5 #> 13532      2         3  24       62744   C2     5 #> 13533      2         3  24       62744   C3     5 #> 13534      2         3  24       62744   C4     3 #> 13535      2         3  24       62744   C5     2 #> 13536      2         3  24       62744   E1     3 #> 13537      2         3  24       62744   E2     2 #> 13538      2         3  24       62744   E3     6 #> 13539      2         3  24       62744   E4     2 #> 13540      2         3  24       62744   E5     6 #> 13541      2         3  24       62744   N1     5 #> 13542      2         3  24       62744   N2     5 #> 13543      2         3  24       62744   N3     5 #> 13544      2         3  24       62744   N4     3 #> 13545      2         3  24       62744   N5     5 #> 13546      2         3  24       62744   O1     6 #> 13547      2         3  24       62744   O2     3 #> 13548      2         3  24       62744   O3     6 #> 13549      2         3  24       62744   O4     6 #> 13550      2         3  24       62744   O5     1 #> 13551      2         3  31       62745   A1     1 #> 13552      2         3  31       62745   A2     6 #> 13553      2         3  31       62745   A3     6 #> 13554      2         3  31       62745   A4     6 #> 13555      2         3  31       62745   A5     5 #> 13556      2         3  31       62745   C1     2 #> 13557      2         3  31       62745   C2     5 #> 13558      2         3  31       62745   C3     6 #> 13559      2         3  31       62745   C4     4 #> 13560      2         3  31       62745   C5     6 #> 13561      2         3  31       62745   E1     6 #> 13562      2         3  31       62745   E2     5 #> 13563      2         3  31       62745   E3     5 #> 13564      2         3  31       62745   E4     5 #> 13565      2         3  31       62745   E5     6 #> 13566      2         3  31       62745   N1     4 #> 13567      2         3  31       62745   N2     5 #> 13568      2         3  31       62745   N3     4 #> 13569      2         3  31       62745   N4     4 #> 13570      2         3  31       62745   N5     6 #> 13571      2         3  31       62745   O1     2 #> 13572      2         3  31       62745   O2     6 #> 13573      2         3  31       62745   O3     4 #> 13574      2         3  31       62745   O4     6 #> 13575      2         3  31       62745   O5     3 #> 13576      2         3  43       62749   A1     1 #> 13577      2         3  43       62749   A2     6 #> 13578      2         3  43       62749   A3     4 #> 13579      2         3  43       62749   A4     6 #> 13580      2         3  43       62749   A5     5 #> 13581      2         3  43       62749   C1     2 #> 13582      2         3  43       62749   C2     5 #> 13583      2         3  43       62749   C3     6 #> 13584      2         3  43       62749   C4     1 #> 13585      2         3  43       62749   C5     2 #> 13586      2         3  43       62749   E1     1 #> 13587      2         3  43       62749   E2     3 #> 13588      2         3  43       62749   E3     4 #> 13589      2         3  43       62749   E4     3 #> 13590      2         3  43       62749   E5     5 #> 13591      2         3  43       62749   N1     2 #> 13592      2         3  43       62749   N2     2 #> 13593      2         3  43       62749   N3     1 #> 13594      2         3  43       62749   N4     6 #> 13595      2         3  43       62749   N5     4 #> 13596      2         3  43       62749   O1     4 #> 13597      2         3  43       62749   O2     1 #> 13598      2         3  43       62749   O3     4 #> 13599      2         3  43       62749   O4     5 #> 13600      2         3  43       62749   O5     2 #> 13601      1         3  60       62750   A1     2 #> 13602      1         3  60       62750   A2     4 #> 13603      1         3  60       62750   A3     4 #> 13604      1         3  60       62750   A4     6 #> 13605      1         3  60       62750   A5     5 #> 13606      1         3  60       62750   C1     5 #> 13607      1         3  60       62750   C2     2 #> 13608      1         3  60       62750   C3     5 #> 13609      1         3  60       62750   C4     1 #> 13610      1         3  60       62750   C5     2 #> 13611      1         3  60       62750   E1     2 #> 13612      1         3  60       62750   E2     2 #> 13613      1         3  60       62750   E3     3 #> 13614      1         3  60       62750   E4     5 #> 13615      1         3  60       62750   E5     4 #> 13616      1         3  60       62750   N1     2 #> 13617      1         3  60       62750   N2     5 #> 13618      1         3  60       62750   N3     2 #> 13619      1         3  60       62750   N4     2 #> 13620      1         3  60       62750   N5     1 #> 13621      1         3  60       62750   O1     4 #> 13622      1         3  60       62750   O2     3 #> 13623      1         3  60       62750   O3     4 #> 13624      1         3  60       62750   O4     4 #> 13625      1         3  60       62750   O5     3 #> 13626      1         3  36       62751   A1     2 #> 13627      1         3  36       62751   A2     4 #> 13628      1         3  36       62751   A3     5 #> 13629      1         3  36       62751   A4     3 #> 13630      1         3  36       62751   A5     5 #> 13631      1         3  36       62751   C1     4 #> 13632      1         3  36       62751   C2     4 #> 13633      1         3  36       62751   C3     5 #> 13634      1         3  36       62751   C4     4 #> 13635      1         3  36       62751   C5     5 #> 13636      1         3  36       62751   E1     5 #> 13637      1         3  36       62751   E2     6 #> 13638      1         3  36       62751   E3     3 #> 13639      1         3  36       62751   E4     4 #> 13640      1         3  36       62751   E5     3 #> 13641      1         3  36       62751   N1     3 #> 13642      1         3  36       62751   N2     4 #> 13643      1         3  36       62751   N3     4 #> 13644      1         3  36       62751   N4     4 #> 13645      1         3  36       62751   N5     3 #> 13646      1         3  36       62751   O1     5 #> 13647      1         3  36       62751   O2     5 #> 13648      1         3  36       62751   O3     4 #> 13649      1         3  36       62751   O4     6 #> 13650      1         3  36       62751   O5     5 #> 13651      2         4  40       62757   A1     5 #> 13652      2         4  40       62757   A2     4 #> 13653      2         4  40       62757   A3     4 #> 13654      2         4  40       62757   A4     6 #> 13655      2         4  40       62757   A5     4 #> 13656      2         4  40       62757   C1     5 #> 13657      2         4  40       62757   C2     5 #> 13658      2         4  40       62757   C3     5 #> 13659      2         4  40       62757   C4     2 #> 13660      2         4  40       62757   C5     2 #> 13661      2         4  40       62757   E1     4 #> 13662      2         4  40       62757   E2     2 #> 13663      2         4  40       62757   E3     3 #> 13664      2         4  40       62757   E4     5 #> 13665      2         4  40       62757   E5     5 #> 13666      2         4  40       62757   N1     4 #> 13667      2         4  40       62757   N2     4 #> 13668      2         4  40       62757   N3    NA #> 13669      2         4  40       62757   N4     4 #> 13670      2         4  40       62757   N5     2 #> 13671      2         4  40       62757   O1     5 #> 13672      2         4  40       62757   O2     5 #> 13673      2         4  40       62757   O3     3 #> 13674      2         4  40       62757   O4     5 #> 13675      2         4  40       62757   O5     2 #> 13676      1         3  21       62758   A1     2 #> 13677      1         3  21       62758   A2     5 #> 13678      1         3  21       62758   A3     4 #> 13679      1         3  21       62758   A4     4 #> 13680      1         3  21       62758   A5     4 #> 13681      1         3  21       62758   C1     2 #> 13682      1         3  21       62758   C2     2 #> 13683      1         3  21       62758   C3     4 #> 13684      1         3  21       62758   C4     2 #> 13685      1         3  21       62758   C5     5 #> 13686      1         3  21       62758   E1     1 #> 13687      1         3  21       62758   E2     1 #> 13688      1         3  21       62758   E3     5 #> 13689      1         3  21       62758   E4     6 #> 13690      1         3  21       62758   E5     5 #> 13691      1         3  21       62758   N1     3 #> 13692      1         3  21       62758   N2     3 #> 13693      1         3  21       62758   N3     1 #> 13694      1         3  21       62758   N4     1 #> 13695      1         3  21       62758   N5     1 #> 13696      1         3  21       62758   O1     5 #> 13697      1         3  21       62758   O2     1 #> 13698      1         3  21       62758   O3     4 #> 13699      1         3  21       62758   O4     4 #> 13700      1         3  21       62758   O5     1 #> 13701      2         4  21       62761   A1     1 #> 13702      2         4  21       62761   A2     6 #> 13703      2         4  21       62761   A3     5 #> 13704      2         4  21       62761   A4     4 #> 13705      2         4  21       62761   A5     5 #> 13706      2         4  21       62761   C1     6 #> 13707      2         4  21       62761   C2     5 #> 13708      2         4  21       62761   C3     6 #> 13709      2         4  21       62761   C4     1 #> 13710      2         4  21       62761   C5     2 #> 13711      2         4  21       62761   E1     1 #> 13712      2         4  21       62761   E2     1 #> 13713      2         4  21       62761   E3     5 #> 13714      2         4  21       62761   E4     5 #> 13715      2         4  21       62761   E5     5 #> 13716      2         4  21       62761   N1     1 #> 13717      2         4  21       62761   N2     1 #> 13718      2         4  21       62761   N3     2 #> 13719      2         4  21       62761   N4     2 #> 13720      2         4  21       62761   N5     3 #> 13721      2         4  21       62761   O1     5 #> 13722      2         4  21       62761   O2     2 #> 13723      2         4  21       62761   O3     5 #> 13724      2         4  21       62761   O4     6 #> 13725      2         4  21       62761   O5     1 #> 13726      2         1  19       62764   A1     4 #> 13727      2         1  19       62764   A2     3 #> 13728      2         1  19       62764   A3     5 #> 13729      2         1  19       62764   A4     6 #> 13730      2         1  19       62764   A5     5 #> 13731      2         1  19       62764   C1     4 #> 13732      2         1  19       62764   C2     5 #> 13733      2         1  19       62764   C3     5 #> 13734      2         1  19       62764   C4     1 #> 13735      2         1  19       62764   C5     2 #> 13736      2         1  19       62764   E1     3 #> 13737      2         1  19       62764   E2     4 #> 13738      2         1  19       62764   E3     4 #> 13739      2         1  19       62764   E4     6 #> 13740      2         1  19       62764   E5     5 #> 13741      2         1  19       62764   N1     5 #> 13742      2         1  19       62764   N2     6 #> 13743      2         1  19       62764   N3     6 #> 13744      2         1  19       62764   N4     3 #> 13745      2         1  19       62764   N5     4 #> 13746      2         1  19       62764   O1     6 #> 13747      2         1  19       62764   O2     3 #> 13748      2         1  19       62764   O3     4 #> 13749      2         1  19       62764   O4     5 #> 13750      2         1  19       62764   O5     3 #> 13751      2         3  36       62765   A1     1 #> 13752      2         3  36       62765   A2     6 #> 13753      2         3  36       62765   A3     6 #> 13754      2         3  36       62765   A4     6 #> 13755      2         3  36       62765   A5     1 #> 13756      2         3  36       62765   C1     1 #> 13757      2         3  36       62765   C2     4 #> 13758      2         3  36       62765   C3     6 #> 13759      2         3  36       62765   C4     1 #> 13760      2         3  36       62765   C5     1 #> 13761      2         3  36       62765   E1     4 #> 13762      2         3  36       62765   E2     1 #> 13763      2         3  36       62765   E3     3 #> 13764      2         3  36       62765   E4     3 #> 13765      2         3  36       62765   E5     2 #> 13766      2         3  36       62765   N1     1 #> 13767      2         3  36       62765   N2     1 #> 13768      2         3  36       62765   N3     1 #> 13769      2         3  36       62765   N4     1 #> 13770      2         3  36       62765   N5     1 #> 13771      2         3  36       62765   O1     1 #> 13772      2         3  36       62765   O2     1 #> 13773      2         3  36       62765   O3     2 #> 13774      2         3  36       62765   O4     5 #> 13775      2         3  36       62765   O5     1 #> 13776      1         4  42       62766   A1     4 #> 13777      1         4  42       62766   A2     5 #> 13778      1         4  42       62766   A3     4 #> 13779      1         4  42       62766   A4     2 #> 13780      1         4  42       62766   A5     2 #> 13781      1         4  42       62766   C1     5 #> 13782      1         4  42       62766   C2     3 #> 13783      1         4  42       62766   C3     3 #> 13784      1         4  42       62766   C4     3 #> 13785      1         4  42       62766   C5     5 #> 13786      1         4  42       62766   E1     5 #> 13787      1         4  42       62766   E2     6 #> 13788      1         4  42       62766   E3     3 #> 13789      1         4  42       62766   E4     5 #> 13790      1         4  42       62766   E5     3 #> 13791      1         4  42       62766   N1     6 #> 13792      1         4  42       62766   N2     6 #> 13793      1         4  42       62766   N3     5 #> 13794      1         4  42       62766   N4     6 #> 13795      1         4  42       62766   N5     3 #> 13796      1         4  42       62766   O1     6 #> 13797      1         4  42       62766   O2     6 #> 13798      1         4  42       62766   O3     5 #> 13799      1         4  42       62766   O4     6 #> 13800      1         4  42       62766   O5     4 #> 13801      1         2  24       62767   A1     4 #> 13802      1         2  24       62767   A2     5 #> 13803      1         2  24       62767   A3     4 #> 13804      1         2  24       62767   A4     6 #> 13805      1         2  24       62767   A5     4 #> 13806      1         2  24       62767   C1     1 #> 13807      1         2  24       62767   C2     1 #> 13808      1         2  24       62767   C3     2 #> 13809      1         2  24       62767   C4     4 #> 13810      1         2  24       62767   C5     5 #> 13811      1         2  24       62767   E1     1 #> 13812      1         2  24       62767   E2     1 #> 13813      1         2  24       62767   E3     3 #> 13814      1         2  24       62767   E4     4 #> 13815      1         2  24       62767   E5     6 #> 13816      1         2  24       62767   N1     6 #> 13817      1         2  24       62767   N2     4 #> 13818      1         2  24       62767   N3     1 #> 13819      1         2  24       62767   N4     1 #> 13820      1         2  24       62767   N5     1 #> 13821      1         2  24       62767   O1     6 #> 13822      1         2  24       62767   O2     5 #> 13823      1         2  24       62767   O3     1 #> 13824      1         2  24       62767   O4     6 #> 13825      1         2  24       62767   O5     6 #> 13826      2         3  44       62768   A1     1 #> 13827      2         3  44       62768   A2     6 #> 13828      2         3  44       62768   A3     6 #> 13829      2         3  44       62768   A4     6 #> 13830      2         3  44       62768   A5     6 #> 13831      2         3  44       62768   C1     6 #> 13832      2         3  44       62768   C2     6 #> 13833      2         3  44       62768   C3     5 #> 13834      2         3  44       62768   C4     1 #> 13835      2         3  44       62768   C5     2 #> 13836      2         3  44       62768   E1     1 #> 13837      2         3  44       62768   E2     1 #> 13838      2         3  44       62768   E3     6 #> 13839      2         3  44       62768   E4     5 #> 13840      2         3  44       62768   E5     5 #> 13841      2         3  44       62768   N1     1 #> 13842      2         3  44       62768   N2     2 #> 13843      2         3  44       62768   N3     1 #> 13844      2         3  44       62768   N4     1 #> 13845      2         3  44       62768   N5     4 #> 13846      2         3  44       62768   O1     5 #> 13847      2         3  44       62768   O2     4 #> 13848      2         3  44       62768   O3     4 #> 13849      2         3  44       62768   O4     5 #> 13850      2         3  44       62768   O5     4 #> 13851      2         3  30       62770   A1     3 #> 13852      2         3  30       62770   A2     6 #> 13853      2         3  30       62770   A3     6 #> 13854      2         3  30       62770   A4     6 #> 13855      2         3  30       62770   A5     5 #> 13856      2         3  30       62770   C1     2 #> 13857      2         3  30       62770   C2     5 #> 13858      2         3  30       62770   C3     5 #> 13859      2         3  30       62770   C4     2 #> 13860      2         3  30       62770   C5     2 #> 13861      2         3  30       62770   E1     3 #> 13862      2         3  30       62770   E2     4 #> 13863      2         3  30       62770   E3     4 #> 13864      2         3  30       62770   E4     5 #> 13865      2         3  30       62770   E5     4 #> 13866      2         3  30       62770   N1     3 #> 13867      2         3  30       62770   N2     4 #> 13868      2         3  30       62770   N3     4 #> 13869      2         3  30       62770   N4     5 #> 13870      2         3  30       62770   N5     6 #> 13871      2         3  30       62770   O1     2 #> 13872      2         3  30       62770   O2     5 #> 13873      2         3  30       62770   O3     4 #> 13874      2         3  30       62770   O4     5 #> 13875      2         3  30       62770   O5     4 #> 13876      2         3  23       62772   A1     1 #> 13877      2         3  23       62772   A2     5 #> 13878      2         3  23       62772   A3     5 #> 13879      2         3  23       62772   A4     6 #> 13880      2         3  23       62772   A5     5 #> 13881      2         3  23       62772   C1     4 #> 13882      2         3  23       62772   C2     5 #> 13883      2         3  23       62772   C3     4 #> 13884      2         3  23       62772   C4     4 #> 13885      2         3  23       62772   C5     4 #> 13886      2         3  23       62772   E1     2 #> 13887      2         3  23       62772   E2     1 #> 13888      2         3  23       62772   E3     5 #> 13889      2         3  23       62772   E4     6 #> 13890      2         3  23       62772   E5     5 #> 13891      2         3  23       62772   N1     2 #> 13892      2         3  23       62772   N2     2 #> 13893      2         3  23       62772   N3     4 #> 13894      2         3  23       62772   N4     4 #> 13895      2         3  23       62772   N5     3 #> 13896      2         3  23       62772   O1     5 #> 13897      2         3  23       62772   O2     5 #> 13898      2         3  23       62772   O3     4 #> 13899      2         3  23       62772   O4     6 #> 13900      2         3  23       62772   O5     2 #> 13901      2         2  26       62776   A1     2 #> 13902      2         2  26       62776   A2     5 #> 13903      2         2  26       62776   A3     3 #> 13904      2         2  26       62776   A4     2 #> 13905      2         2  26       62776   A5     4 #> 13906      2         2  26       62776   C1     2 #> 13907      2         2  26       62776   C2     2 #> 13908      2         2  26       62776   C3     4 #> 13909      2         2  26       62776   C4     4 #> 13910      2         2  26       62776   C5     4 #> 13911      2         2  26       62776   E1     3 #> 13912      2         2  26       62776   E2     3 #> 13913      2         2  26       62776   E3     6 #> 13914      2         2  26       62776   E4     5 #> 13915      2         2  26       62776   E5     4 #> 13916      2         2  26       62776   N1     5 #> 13917      2         2  26       62776   N2     6 #> 13918      2         2  26       62776   N3     4 #> 13919      2         2  26       62776   N4     5 #> 13920      2         2  26       62776   N5     4 #> 13921      2         2  26       62776   O1     6 #> 13922      2         2  26       62776   O2     6 #> 13923      2         2  26       62776   O3     5 #> 13924      2         2  26       62776   O4     6 #> 13925      2         2  26       62776   O5     1 #> 13926      1         3  32       62778   A1     1 #> 13927      1         3  32       62778   A2     6 #> 13928      1         3  32       62778   A3     6 #> 13929      1         3  32       62778   A4     5 #> 13930      1         3  32       62778   A5     6 #> 13931      1         3  32       62778   C1    NA #> 13932      1         3  32       62778   C2     5 #> 13933      1         3  32       62778   C3     5 #> 13934      1         3  32       62778   C4     1 #> 13935      1         3  32       62778   C5     1 #> 13936      1         3  32       62778   E1     2 #> 13937      1         3  32       62778   E2     1 #> 13938      1         3  32       62778   E3     6 #> 13939      1         3  32       62778   E4     5 #> 13940      1         3  32       62778   E5     6 #> 13941      1         3  32       62778   N1     1 #> 13942      1         3  32       62778   N2     5 #> 13943      1         3  32       62778   N3     1 #> 13944      1         3  32       62778   N4     1 #> 13945      1         3  32       62778   N5     3 #> 13946      1         3  32       62778   O1     6 #> 13947      1         3  32       62778   O2     3 #> 13948      1         3  32       62778   O3     5 #> 13949      1         3  32       62778   O4     2 #> 13950      1         3  32       62778   O5     1 #> 13951      1         3  29       62779   A1     1 #> 13952      1         3  29       62779   A2     4 #> 13953      1         3  29       62779   A3     5 #> 13954      1         3  29       62779   A4     4 #> 13955      1         3  29       62779   A5     5 #> 13956      1         3  29       62779   C1     3 #> 13957      1         3  29       62779   C2     5 #> 13958      1         3  29       62779   C3     4 #> 13959      1         3  29       62779   C4     2 #> 13960      1         3  29       62779   C5     4 #> 13961      1         3  29       62779   E1     4 #> 13962      1         3  29       62779   E2     3 #> 13963      1         3  29       62779   E3     4 #> 13964      1         3  29       62779   E4     4 #> 13965      1         3  29       62779   E5     4 #> 13966      1         3  29       62779   N1     2 #> 13967      1         3  29       62779   N2     3 #> 13968      1         3  29       62779   N3     1 #> 13969      1         3  29       62779   N4     4 #> 13970      1         3  29       62779   N5     3 #> 13971      1         3  29       62779   O1     4 #> 13972      1         3  29       62779   O2     4 #> 13973      1         3  29       62779   O3     4 #> 13974      1         3  29       62779   O4     5 #> 13975      1         3  29       62779   O5     3 #> 13976      2         3  47       62780   A1     1 #> 13977      2         3  47       62780   A2     6 #> 13978      2         3  47       62780   A3     6 #> 13979      2         3  47       62780   A4     6 #> 13980      2         3  47       62780   A5     5 #> 13981      2         3  47       62780   C1     6 #> 13982      2         3  47       62780   C2     6 #> 13983      2         3  47       62780   C3     6 #> 13984      2         3  47       62780   C4     1 #> 13985      2         3  47       62780   C5     2 #> 13986      2         3  47       62780   E1     3 #> 13987      2         3  47       62780   E2     3 #> 13988      2         3  47       62780   E3     5 #> 13989      2         3  47       62780   E4     6 #> 13990      2         3  47       62780   E5     6 #> 13991      2         3  47       62780   N1     1 #> 13992      2         3  47       62780   N2     3 #> 13993      2         3  47       62780   N3     4 #> 13994      2         3  47       62780   N4     2 #> 13995      2         3  47       62780   N5     3 #> 13996      2         3  47       62780   O1     6 #> 13997      2         3  47       62780   O2     1 #> 13998      2         3  47       62780   O3     6 #> 13999      2         3  47       62780   O4     6 #> 14000      2         3  47       62780   O5     3 #> 14001      1         3  44       62781   A1     2 #> 14002      1         3  44       62781   A2     5 #> 14003      1         3  44       62781   A3     4 #> 14004      1         3  44       62781   A4     6 #> 14005      1         3  44       62781   A5     4 #> 14006      1         3  44       62781   C1     6 #> 14007      1         3  44       62781   C2     5 #> 14008      1         3  44       62781   C3     4 #> 14009      1         3  44       62781   C4     2 #> 14010      1         3  44       62781   C5     1 #> 14011      1         3  44       62781   E1     3 #> 14012      1         3  44       62781   E2     2 #> 14013      1         3  44       62781   E3     3 #> 14014      1         3  44       62781   E4     4 #> 14015      1         3  44       62781   E5     5 #> 14016      1         3  44       62781   N1     2 #> 14017      1         3  44       62781   N2     3 #> 14018      1         3  44       62781   N3     1 #> 14019      1         3  44       62781   N4     1 #> 14020      1         3  44       62781   N5     1 #> 14021      1         3  44       62781   O1     5 #> 14022      1         3  44       62781   O2     5 #> 14023      1         3  44       62781   O3     4 #> 14024      1         3  44       62781   O4     4 #> 14025      1         3  44       62781   O5     2 #> 14026      1         3  25       62783   A1     5 #> 14027      1         3  25       62783   A2     5 #> 14028      1         3  25       62783   A3     5 #> 14029      1         3  25       62783   A4     5 #> 14030      1         3  25       62783   A5     5 #> 14031      1         3  25       62783   C1     5 #> 14032      1         3  25       62783   C2     5 #> 14033      1         3  25       62783   C3     5 #> 14034      1         3  25       62783   C4     5 #> 14035      1         3  25       62783   C5     5 #> 14036      1         3  25       62783   E1     5 #> 14037      1         3  25       62783   E2     5 #> 14038      1         3  25       62783   E3     5 #> 14039      1         3  25       62783   E4     5 #> 14040      1         3  25       62783   E5     5 #> 14041      1         3  25       62783   N1     5 #> 14042      1         3  25       62783   N2     5 #> 14043      1         3  25       62783   N3     5 #> 14044      1         3  25       62783   N4     5 #> 14045      1         3  25       62783   N5     5 #> 14046      1         3  25       62783   O1     5 #> 14047      1         3  25       62783   O2     5 #> 14048      1         3  25       62783   O3     5 #> 14049      1         3  25       62783   O4     5 #> 14050      1         3  25       62783   O5     5 #> 14051      2         3  28       62785   A1     3 #> 14052      2         3  28       62785   A2     6 #> 14053      2         3  28       62785   A3     5 #> 14054      2         3  28       62785   A4     6 #> 14055      2         3  28       62785   A5     5 #> 14056      2         3  28       62785   C1     5 #> 14057      2         3  28       62785   C2     6 #> 14058      2         3  28       62785   C3     5 #> 14059      2         3  28       62785   C4     1 #> 14060      2         3  28       62785   C5     4 #> 14061      2         3  28       62785   E1     2 #> 14062      2         3  28       62785   E2     2 #> 14063      2         3  28       62785   E3     4 #> 14064      2         3  28       62785   E4     6 #> 14065      2         3  28       62785   E5     5 #> 14066      2         3  28       62785   N1     4 #> 14067      2         3  28       62785   N2     4 #> 14068      2         3  28       62785   N3     4 #> 14069      2         3  28       62785   N4     3 #> 14070      2         3  28       62785   N5     3 #> 14071      2         3  28       62785   O1     6 #> 14072      2         3  28       62785   O2     1 #> 14073      2         3  28       62785   O3     5 #> 14074      2         3  28       62785   O4     4 #> 14075      2         3  28       62785   O5     4 #> 14076      2         4  32       62786   A1     1 #> 14077      2         4  32       62786   A2     5 #> 14078      2         4  32       62786   A3     6 #> 14079      2         4  32       62786   A4     6 #> 14080      2         4  32       62786   A5     6 #> 14081      2         4  32       62786   C1     6 #> 14082      2         4  32       62786   C2     6 #> 14083      2         4  32       62786   C3     5 #> 14084      2         4  32       62786   C4     1 #> 14085      2         4  32       62786   C5     4 #> 14086      2         4  32       62786   E1     3 #> 14087      2         4  32       62786   E2     2 #> 14088      2         4  32       62786   E3     6 #> 14089      2         4  32       62786   E4     5 #> 14090      2         4  32       62786   E5     6 #> 14091      2         4  32       62786   N1     4 #> 14092      2         4  32       62786   N2     4 #> 14093      2         4  32       62786   N3     5 #> 14094      2         4  32       62786   N4     3 #> 14095      2         4  32       62786   N5     1 #> 14096      2         4  32       62786   O1     6 #> 14097      2         4  32       62786   O2     1 #> 14098      2         4  32       62786   O3     6 #> 14099      2         4  32       62786   O4     5 #> 14100      2         4  32       62786   O5     2 #> 14101      2         3  37       62787   A1     1 #> 14102      2         3  37       62787   A2     1 #> 14103      2         3  37       62787   A3     5 #> 14104      2         3  37       62787   A4     6 #> 14105      2         3  37       62787   A5     3 #> 14106      2         3  37       62787   C1     5 #> 14107      2         3  37       62787   C2     3 #> 14108      2         3  37       62787   C3     1 #> 14109      2         3  37       62787   C4     2 #> 14110      2         3  37       62787   C5     5 #> 14111      2         3  37       62787   E1     4 #> 14112      2         3  37       62787   E2     3 #> 14113      2         3  37       62787   E3     1 #> 14114      2         3  37       62787   E4     5 #> 14115      2         3  37       62787   E5     4 #> 14116      2         3  37       62787   N1     3 #> 14117      2         3  37       62787   N2     3 #> 14118      2         3  37       62787   N3     3 #> 14119      2         3  37       62787   N4     3 #> 14120      2         3  37       62787   N5     2 #> 14121      2         3  37       62787   O1     5 #> 14122      2         3  37       62787   O2     5 #> 14123      2         3  37       62787   O3     2 #> 14124      2         3  37       62787   O4     6 #> 14125      2         3  37       62787   O5     6 #> 14126      2         3  39       62788   A1     1 #> 14127      2         3  39       62788   A2     6 #> 14128      2         3  39       62788   A3     6 #> 14129      2         3  39       62788   A4     6 #> 14130      2         3  39       62788   A5     6 #> 14131      2         3  39       62788   C1     6 #> 14132      2         3  39       62788   C2     6 #> 14133      2         3  39       62788   C3     5 #> 14134      2         3  39       62788   C4     2 #> 14135      2         3  39       62788   C5     3 #> 14136      2         3  39       62788   E1     1 #> 14137      2         3  39       62788   E2     2 #> 14138      2         3  39       62788   E3     6 #> 14139      2         3  39       62788   E4     5 #> 14140      2         3  39       62788   E5     5 #> 14141      2         3  39       62788   N1     2 #> 14142      2         3  39       62788   N2     5 #> 14143      2         3  39       62788   N3     5 #> 14144      2         3  39       62788   N4     5 #> 14145      2         3  39       62788   N5     4 #> 14146      2         3  39       62788   O1     5 #> 14147      2         3  39       62788   O2     3 #> 14148      2         3  39       62788   O3     4 #> 14149      2         3  39       62788   O4     6 #> 14150      2         3  39       62788   O5     2 #> 14151      1         2  40       62789   A1     1 #> 14152      1         2  40       62789   A2     6 #> 14153      1         2  40       62789   A3     4 #> 14154      1         2  40       62789   A4     6 #> 14155      1         2  40       62789   A5     6 #> 14156      1         2  40       62789   C1     4 #> 14157      1         2  40       62789   C2     4 #> 14158      1         2  40       62789   C3     3 #> 14159      1         2  40       62789   C4     5 #> 14160      1         2  40       62789   C5     2 #> 14161      1         2  40       62789   E1     1 #> 14162      1         2  40       62789   E2     1 #> 14163      1         2  40       62789   E3     3 #> 14164      1         2  40       62789   E4     6 #> 14165      1         2  40       62789   E5     4 #> 14166      1         2  40       62789   N1     1 #> 14167      1         2  40       62789   N2     2 #> 14168      1         2  40       62789   N3     2 #> 14169      1         2  40       62789   N4     2 #> 14170      1         2  40       62789   N5     1 #> 14171      1         2  40       62789   O1     5 #> 14172      1         2  40       62789   O2     1 #> 14173      1         2  40       62789   O3     5 #> 14174      1         2  40       62789   O4     5 #> 14175      1         2  40       62789   O5     2 #> 14176      2         4  26       62790   A1     1 #> 14177      2         4  26       62790   A2     5 #> 14178      2         4  26       62790   A3     5 #> 14179      2         4  26       62790   A4     6 #> 14180      2         4  26       62790   A5     6 #> 14181      2         4  26       62790   C1     6 #> 14182      2         4  26       62790   C2     5 #> 14183      2         4  26       62790   C3     6 #> 14184      2         4  26       62790   C4     1 #> 14185      2         4  26       62790   C5     1 #> 14186      2         4  26       62790   E1     3 #> 14187      2         4  26       62790   E2     1 #> 14188      2         4  26       62790   E3     6 #> 14189      2         4  26       62790   E4     6 #> 14190      2         4  26       62790   E5     5 #> 14191      2         4  26       62790   N1     4 #> 14192      2         4  26       62790   N2     4 #> 14193      2         4  26       62790   N3     4 #> 14194      2         4  26       62790   N4     3 #> 14195      2         4  26       62790   N5     1 #> 14196      2         4  26       62790   O1     6 #> 14197      2         4  26       62790   O2     1 #> 14198      2         4  26       62790   O3     5 #> 14199      2         4  26       62790   O4     1 #> 14200      2         4  26       62790   O5     4 #> 14201      1         3  27       62792   A1     5 #> 14202      1         3  27       62792   A2     4 #> 14203      1         3  27       62792   A3     4 #> 14204      1         3  27       62792   A4     5 #> 14205      1         3  27       62792   A5     5 #> 14206      1         3  27       62792   C1     4 #> 14207      1         3  27       62792   C2     3 #> 14208      1         3  27       62792   C3     4 #> 14209      1         3  27       62792   C4     4 #> 14210      1         3  27       62792   C5     3 #> 14211      1         3  27       62792   E1     3 #> 14212      1         3  27       62792   E2     1 #> 14213      1         3  27       62792   E3     2 #> 14214      1         3  27       62792   E4     3 #> 14215      1         3  27       62792   E5     5 #> 14216      1         3  27       62792   N1     5 #> 14217      1         3  27       62792   N2     5 #> 14218      1         3  27       62792   N3     3 #> 14219      1         3  27       62792   N4     1 #> 14220      1         3  27       62792   N5     1 #> 14221      1         3  27       62792   O1     4 #> 14222      1         3  27       62792   O2     6 #> 14223      1         3  27       62792   O3     4 #> 14224      1         3  27       62792   O4     5 #> 14225      1         3  27       62792   O5     6 #> 14226      2         3  41       62795   A1     2 #> 14227      2         3  41       62795   A2     5 #> 14228      2         3  41       62795   A3     2 #> 14229      2         3  41       62795   A4     5 #> 14230      2         3  41       62795   A5     4 #> 14231      2         3  41       62795   C1     5 #> 14232      2         3  41       62795   C2     4 #> 14233      2         3  41       62795   C3     1 #> 14234      2         3  41       62795   C4     5 #> 14235      2         3  41       62795   C5     5 #> 14236      2         3  41       62795   E1     2 #> 14237      2         3  41       62795   E2     5 #> 14238      2         3  41       62795   E3     1 #> 14239      2         3  41       62795   E4     6 #> 14240      2         3  41       62795   E5     5 #> 14241      2         3  41       62795   N1     4 #> 14242      2         3  41       62795   N2     4 #> 14243      2         3  41       62795   N3     2 #> 14244      2         3  41       62795   N4     4 #> 14245      2         3  41       62795   N5     5 #> 14246      2         3  41       62795   O1     2 #> 14247      2         3  41       62795   O2     6 #> 14248      2         3  41       62795   O3     1 #> 14249      2         3  41       62795   O4     6 #> 14250      2         3  41       62795   O5     6 #> 14251      2         3  34       62796   A1     1 #> 14252      2         3  34       62796   A2     5 #> 14253      2         3  34       62796   A3     5 #> 14254      2         3  34       62796   A4     5 #> 14255      2         3  34       62796   A5     5 #> 14256      2         3  34       62796   C1     2 #> 14257      2         3  34       62796   C2     4 #> 14258      2         3  34       62796   C3     4 #> 14259      2         3  34       62796   C4     6 #> 14260      2         3  34       62796   C5     5 #> 14261      2         3  34       62796   E1     4 #> 14262      2         3  34       62796   E2     6 #> 14263      2         3  34       62796   E3     3 #> 14264      2         3  34       62796   E4     5 #> 14265      2         3  34       62796   E5     1 #> 14266      2         3  34       62796   N1     4 #> 14267      2         3  34       62796   N2     5 #> 14268      2         3  34       62796   N3     4 #> 14269      2         3  34       62796   N4     4 #> 14270      2         3  34       62796   N5     4 #> 14271      2         3  34       62796   O1     5 #> 14272      2         3  34       62796   O2     6 #> 14273      2         3  34       62796   O3     2 #> 14274      2         3  34       62796   O4     5 #> 14275      2         3  34       62796   O5     4 #> 14276      2         3  23       62797   A1     1 #> 14277      2         3  23       62797   A2     6 #> 14278      2         3  23       62797   A3     6 #> 14279      2         3  23       62797   A4     6 #> 14280      2         3  23       62797   A5     6 #> 14281      2         3  23       62797   C1     3 #> 14282      2         3  23       62797   C2     4 #> 14283      2         3  23       62797   C3     1 #> 14284      2         3  23       62797   C4     1 #> 14285      2         3  23       62797   C5     4 #> 14286      2         3  23       62797   E1     1 #> 14287      2         3  23       62797   E2     1 #> 14288      2         3  23       62797   E3     6 #> 14289      2         3  23       62797   E4     6 #> 14290      2         3  23       62797   E5     6 #> 14291      2         3  23       62797   N1     1 #> 14292      2         3  23       62797   N2     1 #> 14293      2         3  23       62797   N3     2 #> 14294      2         3  23       62797   N4     1 #> 14295      2         3  23       62797   N5     1 #> 14296      2         3  23       62797   O1     5 #> 14297      2         3  23       62797   O2     6 #> 14298      2         3  23       62797   O3     3 #> 14299      2         3  23       62797   O4     4 #> 14300      2         3  23       62797   O5     3 #> 14301      1         3  31       62800   A1     3 #> 14302      1         3  31       62800   A2     5 #> 14303      1         3  31       62800   A3     6 #> 14304      1         3  31       62800   A4     6 #> 14305      1         3  31       62800   A5     4 #> 14306      1         3  31       62800   C1     3 #> 14307      1         3  31       62800   C2     5 #> 14308      1         3  31       62800   C3     3 #> 14309      1         3  31       62800   C4     1 #> 14310      1         3  31       62800   C5     1 #> 14311      1         3  31       62800   E1     1 #> 14312      1         3  31       62800   E2     1 #> 14313      1         3  31       62800   E3     5 #> 14314      1         3  31       62800   E4     5 #> 14315      1         3  31       62800   E5     6 #> 14316      1         3  31       62800   N1     4 #> 14317      1         3  31       62800   N2     6 #> 14318      1         3  31       62800   N3     6 #> 14319      1         3  31       62800   N4     5 #> 14320      1         3  31       62800   N5     3 #> 14321      1         3  31       62800   O1     6 #> 14322      1         3  31       62800   O2     1 #> 14323      1         3  31       62800   O3     5 #> 14324      1         3  31       62800   O4     6 #> 14325      1         3  31       62800   O5     2 #> 14326      2         3  38       62801   A1     6 #> 14327      2         3  38       62801   A2     6 #> 14328      2         3  38       62801   A3     6 #> 14329      2         3  38       62801   A4     6 #> 14330      2         3  38       62801   A5     6 #> 14331      2         3  38       62801   C1     5 #> 14332      2         3  38       62801   C2     6 #> 14333      2         3  38       62801   C3     5 #> 14334      2         3  38       62801   C4     1 #> 14335      2         3  38       62801   C5     1 #> 14336      2         3  38       62801   E1     5 #> 14337      2         3  38       62801   E2     5 #> 14338      2         3  38       62801   E3     4 #> 14339      2         3  38       62801   E4     2 #> 14340      2         3  38       62801   E5     5 #> 14341      2         3  38       62801   N1     2 #> 14342      2         3  38       62801   N2     3 #> 14343      2         3  38       62801   N3     1 #> 14344      2         3  38       62801   N4     3 #> 14345      2         3  38       62801   N5     5 #> 14346      2         3  38       62801   O1     5 #> 14347      2         3  38       62801   O2     5 #> 14348      2         3  38       62801   O3     4 #> 14349      2         3  38       62801   O4     5 #> 14350      2         3  38       62801   O5     3 #> 14351      2         3  44       62803   A1     1 #> 14352      2         3  44       62803   A2     6 #> 14353      2         3  44       62803   A3     5 #> 14354      2         3  44       62803   A4     5 #> 14355      2         3  44       62803   A5     5 #> 14356      2         3  44       62803   C1     4 #> 14357      2         3  44       62803   C2     4 #> 14358      2         3  44       62803   C3     4 #> 14359      2         3  44       62803   C4     4 #> 14360      2         3  44       62803   C5     2 #> 14361      2         3  44       62803   E1     2 #> 14362      2         3  44       62803   E2     3 #> 14363      2         3  44       62803   E3     3 #> 14364      2         3  44       62803   E4     5 #> 14365      2         3  44       62803   E5     2 #> 14366      2         3  44       62803   N1     1 #> 14367      2         3  44       62803   N2     1 #> 14368      2         3  44       62803   N3     2 #> 14369      2         3  44       62803   N4     2 #> 14370      2         3  44       62803   N5     5 #> 14371      2         3  44       62803   O1     3 #> 14372      2         3  44       62803   O2     2 #> 14373      2         3  44       62803   O3     4 #> 14374      2         3  44       62803   O4     5 #> 14375      2         3  44       62803   O5     4 #> 14376      2         3  31       62804   A1     3 #> 14377      2         3  31       62804   A2     6 #> 14378      2         3  31       62804   A3     1 #> 14379      2         3  31       62804   A4     5 #> 14380      2         3  31       62804   A5     6 #> 14381      2         3  31       62804   C1     6 #> 14382      2         3  31       62804   C2     3 #> 14383      2         3  31       62804   C3     3 #> 14384      2         3  31       62804   C4     2 #> 14385      2         3  31       62804   C5     4 #> 14386      2         3  31       62804   E1     1 #> 14387      2         3  31       62804   E2     1 #> 14388      2         3  31       62804   E3     5 #> 14389      2         3  31       62804   E4     6 #> 14390      2         3  31       62804   E5     6 #> 14391      2         3  31       62804   N1     3 #> 14392      2         3  31       62804   N2     5 #> 14393      2         3  31       62804   N3     2 #> 14394      2         3  31       62804   N4     3 #> 14395      2         3  31       62804   N5     2 #> 14396      2         3  31       62804   O1     6 #> 14397      2         3  31       62804   O2     3 #> 14398      2         3  31       62804   O3     6 #> 14399      2         3  31       62804   O4     6 #> 14400      2         3  31       62804   O5     1 #> 14401      1         5  33       62805   A1     1 #> 14402      1         5  33       62805   A2     6 #> 14403      1         5  33       62805   A3     5 #> 14404      1         5  33       62805   A4     6 #> 14405      1         5  33       62805   A5     5 #> 14406      1         5  33       62805   C1     4 #> 14407      1         5  33       62805   C2     5 #> 14408      1         5  33       62805   C3     5 #> 14409      1         5  33       62805   C4     2 #> 14410      1         5  33       62805   C5     4 #> 14411      1         5  33       62805   E1     2 #> 14412      1         5  33       62805   E2     3 #> 14413      1         5  33       62805   E3     5 #> 14414      1         5  33       62805   E4     5 #> 14415      1         5  33       62805   E5     4 #> 14416      1         5  33       62805   N1     4 #> 14417      1         5  33       62805   N2     5 #> 14418      1         5  33       62805   N3     5 #> 14419      1         5  33       62805   N4     4 #> 14420      1         5  33       62805   N5     3 #> 14421      1         5  33       62805   O1     5 #> 14422      1         5  33       62805   O2     1 #> 14423      1         5  33       62805   O3     5 #> 14424      1         5  33       62805   O4     6 #> 14425      1         5  33       62805   O5     2 #> 14426      2         3  28       62809   A1     3 #> 14427      2         3  28       62809   A2     2 #> 14428      2         3  28       62809   A3     3 #> 14429      2         3  28       62809   A4     4 #> 14430      2         3  28       62809   A5     2 #> 14431      2         3  28       62809   C1    NA #> 14432      2         3  28       62809   C2     3 #> 14433      2         3  28       62809   C3     2 #> 14434      2         3  28       62809   C4     2 #> 14435      2         3  28       62809   C5     3 #> 14436      2         3  28       62809   E1     6 #> 14437      2         3  28       62809   E2     6 #> 14438      2         3  28       62809   E3     2 #> 14439      2         3  28       62809   E4     5 #> 14440      2         3  28       62809   E5     3 #> 14441      2         3  28       62809   N1     6 #> 14442      2         3  28       62809   N2     5 #> 14443      2         3  28       62809   N3     5 #> 14444      2         3  28       62809   N4     2 #> 14445      2         3  28       62809   N5     5 #> 14446      2         3  28       62809   O1     3 #> 14447      2         3  28       62809   O2     6 #> 14448      2         3  28       62809   O3     1 #> 14449      2         3  28       62809   O4     1 #> 14450      2         3  28       62809   O5     3 #> 14451      1         3  46       62810   A1     1 #> 14452      1         3  46       62810   A2     5 #> 14453      1         3  46       62810   A3     5 #> 14454      1         3  46       62810   A4     5 #> 14455      1         3  46       62810   A5     6 #> 14456      1         3  46       62810   C1     6 #> 14457      1         3  46       62810   C2     6 #> 14458      1         3  46       62810   C3     6 #> 14459      1         3  46       62810   C4     1 #> 14460      1         3  46       62810   C5     1 #> 14461      1         3  46       62810   E1     1 #> 14462      1         3  46       62810   E2     2 #> 14463      1         3  46       62810   E3     5 #> 14464      1         3  46       62810   E4     6 #> 14465      1         3  46       62810   E5     6 #> 14466      1         3  46       62810   N1     1 #> 14467      1         3  46       62810   N2     1 #> 14468      1         3  46       62810   N3     1 #> 14469      1         3  46       62810   N4     1 #> 14470      1         3  46       62810   N5     1 #> 14471      1         3  46       62810   O1     6 #> 14472      1         3  46       62810   O2     6 #> 14473      1         3  46       62810   O3     5 #> 14474      1         3  46       62810   O4     4 #> 14475      1         3  46       62810   O5     2 #> 14476      2         3  36       62816   A1     2 #> 14477      2         3  36       62816   A2     6 #> 14478      2         3  36       62816   A3     6 #> 14479      2         3  36       62816   A4     5 #> 14480      2         3  36       62816   A5     3 #> 14481      2         3  36       62816   C1     6 #> 14482      2         3  36       62816   C2     5 #> 14483      2         3  36       62816   C3     6 #> 14484      2         3  36       62816   C4     1 #> 14485      2         3  36       62816   C5     6 #> 14486      2         3  36       62816   E1     1 #> 14487      2         3  36       62816   E2     5 #> 14488      2         3  36       62816   E3     4 #> 14489      2         3  36       62816   E4     4 #> 14490      2         3  36       62816   E5     6 #> 14491      2         3  36       62816   N1     6 #> 14492      2         3  36       62816   N2     6 #> 14493      2         3  36       62816   N3     6 #> 14494      2         3  36       62816   N4     6 #> 14495      2         3  36       62816   N5     6 #> 14496      2         3  36       62816   O1     2 #> 14497      2         3  36       62816   O2     1 #> 14498      2         3  36       62816   O3     3 #> 14499      2         3  36       62816   O4     6 #> 14500      2         3  36       62816   O5     2 #> 14501      2         3  28       62817   A1     4 #> 14502      2         3  28       62817   A2     5 #> 14503      2         3  28       62817   A3     5 #> 14504      2         3  28       62817   A4     6 #> 14505      2         3  28       62817   A5     6 #> 14506      2         3  28       62817   C1     3 #> 14507      2         3  28       62817   C2     5 #> 14508      2         3  28       62817   C3     6 #> 14509      2         3  28       62817   C4     5 #> 14510      2         3  28       62817   C5     3 #> 14511      2         3  28       62817   E1     6 #> 14512      2         3  28       62817   E2     6 #> 14513      2         3  28       62817   E3     5 #> 14514      2         3  28       62817   E4     6 #> 14515      2         3  28       62817   E5     5 #> 14516      2         3  28       62817   N1     5 #> 14517      2         3  28       62817   N2     3 #> 14518      2         3  28       62817   N3     2 #> 14519      2         3  28       62817   N4     1 #> 14520      2         3  28       62817   N5     5 #> 14521      2         3  28       62817   O1     5 #> 14522      2         3  28       62817   O2     6 #> 14523      2         3  28       62817   O3     4 #> 14524      2         3  28       62817   O4     1 #> 14525      2         3  28       62817   O5     5 #> 14526      2         3  28       62819   A1     1 #> 14527      2         3  28       62819   A2     4 #> 14528      2         3  28       62819   A3     6 #> 14529      2         3  28       62819   A4     6 #> 14530      2         3  28       62819   A5     6 #> 14531      2         3  28       62819   C1     3 #> 14532      2         3  28       62819   C2     4 #> 14533      2         3  28       62819   C3     2 #> 14534      2         3  28       62819   C4     2 #> 14535      2         3  28       62819   C5     5 #> 14536      2         3  28       62819   E1     4 #> 14537      2         3  28       62819   E2     3 #> 14538      2         3  28       62819   E3     4 #> 14539      2         3  28       62819   E4     4 #> 14540      2         3  28       62819   E5     4 #> 14541      2         3  28       62819   N1     2 #> 14542      2         3  28       62819   N2     2 #> 14543      2         3  28       62819   N3     2 #> 14544      2         3  28       62819   N4     3 #> 14545      2         3  28       62819   N5     1 #> 14546      2         3  28       62819   O1     4 #> 14547      2         3  28       62819   O2     3 #> 14548      2         3  28       62819   O3     5 #> 14549      2         3  28       62819   O4     6 #> 14550      2         3  28       62819   O5     6 #> 14551      2         3  42       62821   A1     3 #> 14552      2         3  42       62821   A2     6 #> 14553      2         3  42       62821   A3     6 #> 14554      2         3  42       62821   A4     6 #> 14555      2         3  42       62821   A5     5 #> 14556      2         3  42       62821   C1     6 #> 14557      2         3  42       62821   C2     5 #> 14558      2         3  42       62821   C3     6 #> 14559      2         3  42       62821   C4     1 #> 14560      2         3  42       62821   C5     2 #> 14561      2         3  42       62821   E1     3 #> 14562      2         3  42       62821   E2     1 #> 14563      2         3  42       62821   E3     5 #> 14564      2         3  42       62821   E4     6 #> 14565      2         3  42       62821   E5     5 #> 14566      2         3  42       62821   N1     1 #> 14567      2         3  42       62821   N2     3 #> 14568      2         3  42       62821   N3     2 #> 14569      2         3  42       62821   N4     1 #> 14570      2         3  42       62821   N5     1 #> 14571      2         3  42       62821   O1     5 #> 14572      2         3  42       62821   O2     1 #> 14573      2         3  42       62821   O3     5 #> 14574      2         3  42       62821   O4     6 #> 14575      2         3  42       62821   O5     1 #> 14576      2         3  32       62822   A1     1 #> 14577      2         3  32       62822   A2     5 #> 14578      2         3  32       62822   A3     5 #> 14579      2         3  32       62822   A4     6 #> 14580      2         3  32       62822   A5     4 #> 14581      2         3  32       62822   C1     4 #> 14582      2         3  32       62822   C2     4 #> 14583      2         3  32       62822   C3     4 #> 14584      2         3  32       62822   C4     3 #> 14585      2         3  32       62822   C5     2 #> 14586      2         3  32       62822   E1     1 #> 14587      2         3  32       62822   E2     2 #> 14588      2         3  32       62822   E3     4 #> 14589      2         3  32       62822   E4     5 #> 14590      2         3  32       62822   E5     4 #> 14591      2         3  32       62822   N1     3 #> 14592      2         3  32       62822   N2     3 #> 14593      2         3  32       62822   N3     3 #> 14594      2         3  32       62822   N4     1 #> 14595      2         3  32       62822   N5     2 #> 14596      2         3  32       62822   O1     4 #> 14597      2         3  32       62822   O2     2 #> 14598      2         3  32       62822   O3     4 #> 14599      2         3  32       62822   O4     4 #> 14600      2         3  32       62822   O5     3 #> 14601      2         3  19       62825   A1     2 #> 14602      2         3  19       62825   A2     5 #> 14603      2         3  19       62825   A3     2 #> 14604      2         3  19       62825   A4     1 #> 14605      2         3  19       62825   A5     3 #> 14606      2         3  19       62825   C1     4 #> 14607      2         3  19       62825   C2     2 #> 14608      2         3  19       62825   C3     4 #> 14609      2         3  19       62825   C4     4 #> 14610      2         3  19       62825   C5     5 #> 14611      2         3  19       62825   E1     4 #> 14612      2         3  19       62825   E2     4 #> 14613      2         3  19       62825   E3     2 #> 14614      2         3  19       62825   E4     4 #> 14615      2         3  19       62825   E5     3 #> 14616      2         3  19       62825   N1     3 #> 14617      2         3  19       62825   N2     5 #> 14618      2         3  19       62825   N3     5 #> 14619      2         3  19       62825   N4     4 #> 14620      2         3  19       62825   N5     1 #> 14621      2         3  19       62825   O1     5 #> 14622      2         3  19       62825   O2     2 #> 14623      2         3  19       62825   O3     3 #> 14624      2         3  19       62825   O4     3 #> 14625      2         3  19       62825   O5     2 #> 14626      2         3  25       62826   A1     2 #> 14627      2         3  25       62826   A2     5 #> 14628      2         3  25       62826   A3     4 #> 14629      2         3  25       62826   A4     6 #> 14630      2         3  25       62826   A5     5 #> 14631      2         3  25       62826   C1     4 #> 14632      2         3  25       62826   C2     5 #> 14633      2         3  25       62826   C3     4 #> 14634      2         3  25       62826   C4     3 #> 14635      2         3  25       62826   C5     4 #> 14636      2         3  25       62826   E1     1 #> 14637      2         3  25       62826   E2     3 #> 14638      2         3  25       62826   E3     4 #> 14639      2         3  25       62826   E4     5 #> 14640      2         3  25       62826   E5     5 #> 14641      2         3  25       62826   N1     5 #> 14642      2         3  25       62826   N2     5 #> 14643      2         3  25       62826   N3     4 #> 14644      2         3  25       62826   N4     3 #> 14645      2         3  25       62826   N5     3 #> 14646      2         3  25       62826   O1     4 #> 14647      2         3  25       62826   O2     4 #> 14648      2         3  25       62826   O3     4 #> 14649      2         3  25       62826   O4     4 #> 14650      2         3  25       62826   O5     4 #> 14651      2         5  37       62827   A1     4 #> 14652      2         5  37       62827   A2     5 #> 14653      2         5  37       62827   A3     5 #> 14654      2         5  37       62827   A4     6 #> 14655      2         5  37       62827   A5     5 #> 14656      2         5  37       62827   C1     4 #> 14657      2         5  37       62827   C2     6 #> 14658      2         5  37       62827   C3     6 #> 14659      2         5  37       62827   C4     4 #> 14660      2         5  37       62827   C5     4 #> 14661      2         5  37       62827   E1     1 #> 14662      2         5  37       62827   E2     5 #> 14663      2         5  37       62827   E3     4 #> 14664      2         5  37       62827   E4     4 #> 14665      2         5  37       62827   E5     5 #> 14666      2         5  37       62827   N1     3 #> 14667      2         5  37       62827   N2     5 #> 14668      2         5  37       62827   N3     4 #> 14669      2         5  37       62827   N4     4 #> 14670      2         5  37       62827   N5     4 #> 14671      2         5  37       62827   O1     5 #> 14672      2         5  37       62827   O2     4 #> 14673      2         5  37       62827   O3     5 #> 14674      2         5  37       62827   O4     4 #> 14675      2         5  37       62827   O5     5 #> 14676      2         3  29       62828   A1     2 #> 14677      2         3  29       62828   A2     6 #> 14678      2         3  29       62828   A3     5 #> 14679      2         3  29       62828   A4     6 #> 14680      2         3  29       62828   A5     6 #> 14681      2         3  29       62828   C1     6 #> 14682      2         3  29       62828   C2     6 #> 14683      2         3  29       62828   C3     5 #> 14684      2         3  29       62828   C4     4 #> 14685      2         3  29       62828   C5     2 #> 14686      2         3  29       62828   E1     1 #> 14687      2         3  29       62828   E2     2 #> 14688      2         3  29       62828   E3     5 #> 14689      2         3  29       62828   E4     6 #> 14690      2         3  29       62828   E5     5 #> 14691      2         3  29       62828   N1     3 #> 14692      2         3  29       62828   N2     5 #> 14693      2         3  29       62828   N3     1 #> 14694      2         3  29       62828   N4     4 #> 14695      2         3  29       62828   N5     3 #> 14696      2         3  29       62828   O1     6 #> 14697      2         3  29       62828   O2     4 #> 14698      2         3  29       62828   O3     5 #> 14699      2         3  29       62828   O4     5 #> 14700      2         3  29       62828   O5     3 #> 14701      2         3  32       62831   A1     6 #> 14702      2         3  32       62831   A2     2 #> 14703      2         3  32       62831   A3     5 #> 14704      2         3  32       62831   A4     6 #> 14705      2         3  32       62831   A5     2 #> 14706      2         3  32       62831   C1     6 #> 14707      2         3  32       62831   C2     5 #> 14708      2         3  32       62831   C3     3 #> 14709      2         3  32       62831   C4     5 #> 14710      2         3  32       62831   C5     3 #> 14711      2         3  32       62831   E1     5 #> 14712      2         3  32       62831   E2     2 #> 14713      2         3  32       62831   E3     4 #> 14714      2         3  32       62831   E4     5 #> 14715      2         3  32       62831   E5     6 #> 14716      2         3  32       62831   N1     6 #> 14717      2         3  32       62831   N2     6 #> 14718      2         3  32       62831   N3     6 #> 14719      2         3  32       62831   N4     5 #> 14720      2         3  32       62831   N5     6 #> 14721      2         3  32       62831   O1     5 #> 14722      2         3  32       62831   O2     5 #> 14723      2         3  32       62831   O3     4 #> 14724      2         3  32       62831   O4     4 #> 14725      2         3  32       62831   O5     6 #> 14726      1         3  27       62832   A1     1 #> 14727      1         3  27       62832   A2     5 #> 14728      1         3  27       62832   A3     5 #> 14729      1         3  27       62832   A4     6 #> 14730      1         3  27       62832   A5     5 #> 14731      1         3  27       62832   C1     5 #> 14732      1         3  27       62832   C2     5 #> 14733      1         3  27       62832   C3     5 #> 14734      1         3  27       62832   C4     3 #> 14735      1         3  27       62832   C5     3 #> 14736      1         3  27       62832   E1     2 #> 14737      1         3  27       62832   E2     1 #> 14738      1         3  27       62832   E3     5 #> 14739      1         3  27       62832   E4     6 #> 14740      1         3  27       62832   E5     6 #> 14741      1         3  27       62832   N1     3 #> 14742      1         3  27       62832   N2     3 #> 14743      1         3  27       62832   N3     1 #> 14744      1         3  27       62832   N4     2 #> 14745      1         3  27       62832   N5     1 #> 14746      1         3  27       62832   O1     6 #> 14747      1         3  27       62832   O2     5 #> 14748      1         3  27       62832   O3     2 #> 14749      1         3  27       62832   O4     6 #> 14750      1         3  27       62832   O5     4 #> 14751      2         3  27       62834   A1     1 #> 14752      2         3  27       62834   A2     6 #> 14753      2         3  27       62834   A3     4 #> 14754      2         3  27       62834   A4     4 #> 14755      2         3  27       62834   A5     4 #> 14756      2         3  27       62834   C1     4 #> 14757      2         3  27       62834   C2     4 #> 14758      2         3  27       62834   C3     6 #> 14759      2         3  27       62834   C4     2 #> 14760      2         3  27       62834   C5     2 #> 14761      2         3  27       62834   E1     6 #> 14762      2         3  27       62834   E2     6 #> 14763      2         3  27       62834   E3     5 #> 14764      2         3  27       62834   E4     5 #> 14765      2         3  27       62834   E5     6 #> 14766      2         3  27       62834   N1     2 #> 14767      2         3  27       62834   N2     4 #> 14768      2         3  27       62834   N3     4 #> 14769      2         3  27       62834   N4     1 #> 14770      2         3  27       62834   N5     2 #> 14771      2         3  27       62834   O1     6 #> 14772      2         3  27       62834   O2     4 #> 14773      2         3  27       62834   O3     5 #> 14774      2         3  27       62834   O4     6 #> 14775      2         3  27       62834   O5     2 #> 14776      1         3  39       62835   A1     1 #> 14777      1         3  39       62835   A2     5 #> 14778      1         3  39       62835   A3     4 #> 14779      1         3  39       62835   A4     5 #> 14780      1         3  39       62835   A5     5 #> 14781      1         3  39       62835   C1     4 #> 14782      1         3  39       62835   C2     4 #> 14783      1         3  39       62835   C3     3 #> 14784      1         3  39       62835   C4    NA #> 14785      1         3  39       62835   C5     4 #> 14786      1         3  39       62835   E1     3 #> 14787      1         3  39       62835   E2     2 #> 14788      1         3  39       62835   E3     4 #> 14789      1         3  39       62835   E4     5 #> 14790      1         3  39       62835   E5     4 #> 14791      1         3  39       62835   N1     4 #> 14792      1         3  39       62835   N2     5 #> 14793      1         3  39       62835   N3     5 #> 14794      1         3  39       62835   N4     6 #> 14795      1         3  39       62835   N5     5 #> 14796      1         3  39       62835   O1     5 #> 14797      1         3  39       62835   O2     6 #> 14798      1         3  39       62835   O3     3 #> 14799      1         3  39       62835   O4     6 #> 14800      1         3  39       62835   O5     2 #> 14801      1        NA  45       62837   A1     2 #> 14802      1        NA  45       62837   A2     5 #> 14803      1        NA  45       62837   A3     6 #> 14804      1        NA  45       62837   A4     5 #> 14805      1        NA  45       62837   A5     4 #> 14806      1        NA  45       62837   C1     1 #> 14807      1        NA  45       62837   C2     5 #> 14808      1        NA  45       62837   C3     6 #> 14809      1        NA  45       62837   C4     1 #> 14810      1        NA  45       62837   C5     4 #> 14811      1        NA  45       62837   E1     4 #> 14812      1        NA  45       62837   E2     1 #> 14813      1        NA  45       62837   E3     5 #> 14814      1        NA  45       62837   E4     4 #> 14815      1        NA  45       62837   E5     6 #> 14816      1        NA  45       62837   N1     4 #> 14817      1        NA  45       62837   N2     4 #> 14818      1        NA  45       62837   N3     4 #> 14819      1        NA  45       62837   N4     4 #> 14820      1        NA  45       62837   N5     1 #> 14821      1        NA  45       62837   O1     6 #> 14822      1        NA  45       62837   O2     1 #> 14823      1        NA  45       62837   O3     5 #> 14824      1        NA  45       62837   O4     6 #> 14825      1        NA  45       62837   O5     1 #> 14826      2         3  38       62839   A1     4 #> 14827      2         3  38       62839   A2     6 #> 14828      2         3  38       62839   A3     6 #> 14829      2         3  38       62839   A4     2 #> 14830      2         3  38       62839   A5     5 #> 14831      2         3  38       62839   C1     5 #> 14832      2         3  38       62839   C2     5 #> 14833      2         3  38       62839   C3     5 #> 14834      2         3  38       62839   C4     2 #> 14835      2         3  38       62839   C5     1 #> 14836      2         3  38       62839   E1     5 #> 14837      2         3  38       62839   E2     2 #> 14838      2         3  38       62839   E3     6 #> 14839      2         3  38       62839   E4     6 #> 14840      2         3  38       62839   E5     6 #> 14841      2         3  38       62839   N1     2 #> 14842      2         3  38       62839   N2     6 #> 14843      2         3  38       62839   N3     6 #> 14844      2         3  38       62839   N4     3 #> 14845      2         3  38       62839   N5     2 #> 14846      2         3  38       62839   O1     6 #> 14847      2         3  38       62839   O2     6 #> 14848      2         3  38       62839   O3     5 #> 14849      2         3  38       62839   O4     4 #> 14850      2         3  38       62839   O5     4 #> 14851      2         1  22       62840   A1     1 #> 14852      2         1  22       62840   A2     5 #> 14853      2         1  22       62840   A3     5 #> 14854      2         1  22       62840   A4     6 #> 14855      2         1  22       62840   A5     5 #> 14856      2         1  22       62840   C1     4 #> 14857      2         1  22       62840   C2     4 #> 14858      2         1  22       62840   C3     4 #> 14859      2         1  22       62840   C4     1 #> 14860      2         1  22       62840   C5     2 #> 14861      2         1  22       62840   E1     4 #> 14862      2         1  22       62840   E2     2 #> 14863      2         1  22       62840   E3     5 #> 14864      2         1  22       62840   E4     6 #> 14865      2         1  22       62840   E5     5 #> 14866      2         1  22       62840   N1     3 #> 14867      2         1  22       62840   N2     3 #> 14868      2         1  22       62840   N3     2 #> 14869      2         1  22       62840   N4     1 #> 14870      2         1  22       62840   N5     2 #> 14871      2         1  22       62840   O1     6 #> 14872      2         1  22       62840   O2     1 #> 14873      2         1  22       62840   O3     5 #> 14874      2         1  22       62840   O4     5 #> 14875      2         1  22       62840   O5     2 #> 14876      2         1  23       62844   A1     1 #> 14877      2         1  23       62844   A2     6 #> 14878      2         1  23       62844   A3     5 #> 14879      2         1  23       62844   A4     6 #> 14880      2         1  23       62844   A5     4 #> 14881      2         1  23       62844   C1     5 #> 14882      2         1  23       62844   C2     5 #> 14883      2         1  23       62844   C3     5 #> 14884      2         1  23       62844   C4     2 #> 14885      2         1  23       62844   C5     2 #> 14886      2         1  23       62844   E1     2 #> 14887      2         1  23       62844   E2     2 #> 14888      2         1  23       62844   E3     3 #> 14889      2         1  23       62844   E4     6 #> 14890      2         1  23       62844   E5     3 #> 14891      2         1  23       62844   N1     5 #> 14892      2         1  23       62844   N2     5 #> 14893      2         1  23       62844   N3     5 #> 14894      2         1  23       62844   N4     1 #> 14895      2         1  23       62844   N5     4 #> 14896      2         1  23       62844   O1     4 #> 14897      2         1  23       62844   O2     6 #> 14898      2         1  23       62844   O3     5 #> 14899      2         1  23       62844   O4     5 #> 14900      2         1  23       62844   O5     2 #> 14901      2         3  25       62846   A1     5 #> 14902      2         3  25       62846   A2     6 #> 14903      2         3  25       62846   A3     2 #> 14904      2         3  25       62846   A4     6 #> 14905      2         3  25       62846   A5     5 #> 14906      2         3  25       62846   C1     4 #> 14907      2         3  25       62846   C2     5 #> 14908      2         3  25       62846   C3     4 #> 14909      2         3  25       62846   C4     2 #> 14910      2         3  25       62846   C5     4 #> 14911      2         3  25       62846   E1     5 #> 14912      2         3  25       62846   E2     2 #> 14913      2         3  25       62846   E3     3 #> 14914      2         3  25       62846   E4     5 #> 14915      2         3  25       62846   E5     4 #> 14916      2         3  25       62846   N1     1 #> 14917      2         3  25       62846   N2     3 #> 14918      2         3  25       62846   N3     1 #> 14919      2         3  25       62846   N4     2 #> 14920      2         3  25       62846   N5     3 #> 14921      2         3  25       62846   O1     6 #> 14922      2         3  25       62846   O2     2 #> 14923      2         3  25       62846   O3     2 #> 14924      2         3  25       62846   O4     1 #> 14925      2         3  25       62846   O5     1 #> 14926      2         3  35       62847   A1    NA #> 14927      2         3  35       62847   A2     6 #> 14928      2         3  35       62847   A3     6 #> 14929      2         3  35       62847   A4    NA #> 14930      2         3  35       62847   A5     6 #> 14931      2         3  35       62847   C1     6 #> 14932      2         3  35       62847   C2     6 #> 14933      2         3  35       62847   C3     5 #> 14934      2         3  35       62847   C4     1 #> 14935      2         3  35       62847   C5     1 #> 14936      2         3  35       62847   E1     1 #> 14937      2         3  35       62847   E2     1 #> 14938      2         3  35       62847   E3     5 #> 14939      2         3  35       62847   E4     6 #> 14940      2         3  35       62847   E5     6 #> 14941      2         3  35       62847   N1     1 #> 14942      2         3  35       62847   N2     1 #> 14943      2         3  35       62847   N3     2 #> 14944      2         3  35       62847   N4     1 #> 14945      2         3  35       62847   N5     4 #> 14946      2         3  35       62847   O1     5 #> 14947      2         3  35       62847   O2     1 #> 14948      2         3  35       62847   O3     6 #> 14949      2         3  35       62847   O4     2 #> 14950      2         3  35       62847   O5     4 #> 14951      2         3  33       62849   A1     2 #> 14952      2         3  33       62849   A2     6 #> 14953      2         3  33       62849   A3     5 #> 14954      2         3  33       62849   A4     6 #> 14955      2         3  33       62849   A5     5 #> 14956      2         3  33       62849   C1     4 #> 14957      2         3  33       62849   C2     6 #> 14958      2         3  33       62849   C3     5 #> 14959      2         3  33       62849   C4     1 #> 14960      2         3  33       62849   C5     2 #> 14961      2         3  33       62849   E1     4 #> 14962      2         3  33       62849   E2     4 #> 14963      2         3  33       62849   E3     5 #> 14964      2         3  33       62849   E4     5 #> 14965      2         3  33       62849   E5     6 #> 14966      2         3  33       62849   N1     2 #> 14967      2         3  33       62849   N2     2 #> 14968      2         3  33       62849   N3     4 #> 14969      2         3  33       62849   N4     2 #> 14970      2         3  33       62849   N5     1 #> 14971      2         3  33       62849   O1     5 #> 14972      2         3  33       62849   O2     1 #> 14973      2         3  33       62849   O3     3 #> 14974      2         3  33       62849   O4     5 #> 14975      2         3  33       62849   O5     4 #> 14976      2         2  28       62851   A1     3 #> 14977      2         2  28       62851   A2     6 #> 14978      2         2  28       62851   A3     6 #> 14979      2         2  28       62851   A4     6 #> 14980      2         2  28       62851   A5     3 #> 14981      2         2  28       62851   C1     6 #> 14982      2         2  28       62851   C2     4 #> 14983      2         2  28       62851   C3     6 #> 14984      2         2  28       62851   C4     4 #> 14985      2         2  28       62851   C5     2 #> 14986      2         2  28       62851   E1     1 #> 14987      2         2  28       62851   E2     1 #> 14988      2         2  28       62851   E3     5 #> 14989      2         2  28       62851   E4     6 #> 14990      2         2  28       62851   E5     6 #> 14991      2         2  28       62851   N1     2 #> 14992      2         2  28       62851   N2     3 #> 14993      2         2  28       62851   N3     2 #> 14994      2         2  28       62851   N4     1 #> 14995      2         2  28       62851   N5     2 #> 14996      2         2  28       62851   O1     6 #> 14997      2         2  28       62851   O2     6 #> 14998      2         2  28       62851   O3     5 #> 14999      2         2  28       62851   O4     1 #> 15000      2         2  28       62851   O5     6 #> 15001      2         3  43       62853   A1     1 #> 15002      2         3  43       62853   A2     6 #> 15003      2         3  43       62853   A3     6 #> 15004      2         3  43       62853   A4     6 #> 15005      2         3  43       62853   A5     6 #> 15006      2         3  43       62853   C1     5 #> 15007      2         3  43       62853   C2     6 #> 15008      2         3  43       62853   C3     6 #> 15009      2         3  43       62853   C4     2 #> 15010      2         3  43       62853   C5     4 #> 15011      2         3  43       62853   E1     6 #> 15012      2         3  43       62853   E2     4 #> 15013      2         3  43       62853   E3     5 #> 15014      2         3  43       62853   E4     5 #> 15015      2         3  43       62853   E5     5 #> 15016      2         3  43       62853   N1     2 #> 15017      2         3  43       62853   N2     1 #> 15018      2         3  43       62853   N3     1 #> 15019      2         3  43       62853   N4     2 #> 15020      2         3  43       62853   N5     4 #> 15021      2         3  43       62853   O1     6 #> 15022      2         3  43       62853   O2     1 #> 15023      2         3  43       62853   O3     4 #> 15024      2         3  43       62853   O4     6 #> 15025      2         3  43       62853   O5     2 #> 15026      1         1  30       62856   A1     2 #> 15027      1         1  30       62856   A2     5 #> 15028      1         1  30       62856   A3     5 #> 15029      1         1  30       62856   A4     6 #> 15030      1         1  30       62856   A5     6 #> 15031      1         1  30       62856   C1     4 #> 15032      1         1  30       62856   C2     5 #> 15033      1         1  30       62856   C3     5 #> 15034      1         1  30       62856   C4     1 #> 15035      1         1  30       62856   C5     2 #> 15036      1         1  30       62856   E1     1 #> 15037      1         1  30       62856   E2     2 #> 15038      1         1  30       62856   E3     4 #> 15039      1         1  30       62856   E4     6 #> 15040      1         1  30       62856   E5     5 #> 15041      1         1  30       62856   N1     1 #> 15042      1         1  30       62856   N2     1 #> 15043      1         1  30       62856   N3     1 #> 15044      1         1  30       62856   N4     1 #> 15045      1         1  30       62856   N5     2 #> 15046      1         1  30       62856   O1     6 #> 15047      1         1  30       62856   O2     4 #> 15048      1         1  30       62856   O3     4 #> 15049      1         1  30       62856   O4     1 #> 15050      1         1  30       62856   O5     3 #> 15051      2         3  27       62857   A1     3 #> 15052      2         3  27       62857   A2     4 #> 15053      2         3  27       62857   A3     4 #> 15054      2         3  27       62857   A4     6 #> 15055      2         3  27       62857   A5     4 #> 15056      2         3  27       62857   C1     4 #> 15057      2         3  27       62857   C2     4 #> 15058      2         3  27       62857   C3     3 #> 15059      2         3  27       62857   C4     4 #> 15060      2         3  27       62857   C5     4 #> 15061      2         3  27       62857   E1     1 #> 15062      2         3  27       62857   E2     5 #> 15063      2         3  27       62857   E3     4 #> 15064      2         3  27       62857   E4     3 #> 15065      2         3  27       62857   E5     3 #> 15066      2         3  27       62857   N1     5 #> 15067      2         3  27       62857   N2     5 #> 15068      2         3  27       62857   N3     4 #> 15069      2         3  27       62857   N4     3 #> 15070      2         3  27       62857   N5     3 #> 15071      2         3  27       62857   O1     4 #> 15072      2         3  27       62857   O2     5 #> 15073      2         3  27       62857   O3     4 #> 15074      2         3  27       62857   O4     4 #> 15075      2         3  27       62857   O5     4 #> 15076      2         3  19       62858   A1     4 #> 15077      2         3  19       62858   A2     6 #> 15078      2         3  19       62858   A3     5 #> 15079      2         3  19       62858   A4     6 #> 15080      2         3  19       62858   A5     6 #> 15081      2         3  19       62858   C1     5 #> 15082      2         3  19       62858   C2     5 #> 15083      2         3  19       62858   C3     5 #> 15084      2         3  19       62858   C4     2 #> 15085      2         3  19       62858   C5     2 #> 15086      2         3  19       62858   E1     1 #> 15087      2         3  19       62858   E2     1 #> 15088      2         3  19       62858   E3     4 #> 15089      2         3  19       62858   E4     6 #> 15090      2         3  19       62858   E5     6 #> 15091      2         3  19       62858   N1     2 #> 15092      2         3  19       62858   N2     3 #> 15093      2         3  19       62858   N3     2 #> 15094      2         3  19       62858   N4     2 #> 15095      2         3  19       62858   N5     2 #> 15096      2         3  19       62858   O1     5 #> 15097      2         3  19       62858   O2     4 #> 15098      2         3  19       62858   O3     4 #> 15099      2         3  19       62858   O4     5 #> 15100      2         3  19       62858   O5     4 #> 15101      2         2  23       62861   A1     2 #> 15102      2         2  23       62861   A2     6 #> 15103      2         2  23       62861   A3     4 #> 15104      2         2  23       62861   A4     6 #> 15105      2         2  23       62861   A5     5 #> 15106      2         2  23       62861   C1     4 #> 15107      2         2  23       62861   C2     4 #> 15108      2         2  23       62861   C3     2 #> 15109      2         2  23       62861   C4     4 #> 15110      2         2  23       62861   C5     4 #> 15111      2         2  23       62861   E1     2 #> 15112      2         2  23       62861   E2     2 #> 15113      2         2  23       62861   E3     6 #> 15114      2         2  23       62861   E4     6 #> 15115      2         2  23       62861   E5     5 #> 15116      2         2  23       62861   N1     1 #> 15117      2         2  23       62861   N2     2 #> 15118      2         2  23       62861   N3     5 #> 15119      2         2  23       62861   N4     4 #> 15120      2         2  23       62861   N5     2 #> 15121      2         2  23       62861   O1     5 #> 15122      2         2  23       62861   O2     2 #> 15123      2         2  23       62861   O3     6 #> 15124      2         2  23       62861   O4     6 #> 15125      2         2  23       62861   O5     1 #> 15126      2         3  31       62863   A1     1 #> 15127      2         3  31       62863   A2     6 #> 15128      2         3  31       62863   A3     5 #> 15129      2         3  31       62863   A4     6 #> 15130      2         3  31       62863   A5     6 #> 15131      2         3  31       62863   C1     3 #> 15132      2         3  31       62863   C2     2 #> 15133      2         3  31       62863   C3     5 #> 15134      2         3  31       62863   C4     2 #> 15135      2         3  31       62863   C5    NA #> 15136      2         3  31       62863   E1     1 #> 15137      2         3  31       62863   E2     1 #> 15138      2         3  31       62863   E3     4 #> 15139      2         3  31       62863   E4     6 #> 15140      2         3  31       62863   E5     6 #> 15141      2         3  31       62863   N1     3 #> 15142      2         3  31       62863   N2     3 #> 15143      2         3  31       62863   N3     3 #> 15144      2         3  31       62863   N4     3 #> 15145      2         3  31       62863   N5     2 #> 15146      2         3  31       62863   O1     6 #> 15147      2         3  31       62863   O2     2 #> 15148      2         3  31       62863   O3     4 #> 15149      2         3  31       62863   O4     6 #> 15150      2         3  31       62863   O5     2 #> 15151      2         1  22       62864   A1     3 #> 15152      2         1  22       62864   A2     4 #> 15153      2         1  22       62864   A3     2 #> 15154      2         1  22       62864   A4     4 #> 15155      2         1  22       62864   A5     4 #> 15156      2         1  22       62864   C1     5 #> 15157      2         1  22       62864   C2     1 #> 15158      2         1  22       62864   C3     4 #> 15159      2         1  22       62864   C4     2 #> 15160      2         1  22       62864   C5     1 #> 15161      2         1  22       62864   E1     1 #> 15162      2         1  22       62864   E2     4 #> 15163      2         1  22       62864   E3     3 #> 15164      2         1  22       62864   E4     3 #> 15165      2         1  22       62864   E5     4 #> 15166      2         1  22       62864   N1     6 #> 15167      2         1  22       62864   N2     6 #> 15168      2         1  22       62864   N3     6 #> 15169      2         1  22       62864   N4     2 #> 15170      2         1  22       62864   N5     1 #> 15171      2         1  22       62864   O1     3 #> 15172      2         1  22       62864   O2     6 #> 15173      2         1  22       62864   O3     1 #> 15174      2         1  22       62864   O4     5 #> 15175      2         1  22       62864   O5     1 #> 15176      1         3  40       62867   A1     1 #> 15177      1         3  40       62867   A2     6 #> 15178      1         3  40       62867   A3     6 #> 15179      1         3  40       62867   A4     4 #> 15180      1         3  40       62867   A5     5 #> 15181      1         3  40       62867   C1     4 #> 15182      1         3  40       62867   C2     3 #> 15183      1         3  40       62867   C3     4 #> 15184      1         3  40       62867   C4     4 #> 15185      1         3  40       62867   C5     4 #> 15186      1         3  40       62867   E1     3 #> 15187      1         3  40       62867   E2     6 #> 15188      1         3  40       62867   E3     6 #> 15189      1         3  40       62867   E4     3 #> 15190      1         3  40       62867   E5     6 #> 15191      1         3  40       62867   N1     1 #> 15192      1         3  40       62867   N2     3 #> 15193      1         3  40       62867   N3     1 #> 15194      1         3  40       62867   N4     3 #> 15195      1         3  40       62867   N5     1 #> 15196      1         3  40       62867   O1     5 #> 15197      1         3  40       62867   O2     3 #> 15198      1         3  40       62867   O3     6 #> 15199      1         3  40       62867   O4     4 #> 15200      1         3  40       62867   O5     1 #> 15201      2         3  40       62869   A1     1 #> 15202      2         3  40       62869   A2     5 #> 15203      2         3  40       62869   A3     6 #> 15204      2         3  40       62869   A4     6 #> 15205      2         3  40       62869   A5     5 #> 15206      2         3  40       62869   C1     4 #> 15207      2         3  40       62869   C2     4 #> 15208      2         3  40       62869   C3     4 #> 15209      2         3  40       62869   C4     2 #> 15210      2         3  40       62869   C5     5 #> 15211      2         3  40       62869   E1     3 #> 15212      2         3  40       62869   E2     5 #> 15213      2         3  40       62869   E3     1 #> 15214      2         3  40       62869   E4     2 #> 15215      2         3  40       62869   E5     4 #> 15216      2         3  40       62869   N1     2 #> 15217      2         3  40       62869   N2     4 #> 15218      2         3  40       62869   N3     4 #> 15219      2         3  40       62869   N4     6 #> 15220      2         3  40       62869   N5     4 #> 15221      2         3  40       62869   O1     4 #> 15222      2         3  40       62869   O2     4 #> 15223      2         3  40       62869   O3     3 #> 15224      2         3  40       62869   O4     4 #> 15225      2         3  40       62869   O5     3 #> 15226      2         3  55       62870   A1     1 #> 15227      2         3  55       62870   A2     6 #> 15228      2         3  55       62870   A3     6 #> 15229      2         3  55       62870   A4     6 #> 15230      2         3  55       62870   A5     6 #> 15231      2         3  55       62870   C1     2 #> 15232      2         3  55       62870   C2    NA #> 15233      2         3  55       62870   C3     6 #> 15234      2         3  55       62870   C4     1 #> 15235      2         3  55       62870   C5    NA #> 15236      2         3  55       62870   E1     6 #> 15237      2         3  55       62870   E2     5 #> 15238      2         3  55       62870   E3     6 #> 15239      2         3  55       62870   E4     6 #> 15240      2         3  55       62870   E5     6 #> 15241      2         3  55       62870   N1     4 #> 15242      2         3  55       62870   N2     1 #> 15243      2         3  55       62870   N3     6 #> 15244      2         3  55       62870   N4     4 #> 15245      2         3  55       62870   N5     5 #> 15246      2         3  55       62870   O1     6 #> 15247      2         3  55       62870   O2     1 #> 15248      2         3  55       62870   O3     5 #> 15249      2         3  55       62870   O4     5 #> 15250      2         3  55       62870   O5     1 #> 15251      2         3  55       62872   A1     1 #> 15252      2         3  55       62872   A2     6 #> 15253      2         3  55       62872   A3     6 #> 15254      2         3  55       62872   A4     6 #> 15255      2         3  55       62872   A5     6 #> 15256      2         3  55       62872   C1     2 #> 15257      2         3  55       62872   C2     5 #> 15258      2         3  55       62872   C3     6 #> 15259      2         3  55       62872   C4     1 #> 15260      2         3  55       62872   C5    NA #> 15261      2         3  55       62872   E1     6 #> 15262      2         3  55       62872   E2     5 #> 15263      2         3  55       62872   E3     6 #> 15264      2         3  55       62872   E4     6 #> 15265      2         3  55       62872   E5     6 #> 15266      2         3  55       62872   N1     4 #> 15267      2         3  55       62872   N2     1 #> 15268      2         3  55       62872   N3     6 #> 15269      2         3  55       62872   N4     4 #> 15270      2         3  55       62872   N5     5 #> 15271      2         3  55       62872   O1     6 #> 15272      2         3  55       62872   O2     1 #> 15273      2         3  55       62872   O3     5 #> 15274      2         3  55       62872   O4     5 #> 15275      2         3  55       62872   O5     1 #> 15276      2         3  54       62874   A1     1 #> 15277      2         3  54       62874   A2     6 #> 15278      2         3  54       62874   A3     6 #> 15279      2         3  54       62874   A4     6 #> 15280      2         3  54       62874   A5     6 #> 15281      2         3  54       62874   C1     5 #> 15282      2         3  54       62874   C2     6 #> 15283      2         3  54       62874   C3     6 #> 15284      2         3  54       62874   C4     1 #> 15285      2         3  54       62874   C5     1 #> 15286      2         3  54       62874   E1     1 #> 15287      2         3  54       62874   E2     1 #> 15288      2         3  54       62874   E3     6 #> 15289      2         3  54       62874   E4     6 #> 15290      2         3  54       62874   E5     6 #> 15291      2         3  54       62874   N1     1 #> 15292      2         3  54       62874   N2     1 #> 15293      2         3  54       62874   N3     1 #> 15294      2         3  54       62874   N4     1 #> 15295      2         3  54       62874   N5     2 #> 15296      2         3  54       62874   O1     5 #> 15297      2         3  54       62874   O2     2 #> 15298      2         3  54       62874   O3     3 #> 15299      2         3  54       62874   O4     4 #> 15300      2         3  54       62874   O5     1 #> 15301      2         1  53       62876   A1     1 #> 15302      2         1  53       62876   A2     5 #> 15303      2         1  53       62876   A3     4 #> 15304      2         1  53       62876   A4     5 #> 15305      2         1  53       62876   A5     4 #> 15306      2         1  53       62876   C1     4 #> 15307      2         1  53       62876   C2     2 #> 15308      2         1  53       62876   C3     3 #> 15309      2         1  53       62876   C4    NA #> 15310      2         1  53       62876   C5     3 #> 15311      2         1  53       62876   E1     3 #> 15312      2         1  53       62876   E2     4 #> 15313      2         1  53       62876   E3     3 #> 15314      2         1  53       62876   E4     1 #> 15315      2         1  53       62876   E5     4 #> 15316      2         1  53       62876   N1     3 #> 15317      2         1  53       62876   N2     4 #> 15318      2         1  53       62876   N3     4 #> 15319      2         1  53       62876   N4     4 #> 15320      2         1  53       62876   N5     4 #> 15321      2         1  53       62876   O1     4 #> 15322      2         1  53       62876   O2     4 #> 15323      2         1  53       62876   O3    NA #> 15324      2         1  53       62876   O4     5 #> 15325      2         1  53       62876   O5     1 #> 15326      1         3  20       62877   A1     2 #> 15327      1         3  20       62877   A2     6 #> 15328      1         3  20       62877   A3     6 #> 15329      1         3  20       62877   A4     6 #> 15330      1         3  20       62877   A5     6 #> 15331      1         3  20       62877   C1     5 #> 15332      1         3  20       62877   C2     4 #> 15333      1         3  20       62877   C3     5 #> 15334      1         3  20       62877   C4     1 #> 15335      1         3  20       62877   C5     4 #> 15336      1         3  20       62877   E1     6 #> 15337      1         3  20       62877   E2     4 #> 15338      1         3  20       62877   E3     6 #> 15339      1         3  20       62877   E4     5 #> 15340      1         3  20       62877   E5     4 #> 15341      1         3  20       62877   N1     3 #> 15342      1         3  20       62877   N2     3 #> 15343      1         3  20       62877   N3     2 #> 15344      1         3  20       62877   N4     5 #> 15345      1         3  20       62877   N5     3 #> 15346      1         3  20       62877   O1    NA #> 15347      1         3  20       62877   O2     1 #> 15348      1         3  20       62877   O3     5 #> 15349      1         3  20       62877   O4     6 #> 15350      1         3  20       62877   O5     1 #> 15351      1         3  52       62878   A1     2 #> 15352      1         3  52       62878   A2     5 #> 15353      1         3  52       62878   A3     5 #> 15354      1         3  52       62878   A4     6 #> 15355      1         3  52       62878   A5     6 #> 15356      1         3  52       62878   C1     2 #> 15357      1         3  52       62878   C2     6 #> 15358      1         3  52       62878   C3     4 #> 15359      1         3  52       62878   C4     1 #> 15360      1         3  52       62878   C5     3 #> 15361      1         3  52       62878   E1     5 #> 15362      1         3  52       62878   E2     4 #> 15363      1         3  52       62878   E3     6 #> 15364      1         3  52       62878   E4     4 #> 15365      1         3  52       62878   E5     5 #> 15366      1         3  52       62878   N1     1 #> 15367      1         3  52       62878   N2     1 #> 15368      1         3  52       62878   N3     1 #> 15369      1         3  52       62878   N4     1 #> 15370      1         3  52       62878   N5     5 #> 15371      1         3  52       62878   O1     6 #> 15372      1         3  52       62878   O2     4 #> 15373      1         3  52       62878   O3     6 #> 15374      1         3  52       62878   O4     4 #> 15375      1         3  52       62878   O5     3 #> 15376      2         3  25       62879   A1     1 #> 15377      2         3  25       62879   A2     6 #> 15378      2         3  25       62879   A3     6 #> 15379      2         3  25       62879   A4     6 #> 15380      2         3  25       62879   A5     6 #> 15381      2         3  25       62879   C1     6 #> 15382      2         3  25       62879   C2     6 #> 15383      2         3  25       62879   C3     5 #> 15384      2         3  25       62879   C4     2 #> 15385      2         3  25       62879   C5     1 #> 15386      2         3  25       62879   E1     5 #> 15387      2         3  25       62879   E2     6 #> 15388      2         3  25       62879   E3     6 #> 15389      2         3  25       62879   E4     5 #> 15390      2         3  25       62879   E5     6 #> 15391      2         3  25       62879   N1     1 #> 15392      2         3  25       62879   N2     2 #> 15393      2         3  25       62879   N3     1 #> 15394      2         3  25       62879   N4     2 #> 15395      2         3  25       62879   N5     1 #> 15396      2         3  25       62879   O1     6 #> 15397      2         3  25       62879   O2     1 #> 15398      2         3  25       62879   O3     6 #> 15399      2         3  25       62879   O4     6 #> 15400      2         3  25       62879   O5     1 #> 15401      1         3  46       62881   A1     2 #> 15402      1         3  46       62881   A2     6 #> 15403      1         3  46       62881   A3     6 #> 15404      1         3  46       62881   A4     6 #> 15405      1         3  46       62881   A5     5 #> 15406      1         3  46       62881   C1     6 #> 15407      1         3  46       62881   C2     5 #> 15408      1         3  46       62881   C3     6 #> 15409      1         3  46       62881   C4     1 #> 15410      1         3  46       62881   C5     1 #> 15411      1         3  46       62881   E1     4 #> 15412      1         3  46       62881   E2     5 #> 15413      1         3  46       62881   E3     4 #> 15414      1         3  46       62881   E4     5 #> 15415      1         3  46       62881   E5     6 #> 15416      1         3  46       62881   N1     3 #> 15417      1         3  46       62881   N2     5 #> 15418      1         3  46       62881   N3     5 #> 15419      1         3  46       62881   N4     4 #> 15420      1         3  46       62881   N5     3 #> 15421      1         3  46       62881   O1     6 #> 15422      1         3  46       62881   O2     1 #> 15423      1         3  46       62881   O3     4 #> 15424      1         3  46       62881   O4     5 #> 15425      1         3  46       62881   O5     1 #> 15426      1         3  19       62883   A1     3 #> 15427      1         3  19       62883   A2     4 #> 15428      1         3  19       62883   A3     4 #> 15429      1         3  19       62883   A4     4 #> 15430      1         3  19       62883   A5     4 #> 15431      1         3  19       62883   C1     4 #> 15432      1         3  19       62883   C2     4 #> 15433      1         3  19       62883   C3     4 #> 15434      1         3  19       62883   C4     2 #> 15435      1         3  19       62883   C5     4 #> 15436      1         3  19       62883   E1     5 #> 15437      1         3  19       62883   E2     3 #> 15438      1         3  19       62883   E3     4 #> 15439      1         3  19       62883   E4     4 #> 15440      1         3  19       62883   E5     4 #> 15441      1         3  19       62883   N1     2 #> 15442      1         3  19       62883   N2     2 #> 15443      1         3  19       62883   N3     3 #> 15444      1         3  19       62883   N4     5 #> 15445      1         3  19       62883   N5     2 #> 15446      1         3  19       62883   O1     5 #> 15447      1         3  19       62883   O2     2 #> 15448      1         3  19       62883   O3     4 #> 15449      1         3  19       62883   O4     5 #> 15450      1         3  19       62883   O5     5 #> 15451      1         3  38       62887   A1     1 #> 15452      1         3  38       62887   A2     4 #> 15453      1         3  38       62887   A3     1 #> 15454      1         3  38       62887   A4     4 #> 15455      1         3  38       62887   A5     5 #> 15456      1         3  38       62887   C1     6 #> 15457      1         3  38       62887   C2     6 #> 15458      1         3  38       62887   C3     5 #> 15459      1         3  38       62887   C4     1 #> 15460      1         3  38       62887   C5     1 #> 15461      1         3  38       62887   E1     2 #> 15462      1         3  38       62887   E2     2 #> 15463      1         3  38       62887   E3     4 #> 15464      1         3  38       62887   E4     6 #> 15465      1         3  38       62887   E5     6 #> 15466      1         3  38       62887   N1    NA #> 15467      1         3  38       62887   N2     5 #> 15468      1         3  38       62887   N3    NA #> 15469      1         3  38       62887   N4     2 #> 15470      1         3  38       62887   N5     4 #> 15471      1         3  38       62887   O1     4 #> 15472      1         3  38       62887   O2     4 #> 15473      1         3  38       62887   O3     4 #> 15474      1         3  38       62887   O4     5 #> 15475      1         3  38       62887   O5     5 #> 15476      2         3  27       62889   A1     2 #> 15477      2         3  27       62889   A2     5 #> 15478      2         3  27       62889   A3     5 #> 15479      2         3  27       62889   A4     4 #> 15480      2         3  27       62889   A5     3 #> 15481      2         3  27       62889   C1     4 #> 15482      2         3  27       62889   C2     2 #> 15483      2         3  27       62889   C3     5 #> 15484      2         3  27       62889   C4     4 #> 15485      2         3  27       62889   C5     3 #> 15486      2         3  27       62889   E1     3 #> 15487      2         3  27       62889   E2     3 #> 15488      2         3  27       62889   E3     4 #> 15489      2         3  27       62889   E4     4 #> 15490      2         3  27       62889   E5     5 #> 15491      2         3  27       62889   N1     4 #> 15492      2         3  27       62889   N2     5 #> 15493      2         3  27       62889   N3     4 #> 15494      2         3  27       62889   N4     3 #> 15495      2         3  27       62889   N5     5 #> 15496      2         3  27       62889   O1     5 #> 15497      2         3  27       62889   O2     1 #> 15498      2         3  27       62889   O3     4 #> 15499      2         3  27       62889   O4     4 #> 15500      2         3  27       62889   O5     2 #> 15501      2         3  27       62890   A1     1 #> 15502      2         3  27       62890   A2     6 #> 15503      2         3  27       62890   A3     6 #> 15504      2         3  27       62890   A4     4 #> 15505      2         3  27       62890   A5     4 #> 15506      2         3  27       62890   C1     5 #> 15507      2         3  27       62890   C2     4 #> 15508      2         3  27       62890   C3     5 #> 15509      2         3  27       62890   C4     4 #> 15510      2         3  27       62890   C5     5 #> 15511      2         3  27       62890   E1     2 #> 15512      2         3  27       62890   E2     4 #> 15513      2         3  27       62890   E3     4 #> 15514      2         3  27       62890   E4     4 #> 15515      2         3  27       62890   E5     5 #> 15516      2         3  27       62890   N1     4 #> 15517      2         3  27       62890   N2     5 #> 15518      2         3  27       62890   N3     4 #> 15519      2         3  27       62890   N4     2 #> 15520      2         3  27       62890   N5     5 #> 15521      2         3  27       62890   O1     6 #> 15522      2         3  27       62890   O2     1 #> 15523      2         3  27       62890   O3     5 #> 15524      2         3  27       62890   O4     5 #> 15525      2         3  27       62890   O5     2 #> 15526      1         3  34       62891   A1     2 #> 15527      1         3  34       62891   A2     5 #> 15528      1         3  34       62891   A3     6 #> 15529      1         3  34       62891   A4     4 #> 15530      1         3  34       62891   A5     6 #> 15531      1         3  34       62891   C1     6 #> 15532      1         3  34       62891   C2     6 #> 15533      1         3  34       62891   C3     6 #> 15534      1         3  34       62891   C4     1 #> 15535      1         3  34       62891   C5     2 #> 15536      1         3  34       62891   E1     4 #> 15537      1         3  34       62891   E2     3 #> 15538      1         3  34       62891   E3     5 #> 15539      1         3  34       62891   E4     4 #> 15540      1         3  34       62891   E5     6 #> 15541      1         3  34       62891   N1     2 #> 15542      1         3  34       62891   N2     3 #> 15543      1         3  34       62891   N3     3 #> 15544      1         3  34       62891   N4     2 #> 15545      1         3  34       62891   N5     2 #> 15546      1         3  34       62891   O1     6 #> 15547      1         3  34       62891   O2     1 #> 15548      1         3  34       62891   O3     6 #> 15549      1         3  34       62891   O4     6 #> 15550      1         3  34       62891   O5     1 #> 15551      2         3  21       62897   A1     2 #> 15552      2         3  21       62897   A2     6 #> 15553      2         3  21       62897   A3     5 #> 15554      2         3  21       62897   A4     6 #> 15555      2         3  21       62897   A5     3 #> 15556      2         3  21       62897   C1     4 #> 15557      2         3  21       62897   C2     5 #> 15558      2         3  21       62897   C3     5 #> 15559      2         3  21       62897   C4     1 #> 15560      2         3  21       62897   C5     1 #> 15561      2         3  21       62897   E1     3 #> 15562      2         3  21       62897   E2     6 #> 15563      2         3  21       62897   E3     4 #> 15564      2         3  21       62897   E4     3 #> 15565      2         3  21       62897   E5     6 #> 15566      2         3  21       62897   N1     5 #> 15567      2         3  21       62897   N2     6 #> 15568      2         3  21       62897   N3     2 #> 15569      2         3  21       62897   N4     2 #> 15570      2         3  21       62897   N5     4 #> 15571      2         3  21       62897   O1     6 #> 15572      2         3  21       62897   O2     2 #> 15573      2         3  21       62897   O3     4 #> 15574      2         3  21       62897   O4     4 #> 15575      2         3  21       62897   O5     1 #> 15576      2         2  29       62898   A1     4 #> 15577      2         2  29       62898   A2     6 #> 15578      2         2  29       62898   A3     6 #> 15579      2         2  29       62898   A4     5 #> 15580      2         2  29       62898   A5     6 #> 15581      2         2  29       62898   C1     6 #> 15582      2         2  29       62898   C2     6 #> 15583      2         2  29       62898   C3     6 #> 15584      2         2  29       62898   C4     1 #> 15585      2         2  29       62898   C5     1 #> 15586      2         2  29       62898   E1     1 #> 15587      2         2  29       62898   E2     1 #> 15588      2         2  29       62898   E3     6 #> 15589      2         2  29       62898   E4     6 #> 15590      2         2  29       62898   E5     6 #> 15591      2         2  29       62898   N1     4 #> 15592      2         2  29       62898   N2     2 #> 15593      2         2  29       62898   N3     1 #> 15594      2         2  29       62898   N4     1 #> 15595      2         2  29       62898   N5     2 #> 15596      2         2  29       62898   O1     6 #> 15597      2         2  29       62898   O2     4 #> 15598      2         2  29       62898   O3     6 #> 15599      2         2  29       62898   O4     6 #> 15600      2         2  29       62898   O5     2 #> 15601      2         3  27       62899   A1     2 #> 15602      2         3  27       62899   A2     1 #> 15603      2         3  27       62899   A3     6 #> 15604      2         3  27       62899   A4     6 #> 15605      2         3  27       62899   A5     6 #> 15606      2         3  27       62899   C1     5 #> 15607      2         3  27       62899   C2     6 #> 15608      2         3  27       62899   C3     5 #> 15609      2         3  27       62899   C4     1 #> 15610      2         3  27       62899   C5     1 #> 15611      2         3  27       62899   E1     5 #> 15612      2         3  27       62899   E2     5 #> 15613      2         3  27       62899   E3     6 #> 15614      2         3  27       62899   E4     5 #> 15615      2         3  27       62899   E5     5 #> 15616      2         3  27       62899   N1     3 #> 15617      2         3  27       62899   N2     4 #> 15618      2         3  27       62899   N3     1 #> 15619      2         3  27       62899   N4     5 #> 15620      2         3  27       62899   N5     6 #> 15621      2         3  27       62899   O1     4 #> 15622      2         3  27       62899   O2     2 #> 15623      2         3  27       62899   O3    NA #> 15624      2         3  27       62899   O4     2 #> 15625      2         3  27       62899   O5     5 #> 15626      2         2  41       62901   A1     2 #> 15627      2         2  41       62901   A2     5 #> 15628      2         2  41       62901   A3     4 #> 15629      2         2  41       62901   A4     4 #> 15630      2         2  41       62901   A5     2 #> 15631      2         2  41       62901   C1     4 #> 15632      2         2  41       62901   C2     3 #> 15633      2         2  41       62901   C3     5 #> 15634      2         2  41       62901   C4     4 #> 15635      2         2  41       62901   C5     3 #> 15636      2         2  41       62901   E1     4 #> 15637      2         2  41       62901   E2     4 #> 15638      2         2  41       62901   E3     3 #> 15639      2         2  41       62901   E4     3 #> 15640      2         2  41       62901   E5     3 #> 15641      2         2  41       62901   N1     5 #> 15642      2         2  41       62901   N2     5 #> 15643      2         2  41       62901   N3     6 #> 15644      2         2  41       62901   N4     6 #> 15645      2         2  41       62901   N5     5 #> 15646      2         2  41       62901   O1     4 #> 15647      2         2  41       62901   O2     2 #> 15648      2         2  41       62901   O3     2 #> 15649      2         2  41       62901   O4     5 #> 15650      2         2  41       62901   O5     4 #> 15651      2         3  52       62903   A1     2 #> 15652      2         3  52       62903   A2     5 #> 15653      2         3  52       62903   A3     5 #> 15654      2         3  52       62903   A4     5 #> 15655      2         3  52       62903   A5     1 #> 15656      2         3  52       62903   C1     5 #> 15657      2         3  52       62903   C2     5 #> 15658      2         3  52       62903   C3     2 #> 15659      2         3  52       62903   C4     4 #> 15660      2         3  52       62903   C5     2 #> 15661      2         3  52       62903   E1     2 #> 15662      2         3  52       62903   E2     2 #> 15663      2         3  52       62903   E3    NA #> 15664      2         3  52       62903   E4     5 #> 15665      2         3  52       62903   E5     5 #> 15666      2         3  52       62903   N1     1 #> 15667      2         3  52       62903   N2     1 #> 15668      2         3  52       62903   N3     1 #> 15669      2         3  52       62903   N4     1 #> 15670      2         3  52       62903   N5     1 #> 15671      2         3  52       62903   O1     5 #> 15672      2         3  52       62903   O2     1 #> 15673      2         3  52       62903   O3     4 #> 15674      2         3  52       62903   O4     4 #> 15675      2         3  52       62903   O5     2 #> 15676      2         3  30       62908   A1     3 #> 15677      2         3  30       62908   A2     5 #> 15678      2         3  30       62908   A3     3 #> 15679      2         3  30       62908   A4     6 #> 15680      2         3  30       62908   A5     1 #> 15681      2         3  30       62908   C1     4 #> 15682      2         3  30       62908   C2     3 #> 15683      2         3  30       62908   C3     3 #> 15684      2         3  30       62908   C4     5 #> 15685      2         3  30       62908   C5     5 #> 15686      2         3  30       62908   E1     3 #> 15687      2         3  30       62908   E2     6 #> 15688      2         3  30       62908   E3     1 #> 15689      2         3  30       62908   E4     2 #> 15690      2         3  30       62908   E5     2 #> 15691      2         3  30       62908   N1     6 #> 15692      2         3  30       62908   N2     6 #> 15693      2         3  30       62908   N3     6 #> 15694      2         3  30       62908   N4     5 #> 15695      2         3  30       62908   N5     6 #> 15696      2         3  30       62908   O1     3 #> 15697      2         3  30       62908   O2     6 #> 15698      2         3  30       62908   O3     1 #> 15699      2         3  30       62908   O4     6 #> 15700      2         3  30       62908   O5     4 #> 15701      2         2  50       62910   A1     3 #> 15702      2         2  50       62910   A2     5 #> 15703      2         2  50       62910   A3     5 #> 15704      2         2  50       62910   A4     6 #> 15705      2         2  50       62910   A5     5 #> 15706      2         2  50       62910   C1     5 #> 15707      2         2  50       62910   C2     5 #> 15708      2         2  50       62910   C3     5 #> 15709      2         2  50       62910   C4     2 #> 15710      2         2  50       62910   C5     2 #> 15711      2         2  50       62910   E1     2 #> 15712      2         2  50       62910   E2     2 #> 15713      2         2  50       62910   E3     4 #> 15714      2         2  50       62910   E4     5 #> 15715      2         2  50       62910   E5     4 #> 15716      2         2  50       62910   N1     2 #> 15717      2         2  50       62910   N2     2 #> 15718      2         2  50       62910   N3     2 #> 15719      2         2  50       62910   N4     2 #> 15720      2         2  50       62910   N5     4 #> 15721      2         2  50       62910   O1     4 #> 15722      2         2  50       62910   O2     2 #> 15723      2         2  50       62910   O3     4 #> 15724      2         2  50       62910   O4     5 #> 15725      2         2  50       62910   O5     2 #> 15726      2         3  43       62911   A1     4 #> 15727      2         3  43       62911   A2     4 #> 15728      2         3  43       62911   A3     4 #> 15729      2         3  43       62911   A4     4 #> 15730      2         3  43       62911   A5     4 #> 15731      2         3  43       62911   C1     5 #> 15732      2         3  43       62911   C2     4 #> 15733      2         3  43       62911   C3     4 #> 15734      2         3  43       62911   C4     1 #> 15735      2         3  43       62911   C5     2 #> 15736      2         3  43       62911   E1     4 #> 15737      2         3  43       62911   E2     3 #> 15738      2         3  43       62911   E3     3 #> 15739      2         3  43       62911   E4     4 #> 15740      2         3  43       62911   E5     5 #> 15741      2         3  43       62911   N1     4 #> 15742      2         3  43       62911   N2     5 #> 15743      2         3  43       62911   N3     3 #> 15744      2         3  43       62911   N4     4 #> 15745      2         3  43       62911   N5     3 #> 15746      2         3  43       62911   O1     3 #> 15747      2         3  43       62911   O2     5 #> 15748      2         3  43       62911   O3     4 #> 15749      2         3  43       62911   O4     3 #> 15750      2         3  43       62911   O5     3 #> 15751      2         5  32       62916   A1     2 #> 15752      2         5  32       62916   A2     6 #> 15753      2         5  32       62916   A3     6 #> 15754      2         5  32       62916   A4     6 #> 15755      2         5  32       62916   A5     5 #> 15756      2         5  32       62916   C1     2 #> 15757      2         5  32       62916   C2     1 #> 15758      2         5  32       62916   C3     4 #> 15759      2         5  32       62916   C4     4 #> 15760      2         5  32       62916   C5     1 #> 15761      2         5  32       62916   E1     2 #> 15762      2         5  32       62916   E2     2 #> 15763      2         5  32       62916   E3     5 #> 15764      2         5  32       62916   E4     6 #> 15765      2         5  32       62916   E5     4 #> 15766      2         5  32       62916   N1     4 #> 15767      2         5  32       62916   N2     3 #> 15768      2         5  32       62916   N3     2 #> 15769      2         5  32       62916   N4     1 #> 15770      2         5  32       62916   N5     2 #> 15771      2         5  32       62916   O1     6 #> 15772      2         5  32       62916   O2     1 #> 15773      2         5  32       62916   O3     5 #> 15774      2         5  32       62916   O4     6 #> 15775      2         5  32       62916   O5     2 #> 15776      1         3  19       62918   A1     4 #> 15777      1         3  19       62918   A2     3 #> 15778      1         3  19       62918   A3     1 #> 15779      1         3  19       62918   A4     3 #> 15780      1         3  19       62918   A5     3 #> 15781      1         3  19       62918   C1     6 #> 15782      1         3  19       62918   C2     4 #> 15783      1         3  19       62918   C3     4 #> 15784      1         3  19       62918   C4     2 #> 15785      1         3  19       62918   C5     2 #> 15786      1         3  19       62918   E1     3 #> 15787      1         3  19       62918   E2     4 #> 15788      1         3  19       62918   E3     3 #> 15789      1         3  19       62918   E4     4 #> 15790      1         3  19       62918   E5     5 #> 15791      1         3  19       62918   N1     5 #> 15792      1         3  19       62918   N2     5 #> 15793      1         3  19       62918   N3     6 #> 15794      1         3  19       62918   N4     6 #> 15795      1         3  19       62918   N5     6 #> 15796      1         3  19       62918   O1     5 #> 15797      1         3  19       62918   O2     5 #> 15798      1         3  19       62918   O3     4 #> 15799      1         3  19       62918   O4     6 #> 15800      1         3  19       62918   O5     3 #> 15801      2         4  22       62920   A1     2 #> 15802      2         4  22       62920   A2     6 #> 15803      2         4  22       62920   A3     6 #> 15804      2         4  22       62920   A4     6 #> 15805      2         4  22       62920   A5     6 #> 15806      2         4  22       62920   C1     5 #> 15807      2         4  22       62920   C2     5 #> 15808      2         4  22       62920   C3     5 #> 15809      2         4  22       62920   C4     2 #> 15810      2         4  22       62920   C5     3 #> 15811      2         4  22       62920   E1     5 #> 15812      2         4  22       62920   E2     5 #> 15813      2         4  22       62920   E3     5 #> 15814      2         4  22       62920   E4     5 #> 15815      2         4  22       62920   E5     4 #> 15816      2         4  22       62920   N1     1 #> 15817      2         4  22       62920   N2     5 #> 15818      2         4  22       62920   N3     4 #> 15819      2         4  22       62920   N4     6 #> 15820      2         4  22       62920   N5     6 #> 15821      2         4  22       62920   O1     6 #> 15822      2         4  22       62920   O2     2 #> 15823      2         4  22       62920   O3     6 #> 15824      2         4  22       62920   O4     6 #> 15825      2         4  22       62920   O5     2 #> 15826      2         3  21       62922   A1     3 #> 15827      2         3  21       62922   A2     6 #> 15828      2         3  21       62922   A3     4 #> 15829      2         3  21       62922   A4     6 #> 15830      2         3  21       62922   A5     5 #> 15831      2         3  21       62922   C1     5 #> 15832      2         3  21       62922   C2     5 #> 15833      2         3  21       62922   C3     5 #> 15834      2         3  21       62922   C4     2 #> 15835      2         3  21       62922   C5     4 #> 15836      2         3  21       62922   E1     5 #> 15837      2         3  21       62922   E2     2 #> 15838      2         3  21       62922   E3     4 #> 15839      2         3  21       62922   E4     5 #> 15840      2         3  21       62922   E5     5 #> 15841      2         3  21       62922   N1     1 #> 15842      2         3  21       62922   N2     1 #> 15843      2         3  21       62922   N3     2 #> 15844      2         3  21       62922   N4     2 #> 15845      2         3  21       62922   N5     5 #> 15846      2         3  21       62922   O1     4 #> 15847      2         3  21       62922   O2     4 #> 15848      2         3  21       62922   O3     5 #> 15849      2         3  21       62922   O4     4 #> 15850      2         3  21       62922   O5     4 #> 15851      1         5  50       62926   A1     1 #> 15852      1         5  50       62926   A2     3 #> 15853      1         5  50       62926   A3     2 #> 15854      1         5  50       62926   A4     3 #> 15855      1         5  50       62926   A5     2 #> 15856      1         5  50       62926   C1     5 #> 15857      1         5  50       62926   C2     5 #> 15858      1         5  50       62926   C3     2 #> 15859      1         5  50       62926   C4     4 #> 15860      1         5  50       62926   C5     5 #> 15861      1         5  50       62926   E1     5 #> 15862      1         5  50       62926   E2     5 #> 15863      1         5  50       62926   E3     3 #> 15864      1         5  50       62926   E4     2 #> 15865      1         5  50       62926   E5     3 #> 15866      1         5  50       62926   N1     4 #> 15867      1         5  50       62926   N2     4 #> 15868      1         5  50       62926   N3     5 #> 15869      1         5  50       62926   N4     6 #> 15870      1         5  50       62926   N5     2 #> 15871      1         5  50       62926   O1     6 #> 15872      1         5  50       62926   O2     2 #> 15873      1         5  50       62926   O3     5 #> 15874      1         5  50       62926   O4     6 #> 15875      1         5  50       62926   O5     2 #> 15876      2         4  27       62931   A1     1 #> 15877      2         4  27       62931   A2     6 #> 15878      2         4  27       62931   A3     6 #> 15879      2         4  27       62931   A4     4 #> 15880      2         4  27       62931   A5     6 #> 15881      2         4  27       62931   C1     5 #> 15882      2         4  27       62931   C2     5 #> 15883      2         4  27       62931   C3     6 #> 15884      2         4  27       62931   C4     1 #> 15885      2         4  27       62931   C5     2 #> 15886      2         4  27       62931   E1     5 #> 15887      2         4  27       62931   E2     2 #> 15888      2         4  27       62931   E3     4 #> 15889      2         4  27       62931   E4     5 #> 15890      2         4  27       62931   E5     3 #> 15891      2         4  27       62931   N1     4 #> 15892      2         4  27       62931   N2     3 #> 15893      2         4  27       62931   N3     5 #> 15894      2         4  27       62931   N4     4 #> 15895      2         4  27       62931   N5     6 #> 15896      2         4  27       62931   O1     5 #> 15897      2         4  27       62931   O2     1 #> 15898      2         4  27       62931   O3     3 #> 15899      2         4  27       62931   O4     5 #> 15900      2         4  27       62931   O5     6 #> 15901      2         3  48       62933   A1     1 #> 15902      2         3  48       62933   A2     5 #> 15903      2         3  48       62933   A3     4 #> 15904      2         3  48       62933   A4     5 #> 15905      2         3  48       62933   A5     5 #> 15906      2         3  48       62933   C1     1 #> 15907      2         3  48       62933   C2     6 #> 15908      2         3  48       62933   C3     4 #> 15909      2         3  48       62933   C4     1 #> 15910      2         3  48       62933   C5     1 #> 15911      2         3  48       62933   E1     4 #> 15912      2         3  48       62933   E2     4 #> 15913      2         3  48       62933   E3     1 #> 15914      2         3  48       62933   E4     5 #> 15915      2         3  48       62933   E5     5 #> 15916      2         3  48       62933   N1     1 #> 15917      2         3  48       62933   N2     2 #> 15918      2         3  48       62933   N3     1 #> 15919      2         3  48       62933   N4     3 #> 15920      2         3  48       62933   N5     4 #> 15921      2         3  48       62933   O1     3 #> 15922      2         3  48       62933   O2     3 #> 15923      2         3  48       62933   O3     3 #> 15924      2         3  48       62933   O4     1 #> 15925      2         3  48       62933   O5     2 #> 15926      2        NA  12       62934   A1     4 #> 15927      2        NA  12       62934   A2     4 #> 15928      2        NA  12       62934   A3     2 #> 15929      2        NA  12       62934   A4     4 #> 15930      2        NA  12       62934   A5     4 #> 15931      2        NA  12       62934   C1     4 #> 15932      2        NA  12       62934   C2     4 #> 15933      2        NA  12       62934   C3     4 #> 15934      2        NA  12       62934   C4     3 #> 15935      2        NA  12       62934   C5     4 #> 15936      2        NA  12       62934   E1     3 #> 15937      2        NA  12       62934   E2     5 #> 15938      2        NA  12       62934   E3     2 #> 15939      2        NA  12       62934   E4     2 #> 15940      2        NA  12       62934   E5     4 #> 15941      2        NA  12       62934   N1     2 #> 15942      2        NA  12       62934   N2     3 #> 15943      2        NA  12       62934   N3     3 #> 15944      2        NA  12       62934   N4     4 #> 15945      2        NA  12       62934   N5     4 #> 15946      2        NA  12       62934   O1     2 #> 15947      2        NA  12       62934   O2     2 #> 15948      2        NA  12       62934   O3     4 #> 15949      2        NA  12       62934   O4     4 #> 15950      2        NA  12       62934   O5     2 #> 15951      2         3  43       62936   A1     1 #> 15952      2         3  43       62936   A2     4 #> 15953      2         3  43       62936   A3     4 #> 15954      2         3  43       62936   A4     5 #> 15955      2         3  43       62936   A5     5 #> 15956      2         3  43       62936   C1     3 #> 15957      2         3  43       62936   C2     1 #> 15958      2         3  43       62936   C3     2 #> 15959      2         3  43       62936   C4     2 #> 15960      2         3  43       62936   C5     2 #> 15961      2         3  43       62936   E1     4 #> 15962      2         3  43       62936   E2     5 #> 15963      2         3  43       62936   E3     2 #> 15964      2         3  43       62936   E4     3 #> 15965      2         3  43       62936   E5     5 #> 15966      2         3  43       62936   N1     2 #> 15967      2         3  43       62936   N2    NA #> 15968      2         3  43       62936   N3     1 #> 15969      2         3  43       62936   N4     4 #> 15970      2         3  43       62936   N5     1 #> 15971      2         3  43       62936   O1     2 #> 15972      2         3  43       62936   O2     4 #> 15973      2         3  43       62936   O3     4 #> 15974      2         3  43       62936   O4     5 #> 15975      2         3  43       62936   O5     2 #> 15976      1         3  37       62938   A1     2 #> 15977      1         3  37       62938   A2     5 #> 15978      1         3  37       62938   A3     5 #> 15979      1         3  37       62938   A4     6 #> 15980      1         3  37       62938   A5     5 #> 15981      1         3  37       62938   C1     6 #> 15982      1         3  37       62938   C2     5 #> 15983      1         3  37       62938   C3     4 #> 15984      1         3  37       62938   C4     2 #> 15985      1         3  37       62938   C5     2 #> 15986      1         3  37       62938   E1     1 #> 15987      1         3  37       62938   E2     1 #> 15988      1         3  37       62938   E3     6 #> 15989      1         3  37       62938   E4     6 #> 15990      1         3  37       62938   E5     6 #> 15991      1         3  37       62938   N1     2 #> 15992      1         3  37       62938   N2     2 #> 15993      1         3  37       62938   N3     2 #> 15994      1         3  37       62938   N4     2 #> 15995      1         3  37       62938   N5     1 #> 15996      1         3  37       62938   O1     6 #> 15997      1         3  37       62938   O2     2 #> 15998      1         3  37       62938   O3     6 #> 15999      1         3  37       62938   O4     4 #> 16000      1         3  37       62938   O5     2 #> 16001      2         3  18       62939   A1     3 #> 16002      2         3  18       62939   A2     5 #> 16003      2         3  18       62939   A3     5 #> 16004      2         3  18       62939   A4     5 #> 16005      2         3  18       62939   A5     4 #> 16006      2         3  18       62939   C1     4 #> 16007      2         3  18       62939   C2     4 #> 16008      2         3  18       62939   C3     4 #> 16009      2         3  18       62939   C4     3 #> 16010      2         3  18       62939   C5     3 #> 16011      2         3  18       62939   E1     4 #> 16012      2         3  18       62939   E2     2 #> 16013      2         3  18       62939   E3     4 #> 16014      2         3  18       62939   E4     4 #> 16015      2         3  18       62939   E5     5 #> 16016      2         3  18       62939   N1     3 #> 16017      2         3  18       62939   N2     3 #> 16018      2         3  18       62939   N3     3 #> 16019      2         3  18       62939   N4     2 #> 16020      2         3  18       62939   N5     2 #> 16021      2         3  18       62939   O1     5 #> 16022      2         3  18       62939   O2     2 #> 16023      2         3  18       62939   O3     5 #> 16024      2         3  18       62939   O4     5 #> 16025      2         3  18       62939   O5     1 #> 16026      2         3  18       62941   A1     2 #> 16027      2         3  18       62941   A2     5 #> 16028      2         3  18       62941   A3     4 #> 16029      2         3  18       62941   A4     5 #> 16030      2         3  18       62941   A5     5 #> 16031      2         3  18       62941   C1     4 #> 16032      2         3  18       62941   C2     4 #> 16033      2         3  18       62941   C3     4 #> 16034      2         3  18       62941   C4     3 #> 16035      2         3  18       62941   C5     3 #> 16036      2         3  18       62941   E1     4 #> 16037      2         3  18       62941   E2     3 #> 16038      2         3  18       62941   E3     3 #> 16039      2         3  18       62941   E4     4 #> 16040      2         3  18       62941   E5     5 #> 16041      2         3  18       62941   N1     3 #> 16042      2         3  18       62941   N2     4 #> 16043      2         3  18       62941   N3     2 #> 16044      2         3  18       62941   N4     3 #> 16045      2         3  18       62941   N5     3 #> 16046      2         3  18       62941   O1     5 #> 16047      2         3  18       62941   O2     2 #> 16048      2         3  18       62941   O3     5 #> 16049      2         3  18       62941   O4     5 #> 16050      2         3  18       62941   O5     2 #> 16051      2         3  24       62942   A1     1 #> 16052      2         3  24       62942   A2     5 #> 16053      2         3  24       62942   A3     5 #> 16054      2         3  24       62942   A4     6 #> 16055      2         3  24       62942   A5     6 #> 16056      2         3  24       62942   C1     5 #> 16057      2         3  24       62942   C2     6 #> 16058      2         3  24       62942   C3     4 #> 16059      2         3  24       62942   C4     2 #> 16060      2         3  24       62942   C5     2 #> 16061      2         3  24       62942   E1     5 #> 16062      2         3  24       62942   E2     4 #> 16063      2         3  24       62942   E3     5 #> 16064      2         3  24       62942   E4     5 #> 16065      2         3  24       62942   E5     5 #> 16066      2         3  24       62942   N1     4 #> 16067      2         3  24       62942   N2     4 #> 16068      2         3  24       62942   N3     2 #> 16069      2         3  24       62942   N4     5 #> 16070      2         3  24       62942   N5     4 #> 16071      2         3  24       62942   O1     4 #> 16072      2         3  24       62942   O2     1 #> 16073      2         3  24       62942   O3     3 #> 16074      2         3  24       62942   O4     4 #> 16075      2         3  24       62942   O5     2 #> 16076      2         3  23       62948   A1     4 #> 16077      2         3  23       62948   A2     6 #> 16078      2         3  23       62948   A3     6 #> 16079      2         3  23       62948   A4     6 #> 16080      2         3  23       62948   A5     6 #> 16081      2         3  23       62948   C1     4 #> 16082      2         3  23       62948   C2     3 #> 16083      2         3  23       62948   C3     1 #> 16084      2         3  23       62948   C4     3 #> 16085      2         3  23       62948   C5     2 #> 16086      2         3  23       62948   E1     1 #> 16087      2         3  23       62948   E2     4 #> 16088      2         3  23       62948   E3     4 #> 16089      2         3  23       62948   E4     6 #> 16090      2         3  23       62948   E5     5 #> 16091      2         3  23       62948   N1     3 #> 16092      2         3  23       62948   N2     6 #> 16093      2         3  23       62948   N3     6 #> 16094      2         3  23       62948   N4     2 #> 16095      2         3  23       62948   N5     1 #> 16096      2         3  23       62948   O1     5 #> 16097      2         3  23       62948   O2     4 #> 16098      2         3  23       62948   O3     3 #> 16099      2         3  23       62948   O4     5 #> 16100      2         3  23       62948   O5     1 #> 16101      2         3  20       62949   A1     3 #> 16102      2         3  20       62949   A2     4 #> 16103      2         3  20       62949   A3     6 #> 16104      2         3  20       62949   A4     3 #> 16105      2         3  20       62949   A5     3 #> 16106      2         3  20       62949   C1     6 #> 16107      2         3  20       62949   C2     4 #> 16108      2         3  20       62949   C3     4 #> 16109      2         3  20       62949   C4     2 #> 16110      2         3  20       62949   C5     3 #> 16111      2         3  20       62949   E1     1 #> 16112      2         3  20       62949   E2     2 #> 16113      2         3  20       62949   E3     6 #> 16114      2         3  20       62949   E4     6 #> 16115      2         3  20       62949   E5     6 #> 16116      2         3  20       62949   N1    NA #> 16117      2         3  20       62949   N2     6 #> 16118      2         3  20       62949   N3     4 #> 16119      2         3  20       62949   N4     2 #> 16120      2         3  20       62949   N5     3 #> 16121      2         3  20       62949   O1     6 #> 16122      2         3  20       62949   O2     3 #> 16123      2         3  20       62949   O3     6 #> 16124      2         3  20       62949   O4     4 #> 16125      2         3  20       62949   O5    NA #> 16126      2         2  32       62950   A1     1 #> 16127      2         2  32       62950   A2     6 #> 16128      2         2  32       62950   A3     6 #> 16129      2         2  32       62950   A4     6 #> 16130      2         2  32       62950   A5     6 #> 16131      2         2  32       62950   C1     6 #> 16132      2         2  32       62950   C2     5 #> 16133      2         2  32       62950   C3     4 #> 16134      2         2  32       62950   C4     1 #> 16135      2         2  32       62950   C5     1 #> 16136      2         2  32       62950   E1     5 #> 16137      2         2  32       62950   E2     3 #> 16138      2         2  32       62950   E3     4 #> 16139      2         2  32       62950   E4     5 #> 16140      2         2  32       62950   E5     6 #> 16141      2         2  32       62950   N1     3 #> 16142      2         2  32       62950   N2     5 #> 16143      2         2  32       62950   N3     2 #> 16144      2         2  32       62950   N4     2 #> 16145      2         2  32       62950   N5     2 #> 16146      2         2  32       62950   O1     1 #> 16147      2         2  32       62950   O2     2 #> 16148      2         2  32       62950   O3     5 #> 16149      2         2  32       62950   O4     3 #> 16150      2         2  32       62950   O5     2 #> 16151      1         4  37       62951   A1     1 #> 16152      1         4  37       62951   A2     4 #> 16153      1         4  37       62951   A3     5 #> 16154      1         4  37       62951   A4     5 #> 16155      1         4  37       62951   A5     5 #> 16156      1         4  37       62951   C1     4 #> 16157      1         4  37       62951   C2     2 #> 16158      1         4  37       62951   C3     4 #> 16159      1         4  37       62951   C4     2 #> 16160      1         4  37       62951   C5     4 #> 16161      1         4  37       62951   E1     4 #> 16162      1         4  37       62951   E2     2 #> 16163      1         4  37       62951   E3     2 #> 16164      1         4  37       62951   E4     5 #> 16165      1         4  37       62951   E5     3 #> 16166      1         4  37       62951   N1     2 #> 16167      1         4  37       62951   N2     2 #> 16168      1         4  37       62951   N3     2 #> 16169      1         4  37       62951   N4     2 #> 16170      1         4  37       62951   N5     1 #> 16171      1         4  37       62951   O1     5 #> 16172      1         4  37       62951   O2     1 #> 16173      1         4  37       62951   O3     5 #> 16174      1         4  37       62951   O4     6 #> 16175      1         4  37       62951   O5     2 #> 16176      1         3  19       62953   A1     1 #> 16177      1         3  19       62953   A2     6 #> 16178      1         3  19       62953   A3     6 #> 16179      1         3  19       62953   A4     6 #> 16180      1         3  19       62953   A5     5 #> 16181      1         3  19       62953   C1     6 #> 16182      1         3  19       62953   C2     5 #> 16183      1         3  19       62953   C3     5 #> 16184      1         3  19       62953   C4     1 #> 16185      1         3  19       62953   C5     2 #> 16186      1         3  19       62953   E1     5 #> 16187      1         3  19       62953   E2     5 #> 16188      1         3  19       62953   E3     4 #> 16189      1         3  19       62953   E4     5 #> 16190      1         3  19       62953   E5     2 #> 16191      1         3  19       62953   N1     2 #> 16192      1         3  19       62953   N2     3 #> 16193      1         3  19       62953   N3     5 #> 16194      1         3  19       62953   N4     2 #> 16195      1         3  19       62953   N5     2 #> 16196      1         3  19       62953   O1     6 #> 16197      1         3  19       62953   O2     2 #> 16198      1         3  19       62953   O3     5 #> 16199      1         3  19       62953   O4     4 #> 16200      1         3  19       62953   O5     2 #> 16201      2         3  29       62954   A1     2 #> 16202      2         3  29       62954   A2     4 #> 16203      2         3  29       62954   A3     4 #> 16204      2         3  29       62954   A4     5 #> 16205      2         3  29       62954   A5     2 #> 16206      2         3  29       62954   C1     5 #> 16207      2         3  29       62954   C2     5 #> 16208      2         3  29       62954   C3     1 #> 16209      2         3  29       62954   C4     1 #> 16210      2         3  29       62954   C5     6 #> 16211      2         3  29       62954   E1     3 #> 16212      2         3  29       62954   E2     4 #> 16213      2         3  29       62954   E3     3 #> 16214      2         3  29       62954   E4     2 #> 16215      2         3  29       62954   E5     1 #> 16216      2         3  29       62954   N1     2 #> 16217      2         3  29       62954   N2     2 #> 16218      2         3  29       62954   N3     4 #> 16219      2         3  29       62954   N4     6 #> 16220      2         3  29       62954   N5     4 #> 16221      2         3  29       62954   O1     2 #> 16222      2         3  29       62954   O2     2 #> 16223      2         3  29       62954   O3     6 #> 16224      2         3  29       62954   O4     5 #> 16225      2         3  29       62954   O5     1 #> 16226      2         3  20       62957   A1     2 #> 16227      2         3  20       62957   A2     5 #> 16228      2         3  20       62957   A3     2 #> 16229      2         3  20       62957   A4     6 #> 16230      2         3  20       62957   A5     4 #> 16231      2         3  20       62957   C1     6 #> 16232      2         3  20       62957   C2     5 #> 16233      2         3  20       62957   C3     4 #> 16234      2         3  20       62957   C4     1 #> 16235      2         3  20       62957   C5     2 #> 16236      2         3  20       62957   E1     2 #> 16237      2         3  20       62957   E2     2 #> 16238      2         3  20       62957   E3     5 #> 16239      2         3  20       62957   E4     5 #> 16240      2         3  20       62957   E5     5 #> 16241      2         3  20       62957   N1     1 #> 16242      2         3  20       62957   N2     1 #> 16243      2         3  20       62957   N3     1 #> 16244      2         3  20       62957   N4     1 #> 16245      2         3  20       62957   N5     1 #> 16246      2         3  20       62957   O1     5 #> 16247      2         3  20       62957   O2     1 #> 16248      2         3  20       62957   O3     3 #> 16249      2         3  20       62957   O4     5 #> 16250      2         3  20       62957   O5     2 #> 16251      2         4  26       62962   A1     1 #> 16252      2         4  26       62962   A2     5 #> 16253      2         4  26       62962   A3     6 #> 16254      2         4  26       62962   A4     6 #> 16255      2         4  26       62962   A5     5 #> 16256      2         4  26       62962   C1     4 #> 16257      2         4  26       62962   C2     2 #> 16258      2         4  26       62962   C3     4 #> 16259      2         4  26       62962   C4     1 #> 16260      2         4  26       62962   C5     1 #> 16261      2         4  26       62962   E1     2 #> 16262      2         4  26       62962   E2     2 #> 16263      2         4  26       62962   E3     5 #> 16264      2         4  26       62962   E4     6 #> 16265      2         4  26       62962   E5     5 #> 16266      2         4  26       62962   N1     1 #> 16267      2         4  26       62962   N2     2 #> 16268      2         4  26       62962   N3     1 #> 16269      2         4  26       62962   N4     1 #> 16270      2         4  26       62962   N5     2 #> 16271      2         4  26       62962   O1     4 #> 16272      2         4  26       62962   O2     4 #> 16273      2         4  26       62962   O3     6 #> 16274      2         4  26       62962   O4     4 #> 16275      2         4  26       62962   O5     1 #> 16276      2         4  33       62965   A1     2 #> 16277      2         4  33       62965   A2     4 #> 16278      2         4  33       62965   A3     2 #> 16279      2         4  33       62965   A4     5 #> 16280      2         4  33       62965   A5     4 #> 16281      2         4  33       62965   C1     5 #> 16282      2         4  33       62965   C2     5 #> 16283      2         4  33       62965   C3     4 #> 16284      2         4  33       62965   C4     4 #> 16285      2         4  33       62965   C5     6 #> 16286      2         4  33       62965   E1     5 #> 16287      2         4  33       62965   E2     5 #> 16288      2         4  33       62965   E3     2 #> 16289      2         4  33       62965   E4     2 #> 16290      2         4  33       62965   E5     4 #> 16291      2         4  33       62965   N1     4 #> 16292      2         4  33       62965   N2     5 #> 16293      2         4  33       62965   N3     5 #> 16294      2         4  33       62965   N4     6 #> 16295      2         4  33       62965   N5     4 #> 16296      2         4  33       62965   O1     6 #> 16297      2         4  33       62965   O2     1 #> 16298      2         4  33       62965   O3     6 #> 16299      2         4  33       62965   O4     6 #> 16300      2         4  33       62965   O5     1 #> 16301      2         5  38       62968   A1     4 #> 16302      2         5  38       62968   A2     4 #> 16303      2         5  38       62968   A3     4 #> 16304      2         5  38       62968   A4     6 #> 16305      2         5  38       62968   A5     2 #> 16306      2         5  38       62968   C1     2 #> 16307      2         5  38       62968   C2     4 #> 16308      2         5  38       62968   C3     4 #> 16309      2         5  38       62968   C4     3 #> 16310      2         5  38       62968   C5     4 #> 16311      2         5  38       62968   E1     5 #> 16312      2         5  38       62968   E2     4 #> 16313      2         5  38       62968   E3     2 #> 16314      2         5  38       62968   E4     4 #> 16315      2         5  38       62968   E5     4 #> 16316      2         5  38       62968   N1     2 #> 16317      2         5  38       62968   N2     3 #> 16318      2         5  38       62968   N3     4 #> 16319      2         5  38       62968   N4     5 #> 16320      2         5  38       62968   N5     3 #> 16321      2         5  38       62968   O1     4 #> 16322      2         5  38       62968   O2     2 #> 16323      2         5  38       62968   O3     5 #> 16324      2         5  38       62968   O4     5 #> 16325      2         5  38       62968   O5     4 #> 16326      1         2  26       62969   A1     1 #> 16327      1         2  26       62969   A2     5 #> 16328      1         2  26       62969   A3     6 #> 16329      1         2  26       62969   A4     6 #> 16330      1         2  26       62969   A5     6 #> 16331      1         2  26       62969   C1     6 #> 16332      1         2  26       62969   C2     5 #> 16333      1         2  26       62969   C3     5 #> 16334      1         2  26       62969   C4     1 #> 16335      1         2  26       62969   C5     5 #> 16336      1         2  26       62969   E1     1 #> 16337      1         2  26       62969   E2     6 #> 16338      1         2  26       62969   E3     6 #> 16339      1         2  26       62969   E4     6 #> 16340      1         2  26       62969   E5     5 #> 16341      1         2  26       62969   N1     4 #> 16342      1         2  26       62969   N2     5 #> 16343      1         2  26       62969   N3     5 #> 16344      1         2  26       62969   N4     1 #> 16345      1         2  26       62969   N5     1 #> 16346      1         2  26       62969   O1     4 #> 16347      1         2  26       62969   O2     1 #> 16348      1         2  26       62969   O3     6 #> 16349      1         2  26       62969   O4     3 #> 16350      1         2  26       62969   O5     3 #> 16351      2         3  30       62971   A1     3 #> 16352      2         3  30       62971   A2     5 #> 16353      2         3  30       62971   A3     5 #> 16354      2         3  30       62971   A4     6 #> 16355      2         3  30       62971   A5     6 #> 16356      2         3  30       62971   C1     5 #> 16357      2         3  30       62971   C2     5 #> 16358      2         3  30       62971   C3     5 #> 16359      2         3  30       62971   C4     2 #> 16360      2         3  30       62971   C5     2 #> 16361      2         3  30       62971   E1     1 #> 16362      2         3  30       62971   E2     2 #> 16363      2         3  30       62971   E3     5 #> 16364      2         3  30       62971   E4     6 #> 16365      2         3  30       62971   E5     6 #> 16366      2         3  30       62971   N1     6 #> 16367      2         3  30       62971   N2     6 #> 16368      2         3  30       62971   N3     5 #> 16369      2         3  30       62971   N4     5 #> 16370      2         3  30       62971   N5     5 #> 16371      2         3  30       62971   O1     6 #> 16372      2         3  30       62971   O2     6 #> 16373      2         3  30       62971   O3    NA #> 16374      2         3  30       62971   O4     6 #> 16375      2         3  30       62971   O5     5 #> 16376      1         3  36       62974   A1     5 #> 16377      1         3  36       62974   A2     6 #> 16378      1         3  36       62974   A3     6 #> 16379      1         3  36       62974   A4     6 #> 16380      1         3  36       62974   A5     6 #> 16381      1         3  36       62974   C1     5 #> 16382      1         3  36       62974   C2     5 #> 16383      1         3  36       62974   C3     4 #> 16384      1         3  36       62974   C4     1 #> 16385      1         3  36       62974   C5     1 #> 16386      1         3  36       62974   E1     5 #> 16387      1         3  36       62974   E2     1 #> 16388      1         3  36       62974   E3     5 #> 16389      1         3  36       62974   E4     6 #> 16390      1         3  36       62974   E5     3 #> 16391      1         3  36       62974   N1     1 #> 16392      1         3  36       62974   N2     1 #> 16393      1         3  36       62974   N3     3 #> 16394      1         3  36       62974   N4     1 #> 16395      1         3  36       62974   N5     1 #> 16396      1         3  36       62974   O1     5 #> 16397      1         3  36       62974   O2     1 #> 16398      1         3  36       62974   O3     6 #> 16399      1         3  36       62974   O4     4 #> 16400      1         3  36       62974   O5     4 #> 16401      2         3  41       62976   A1     1 #> 16402      2         3  41       62976   A2     6 #> 16403      2         3  41       62976   A3     6 #> 16404      2         3  41       62976   A4     6 #> 16405      2         3  41       62976   A5     6 #> 16406      2         3  41       62976   C1     6 #> 16407      2         3  41       62976   C2     5 #> 16408      2         3  41       62976   C3     5 #> 16409      2         3  41       62976   C4     3 #> 16410      2         3  41       62976   C5     1 #> 16411      2         3  41       62976   E1     5 #> 16412      2         3  41       62976   E2     4 #> 16413      2         3  41       62976   E3     4 #> 16414      2         3  41       62976   E4     4 #> 16415      2         3  41       62976   E5     5 #> 16416      2         3  41       62976   N1     1 #> 16417      2         3  41       62976   N2     1 #> 16418      2         3  41       62976   N3     2 #> 16419      2         3  41       62976   N4     4 #> 16420      2         3  41       62976   N5     2 #> 16421      2         3  41       62976   O1     6 #> 16422      2         3  41       62976   O2     2 #> 16423      2         3  41       62976   O3     4 #> 16424      2         3  41       62976   O4     6 #> 16425      2         3  41       62976   O5     1 #> 16426      2         5  34       62983   A1     1 #> 16427      2         5  34       62983   A2     5 #> 16428      2         5  34       62983   A3     5 #> 16429      2         5  34       62983   A4     6 #> 16430      2         5  34       62983   A5     6 #> 16431      2         5  34       62983   C1     5 #> 16432      2         5  34       62983   C2     3 #> 16433      2         5  34       62983   C3     4 #> 16434      2         5  34       62983   C4     2 #> 16435      2         5  34       62983   C5     3 #> 16436      2         5  34       62983   E1     3 #> 16437      2         5  34       62983   E2     5 #> 16438      2         5  34       62983   E3     4 #> 16439      2         5  34       62983   E4     5 #> 16440      2         5  34       62983   E5     4 #> 16441      2         5  34       62983   N1     4 #> 16442      2         5  34       62983   N2     5 #> 16443      2         5  34       62983   N3     3 #> 16444      2         5  34       62983   N4     2 #> 16445      2         5  34       62983   N5     5 #> 16446      2         5  34       62983   O1     4 #> 16447      2         5  34       62983   O2     2 #> 16448      2         5  34       62983   O3     5 #> 16449      2         5  34       62983   O4     6 #> 16450      2         5  34       62983   O5     2 #> 16451      2         3  35       62984   A1     1 #> 16452      2         3  35       62984   A2     6 #> 16453      2         3  35       62984   A3     5 #> 16454      2         3  35       62984   A4     6 #> 16455      2         3  35       62984   A5     4 #> 16456      2         3  35       62984   C1     4 #> 16457      2         3  35       62984   C2     2 #> 16458      2         3  35       62984   C3     4 #> 16459      2         3  35       62984   C4     5 #> 16460      2         3  35       62984   C5     3 #> 16461      2         3  35       62984   E1     1 #> 16462      2         3  35       62984   E2     6 #> 16463      2         3  35       62984   E3     4 #> 16464      2         3  35       62984   E4     4 #> 16465      2         3  35       62984   E5     1 #> 16466      2         3  35       62984   N1     4 #> 16467      2         3  35       62984   N2     5 #> 16468      2         3  35       62984   N3     4 #> 16469      2         3  35       62984   N4     4 #> 16470      2         3  35       62984   N5     1 #> 16471      2         3  35       62984   O1     1 #> 16472      2         3  35       62984   O2     1 #> 16473      2         3  35       62984   O3     2 #> 16474      2         3  35       62984   O4     5 #> 16475      2         3  35       62984   O5     1 #> 16476      2         3  40       62989   A1     3 #> 16477      2         3  40       62989   A2     6 #> 16478      2         3  40       62989   A3     5 #> 16479      2         3  40       62989   A4     6 #> 16480      2         3  40       62989   A5     5 #> 16481      2         3  40       62989   C1     3 #> 16482      2         3  40       62989   C2     5 #> 16483      2         3  40       62989   C3     5 #> 16484      2         3  40       62989   C4     1 #> 16485      2         3  40       62989   C5     1 #> 16486      2         3  40       62989   E1     3 #> 16487      2         3  40       62989   E2     1 #> 16488      2         3  40       62989   E3     3 #> 16489      2         3  40       62989   E4     4 #> 16490      2         3  40       62989   E5     4 #> 16491      2         3  40       62989   N1     3 #> 16492      2         3  40       62989   N2     3 #> 16493      2         3  40       62989   N3     2 #> 16494      2         3  40       62989   N4     3 #> 16495      2         3  40       62989   N5     4 #> 16496      2         3  40       62989   O1     4 #> 16497      2         3  40       62989   O2     4 #> 16498      2         3  40       62989   O3     4 #> 16499      2         3  40       62989   O4     4 #> 16500      2         3  40       62989   O5     2 #> 16501      2         3  33       62990   A1     6 #> 16502      2         3  33       62990   A2     1 #> 16503      2         3  33       62990   A3     1 #> 16504      2         3  33       62990   A4     1 #> 16505      2         3  33       62990   A5     1 #> 16506      2         3  33       62990   C1     6 #> 16507      2         3  33       62990   C2     6 #> 16508      2         3  33       62990   C3     6 #> 16509      2         3  33       62990   C4     1 #> 16510      2         3  33       62990   C5     1 #> 16511      2         3  33       62990   E1     6 #> 16512      2         3  33       62990   E2     6 #> 16513      2         3  33       62990   E3     1 #> 16514      2         3  33       62990   E4     1 #> 16515      2         3  33       62990   E5     6 #> 16516      2         3  33       62990   N1     6 #> 16517      2         3  33       62990   N2     6 #> 16518      2         3  33       62990   N3     6 #> 16519      2         3  33       62990   N4     6 #> 16520      2         3  33       62990   N5     1 #> 16521      2         3  33       62990   O1     6 #> 16522      2         3  33       62990   O2     1 #> 16523      2         3  33       62990   O3     6 #> 16524      2         3  33       62990   O4     6 #> 16525      2         3  33       62990   O5     1 #> 16526      2         3  28       62991   A1     5 #> 16527      2         3  28       62991   A2     5 #> 16528      2         3  28       62991   A3     4 #> 16529      2         3  28       62991   A4     5 #> 16530      2         3  28       62991   A5     5 #> 16531      2         3  28       62991   C1     5 #> 16532      2         3  28       62991   C2     5 #> 16533      2         3  28       62991   C3     5 #> 16534      2         3  28       62991   C4     3 #> 16535      2         3  28       62991   C5     1 #> 16536      2         3  28       62991   E1     4 #> 16537      2         3  28       62991   E2     5 #> 16538      2         3  28       62991   E3     4 #> 16539      2         3  28       62991   E4     4 #> 16540      2         3  28       62991   E5     5 #> 16541      2         3  28       62991   N1     4 #> 16542      2         3  28       62991   N2     4 #> 16543      2         3  28       62991   N3     4 #> 16544      2         3  28       62991   N4     4 #> 16545      2         3  28       62991   N5     4 #> 16546      2         3  28       62991   O1     4 #> 16547      2         3  28       62991   O2     5 #> 16548      2         3  28       62991   O3     4 #> 16549      2         3  28       62991   O4     4 #> 16550      2         3  28       62991   O5     4 #> 16551      2         3  29       62994   A1     1 #> 16552      2         3  29       62994   A2     6 #> 16553      2         3  29       62994   A3     5 #> 16554      2         3  29       62994   A4     2 #> 16555      2         3  29       62994   A5     5 #> 16556      2         3  29       62994   C1     5 #> 16557      2         3  29       62994   C2     5 #> 16558      2         3  29       62994   C3     3 #> 16559      2         3  29       62994   C4     3 #> 16560      2         3  29       62994   C5     5 #> 16561      2         3  29       62994   E1     1 #> 16562      2         3  29       62994   E2     1 #> 16563      2         3  29       62994   E3     4 #> 16564      2         3  29       62994   E4     6 #> 16565      2         3  29       62994   E5     4 #> 16566      2         3  29       62994   N1     5 #> 16567      2         3  29       62994   N2     5 #> 16568      2         3  29       62994   N3     6 #> 16569      2         3  29       62994   N4     5 #> 16570      2         3  29       62994   N5     5 #> 16571      2         3  29       62994   O1     4 #> 16572      2         3  29       62994   O2     2 #> 16573      2         3  29       62994   O3     4 #> 16574      2         3  29       62994   O4     3 #> 16575      2         3  29       62994   O5     2 #> 16576      2        NA  17       62995   A1     6 #> 16577      2        NA  17       62995   A2     1 #> 16578      2        NA  17       62995   A3     1 #> 16579      2        NA  17       62995   A4     2 #> 16580      2        NA  17       62995   A5     3 #> 16581      2        NA  17       62995   C1     2 #> 16582      2        NA  17       62995   C2     4 #> 16583      2        NA  17       62995   C3     2 #> 16584      2        NA  17       62995   C4     4 #> 16585      2        NA  17       62995   C5     4 #> 16586      2        NA  17       62995   E1     3 #> 16587      2        NA  17       62995   E2     5 #> 16588      2        NA  17       62995   E3     4 #> 16589      2        NA  17       62995   E4     3 #> 16590      2        NA  17       62995   E5     4 #> 16591      2        NA  17       62995   N1     5 #> 16592      2        NA  17       62995   N2     6 #> 16593      2        NA  17       62995   N3     4 #> 16594      2        NA  17       62995   N4     4 #> 16595      2        NA  17       62995   N5     4 #> 16596      2        NA  17       62995   O1     5 #> 16597      2        NA  17       62995   O2     1 #> 16598      2        NA  17       62995   O3     4 #> 16599      2        NA  17       62995   O4     4 #> 16600      2        NA  17       62995   O5     2 #> 16601      2         3  50       62996   A1     2 #> 16602      2         3  50       62996   A2     5 #> 16603      2         3  50       62996   A3     5 #> 16604      2         3  50       62996   A4     5 #> 16605      2         3  50       62996   A5     4 #> 16606      2         3  50       62996   C1     4 #> 16607      2         3  50       62996   C2     5 #> 16608      2         3  50       62996   C3     5 #> 16609      2         3  50       62996   C4     2 #> 16610      2         3  50       62996   C5     1 #> 16611      2         3  50       62996   E1     1 #> 16612      2         3  50       62996   E2     1 #> 16613      2         3  50       62996   E3     3 #> 16614      2         3  50       62996   E4     5 #> 16615      2         3  50       62996   E5     4 #> 16616      2         3  50       62996   N1     1 #> 16617      2         3  50       62996   N2     2 #> 16618      2         3  50       62996   N3     2 #> 16619      2         3  50       62996   N4     4 #> 16620      2         3  50       62996   N5     1 #> 16621      2         3  50       62996   O1     3 #> 16622      2         3  50       62996   O2     2 #> 16623      2         3  50       62996   O3     4 #> 16624      2         3  50       62996   O4     5 #> 16625      2         3  50       62996   O5     2 #> 16626      1         3  20       62997   A1     2 #> 16627      1         3  20       62997   A2    NA #> 16628      1         3  20       62997   A3     5 #> 16629      1         3  20       62997   A4     3 #> 16630      1         3  20       62997   A5     6 #> 16631      1         3  20       62997   C1     2 #> 16632      1         3  20       62997   C2     1 #> 16633      1         3  20       62997   C3     5 #> 16634      1         3  20       62997   C4     2 #> 16635      1         3  20       62997   C5     6 #> 16636      1         3  20       62997   E1     1 #> 16637      1         3  20       62997   E2     1 #> 16638      1         3  20       62997   E3     5 #> 16639      1         3  20       62997   E4     6 #> 16640      1         3  20       62997   E5     6 #> 16641      1         3  20       62997   N1     6 #> 16642      1         3  20       62997   N2     6 #> 16643      1         3  20       62997   N3     5 #> 16644      1         3  20       62997   N4     5 #> 16645      1         3  20       62997   N5     3 #> 16646      1         3  20       62997   O1     4 #> 16647      1         3  20       62997   O2     6 #> 16648      1         3  20       62997   O3     3 #> 16649      1         3  20       62997   O4     3 #> 16650      1         3  20       62997   O5     3 #> 16651      2         3  19       63004   A1     5 #> 16652      2         3  19       63004   A2     5 #> 16653      2         3  19       63004   A3     5 #> 16654      2         3  19       63004   A4     6 #> 16655      2         3  19       63004   A5     3 #> 16656      2         3  19       63004   C1     4 #> 16657      2         3  19       63004   C2     5 #> 16658      2         3  19       63004   C3     5 #> 16659      2         3  19       63004   C4     2 #> 16660      2         3  19       63004   C5     2 #> 16661      2         3  19       63004   E1     4 #> 16662      2         3  19       63004   E2     6 #> 16663      2         3  19       63004   E3     4 #> 16664      2         3  19       63004   E4     5 #> 16665      2         3  19       63004   E5     4 #> 16666      2         3  19       63004   N1     5 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 53334 rows ]  data_to_long(   tidyr::who,   select = new_sp_m014:newrel_f65,   names_to = c(\"diagnosis\", \"gender\", \"age\"),   names_pattern = \"new_?(.*)_(.)(.*)\",   values_to = \"count\" ) #> # A tibble: 405,440 × 8 #>    country     iso2  iso3   year diagnosis gender age   count #>    <chr>       <chr> <chr> <dbl> <chr>     <chr>  <chr> <dbl> #>  1 Afghanistan AF    AFG    1980 sp        m      014      NA #>  2 Afghanistan AF    AFG    1980 sp        m      1524     NA #>  3 Afghanistan AF    AFG    1980 sp        m      2534     NA #>  4 Afghanistan AF    AFG    1980 sp        m      3544     NA #>  5 Afghanistan AF    AFG    1980 sp        m      4554     NA #>  6 Afghanistan AF    AFG    1980 sp        m      5564     NA #>  7 Afghanistan AF    AFG    1980 sp        m      65       NA #>  8 Afghanistan AF    AFG    1980 sp        f      014      NA #>  9 Afghanistan AF    AFG    1980 sp        f      1524     NA #> 10 Afghanistan AF    AFG    1980 sp        f      2534     NA #> # ℹ 405,430 more rows"},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape (pivot) data from long to wide — data_to_wide","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"function \"widens\" data, increasing number columns decreasing number rows. dependency-free base-R equivalent tidyr::pivot_wider().","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"","code":"data_to_wide(   data,   id_cols = NULL,   values_from = \"Value\",   names_from = \"Name\",   names_sep = \"_\",   names_prefix = \"\",   names_glue = NULL,   values_fill = NULL,   verbose = TRUE,   ...,   colnames_from,   rows_from,   sep )  reshape_wider(   data,   id_cols = NULL,   values_from = \"Value\",   names_from = \"Name\",   names_sep = \"_\",   names_prefix = \"\",   names_glue = NULL,   values_fill = NULL,   verbose = TRUE,   ...,   colnames_from,   rows_from,   sep )"},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"data data frame pivot. id_cols name column identifies rows. NULL, use unique rows. values_from name column contains values used future variable values. names_from name column contains levels used future column names. names_sep names_from values_from contains multiple variables, used join values together single string use column name. names_prefix String added start every variable name. particularly useful names_from numeric vector want create syntactic variable names. names_glue Instead names_sep names_prefix, can supply glue specification uses names_from columns create custom column names. Note delimiters supported names_glue curly brackets, { }. values_fill Optionally, (scalar) value used replace missing values new columns created. verbose Toggle warnings. ... used now. colnames_from Deprecated. Use names_from instead. rows_from Deprecated. Use id_cols instead. sep Deprecated. Use names_sep instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"tibble provided input, reshape_wider() also returns tibble. Otherwise, returns data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_to_wide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape (pivot) data from long to wide — data_to_wide","text":"","code":"data_long <- read.table(header = TRUE, text = \"  subject sex condition measurement        1   M   control         7.9        1   M     cond1        12.3        1   M     cond2        10.7        2   F   control         6.3        2   F     cond1        10.6        2   F     cond2        11.1        3   F   control         9.5        3   F     cond1        13.1        3   F     cond2        13.8        4   M   control        11.5        4   M     cond1        13.4        4   M     cond2        12.9\")   data_to_wide(   data_long,   id_cols = \"subject\",   names_from = \"condition\",   values_from = \"measurement\" ) #>   subject control cond1 cond2 #> 1       1     7.9  12.3  10.7 #> 2       2     6.3  10.6  11.1 #> 3       3     9.5  13.1  13.8 #> 4       4    11.5  13.4  12.9  data_to_wide(   data_long,   id_cols = \"subject\",   names_from = \"condition\",   values_from = \"measurement\",   names_prefix = \"Var.\",   names_sep = \".\" ) #>   subject Var.control Var.cond1 Var.cond2 #> 1       1         7.9      12.3      10.7 #> 2       2         6.3      10.6      11.1 #> 3       3         9.5      13.1      13.8 #> 4       4        11.5      13.4      12.9  production <- expand.grid(   product = c(\"A\", \"B\"),   country = c(\"AI\", \"EI\"),   year = 2000:2014 ) production <- data_filter(production, (product == \"A\" & country == \"AI\") | product == \"B\")  production$production <- rnorm(nrow(production))  data_to_wide(   production,   names_from = c(\"product\", \"country\"),   values_from = \"production\",   names_glue = \"prod_{product}_{country}\" ) #>    year  prod_A_AI    prod_B_AI  prod_B_EI #> 1  2000 -0.8408539  1.430252916  0.3920247 #> 2  2001 -0.4726417 -0.996105337 -0.1950098 #> 3  2002  1.3394131 -0.711765324 -0.9245581 #> 4  2003  0.8737440 -1.043327370  1.0166035 #> 5  2004 -2.2241873  1.878421273 -0.5218175 #> 6  2005 -0.6546695  0.993425211 -0.2819180 #> 7  2006 -1.0952392  1.164258300  0.2246749 #> 8  2007 -1.1649528  0.748724154 -1.3051249 #> 9  2008 -0.3766038  0.004485138  1.5616184 #> 10 2009 -0.7426178 -0.331893557  0.1463996 #> 11 2010  0.4176823  0.036978385 -1.7524488 #> 12 2011  0.1575659  0.411082845 -0.9077312 #> 13 2012  1.4151629 -0.205867410 -0.8926030 #> 14 2013  0.5674379  0.764974595 -1.9997762 #> 15 2014 -1.5185747  0.560874533 -0.8971569"},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":null,"dir":"Reference","previous_headings":"","what":"Keep only one row from all with duplicated IDs — data_unique","title":"Keep only one row from all with duplicated IDs — data_unique","text":"rows least one duplicated ID, keep one. Methods selecting duplicated row either first duplicate, last duplicate, \"best\" duplicate (default), based duplicate smallest number NA. case ties, picks first duplicate, one likely valid authentic, given practice effects. Contrarily dplyr::distinct(), data_unique() keeps columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keep only one row from all with duplicated IDs — data_unique","text":"","code":"data_unique(   data,   select = NULL,   keep = \"best\",   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keep only one row from all with duplicated IDs — data_unique","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". keep method used duplicate selection, either \"best\" (default), \"first\", \"last\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keep only one row from all with duplicated IDs — data_unique","text":"data frame, containing chosen duplicates.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_unique.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keep only one row from all with duplicated IDs — data_unique","text":"","code":"df1 <- data.frame(   id = c(1, 2, 3, 1, 3),   item1 = c(NA, 1, 1, 2, 3),   item2 = c(NA, 1, 1, 2, 3),   item3 = c(NA, 1, 1, 2, 3) )  data_unique(df1, select = \"id\") #> (2 duplicates removed, with method 'best') #>   id item1 item2 item3 #> 1  1     2     2     2 #> 2  2     1     1     1 #> 3  3     1     1     1"},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":null,"dir":"Reference","previous_headings":"","what":"Unite (","title":"Unite (","text":"Merge values multiple variables per observation one new variable.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unite (","text":"","code":"data_unite(   data,   new_column = NULL,   select = NULL,   exclude = NULL,   separator = \"_\",   append = FALSE,   remove_na = FALSE,   ignore_case = FALSE,   verbose = TRUE,   regex = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unite (","text":"data data frame. new_column name new column, string. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. separator character use values. append Logical, FALSE (default), removes original columns united. TRUE, columns preserved new column appended data frame. remove_na Logical, TRUE, missing values (NA) included united values. FALSE, missing values represented \"NA\" united values. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. verbose Toggle warnings. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unite (","text":"data, newly created variable.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/data_unite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unite (","text":"","code":"d <- data.frame(   x = 1:3,   y = letters[1:3],   z = 6:8 ) d #>   x y z #> 1 1 a 6 #> 2 2 b 7 #> 3 3 c 8 data_unite(d, new_column = \"xyz\") #>     xyz #> 1 1_a_6 #> 2 2_b_7 #> 3 3_c_8 data_unite(d, new_column = \"xyz\", remove = FALSE) #>     xyz #> 1 1_a_6 #> 2 2_b_7 #> 3 3_c_8 data_unite(d, new_column = \"xyz\", select = c(\"x\", \"z\")) #>   y xyz #> 1 a 1_6 #> 2 b 2_7 #> 3 c 3_8 data_unite(d, new_column = \"xyz\", select = c(\"x\", \"z\"), append = TRUE) #>   x y z xyz #> 1 1 a 6 1_6 #> 2 2 b 7 2_7 #> 3 3 c 8 3_8"},{"path":"https://easystats.github.io/datawizard/reference/datawizard-package.html","id":null,"dir":"Reference","previous_headings":"","what":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","title":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","text":"lightweight package assist key steps involved data analysis workflow: wrangling raw data get needed form, applying preprocessing steps statistical transformations, compute statistical summaries data properties distributions. also data wrangling backend packages 'easystats' ecosystem. References: Patil et al. (2022) doi:10.21105/joss.04684 .","code":""},{"path":"https://easystats.github.io/datawizard/reference/datawizard-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","text":"datawizard","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/datawizard-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"datawizard: Easy Data Wrangling and Statistical Transformations — datawizard-package","text":"Maintainer: Etienne Bacher etienne.bacher@protonmail.com (ORCID) Authors: Indrajeet Patil patilindrajeet.science@gmail.com (ORCID) (@patilindrajeets) Dominique Makowski dom.makowski@gmail.com (ORCID) (@Dom_Makowski) Daniel Lüdecke d.luedecke@uke.de (ORCID) (@strengejacke) Mattan S. Ben-Shachar matanshm@post.bgu.ac.il (ORCID) Brenton M. Wiernik brenton@wiernik.org (ORCID) (@bmwiernik) contributors: Rémi Thériault remi.theriault@mail.mcgill.ca (ORCID) (@rempsyc) [contributor] Thomas J. Faulkenberry faulkenberry@tarleton.edu [reviewer] Robert Garrett rcg4@illinois.edu [reviewer]","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute group-meaned and de-meaned variables — demean","title":"Compute group-meaned and de-meaned variables — demean","text":"demean() computes group- de-meaned versions variable can used regression analysis model - within-subject effect. degroup() generic terms centering-operation. demean() always uses mean-centering, degroup() can also use mode median centering.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute group-meaned and de-meaned variables — demean","text":"","code":"demean(   x,   select,   group,   suffix_demean = \"_within\",   suffix_groupmean = \"_between\",   add_attributes = TRUE,   verbose = TRUE )  degroup(   x,   select,   group,   center = \"mean\",   suffix_demean = \"_within\",   suffix_groupmean = \"_between\",   add_attributes = TRUE,   verbose = TRUE )  detrend(   x,   select,   group,   center = \"mean\",   suffix_demean = \"_within\",   suffix_groupmean = \"_between\",   add_attributes = TRUE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute group-meaned and de-meaned variables — demean","text":"x data frame. select Character vector (formula) names variables select group- de-meaned. group Character vector (formula) name variable indicates group- cluster-ID. suffix_demean, suffix_groupmean String value, appended names group-meaned de-meaned variables x. default, de-meaned variables suffixed \"_within\" grouped-meaned variables \"_between\". add_attributes Logical, TRUE, returned variables gain attributes indicate within- -effects. relevant printing model_parameters() - cases, within- -effects printed separated blocks. verbose Toggle warnings messages. center Method centering. demean() always performs mean-centering, degroup() can use center = \"median\" center = \"mode\" median- mode-centering, also \"min\" \"max\".","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute group-meaned and de-meaned variables — demean","text":"data frame group-/de-meaned variables, get suffix \"_between\" (group-meaned variable) \"_within\" (de-meaned variable) default.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"heterogeneity-bias","dir":"Reference","previous_headings":"","what":"Heterogeneity Bias","title":"Compute group-meaned and de-meaned variables — demean","text":"Mixed models include different levels sources variability, .e. error terms level. macro-indicators (level-2 predictors, higher-level units, general: group-level predictors vary within across groups) included fixed effects (.e. treated covariate level-1), variance left unaccounted covariate absorbed error terms level-1 level-2 (Bafumi Gelman 2006; Gelman Hill 2007, Chapter 12.6.): “covariates contain two parts: one specific higher-level entity vary occasions, one represents difference occasions, within higher-level entities” (Bell et al. 2015). Hence, error terms correlated covariate, violates one assumptions mixed models (iid, independent identically distributed error terms). bias also called heterogeneity bias (Bell et al. 2015). resolve problem, level-2 predictors used (level-1) covariates separated \"within\" \"\" effects \"de-meaning\" \"group-meaning\": demeaning time-varying predictors, “higher level, mean term longer constrained Level 1 effects, free account higher-level variance associated variable” (Bell et al. 2015).","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"panel-data-and-correlating-fixed-and-group-effects","dir":"Reference","previous_headings":"","what":"Panel data and correlating fixed and group effects","title":"Compute group-meaned and de-meaned variables — demean","text":"demean() intended create group- de-meaned variables panel regression models (fixed effects models), complex random-effect-within-models (see Bell et al. 2015, 2018), group-effects (random effects) fixed effects correlate (see Bafumi Gelman 2006). can happen, instance, analyzing panel data, can lead Heterogeneity Bias. control correlating predictors group effects, recommended include group-meaned de-meaned version time-varying covariates (group-meaned version time-invariant covariates higher level, e.g. level-2 predictors) model. , one can fit complex multilevel models panel data, including time-varying predictors, time-invariant predictors random effects.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"why-mixed-models-are-preferred-over-fixed-effects-models","dir":"Reference","previous_headings":"","what":"Why mixed models are preferred over fixed effects models","title":"Compute group-meaned and de-meaned variables — demean","text":"mixed models approach can model causes endogeneity explicitly including (separated) within- -effects time-varying fixed effects including time-constant fixed effects. Furthermore, mixed models also include random effects, thus mixed models approach superior classic fixed-effects models, lack information variation group-effects -subject effects. Furthermore, fixed effects regression include random slopes, means fixed effects regressions neglecting “cross-cluster differences effects lower-level controls () reduces precision estimated context effects, resulting unnecessarily wide confidence intervals low statistical power” (Heisig et al. 2017).","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"terminology","dir":"Reference","previous_headings":"","what":"Terminology","title":"Compute group-meaned and de-meaned variables — demean","text":"group-meaned variable simply mean independent variable within group (id-level cluster) represented group. represents cluster-mean independent variable. regression coefficient group-meaned variable -subject-effect. de-meaned variable centered version group-meaned variable. De-meaning sometimes also called person-mean centering centering within clusters. regression coefficient de-meaned variable represents within-subject-effect.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-with-continuous-predictors","dir":"Reference","previous_headings":"","what":"De-meaning with continuous predictors","title":"Compute group-meaned and de-meaned variables — demean","text":"continuous time-varying predictors, recommendation include de-meaned group-meaned versions fixed effects, raw (untransformed) time-varying predictors . de-meaned predictor also included random effect (random slope). regression models, coefficient de-meaned predictors indicates within-subject effect, coefficient group-meaned predictor indicates -subject effect.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-with-binary-predictors","dir":"Reference","previous_headings":"","what":"De-meaning with binary predictors","title":"Compute group-meaned and de-meaned variables — demean","text":"binary time-varying predictors, two recommendations. First include raw (untransformed) binary predictor fixed effect de-meaned variable random effect (random slope). alternative add de-meaned version(s) binary time-varying covariates additional fixed effect well (instead adding random slope). Centering time-varying binary variables obtain within-effects (level 1) necessary. sensible interpretation left typical 0/1 format (Hoffmann 2015, chapter 8-2.). demean() thus coerce categorical time-varying predictors numeric compute de- group-meaned versions variables, raw (untransformed) binary predictor de-meaned version added model.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-of-factors-with-more-than-levels","dir":"Reference","previous_headings":"","what":"De-meaning of factors with more than 2 levels","title":"Compute group-meaned and de-meaned variables — demean","text":"Factors two levels demeaned two ways: first, also converted numeric de-meaned; second, dummy variables created (binary, 0/1 coding level) binary dummy-variables de-meaned way (described ). Packages like panelr internally convert factors dummies demeaning, behaviour can mimicked .","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"de-meaning-interaction-terms","dir":"Reference","previous_headings":"","what":"De-meaning interaction terms","title":"Compute group-meaned and de-meaned variables — demean","text":"multiple ways deal interaction terms within- -effects. classical approach simply use product term de-meaned variables (.e. introducing de-meaned variables interaction term model formula, e.g. y ~ x_within * time_within). approach, however, might subject bias (see Giesselmann & Schmidt-Catran 2020).  Another option first calculate product term apply de-meaning . approach produces estimator “reflects unit-level differences interacted variables whose moderators vary within units”, desirable within interaction two time-dependent variables required.  third option, interaction result genuine within estimator, \"double de-mean\" interaction terms (Giesselmann & Schmidt-Catran 2018), however, currently supported demean(). required, wmb() function panelr package used.  de-mean interaction terms within-models, simply specify term interaction select-argument, e.g. select = \"*b\" (see 'Examples').","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"analysing-panel-data-with-mixed-models-using-lme-","dir":"Reference","previous_headings":"","what":"Analysing panel data with mixed models using lme4","title":"Compute group-meaned and de-meaned variables — demean","text":"description translate formulas described Bell et al. 2018 R using lmer() lme4 can found vignette.","code":""},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute group-meaned and de-meaned variables — demean","text":"Bafumi J, Gelman . 2006. Fitting Multilevel Models Predictors Group Effects Correlate. . Philadelphia, PA: Annual meeting American Political Science Association. Bell , Fairbrother M, Jones K. 2019. Fixed Random Effects Models: Making Informed Choice. Quality & Quantity (53); 1051-1074 Bell , Jones K. 2015. Explaining Fixed Effects: Random Effects Modeling Time-Series Cross-Sectional Panel Data. Political Science Research Methods, 3(1), 133–153. Gelman , Hill J. 2007. Data Analysis Using Regression Multilevel/Hierarchical Models. Analytical Methods Social Research. Cambridge, New York: Cambridge University Press Giesselmann M, Schmidt-Catran, AW. 2020. Interactions fixed effects regression models. Sociological Methods & Research, 1–28. https://doi.org/10.1177/0049124120914934 Heisig JP, Schaeffer M, Giesecke J. 2017. Costs Simplicity: Multilevel Models May Benefit Accounting Cross-Cluster Differences Effects Controls. American Sociological Review 82 (4): 796–827. Hoffman L. 2015. Longitudinal analysis: modeling within-person fluctuation change. New York: Routledge","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/demean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute group-meaned and de-meaned variables — demean","text":"","code":"data(iris) iris$ID <- sample(1:4, nrow(iris), replace = TRUE) # fake-ID iris$binary <- as.factor(rbinom(150, 1, .35)) # binary variable  x <- demean(iris, select = c(\"Sepal.Length\", \"Petal.Length\"), group = \"ID\") head(x) #>   Sepal.Length_between Petal.Length_between Sepal.Length_within #> 1             5.809375             3.687500          -0.7093750 #> 2             5.692500             3.385000          -0.7925000 #> 3             5.809375             3.687500          -1.1093750 #> 4             5.692500             3.385000          -1.0925000 #> 5             5.895238             3.811905          -0.8952381 #> 6             5.980556             4.172222          -0.5805556 #>   Petal.Length_within #> 1           -2.287500 #> 2           -1.985000 #> 3           -2.387500 #> 4           -1.885000 #> 5           -2.411905 #> 6           -2.472222  x <- demean(iris, select = c(\"Sepal.Length\", \"binary\", \"Species\"), group = \"ID\") #> Categorical predictors (Species, binary) have been coerced to numeric #>   values to compute de- and group-meaned variables. head(x) #>   Sepal.Length_between Species_between binary_between Species_setosa_between #> 1             5.809375        0.968750      0.3125000              0.3437500 #> 2             5.692500        0.875000      0.2500000              0.4250000 #> 3             5.809375        0.968750      0.3125000              0.3437500 #> 4             5.692500        0.875000      0.2500000              0.4250000 #> 5             5.895238        1.047619      0.3333333              0.3571429 #> 6             5.980556        1.111111      0.4166667              0.1944444 #>   Species_versicolor_between Species_virginica_between Sepal.Length_within #> 1                  0.3437500                 0.3125000          -0.7093750 #> 2                  0.2750000                 0.3000000          -0.7925000 #> 3                  0.3437500                 0.3125000          -1.1093750 #> 4                  0.2750000                 0.3000000          -1.0925000 #> 5                  0.2380952                 0.4047619          -0.8952381 #> 6                  0.5000000                 0.3055556          -0.5805556 #>   Species_within binary_within Species_setosa_within Species_versicolor_within #> 1      -0.968750    -0.3125000             0.6562500                -0.3437500 #> 2      -0.875000    -0.2500000             0.5750000                -0.2750000 #> 3      -0.968750    -0.3125000             0.6562500                -0.3437500 #> 4      -0.875000     0.7500000             0.5750000                -0.2750000 #> 5      -1.047619     0.6666667             0.6428571                -0.2380952 #> 6      -1.111111    -0.4166667             0.8055556                -0.5000000 #>   Species_virginica_within #> 1               -0.3125000 #> 2               -0.3000000 #> 3               -0.3125000 #> 4               -0.3000000 #> 5               -0.4047619 #> 6               -0.3055556   # demean interaction term x*y dat <- data.frame(   a = c(1, 2, 3, 4, 1, 2, 3, 4),   x = c(4, 3, 3, 4, 1, 2, 1, 2),   y = c(1, 2, 1, 2, 4, 3, 2, 1),   ID = c(1, 2, 3, 1, 2, 3, 1, 2) ) demean(dat, select = c(\"a\", \"x*y\"), group = \"ID\") #>   a_between x_y_between   a_within x_y_within #> 1  2.666667    4.666667 -1.6666667 -0.6666667 #> 2  2.333333    4.000000 -0.3333333  2.0000000 #> 3  2.500000    4.500000  0.5000000 -1.5000000 #> 4  2.666667    4.666667  1.3333333  3.3333333 #> 5  2.333333    4.000000 -1.3333333  0.0000000 #> 6  2.500000    4.500000 -0.5000000  1.5000000 #> 7  2.666667    4.666667  0.3333333 -2.6666667 #> 8  2.333333    4.000000  1.6666667 -2.0000000  # or in formula-notation demean(dat, select = ~ a + x * y, group = ~ID) #>   a_between x_y_between   a_within x_y_within #> 1  2.666667    4.666667 -1.6666667 -0.6666667 #> 2  2.333333    4.000000 -0.3333333  2.0000000 #> 3  2.500000    4.500000  0.5000000 -1.5000000 #> 4  2.666667    4.666667  1.3333333  3.3333333 #> 5  2.333333    4.000000 -1.3333333  0.0000000 #> 6  2.500000    4.500000 -0.5000000  1.5000000 #> 7  2.666667    4.666667  0.3333333 -2.6666667 #> 8  2.333333    4.000000  1.6666667 -2.0000000"},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"Describe a distribution — describe_distribution","title":"Describe a distribution — describe_distribution","text":"function describes distribution set indices (e.g., measures centrality, dispersion, range, skewness, kurtosis).","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Describe a distribution — describe_distribution","text":"","code":"describe_distribution(x, ...)  # S3 method for numeric describe_distribution(   x,   centrality = \"mean\",   dispersion = TRUE,   iqr = TRUE,   range = TRUE,   quartiles = FALSE,   ci = NULL,   iterations = 100,   threshold = 0.1,   verbose = TRUE,   ... )  # S3 method for factor describe_distribution(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...)  # S3 method for data.frame describe_distribution(   x,   select = NULL,   exclude = NULL,   centrality = \"mean\",   dispersion = TRUE,   iqr = TRUE,   range = TRUE,   quartiles = FALSE,   include_factors = FALSE,   ci = NULL,   iterations = 100,   threshold = 0.1,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Describe a distribution — describe_distribution","text":"x numeric vector, character vector, data frame, list. See Details. ... Additional arguments passed methods. centrality point-estimates (centrality indices) compute. Character (vector) list one options: \"median\", \"mean\", \"MAP\" (see map_estimate()), \"trimmed\" (just mean(x, trim = threshold)), \"mode\" \"\". dispersion Logical, TRUE, computes indices dispersion related estimate(s) (SD MAD mean median, respectively). Dispersion available \"MAP\" \"mode\" centrality indices. iqr Logical, TRUE, interquartile range calculated (based stats::IQR(), using type = 6). range Return range (min max). quartiles Return first third quartiles (25th 75pth percentiles). ci Confidence Interval (CI) level. Default NULL, .e. confidence intervals computed. NULL, confidence intervals based bootstrap replicates (see iterations). centrality = \"\", bootstrapped confidence interval refers first centrality index (typically median). iterations number bootstrap replicates computing confidence intervals. applies ci NULL. threshold centrality = \"trimmed\" (.e. trimmed mean), indicates fraction (0 0.5) observations trimmed end vector mean computed. verbose Toggle warnings messages. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. include_factors Logical, TRUE, factors included output, however, columns range (first last factor levels) well n missing contain information. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Describe a distribution — describe_distribution","text":"data frame columns describe properties variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Describe a distribution — describe_distribution","text":"x data frame, numeric variables kept displayed summary. x list, behavior different whether x stored list. x stored (example, describe_distribution(mylist) mylist created ), artificial variable names used summary (Var_1, Var_2, etc.). x unstored list (example, describe_distribution(list(mtcars$mpg))), \"mtcars$mpg\" used variable name.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Describe a distribution — describe_distribution","text":"also plot()-method implemented see-package.","code":""},{"path":"https://easystats.github.io/datawizard/reference/describe_distribution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Describe a distribution — describe_distribution","text":"","code":"describe_distribution(rnorm(100)) #>  Mean |   SD |  IQR |         Range | Skewness | Kurtosis |   n | n_Missing #> --------------------------------------------------------------------------- #> -0.11 | 1.06 | 1.43 | [-3.51, 2.50] |    -0.17 |     0.50 | 100 |         0  data(iris) describe_distribution(iris) #> Variable     | Mean |   SD |  IQR |        Range | Skewness | Kurtosis |   n | n_Missing #> ---------------------------------------------------------------------------------------- #> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] |     0.31 |    -0.55 | 150 |         0 #> Sepal.Width  | 3.06 | 0.44 | 0.52 | [2.00, 4.40] |     0.32 |     0.23 | 150 |         0 #> Petal.Length | 3.76 | 1.77 | 3.52 | [1.00, 6.90] |    -0.27 |    -1.40 | 150 |         0 #> Petal.Width  | 1.20 | 0.76 | 1.50 | [0.10, 2.50] |    -0.10 |    -1.34 | 150 |         0 describe_distribution(iris, include_factors = TRUE, quartiles = TRUE) #> Variable     | Mean |   SD |  IQR |               Range |  Quartiles | Skewness | Kurtosis |   n | n_Missing #> ------------------------------------------------------------------------------------------------------------ #> Sepal.Length | 5.84 | 0.83 | 1.30 |          [4.3, 7.9] | 5.10, 6.40 |     0.31 |    -0.55 | 150 |         0 #> Sepal.Width  | 3.06 | 0.44 | 0.52 |            [2, 4.4] | 2.80, 3.30 |     0.32 |     0.23 | 150 |         0 #> Petal.Length | 3.76 | 1.77 | 3.52 |            [1, 6.9] | 1.60, 5.10 |    -0.27 |    -1.40 | 150 |         0 #> Petal.Width  | 1.20 | 0.76 | 1.50 |          [0.1, 2.5] | 0.30, 1.80 |    -0.10 |    -1.34 | 150 |         0 #> Species      |      |      |      | [setosa, virginica] |            |     0.00 |    -1.51 | 150 |         0 describe_distribution(list(mtcars$mpg, mtcars$cyl)) #> Variable   |  Mean |   SD |  IQR |          Range | Skewness | Kurtosis |  n | n_Missing #> ---------------------------------------------------------------------------------------- #> mtcars$mpg | 20.09 | 6.03 | 7.53 | [10.40, 33.90] |     0.67 |    -0.02 | 32 |         0 #> mtcars$cyl |  6.19 | 1.79 | 4.00 |   [4.00, 8.00] |    -0.19 |    -1.76 | 32 |         0"},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute mode for a statistical distribution — distribution_mode","title":"Compute mode for a statistical distribution — distribution_mode","text":"Compute mode statistical distribution","code":""},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute mode for a statistical distribution — distribution_mode","text":"","code":"distribution_mode(x)"},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute mode for a statistical distribution — distribution_mode","text":"x atomic vector, list, data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute mode for a statistical distribution — distribution_mode","text":"value appears frequently provided data. returned data structure entered one.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/distribution_mode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute mode for a statistical distribution — distribution_mode","text":"","code":"distribution_mode(c(1, 2, 3, 3, 4, 5)) #> [1] 3 distribution_mode(c(1.5, 2.3, 3.7, 3.7, 4.0, 5)) #> [1] 3.7"},{"path":"https://easystats.github.io/datawizard/reference/dot-is_deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","title":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","text":"Print message saying argument deprecated user use replacement instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/dot-is_deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","text":"","code":".is_deprecated(arg, replacement)"},{"path":"https://easystats.github.io/datawizard/reference/dot-is_deprecated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a message saying that an argument is deprecated and that the user should use its replacement instead. — .is_deprecated","text":"arg Argument deprecated replacement Argument replaces deprecated argument","code":""},{"path":"https://easystats.github.io/datawizard/reference/efc.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample dataset from the EFC Survey — efc","title":"Sample dataset from the EFC Survey — efc","text":"Selected variables EUROFAMCARE survey. Useful testing \"real-life\" data sets, including random missing values. data set also value variable label attributes.","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":null,"dir":"Reference","previous_headings":"","what":"Find or get columns in a data frame based on search patterns — find_columns","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"find_columns() returns column names data set match certain search pattern, get_columns() returns found data. data_select() alias get_columns(), data_find() alias find_columns().","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"","code":"find_columns(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_find(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  get_columns(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  data_select(   data,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"data data frame. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings. ... Arguments passed functions. Mostly used yet.","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"find_columns() returns character vector column names matched pattern select exclude, NULL matching column name found. get_columns() returns data frame matching columns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"Note possible either pass entire select helper pattern inside select helper function argument:   means also possible use loop values arguments patterns:   However, behavior limited \"single-level function\". work nested functions, like :   case, better pass whole select helper argument outer():","code":"foo <- function(data, pattern) {   find_columns(data, select = starts_with(pattern)) } foo(iris, pattern = \"Sep\")  foo2 <- function(data, pattern) {   find_columns(data, select = pattern) } foo2(iris, pattern = starts_with(\"Sep\")) for (i in c(\"Sepal\", \"Sp\")) {   head(iris) |>     find_columns(select = starts_with(i)) |>     print() } inner <- function(data, arg) {   find_columns(data, select = arg) } outer <- function(data, arg) {   inner(data, starts_with(arg)) } outer(iris, \"Sep\") outer <- function(data, arg) {   inner(data, arg) } outer(iris, starts_with(\"Sep\"))"},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/find_columns.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find or get columns in a data frame based on search patterns — find_columns","text":"","code":"# Find columns names by pattern find_columns(iris, starts_with(\"Sepal\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  find_columns(iris, ends_with(\"Width\")) #> [1] \"Sepal.Width\" \"Petal.Width\" find_columns(iris, regex(\"\\\\.\")) #> [1] \"Sepal.Length\" \"Sepal.Width\"  \"Petal.Length\" \"Petal.Width\"  find_columns(iris, c(\"Petal.Width\", \"Sepal.Length\")) #> [1] \"Petal.Width\"  \"Sepal.Length\"  # starts with \"Sepal\", but not allowed to end with \"width\" find_columns(iris, starts_with(\"Sepal\"), exclude = contains(\"Width\")) #> [1] \"Sepal.Length\"  # find numeric with mean > 3.5 numeric_mean_35 <- function(x) is.numeric(x) && mean(x, na.rm = TRUE) > 3.5 find_columns(iris, numeric_mean_35) #> [1] \"Sepal.Length\" \"Petal.Length\""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert value labels into factor levels — labels_to_levels","title":"Convert value labels into factor levels — labels_to_levels","text":"Convert value labels factor levels","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert value labels into factor levels — labels_to_levels","text":"","code":"labels_to_levels(x, ...)  # S3 method for factor labels_to_levels(x, verbose = TRUE, ...)  # S3 method for data.frame labels_to_levels(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   append = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert value labels into factor levels — labels_to_levels","text":"x data frame factor. variable types (e.g. numerics) allowed. ... Currently used. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert value labels into factor levels — labels_to_levels","text":"x, factors former levels replaced value labels.","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert value labels into factor levels — labels_to_levels","text":"labels_to_levels() allows use value labels factors levels.","code":""},{"path":"https://easystats.github.io/datawizard/reference/labels_to_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert value labels into factor levels — labels_to_levels","text":"","code":"data(efc) # create factor x <- as.factor(efc$c172code) # add value labels - these are not factor levels yet x <- assign_labels(x, values = c(`1` = \"low\", `2` = \"mid\", `3` = \"high\")) levels(x) #> [1] \"1\" \"2\" \"3\" data_tabulate(x) #> x <categorical> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> 1     |  8 |  8.00 |    8.89 |         8.89 #> 2     | 66 | 66.00 |   73.33 |        82.22 #> 3     | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>  x <- labels_to_levels(x) levels(x) #> [1] \"low\"  \"mid\"  \"high\" data_tabulate(x) #> x <categorical> #> # total N=100 valid N=90 #>  #> Value |  N | Raw % | Valid % | Cumulative % #> ------+----+-------+---------+------------- #> low   |  8 |  8.00 |    8.89 |         8.89 #> mid   | 66 | 66.00 |   73.33 |        82.22 #> high  | 16 | 16.00 |   17.78 |       100.00 #> <NA>  | 10 | 10.00 |    <NA> |         <NA>"},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"function allows use () datawizard's transformers inside model formula. See examples .  Currently, center(), standardize(), normalize(), & rescale() supported.","code":""},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"","code":"# S3 method for dw_transformer makepredictcall(var, call)"},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"var variable. call term formula, call.","code":""},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"replacement call predvars attribute   terms.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Utility Function for Safe Prediction with datawizard transformers — makepredictcall.dw_transformer","text":"","code":"data(\"mtcars\") train <- mtcars[1:30, ] test <- mtcars[31:32, ]  m1 <- lm(mpg ~ center(hp), data = train) predict(m1, newdata = test) # Data is \"centered\" before the prediction is made, #> Maserati Bora    Volvo 142E  #>      4.269496     22.911189  # according to the center of the old data  m2 <- lm(mpg ~ standardize(hp), data = train) m3 <- lm(mpg ~ scale(hp), data = train) # same as above predict(m2, newdata = test) # Data is \"standardized\" before the prediction is made. #> Maserati Bora    Volvo 142E  #>      4.269496     22.911189  predict(m3, newdata = test) # Data is \"standardized\" before the prediction is made. #> Maserati Bora    Volvo 142E  #>      4.269496     22.911189    m4 <- lm(mpg ~ normalize(hp), data = mtcars) m5 <- lm(mpg ~ rescale(hp, to = c(-3, 3)), data = mtcars)  (newdata <- data.frame(hp = c(range(mtcars$hp), 400))) # 400 is outside original range! #>    hp #> 1  52 #> 2 335 #> 3 400  model.frame(delete.response(terms(m4)), data = newdata) #>   normalize(hp) #> 1      0.000000 #> 2      1.000000 #> 3      1.229682 model.frame(delete.response(terms(m5)), data = newdata) #>   rescale(hp, to = c(-3, 3)) #> 1                  -3.000000 #> 2                   3.000000 #> 3                   4.378092"},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Helpers — mean_sd","title":"Summary Helpers — mean_sd","text":"Summary Helpers","code":""},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Helpers — mean_sd","text":"","code":"mean_sd(x, times = 1L, remove_na = TRUE, named = TRUE, na.rm = TRUE, ...)  median_mad(   x,   times = 1L,   remove_na = TRUE,   constant = 1.4826,   named = TRUE,   na.rm = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Helpers — mean_sd","text":"x numeric vector (one can coerced one via .numeric()) summarized. times many SDs Mean (MADs around Median) remove_na Logical. NA values removed computing (TRUE) (FALSE, default)? named vector named? (E.g., c(\"-SD\" = -1, Mean = 1, \"+SD\" = 2).) na.rm Deprecated. Please use remove_na instead. ... used. constant scale factor.","code":""},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Helpers — mean_sd","text":"(possibly named) numeric vector length 2*times + 1 SDs mean, mean, SDs mean (median MAD).","code":""},{"path":"https://easystats.github.io/datawizard/reference/mean_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Helpers — mean_sd","text":"","code":"mean_sd(mtcars$mpg) #>      -SD     Mean      +SD  #> 14.06368 20.09062 26.11757   mean_sd(mtcars$mpg, times = 2L) #>     -2 SD     -1 SD      Mean     +1 SD     +2 SD  #>  8.036729 14.063677 20.090625 26.117573 32.144521   median_mad(mtcars$mpg) #>     -MAD   Median     +MAD  #> 13.78851 19.20000 24.61149"},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of mean values by group — means_by_group","title":"Summary of mean values by group — means_by_group","text":"Computes summary table means groups.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary of mean values by group — means_by_group","text":"","code":"means_by_group(x, ...)  # S3 method for numeric means_by_group(x, group = NULL, ci = 0.95, weights = NULL, digits = NULL, ...)  # S3 method for data.frame means_by_group(   x,   select = NULL,   group = NULL,   ci = 0.95,   weights = NULL,   digits = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary of mean values by group — means_by_group","text":"x vector data frame. ... Currently used group x numeric vector, group factor indicates group-classifying categories. x data frame, group character string, naming variable x used grouping. Numeric vectors coerced factors. group refer single variable. ci Level confidence interval mean estimates. Default 0.95. Use ci = NA suppress confidence intervals. weights x numeric vector, weights vector weights applied weight observations. x data frame, weights can also character string indicating name variable x used weighting. Default NULL, weights used. digits Optional scalar, indicating amount digits decimal point rounding estimates values. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary of mean values by group — means_by_group","text":"data frame information mean summary statistics sub-group.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summary of mean values by group — means_by_group","text":"function comparable aggregate(x, group, mean), provides information, including summary statistics One-Way-ANOVA using x dependent group independent variable. emmeans::contrast() used get p-values sub-group. P-values indicate whether group-mean significantly different total mean.","code":""},{"path":"https://easystats.github.io/datawizard/reference/means_by_group.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary of mean values by group — means_by_group","text":"","code":"data(efc) means_by_group(efc, \"c12hour\", \"e42dep\") #> # Mean of average number of hours of care per week by elder's dependency #>  #> Category             |   Mean |  N |    SD |           95% CI |      p #> ---------------------------------------------------------------------- #> independent          |  17.00 |  2 | 11.31 | [-68.46, 102.46] | 0.573  #> slightly dependent   |  34.25 |  4 | 29.97 | [-26.18,  94.68] | 0.626  #> moderately dependent |  52.75 | 28 | 51.83 | [ 29.91,  75.59] | > .999 #> severely dependent   | 106.97 | 63 | 65.88 | [ 91.74, 122.19] | 0.001  #> Total                |  86.46 | 97 | 66.40 |                  |        #>  #> Anova: R2=0.186; adj.R2=0.160; F=7.098; p<.001  data(iris) means_by_group(iris, \"Sepal.Width\", \"Species\") #> # Mean of Sepal.Width by Species #>  #> Category   | Mean |   N |   SD |       95% CI |      p #> ------------------------------------------------------ #> setosa     | 3.43 |  50 | 0.38 | [3.33, 3.52] | < .001 #> versicolor | 2.77 |  50 | 0.31 | [2.68, 2.86] | < .001 #> virginica  | 2.97 |  50 | 0.32 | [2.88, 3.07] | 0.035  #> Total      | 3.06 | 150 | 0.44 |              |        #>  #> Anova: R2=0.401; adj.R2=0.393; F=49.160; p<.001  # weighting efc$weight <- abs(rnorm(n = nrow(efc), mean = 1, sd = .5)) means_by_group(efc, \"c12hour\", \"e42dep\", weights = \"weight\") #> # Mean of average number of hours of care per week by elder's dependency #>  #> Category             |   Mean |  N |    SD |           95% CI |     p #> --------------------------------------------------------------------- #> independent          |  19.00 |  1 | 11.31 | [-84.29, 122.30] | 0.685 #> slightly dependent   |  32.41 |  3 | 29.36 | [-34.71,  99.53] | 0.685 #> moderately dependent |  53.32 | 30 | 51.24 | [ 30.70,  75.93] | 0.907 #> severely dependent   | 100.17 | 65 | 66.62 | [ 84.84, 115.50] | 0.018 #> Total                |  82.66 | 97 | 65.34 |                  |       #>  #> Anova: R2=0.143; adj.R2=0.115; F=5.163; p=0.002"},{"path":"https://easystats.github.io/datawizard/reference/nhanes_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample dataset from the National Health and Nutrition Examination Survey — nhanes_sample","title":"Sample dataset from the National Health and Nutrition Examination Survey — nhanes_sample","text":"Selected variables National Health Nutrition Examination Survey used example Lumley (2010), Appendix E.","code":""},{"path":"https://easystats.github.io/datawizard/reference/nhanes_sample.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample dataset from the National Health and Nutrition Examination Survey — nhanes_sample","text":"Lumley T (2010). Complex Surveys: guide analysis using R. Wiley","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize numeric variable to 0-1 range — normalize","title":"Normalize numeric variable to 0-1 range — normalize","text":"Performs normalization data, .e., scales variables range 0 - 1. special case rescale(). unnormalize() counterpart, works variables normalized normalize().","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize numeric variable to 0-1 range — normalize","text":"","code":"normalize(x, ...)  # S3 method for numeric normalize(x, include_bounds = TRUE, verbose = TRUE, ...)  # S3 method for data.frame normalize(   x,   select = NULL,   exclude = NULL,   include_bounds = TRUE,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  unnormalize(x, ...)  # S3 method for numeric unnormalize(x, verbose = TRUE, ...)  # S3 method for data.frame unnormalize(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  # S3 method for grouped_df unnormalize(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize numeric variable to 0-1 range — normalize","text":"x numeric vector, (grouped) data frame, matrix. See 'Details'. ... Arguments passed methods. include_bounds Numeric logical. Using can useful case beta-regression, response variable allowed include zeros ones. TRUE, input normalized range includes zero one. FALSE, return value compressed, using Smithson Verkuilen's (2006) formula (x * (n - 1) + 0.5) / n, avoid zeros ones normalized variables. Else, numeric (e.g., 0.001), include_bounds defines \"distance\" lower upper bound, .e. normalized vectors rescaled range 0 + include_bounds 1 - include_bounds. verbose Toggle warnings messages . select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, standardized variables get new column names (suffix \"_z\") appended (column bind) x, thus returning original standardized variables. FALSE, original variables x overwritten standardized versions. character value, standardized variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize numeric variable to 0-1 range — normalize","text":"normalized object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Normalize numeric variable to 0-1 range — normalize","text":"x matrix, normalization performed across values (column- row-wise). column-wise normalization, convert matrix data.frame. x grouped data frame (grouped_df), normalization performed separately group.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Normalize numeric variable to 0-1 range — normalize","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Normalize numeric variable to 0-1 range — normalize","text":"Smithson M, Verkuilen J (2006). Better Lemon Squeezer? Maximum-Likelihood Regression Beta-Distributed Dependent Variables. Psychological Methods, 11(1), 54–71.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/normalize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalize numeric variable to 0-1 range — normalize","text":"","code":"normalize(c(0, 1, 5, -5, -2)) #> [1] 0.5 0.6 1.0 0.0 0.3 #> (original range = -5 to 5) #>  normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE) #> [1] 0.50 0.58 0.90 0.10 0.34 #> (original range = -5 to 5) #>  # use a value defining the bounds normalize(c(0, 1, 5, -5, -2), include_bounds = .001) #> [1] 0.5000 0.5998 0.9990 0.0010 0.3004 #> (original range = -5 to 5) #>   head(normalize(trees)) #>        Girth     Height      Volume #> 1 0.00000000 0.29166667 0.001497006 #> 2 0.02439024 0.08333333 0.001497006 #> 3 0.04065041 0.00000000 0.000000000 #> 4 0.17886179 0.37500000 0.092814371 #> 5 0.19512195 0.75000000 0.128742515 #> 6 0.20325203 0.83333333 0.142215569"},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":null,"dir":"Reference","previous_headings":"","what":"(Signed) rank transformation — ranktransform","title":"(Signed) rank transformation — ranktransform","text":"Transform numeric values integers rank (.e., 1st smallest, 2nd smallest, 3rd smallest, etc.). Setting sign argument TRUE give signed ranks, ranking done according absolute size sign preserved (.e., 2, 1, -3, 4).","code":""},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Signed) rank transformation — ranktransform","text":"","code":"ranktransform(x, ...)  # S3 method for numeric ranktransform(x, sign = FALSE, method = \"average\", verbose = TRUE, ...)  # S3 method for data.frame ranktransform(   x,   select = NULL,   exclude = NULL,   sign = FALSE,   method = \"average\",   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Signed) rank transformation — ranktransform","text":"x Object. ... Arguments passed methods. sign Logical, TRUE, return signed ranks. method Treatment ties. Can one \"average\" (default), \"first\", \"last\", \"random\", \"max\" \"min\". See rank() details. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"(Signed) rank transformation — ranktransform","text":"rank-transformed object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"(Signed) rank transformation — ranktransform","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/ranktransform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Signed) rank transformation — ranktransform","text":"","code":"ranktransform(c(0, 1, 5, -5, -2)) #> [1] 3 4 5 1 2  # Won't work # ranktransform(c(0, 1, 5, -5, -2), sign = TRUE)  head(ranktransform(trees)) #>   Girth Height Volume #> 1     1    6.0    2.5 #> 2     2    3.0    2.5 #> 3     3    1.0    1.0 #> 4     4    8.5    5.0 #> 5     5   25.5    7.0 #> 6     6   28.0    9.0"},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode values from one or more variables into a new variable — recode_into","title":"Recode values from one or more variables into a new variable — recode_into","text":"functions recodes values one variables new variable. convenient function avoid nested ifelse() statements, similar dplyr::case_when().","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode values from one or more variables into a new variable — recode_into","text":"","code":"recode_into(   ...,   data = NULL,   default = NA,   overwrite = TRUE,   preserve_na = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode values from one or more variables into a new variable — recode_into","text":"... sequence two-sided formulas, left hand side (LHS) logical matching condition determines values match case. LHS formula also called \"recode pattern\" (e.g., messages). right hand side (RHS) indicates replacement value. data Optional, name data frame. can used avoid writing data name multiple times .... See 'Examples'. default Indicates default value chosen match formulas ... found. provided, NA used default value. overwrite Logical, TRUE (default) one recode pattern apply case, already recoded values overwritten subsequent recode patterns. FALSE, former recoded cases altered later recode patterns apply cases . warning message printed alert situations avoid unintentional recodings. preserve_na Logical, TRUE default NA, missing values original variable set back NA recoded variable (unless overwritten recode patterns). FALSE, missing values original variable recoded default. Setting preserve_na = TRUE prevents unintentional overwriting missing values default, means find valid values original data missing values. See 'Examples'. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode values from one or more variables into a new variable — recode_into","text":"vector recoded values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_into.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode values from one or more variables into a new variable — recode_into","text":"","code":"x <- 1:30 recode_into(   x > 15 ~ \"a\",   x > 10 & x <= 15 ~ \"b\",   default = \"c\" ) #>  [1] \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"c\" \"b\" \"b\" \"b\" \"b\" \"b\" \"a\" \"a\" \"a\" \"a\" #> [20] \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\" \"a\"  x <- 1:10 # default behaviour: second recode pattern \"x > 5\" overwrites # some of the formerly recoded cases from pattern \"x >= 3 & x <= 7\" recode_into(   x >= 3 & x <= 7 ~ 1,   x > 5 ~ 2,   default = 0,   verbose = FALSE ) #>  [1] 0 0 1 1 1 2 2 2 2 2  # setting \"overwrite = FALSE\" will not alter formerly recoded cases recode_into(   x >= 3 & x <= 7 ~ 1,   x > 5 ~ 2,   default = 0,   overwrite = FALSE,   verbose = FALSE ) #>  [1] 0 0 1 1 1 1 1 2 2 2  set.seed(123) d <- data.frame(   x = sample(1:5, 30, TRUE),   y = sample(letters[1:5], 30, TRUE),   stringsAsFactors = FALSE )  # from different variables into new vector recode_into(   d$x %in% 1:3 & d$y %in% c(\"a\", \"b\") ~ 1,   d$x > 3 ~ 2,   default = 0 ) #>  [1] 1 1 1 0 0 2 2 0 1 1 2 0 0 0 2 1 1 2 1 0 1 1 0 2 0 1 2 2 1 2  # no need to write name of data frame each time recode_into(   x %in% 1:3 & y %in% c(\"a\", \"b\") ~ 1,   x > 3 ~ 2,   data = d,   default = 0 ) #>  [1] 1 1 1 0 0 2 2 0 1 1 2 0 0 0 2 1 1 2 1 0 1 1 0 2 0 1 2 2 1 2  # handling of missing values d <- data.frame(   x = c(1, NA, 2, NA, 3, 4),   y = c(1, 11, 3, NA, 5, 6) ) # first NA in x is overwritten by valid value from y # we have no known value for second NA in x and y, # thus we get one NA in the result recode_into(   x <= 3 ~ 1,   y > 5 ~ 2,   data = d,   default = 0,   preserve_na = TRUE ) #> [1]  1  2  1 NA  1  2 # first NA in x is overwritten by valid value from y # default value is used for second NA recode_into(   x <= 3 ~ 1,   y > 5 ~ 2,   data = d,   default = 0,   preserve_na = FALSE ) #> Missing values in original variable are overwritten by default value. If #>   you want to preserve missing values, set `preserve_na = TRUE`. #> [1] 1 2 1 0 1 2"},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode old values of variables into new values — recode_values","title":"Recode old values of variables into new values — recode_values","text":"functions recodes old values new values can used recode numeric character vectors, factors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode old values of variables into new values — recode_values","text":"","code":"recode_values(x, ...)  # S3 method for numeric recode_values(   x,   recode = NULL,   default = NULL,   preserve_na = TRUE,   verbose = TRUE,   ... )  # S3 method for data.frame recode_values(   x,   select = NULL,   exclude = NULL,   recode = NULL,   default = NULL,   preserve_na = TRUE,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  change_code(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode old values of variables into new values — recode_values","text":"x data frame, numeric character vector, factor. ... used. recode list named vectors, indicate recode pairs. names list-elements (.e. left-hand side) represent new values, values list-elements indicate original (old) values replaced. recoding numeric vectors, element names surrounded backticks. example, recode=list(`0`=1) recode 1 0 numeric vector. See also 'Examples' 'Details'. default Defines default value values match recode-pairs. Note , preserve_na=FALSE, missing values (NA) also captured default argument, thus also recoded specified value. See 'Examples' 'Details'. preserve_na Logical, TRUE, NA (missing values) preserved. overrides arguments, including default. Hence, preserve_na=TRUE, default longer convert NA specified default value. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode old values of variables into new values — recode_values","text":"x, old values replaced new values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Recode old values of variables into new values — recode_values","text":"section describes pattern recode arguments, also provides shortcuts, particular recoding numeric values. Single values Single values either need wrapped backticks (case numeric values) \"\" (character factor levels). Example: recode=list(`0`=1,`1`=2) recode 1 0, 2 1. factors character vectors, example : recode=list(x=\"\",y=\"b\") (recode \"\" \"x\" \"b\" \"y\"). Multiple values Multiple values recoded new value can separated comma. Example: recode=list(`1`=c(1,4),`2`=c(2,3)) recode values 1 4 1, 2 3 2. also possible define  old values character string, like:  recode=list(`1`=\"1,4\",`2`=\"2,3\") factors character vectors, example : recode=list(x=c(\"\",\"b\"),y=c(\"c\",\"d\")). Value range Numeric value ranges can defined using :. Example: recode=list(`1`=1:3,`2`=4:6) recode values 1 3 1, 4 6 2. min max placeholder use minimum maximum value (numeric) variable. Useful, e.g., recoding ranges values. Example: recode=list(`1`=\"min:10\",`2`=\"11:max\"). default values default argument defines default value values match recode-pairs. example, recode=list(`1`=c(1,2),`2`=c(3,4)), default=9 recode values 1 2 1, 3 4 2, values 9. preserve_na set FALSE, NA (missing values) also recoded specified default value. Reversing rescaling See reverse() rescale().","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Recode old values of variables into new values — recode_values","text":"can use options(data_recode_pattern = \"old=new\") switch behaviour recode-argument, .e. recode-pairs now following pattern old values = new values, e.g. getOption(\"data_recode_pattern\") set \"old=new\", recode(`1`=0) recode 1 0. default recode(`1`=0) recode 0 1.","code":""},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Recode old values of variables into new values — recode_values","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/recode_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode old values of variables into new values — recode_values","text":"","code":"# numeric ---------- set.seed(123) x <- sample(c(1:4, NA), 15, TRUE) table(x, useNA = \"always\") #> x #>    1    2    3    4 <NA>  #>    2    3    6    2    2   out <- recode_values(x, list(`0` = 1, `1` = 2:3, `2` = 4)) out #>  [1]  1  1  1  1  1 NA  2  0  1  1 NA  1  1  0  2 table(out, useNA = \"always\") #> out #>    0    1    2 <NA>  #>    2    9    2    2   # to recode NA values, set preserve_na to FALSE out <- recode_values(   x,   list(`0` = 1, `1` = 2:3, `2` = 4, `9` = NA),   preserve_na = FALSE ) out #>  [1] 1 1 1 1 1 9 2 0 1 1 9 1 1 0 2 table(out, useNA = \"always\") #> out #>    0    1    2    9 <NA>  #>    2    9    2    2    0   # preserve na ---------- out <- recode_values(x, list(`0` = 1, `1` = 2:3), default = 77) out #>  [1]  1  1  1  1  1 NA 77  0  1  1 NA  1  1  0 77 table(out, useNA = \"always\") #> out #>    0    1   77 <NA>  #>    2    9    2    2   # recode na into default ---------- out <- recode_values(   x,   list(`0` = 1, `1` = 2:3),   default = 77,   preserve_na = FALSE ) out #>  [1]  1  1  1  1  1 77 77  0  1  1 77  1  1  0 77 table(out, useNA = \"always\") #> out #>    0    1   77 <NA>  #>    2    9    4    0    # factors (character vectors are similar) ---------- set.seed(123) x <- as.factor(sample(c(\"a\", \"b\", \"c\"), 15, TRUE)) table(x) #> x #> a b c  #> 2 7 6   out <- recode_values(x, list(x = \"a\", y = c(\"b\", \"c\"))) out #>  [1] y y y y y y y y y x y y x y y #> Levels: x y table(out) #> out #>  x  y  #>  2 13   out <- recode_values(x, list(x = \"a\", y = \"b\", z = \"c\")) out #>  [1] z z z y z y y y z x y y x y z #> Levels: x y z table(out) #> out #> x y z  #> 2 7 6   out <- recode_values(x, list(y = \"b,c\"), default = 77) # same as # recode_values(x, list(y = c(\"b\", \"c\")), default = 77) out #>  [1] y  y  y  y  y  y  y  y  y  77 y  y  77 y  y  #> Levels: 77 y table(out) #> out #> 77  y  #>  2 13    # data frames ---------- set.seed(123) d <- data.frame(   x = sample(c(1:4, NA), 12, TRUE),   y = as.factor(sample(c(\"a\", \"b\", \"c\"), 12, TRUE)),   stringsAsFactors = FALSE )  recode_values(   d,   recode = list(`0` = 1, `1` = 2:3, `2` = 4, x = \"a\", y = c(\"b\", \"c\")),   append = TRUE ) #>     x y x_r y_r #> 1   3 c   1   y #> 2   3 a   1   x #> 3   2 a   1   x #> 4   2 a   1   x #> 5   3 a   1   x #> 6  NA c  NA   y #> 7   4 b   2   y #> 8   1 c   0   y #> 9   2 b   1   y #> 10  3 a   1   x #> 11 NA b  NA   y #> 12  3 c   1   y   # switch recode pattern to \"old=new\" ---------- options(data_recode_pattern = \"old=new\")  # numeric set.seed(123) x <- sample(c(1:4, NA), 15, TRUE) table(x, useNA = \"always\") #> x #>    1    2    3    4 <NA>  #>    2    3    6    2    2   out <- recode_values(x, list(`1` = 0, `2:3` = 1, `4` = 2)) table(out, useNA = \"always\") #> out #>    0    1    2 <NA>  #>    2    9    2    2   # factors (character vectors are similar) set.seed(123) x <- as.factor(sample(c(\"a\", \"b\", \"c\"), 15, TRUE)) table(x) #> x #> a b c  #> 2 7 6   out <- recode_values(x, list(a = \"x\", `b, c` = \"y\")) table(out) #> out #>  x  y  #>  2 13   # reset options options(data_recode_pattern = NULL)"},{"path":"https://easystats.github.io/datawizard/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. insight print_html, print_md","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Return or remove variables or observations that are completely missing — remove_empty","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"functions check rows columns data frame completely contain missing values, .e. observations variables completely missing values, either (1) returns indices; (2) removes data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"","code":"empty_columns(x)  empty_rows(x)  remove_empty_columns(x)  remove_empty_rows(x)  remove_empty(x)"},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"x data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"empty_columns() empty_rows(), numeric (named) vector row column indices variables completely missing values. remove_empty_columns() remove_empty_rows(), data frame \"empty\" columns rows removed, respectively. remove_empty(), empty rows columns removed.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"character vectors, empty string values (.e. \"\") also considered missing value. Thus, character vector contains NA \"\"``, considered empty variable removed. applies observations (rows) contain NAor\"\"`.","code":""},{"path":"https://easystats.github.io/datawizard/reference/remove_empty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return or remove variables or observations that are completely missing — remove_empty","text":"","code":"tmp <- data.frame(   a = c(1, 2, 3, NA, 5),   b = c(1, NA, 3, NA, 5),   c = c(NA, NA, NA, NA, NA),   d = c(1, NA, 3, NA, 5) )  tmp #>    a  b  c  d #> 1  1  1 NA  1 #> 2  2 NA NA NA #> 3  3  3 NA  3 #> 4 NA NA NA NA #> 5  5  5 NA  5  # indices of empty columns or rows empty_columns(tmp) #> c  #> 3  empty_rows(tmp) #> [1] 4  # remove empty columns or rows remove_empty_columns(tmp) #>    a  b  d #> 1  1  1  1 #> 2  2 NA NA #> 3  3  3  3 #> 4 NA NA NA #> 5  5  5  5 remove_empty_rows(tmp) #>   a  b  c  d #> 1 1  1 NA  1 #> 2 2 NA NA NA #> 3 3  3 NA  3 #> 5 5  5 NA  5  # remove empty columns and rows remove_empty(tmp) #>   a  b  d #> 1 1  1  1 #> 2 2 NA NA #> 3 3  3  3 #> 5 5  5  5  # also remove \"empty\" character vectors tmp <- data.frame(   a = c(1, 2, 3, NA, 5),   b = c(1, NA, 3, NA, 5),   c = c(\"\", \"\", \"\", \"\", \"\"),   stringsAsFactors = FALSE ) empty_columns(tmp) #> c  #> 3"},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert infinite or NaN values into NA — replace_nan_inf","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"Replaces infinite (Inf -Inf) NaN values NA.","code":""},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"","code":"replace_nan_inf(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"x vector dataframe ... Currently used.","code":""},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"Data Inf, -Inf, NaN converted NA.","code":""},{"path":"https://easystats.github.io/datawizard/reference/replace_nan_inf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert infinite or NaN values into NA — replace_nan_inf","text":"","code":"# a vector x <- c(1, 2, NA, 3, NaN, 4, NA, 5, Inf, -Inf, 6, 7) replace_nan_inf(x) #>  [1]  1  2 NA  3 NA  4 NA  5 NA NA  6  7  # a data frame df <- data.frame(   x = c(1, NA, 5, Inf, 2, NA),   y = c(3, NaN, 4, -Inf, 6, 7),   stringsAsFactors = FALSE ) replace_nan_inf(df) #>    x  y #> 1  1  3 #> 2 NA NA #> 3  5  4 #> 4 NA NA #> 5  2  6 #> 6 NA  7"},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale Variables to a New Range — rescale","title":"Rescale Variables to a New Range — rescale","text":"Rescale variables new range. Can also used reverse-score variables (change keying/scoring direction).","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale Variables to a New Range — rescale","text":"","code":"rescale(x, ...)  change_scale(x, ...)  # S3 method for numeric rescale(x, to = c(0, 100), range = NULL, verbose = TRUE, ...)  # S3 method for data.frame rescale(   x,   select = NULL,   exclude = NULL,   to = c(0, 100),   range = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale Variables to a New Range — rescale","text":"x (grouped) data frame, numeric vector factor. ... Arguments passed methods. Numeric vector length 2 giving new range variable rescaling. reverse-score variable, range given maximum value first. See examples. range Initial (old) range values. NULL, take range input vector (range(x)). verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale Variables to a New Range — rescale","text":"rescaled object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Rescale Variables to a New Range — rescale","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/rescale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale Variables to a New Range — rescale","text":"","code":"rescale(c(0, 1, 5, -5, -2)) #> [1]  50  60 100   0  30 #> (original range = -5 to 5) #>  rescale(c(0, 1, 5, -5, -2), to = c(-5, 5)) #> [1]  0  1  5 -5 -2 #> (original range = -5 to 5) #>  rescale(c(1, 2, 3, 4, 5), to = c(-2, 2)) #> [1] -2 -1  0  1  2 #> (original range = 1 to 5) #>   # Specify the \"theoretical\" range of the input vector rescale(c(1, 3, 4), to = c(0, 40), range = c(0, 4)) #> [1] 10 30 40 #> (original range = 0 to 4) #>   # Reverse-score a variable rescale(c(1, 2, 3, 4, 5), to = c(5, 1)) #> [1] 5 4 3 2 1 #> (original range = 1 to 5) #>  rescale(c(1, 2, 3, 4, 5), to = c(2, -2)) #> [1]  2  1  0 -1 -2 #> (original range = 1 to 5) #>   # Data frames head(rescale(iris, to = c(0, 1))) #> Variables of class `factor` can't be rescaled and remain unchanged. #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   0.22222222   0.6250000   0.06779661  0.04166667  setosa #> 2   0.16666667   0.4166667   0.06779661  0.04166667  setosa #> 3   0.11111111   0.5000000   0.05084746  0.04166667  setosa #> 4   0.08333333   0.4583333   0.08474576  0.04166667  setosa #> 5   0.19444444   0.6666667   0.06779661  0.04166667  setosa #> 6   0.30555556   0.7916667   0.11864407  0.12500000  setosa head(rescale(iris, to = c(0, 1), select = \"Sepal.Length\")) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   0.22222222         3.5          1.4         0.2  setosa #> 2   0.16666667         3.0          1.4         0.2  setosa #> 3   0.11111111         3.2          1.3         0.2  setosa #> 4   0.08333333         3.1          1.5         0.2  setosa #> 5   0.19444444         3.6          1.4         0.2  setosa #> 6   0.30555556         3.9          1.7         0.4  setosa  # One can specify a list of ranges head(rescale(iris, to = list(   \"Sepal.Length\" = c(0, 1),   \"Petal.Length\" = c(-1, 0) ))) #> Variables of class `factor` can't be rescaled and remain unchanged. #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1   0.22222222         3.5   -0.9322034         0.2  setosa #> 2   0.16666667         3.0   -0.9322034         0.2  setosa #> 3   0.11111111         3.2   -0.9491525         0.2  setosa #> 4   0.08333333         3.1   -0.9152542         0.2  setosa #> 5   0.19444444         3.6   -0.9322034         0.2  setosa #> 6   0.30555556         3.9   -0.8813559         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale design weights for multilevel analysis — rescale_weights","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"functions fit multilevel mixed effects models allow specify frequency weights, design (.e. sampling probability) weights, used analyzing complex samples survey data. rescale_weights() implements algorithm proposed Asparouhov (2006) Carle (2009) rescale design weights survey data account grouping structure multilevel models, can used multilevel modelling.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"","code":"rescale_weights(data, group, probability_weights, nest = FALSE)"},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"data data frame. group Variable names (character vector, formula), indicating grouping structure (strata) survey data (level-2-cluster variable). also possible create weights multiple group variables; cases, created weighting variable suffixed name group variable. probability_weights Variable indicating probability (design sampling) weights survey data (level-1-weight). nest Logical, TRUE group indicates least two group variables, groups \"nested\", .e. groups now combination group level variables group.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"data, including new weighting variables: pweights_a pweights_b, represent rescaled design weights use multilevel models (use variables weights argument).","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"Rescaling based two methods: pweights_a, sample weights probability_weights adjusted factor represents proportion group size divided sum sampling weights within group. adjustment factor pweights_b sum sample weights within group divided sum squared sample weights within group (see Carle (2009), Appendix B). words, pweights_a \"scales weights new weights sum cluster sample size\" pweights_b \"scales weights new weights sum effective cluster size\". Regarding choice scaling methods B, Carle suggests \"analysts wish discuss point estimates report results based weighting method . analysts interested residual -group variance, method B may generally provide least biased estimates\". general, recommended fit non-weighted model weighted models scaling methods comparing models, see whether \"inferential decisions converge\", gain confidence results. Though bias scaled weights decreases increasing group size, method preferred insufficient low group size concern. group ID probably PSU may used random effects (e.g. nested design, group PSU varying intercepts), depending survey design mimicked.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"Carle .C. (2009). Fitting multilevel models complex survey data design weights: Recommendations. BMC Medical Research Methodology 9(49): 1-13 Asparouhov T. (2006). General Multi-Level Modeling Sampling Weights. Communications Statistics - Theory Methods 35: 439-460","code":""},{"path":"https://easystats.github.io/datawizard/reference/rescale_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale design weights for multilevel analysis — rescale_weights","text":"","code":"if (require(\"lme4\")) {   data(nhanes_sample)   head(rescale_weights(nhanes_sample, \"SDMVSTRA\", \"WTINT2YR\"))    # also works with multiple group-variables   head(rescale_weights(nhanes_sample, c(\"SDMVSTRA\", \"SDMVPSU\"), \"WTINT2YR\"))    # or nested structures.   x <- rescale_weights(     data = nhanes_sample,     group = c(\"SDMVSTRA\", \"SDMVPSU\"),     probability_weights = \"WTINT2YR\",     nest = TRUE   )   head(x)    nhanes_sample <- rescale_weights(nhanes_sample, \"SDMVSTRA\", \"WTINT2YR\")    glmer(     total ~ factor(RIAGENDR) * (log(age) + factor(RIDRETH1)) + (1 | SDMVPSU),     family = poisson(),     data = nhanes_sample,     weights = pweights_a   ) } #> Generalized linear mixed model fit by maximum likelihood (Laplace #>   Approximation) [glmerMod] #>  Family: poisson  ( log ) #> Formula: total ~ factor(RIAGENDR) * (log(age) + factor(RIDRETH1)) + (1 |   #>     SDMVPSU) #>    Data: nhanes_sample #> Weights: pweights_a #>       AIC       BIC    logLik  deviance  df.resid  #>  78844.27  78920.47 -39409.14  78818.27      2582  #> Random effects: #>  Groups  Name        Std.Dev. #>  SDMVPSU (Intercept) 0.1018   #> Number of obs: 2595, groups:  SDMVPSU, 2 #> Fixed Effects: #>                         (Intercept)                    factor(RIAGENDR)2   #>                            2.491801                            -1.021308   #>                            log(age)                    factor(RIDRETH1)2   #>                            0.838726                            -0.088627   #>                   factor(RIDRETH1)3                    factor(RIDRETH1)4   #>                           -0.013333                             0.722511   #>                   factor(RIDRETH1)5           factor(RIAGENDR)2:log(age)   #>                           -0.106521                            -1.012695   #> factor(RIAGENDR)2:factor(RIDRETH1)2  factor(RIAGENDR)2:factor(RIDRETH1)3   #>                           -0.009086                             0.732985   #> factor(RIAGENDR)2:factor(RIDRETH1)4  factor(RIAGENDR)2:factor(RIDRETH1)5   #>                            0.275967                             0.542074"},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape CI between wide/long formats — reshape_ci","title":"Reshape CI between wide/long formats — reshape_ci","text":"Reshape CI wide/long formats.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape CI between wide/long formats — reshape_ci","text":"","code":"reshape_ci(x, ci_type = \"CI\")"},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape CI between wide/long formats — reshape_ci","text":"x data frame containing columns named CI_low CI_high (similar, see ci_type). ci_type String indicating \"type\" (.e. prefix) interval columns. Per easystats convention, confidence credible intervals named CI_low CI_high, related ci_type \"CI\". column names intervals differ, ci_type can used indicate name, e.g. ci_type = \"SI\" can used support intervals, column names data frame SI_low SI_high.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reshape CI between wide/long formats — reshape_ci","text":"data frame columns corresponding confidence intervals reshaped either wide long format.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reshape_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape CI between wide/long formats — reshape_ci","text":"","code":"x <- data.frame(   Parameter = c(\"Term 1\", \"Term 2\", \"Term 1\", \"Term 2\"),   CI = c(.8, .8, .9, .9),   CI_low = c(.2, .3, .1, .15),   CI_high = c(.5, .6, .8, .85),   stringsAsFactors = FALSE )  reshape_ci(x) #>   Parameter CI_low_0.8 CI_high_0.8 CI_low_0.9 CI_high_0.9 #> 1    Term 1        0.2         0.5       0.10        0.80 #> 2    Term 2        0.3         0.6       0.15        0.85 reshape_ci(reshape_ci(x)) #>   Parameter  CI CI_low CI_high #> 1    Term 1 0.8   0.20    0.50 #> 2    Term 1 0.9   0.10    0.80 #> 3    Term 2 0.8   0.30    0.60 #> 4    Term 2 0.9   0.15    0.85"},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse-Score Variables — reverse","title":"Reverse-Score Variables — reverse","text":"Reverse-score variables (change keying/scoring direction).","code":""},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse-Score Variables — reverse","text":"","code":"reverse(x, ...)  reverse_scale(x, ...)  # S3 method for numeric reverse(x, range = NULL, verbose = TRUE, ...)  # S3 method for data.frame reverse(   x,   select = NULL,   exclude = NULL,   range = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = FALSE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse-Score Variables — reverse","text":"x (grouped) data frame, numeric vector factor. ... Arguments passed methods. range Range values used reference reversing scale. numeric variables, can NULL numeric vector length two, indicating lowest highest value reference range. NULL, take range input vector (range(x)). factors, range can NULL, numeric vector length two, (numeric) vector least length factor levels (.e. must equal larger nlevels(x)). Note providing range factors usually makes sense factor levels numeric, characters. verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse-Score Variables — reverse","text":"reverse-scored object.","code":""},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Reverse-Score Variables — reverse","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/reverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse-Score Variables — reverse","text":"","code":"reverse(c(1, 2, 3, 4, 5)) #> [1] 5 4 3 2 1 reverse(c(-2, -1, 0, 2, 1)) #> [1]  2  1  0 -2 -1  # Specify the \"theoretical\" range of the input vector reverse(c(1, 3, 4), range = c(0, 4)) #> [1] 3 1 0  # Factor variables reverse(factor(c(1, 2, 3, 4, 5))) #> [1] 5 4 3 2 1 #> Levels: 1 2 3 4 5 reverse(factor(c(1, 2, 3, 4, 5)), range = 0:10) #> [1] 9 8 7 6 5 #> Levels: 0 1 2 3 4 5 6 7 8 9 10  # Data frames head(reverse(iris)) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species #> 1          7.1         2.9          6.5         2.4 virginica #> 2          7.3         3.4          6.5         2.4 virginica #> 3          7.5         3.2          6.6         2.4 virginica #> 4          7.6         3.3          6.4         2.4 virginica #> 5          7.2         2.8          6.5         2.4 virginica #> 6          6.8         2.5          6.2         2.2 virginica head(reverse(iris, select = \"Sepal.Length\")) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          7.1         3.5          1.4         0.2  setosa #> 2          7.3         3.0          1.4         0.2  setosa #> 3          7.5         3.2          1.3         0.2  setosa #> 4          7.6         3.1          1.5         0.2  setosa #> 5          7.2         3.6          1.4         0.2  setosa #> 6          6.8         3.9          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":null,"dir":"Reference","previous_headings":"","what":"Row means (optionally with minimum amount of valid values) — row_means","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"function similar SPSS MEAN.n function computes row means data frame matrix least min_valid values row valid (NA).","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"","code":"row_means(   data,   select = NULL,   exclude = NULL,   min_valid = NULL,   digits = NULL,   ignore_case = FALSE,   regex = FALSE,   remove_na = FALSE,   verbose = TRUE )"},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"data data frame least two columns, row means applied. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. min_valid Optional, numeric value length 1. May either numeric value indicates amount valid values per row calculate row mean; value 0 1, indicating proportion valid values per row calculate row mean (see 'Details'). NULL (default), cases considered. row's sum valid values less min_valid, NA returned. digits Numeric value indicating number decimal places used rounding mean values. Negative values allowed (see 'Details'). default, digits = NULL rounding used. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. remove_na Logical, TRUE (default), removes missing (NA) values calculating row means. applies min_valuid specified. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"vector row means rows least n valid values.","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"Rounding negative number digits means rounding power ten, example row_means(df, 3, digits = -2) rounds nearest hundred. min_valid, NULL, min_valid must numeric value 0 ncol(data). row data frame least min_valid non-missing values, row mean returned. min_valid non-integer value 0 1, min_valid considered indicate proportion required non-missing values per row. E.g., min_valid = 0.75, row must least ncol(data) * min_valid non-missing values row mean calculated. See 'Examples'.","code":""},{"path":"https://easystats.github.io/datawizard/reference/row_means.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row means (optionally with minimum amount of valid values) — row_means","text":"","code":"dat <- data.frame(   c1 = c(1, 2, NA, 4),   c2 = c(NA, 2, NA, 5),   c3 = c(NA, 4, NA, NA),   c4 = c(2, 3, 7, 8) )  # default, all means are shown, if no NA values are present row_means(dat) #> [1]   NA 2.75   NA   NA  # remove all NA before computing row means row_means(dat, remove_na = TRUE) #> [1] 1.500000 2.750000 7.000000 5.666667  # needs at least 4 non-missing values per row row_means(dat, min_valid = 4) # 1 valid return value #> [1]   NA 2.75   NA   NA  # needs at least 3 non-missing values per row row_means(dat, min_valid = 3) # 2 valid return values #> [1]       NA 2.750000       NA 5.666667  # needs at least 2 non-missing values per row row_means(dat, min_valid = 2) #> [1] 1.500000 2.750000       NA 5.666667  # needs at least 1 non-missing value per row, for two selected variables row_means(dat, select = c(\"c1\", \"c3\"), min_valid = 1) #> [1]  1  3 NA  4  # needs at least 50% of non-missing values per row row_means(dat, min_valid = 0.5) # 3 valid return values #> [1] 1.500000 2.750000       NA 5.666667  # needs at least 75% of non-missing values per row row_means(dat, min_valid = 0.75) # 2 valid return values #> [1]       NA 2.750000       NA 5.666667"},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for working with row names or row ids — rownames_as_column","title":"Tools for working with row names or row ids — rownames_as_column","text":"Tools working row names row ids","code":""},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for working with row names or row ids — rownames_as_column","text":"","code":"rownames_as_column(x, var = \"rowname\")  column_as_rownames(x, var = \"rowname\")  rowid_as_column(x, var = \"rowid\")"},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for working with row names or row ids — rownames_as_column","text":"x data frame. var Name column use row names/ids. column_as_rownames(), argument can variable name column number. rownames_as_column() rowid_as_column(), column name must already exist data.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tools for working with row names or row ids — rownames_as_column","text":"data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tools for working with row names or row ids — rownames_as_column","text":"similar tibble's functions column_to_rownames(), rownames_to_column() rowid_to_column(). Note behavior rowid_as_column() different grouped dataframe: instead making rowid unique across full dataframe, creates rowid per group. Therefore, can several rows rowid belong different groups. familiar dplyr, similar following:","code":"data |>   group_by(grp) |>   mutate(id = row_number()) |>   ungroup()"},{"path":"https://easystats.github.io/datawizard/reference/rownames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for working with row names or row ids — rownames_as_column","text":"","code":"# Convert between row names and column -------------------------------- test <- rownames_as_column(mtcars, var = \"car\") test #>                    car  mpg cyl  disp  hp drat    wt  qsec vs am gear carb #> 1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 #> 2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 #> 3           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 #> 4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 #> 5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 #> 6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 #> 7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 #> 8            Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 #> 9             Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 #> 10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 #> 11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 #> 12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 #> 13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 #> 14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 #> 15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 #> 16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 #> 17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 #> 18            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 #> 19         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 #> 20      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 #> 21       Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1 #> 22    Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 #> 23         AMC Javelin 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 #> 24          Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 #> 25    Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 #> 26           Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 #> 27       Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 #> 28        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 #> 29      Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 #> 30        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 #> 31       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 #> 32          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2 head(column_as_rownames(test, var = \"car\")) #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  test_data <- head(iris)  rowid_as_column(test_data) #>   rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1     1          5.1         3.5          1.4         0.2  setosa #> 2     2          4.9         3.0          1.4         0.2  setosa #> 3     3          4.7         3.2          1.3         0.2  setosa #> 4     4          4.6         3.1          1.5         0.2  setosa #> 5     5          5.0         3.6          1.4         0.2  setosa #> 6     6          5.4         3.9          1.7         0.4  setosa rowid_as_column(test_data, var = \"my_id\") #>   my_id Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1     1          5.1         3.5          1.4         0.2  setosa #> 2     2          4.9         3.0          1.4         0.2  setosa #> 3     3          4.7         3.2          1.3         0.2  setosa #> 4     4          4.6         3.1          1.5         0.2  setosa #> 5     5          5.0         3.6          1.4         0.2  setosa #> 6     6          5.4         3.9          1.7         0.4  setosa"},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Skewness and (Excess) Kurtosis — skewness","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"Compute Skewness (Excess) Kurtosis","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"","code":"skewness(x, ...)  # S3 method for numeric skewness(   x,   remove_na = TRUE,   type = \"2\",   iterations = NULL,   verbose = TRUE,   na.rm = TRUE,   ... )  kurtosis(x, ...)  # S3 method for numeric kurtosis(   x,   remove_na = TRUE,   type = \"2\",   iterations = NULL,   verbose = TRUE,   na.rm = TRUE,   ... )  # S3 method for parameters_kurtosis print(x, digits = 3, test = FALSE, ...)  # S3 method for parameters_skewness print(x, digits = 3, test = FALSE, ...)  # S3 method for parameters_skewness summary(object, test = FALSE, ...)  # S3 method for parameters_kurtosis summary(object, test = FALSE, ...)"},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"x numeric vector data.frame. ... Arguments passed methods. remove_na Logical. NA values removed computing (TRUE) (FALSE, default)? type Type algorithm computing skewness. May one 1 (\"1\", \"\" \"classic\"), 2 (\"2\", \"II\" \"SPSS\" \"SAS\") 3 ( \"3\", \"III\" \"Minitab\"). See 'Details'. iterations number bootstrap replicates computing standard errors. NULL (default), parametric standard errors computed. verbose Toggle warnings messages. na.rm Deprecated. Please use remove_na instead. digits Number decimal places. test Logical, TRUE, tests skewness kurtosis significantly different zero. object object returned skewness() kurtosis().","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"Values skewness kurtosis.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"skewness","dir":"Reference","previous_headings":"","what":"Skewness","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"Symmetric distributions skewness around zero, negative skewness values indicates \"left-skewed\" distribution, positive skewness values indicates \"right-skewed\" distribution. Examples relationship skewness distributions : Normal distribution (symmetric distribution) skewness 0 Half-normal distribution skewness just 1 Exponential distribution skewness 2 Lognormal distribution can skewness positive value, depending parameters (https://en.wikipedia.org/wiki/Skewness)","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"types-of-skewness","dir":"Reference","previous_headings":"","what":"Types of Skewness","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"skewness() supports three different methods estimating skewness, discussed Joanes Gill (1988): Type \"1\" \"classical\" method, g1 = (sum((x - mean(x))^3) / n) / (sum((x - mean(x))^2) / n)^1.5 Type \"2\" first calculates type-1 skewness, adjusts result: G1 = g1 * sqrt(n * (n - 1)) / (n - 2). SAS SPSS usually return. Type \"3\" first calculates type-1 skewness, adjusts result: b1 = g1 * ((1 - 1 / n))^1.5. Minitab usually returns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"kurtosis","dir":"Reference","previous_headings":"","what":"Kurtosis","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"kurtosis measure \"tailedness\" distribution. distribution kurtosis values zero called \"mesokurtic\". kurtosis value larger zero indicates \"leptokurtic\" distribution fatter tails. kurtosis value zero indicates \"platykurtic\" distribution thinner tails (https://en.wikipedia.org/wiki/Kurtosis).","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"types-of-kurtosis","dir":"Reference","previous_headings":"","what":"Types of Kurtosis","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"kurtosis() supports three different methods estimating kurtosis, discussed Joanes Gill (1988): Type \"1\" \"classical\" method, g2 = n * sum((x - mean(x))^4) / (sum((x - mean(x))^2)^2) - 3. Type \"2\" first calculates type-1 kurtosis, adjusts result: G2 = ((n + 1) * g2 + 6) * (n - 1)/((n - 2) * (n - 3)). SAS SPSS usually return Type \"3\" first calculates type-1 kurtosis, adjusts result: b2 = (g2 + 3) * (1 - 1 / n)^2 - 3. Minitab usually returns.","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"standard-errors","dir":"Reference","previous_headings":"","what":"Standard Errors","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"recommended compute empirical (bootstrapped) standard errors (via iterations argument) relying analytic standard errors (Wright & Herrington, 2011).","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"D. N. Joanes C. . Gill (1998). Comparing measures sample skewness kurtosis. Statistician, 47, 183–189. Wright, D. B., & Herrington, J. . (2011). Problematic standard errors confidence intervals skewness kurtosis. Behavior research methods, 43(1), 8-17.","code":""},{"path":"https://easystats.github.io/datawizard/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Skewness and (Excess) Kurtosis — skewness","text":"","code":"skewness(rnorm(1000)) #> Skewness |    SE #> ---------------- #>    0.063 | 0.077 kurtosis(rnorm(1000)) #> Kurtosis |    SE #> ---------------- #>   -0.071 | 0.154"},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":null,"dir":"Reference","previous_headings":"","what":"Shift numeric value range — slide","title":"Shift numeric value range — slide","text":"functions shifts value range numeric variable, new range starts given value.","code":""},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shift numeric value range — slide","text":"","code":"slide(x, ...)  # S3 method for numeric slide(x, lowest = 0, ...)  # S3 method for data.frame slide(   x,   select = NULL,   exclude = NULL,   lowest = 0,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shift numeric value range — slide","text":"x data frame numeric vector. ... used. lowest Numeric, indicating lowest (minimum) value converting factors character vectors numeric values. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shift numeric value range — slide","text":"x, range numeric variables starts new value.","code":""},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Shift numeric value range — slide","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/slide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shift numeric value range — slide","text":"","code":"# numeric head(mtcars$gear) #> [1] 4 4 4 3 3 3 head(slide(mtcars$gear)) #> [1] 1 1 1 0 0 0 head(slide(mtcars$gear, lowest = 10)) #> [1] 11 11 11 10 10 10  # data frame sapply(slide(mtcars, lowest = 1), min) #>  mpg  cyl disp   hp drat   wt qsec   vs   am gear carb  #>    1    1    1    1    1    1    1    1    1    1    1  sapply(mtcars, min) #>    mpg    cyl   disp     hp   drat     wt   qsec     vs     am   gear   carb  #> 10.400  4.000 71.100 52.000  2.760  1.513 14.500  0.000  0.000  3.000  1.000"},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantify the smoothness of a vector — smoothness","title":"Quantify the smoothness of a vector — smoothness","text":"Quantify smoothness vector","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantify the smoothness of a vector — smoothness","text":"","code":"smoothness(x, method = \"cor\", lag = 1, iterations = NULL, ...)"},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantify the smoothness of a vector — smoothness","text":"x Numeric vector (similar time series). method Can \"diff\" (standard deviation standardized differences) \"cor\" (default, lag-one autocorrelation). lag integer indicating lag use. less 1, interpreted expressed percentage length vector. iterations number bootstrap replicates computing standard errors. NULL (default), parametric standard errors computed. ... Arguments passed methods.","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantify the smoothness of a vector — smoothness","text":"Value smoothness.","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantify the smoothness of a vector — smoothness","text":"https://stats.stackexchange.com/questions/24607/--measure-smoothness---time-series--r","code":""},{"path":"https://easystats.github.io/datawizard/reference/smoothness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantify the smoothness of a vector — smoothness","text":"","code":"x <- (-10:10)^3 + rnorm(21, 0, 100) plot(x)  smoothness(x, method = \"cor\") #> [1] 0.9291692 #> attr(,\"class\") #> [1] \"parameters_smoothness\" \"numeric\"               smoothness(x, method = \"diff\") #> [1] 1.584401 #> attr(,\"class\") #> [1] \"parameters_smoothness\" \"numeric\""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-fit a model with standardized data — standardize.default","title":"Re-fit a model with standardized data — standardize.default","text":"Performs standardization data (z-scoring) using standardize() re-fits model standardized data.  Standardization done completely refitting model standardized data. Hence, approach equal standardizing variables fitting model return new model object. method particularly recommended complex models include interactions transformations (e.g., polynomial spline terms). robust (default FALSE) argument enables robust standardization data, based median MAD instead mean SD.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Re-fit a model with standardized data — standardize.default","text":"","code":"# S3 method for default standardize(   x,   robust = FALSE,   two_sd = FALSE,   weights = TRUE,   verbose = TRUE,   include_response = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Re-fit a model with standardized data — standardize.default","text":"x statistical model. robust Logical, TRUE, centering done subtracting median variables dividing median absolute deviation (MAD). FALSE, variables standardized subtracting mean dividing standard deviation (SD). two_sd TRUE, variables scaled two times deviation (SD MAD depending robust). method can useful obtain model coefficients continuous parameters comparable coefficients related binary predictors, applied predictors (outcome) (Gelman, 2008). weights TRUE (default), weighted-standardization carried . verbose Toggle warnings messages . include_response TRUE (default), response value also standardized. FALSE, predictors standardized. Note GLMs models non-linear link functions, response value standardized, make re-fitting model work. model contains stats::offset(), offset variable(s) standardized response standardized. two_sd = TRUE, offsets standardized one-sd (similar response). (mediate models, include_response refers outcome y model; m model's response always standardized possible). ... Arguments passed methods.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Re-fit a model with standardized data — standardize.default","text":"statistical model fitted standardized data","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"generalized-linear-models","dir":"Reference","previous_headings":"","what":"Generalized Linear Models","title":"Re-fit a model with standardized data — standardize.default","text":"Standardization generalized linear models (GLM, GLMM, etc) done respect predictors (outcome remains -, unstandardized) - maintaining interpretability coefficients (e.g., binomial model: exponent standardized parameter change 1 SD predictor, etc.)","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"dealing-with-factors","dir":"Reference","previous_headings":"","what":"Dealing with Factors","title":"Re-fit a model with standardized data — standardize.default","text":"standardize(model) standardize_parameters(model, method = \"refit\") standardize categorical predictors (.e. factors) / dummy-variables, may different behaviour compared R packages (lm.beta) software packages (like SPSS). mimic behaviours, either use standardize_parameters(model, method = \"basic\") obtain post-hoc standardized parameters, standardize data standardize(data, force = TRUE) fitting model.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"transformed-variables","dir":"Reference","previous_headings":"","what":"Transformed Variables","title":"Re-fit a model with standardized data — standardize.default","text":"model's formula contains transformations (e.g. y ~ exp(X)) transformation effectively takes place standardization (e.g., exp(scale(X))). Since transformations undefined none positive values, log() sqrt(), relevel variables shifted (post standardization) Z - min(Z) + 1 Z - min(Z) (respectively).","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/standardize.default.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Re-fit a model with standardized data — standardize.default","text":"","code":"model <- lm(Infant.Mortality ~ Education * Fertility, data = swiss) coef(standardize(model)) #>         (Intercept)           Education           Fertility Education:Fertility  #>          0.06386069          0.47482848          0.63270919          0.09829777"},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardization (Z-scoring) — standardize","title":"Standardization (Z-scoring) — standardize","text":"Performs standardization data (z-scoring), .e., centering scaling, data expressed terms standard deviation (.e., mean = 0, SD = 1) Median Absolute Deviance (median = 0, MAD = 1). applied statistical model, function extracts dataset, standardizes , refits model standardized version dataset. normalize() function can also used scale numeric variables within 0 - 1 range.  model standardization, see standardize.default().","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardization (Z-scoring) — standardize","text":"","code":"standardize(x, ...)  standardise(x, ...)  # S3 method for numeric standardize(   x,   robust = FALSE,   two_sd = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   scale = NULL,   verbose = TRUE,   ... )  # S3 method for factor standardize(   x,   robust = FALSE,   two_sd = FALSE,   weights = NULL,   force = FALSE,   verbose = TRUE,   ... )  # S3 method for data.frame standardize(   x,   select = NULL,   exclude = NULL,   robust = FALSE,   two_sd = FALSE,   weights = NULL,   reference = NULL,   center = NULL,   scale = NULL,   remove_na = c(\"none\", \"selected\", \"all\"),   force = FALSE,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )  unstandardize(x, ...)  unstandardise(x, ...)  # S3 method for numeric unstandardize(   x,   center = NULL,   scale = NULL,   reference = NULL,   robust = FALSE,   two_sd = FALSE,   ... )  # S3 method for data.frame unstandardize(   x,   center = NULL,   scale = NULL,   reference = NULL,   robust = FALSE,   two_sd = FALSE,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardization (Z-scoring) — standardize","text":"x (grouped) data frame, vector statistical model (unstandardize() model). ... Arguments passed methods. robust Logical, TRUE, centering done subtracting median variables dividing median absolute deviation (MAD). FALSE, variables standardized subtracting mean dividing standard deviation (SD). two_sd TRUE, variables scaled two times deviation (SD MAD depending robust). method can useful obtain model coefficients continuous parameters comparable coefficients related binary predictors, applied predictors (outcome) (Gelman, 2008). weights Can NULL (weighting), : model: TRUE (default), weighted-standardization carried . data.frames: numeric vector weights, character name column data.frame contains weights. numeric vectors: numeric vector weights. reference data frame variable centrality deviation computed instead input variable. Useful standardizing subset new data according another data frame. center, scale standardize():  Numeric values, can used alternative reference define reference centrality deviation. scale center length 1, recycled match length selected variables standardization. Else, center scale must length number selected variables. Values center scale matched selected variables provided order, unless named vector given. case, names matched names selected variables. unstandardize(): center scale correspond center (mean / median) scale (SD / MAD) original non-standardized data (data frames, named, column order correspond numeric column). However, one can also directly provide original data reference, center scale computed (according robust two_sd). Alternatively, input contains attributes center scale (output standardize()), take rest arguments absent. verbose Toggle warnings messages . force Logical, TRUE, forces recoding factors character vectors well. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. remove_na missing values (NA) treated: \"none\" (default): column's standardization done separately, ignoring NAs. Else, rows NA columns selected select / exclude (\"selected\") columns (\"\") dropped standardization, resulting data frame include cases. append Logical string. TRUE, standardized variables get new column names (suffix \"_z\") appended (column bind) x, thus returning original standardized variables. FALSE, original variables x overwritten standardized versions. character value, standardized variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardization (Z-scoring) — standardize","text":"standardized object (either standardize data frame statistical model fitted standardized data).","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Standardization (Z-scoring) — standardize","text":"x vector data frame remove_na = \"none\"), missing values preserved, return value length / number rows original input.","code":""},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Standardization (Z-scoring) — standardize","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/standardize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Standardization (Z-scoring) — standardize","text":"","code":"d <- iris[1:4, ]  # vectors standardise(d$Petal.Length) #> [1]  0.000000  0.000000 -1.224745  1.224745 #> (center: 1.4, scale = 0.082) #>   # Data frames # overwrite standardise(d, select = c(\"Sepal.Length\", \"Sepal.Width\")) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1    1.2402159   1.3887301          1.4         0.2  setosa #> 2    0.3382407  -0.9258201          1.4         0.2  setosa #> 3   -0.5637345   0.0000000          1.3         0.2  setosa #> 4   -1.0147221  -0.4629100          1.5         0.2  setosa  # append standardise(d, select = c(\"Sepal.Length\", \"Sepal.Width\"), append = TRUE) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_z #> 1          5.1         3.5          1.4         0.2  setosa      1.2402159 #> 2          4.9         3.0          1.4         0.2  setosa      0.3382407 #> 3          4.7         3.2          1.3         0.2  setosa     -0.5637345 #> 4          4.6         3.1          1.5         0.2  setosa     -1.0147221 #>   Sepal.Width_z #> 1     1.3887301 #> 2    -0.9258201 #> 3     0.0000000 #> 4    -0.4629100  # append, suffix standardise(d, select = c(\"Sepal.Length\", \"Sepal.Width\"), append = \"_std\") #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_std #> 1          5.1         3.5          1.4         0.2  setosa        1.2402159 #> 2          4.9         3.0          1.4         0.2  setosa        0.3382407 #> 3          4.7         3.2          1.3         0.2  setosa       -0.5637345 #> 4          4.6         3.1          1.5         0.2  setosa       -1.0147221 #>   Sepal.Width_std #> 1       1.3887301 #> 2      -0.9258201 #> 3       0.0000000 #> 4      -0.4629100  # standardizing with reference center and scale d <- data.frame(   a = c(-2, -1, 0, 1, 2),   b = c(3, 4, 5, 6, 7) )  # default standardization, based on mean and sd of each variable standardize(d) # means are 0 and 5, sd ~ 1.581139 #>            a          b #> 1 -1.2649111 -1.2649111 #> 2 -0.6324555 -0.6324555 #> 3  0.0000000  0.0000000 #> 4  0.6324555  0.6324555 #> 5  1.2649111  1.2649111  # standardization, based on mean and sd set to the same values standardize(d, center = c(0, 5), scale = c(1.581, 1.581)) #>            a          b #> 1 -1.2650221 -1.2650221 #> 2 -0.6325111 -0.6325111 #> 3  0.0000000  0.0000000 #> 4  0.6325111  0.6325111 #> 5  1.2650221  1.2650221  # standardization, mean and sd for each variable newly defined standardize(d, center = c(3, 4), scale = c(2, 4)) #>      a     b #> 1 -2.5 -0.25 #> 2 -2.0  0.00 #> 3 -1.5  0.25 #> 4 -1.0  0.50 #> 5 -0.5  0.75  # standardization, taking same mean and sd for each variable standardize(d, center = 1, scale = 3) #>            a         b #> 1 -1.0000000 0.6666667 #> 2 -0.6666667 1.0000000 #> 3 -0.3333333 1.3333333 #> 4  0.0000000 1.6666667 #> 5  0.3333333 2.0000000"},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Convenient text formatting functionalities — text_format","title":"Convenient text formatting functionalities — text_format","text":"Convenience functions manipulate format text.","code":""},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convenient text formatting functionalities — text_format","text":"","code":"text_format(   text,   sep = \", \",   last = \" and \",   width = NULL,   enclose = NULL,   ... )  format_text(   text,   sep = \", \",   last = \" and \",   width = NULL,   enclose = NULL,   ... )  text_fullstop(text)  text_lastchar(text, n = 1)  text_concatenate(text, sep = \", \", last = \" and \", enclose = NULL)  text_paste(text, text2 = NULL, sep = \", \", enclose = NULL, ...)  text_remove(text, pattern = \"\", ...)  text_wrap(text, width = NULL, ...)"},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convenient text formatting functionalities — text_format","text":"text, text2 character string. sep Separator. last Last separator. width Positive integer giving target column width wrapping lines output. Can \"auto\", case select 90\\ default width. enclose Character used wrap elements text, can , e.g., enclosed quotes backticks. NULL (default), text elements enclosed. ... arguments passed functions. n number characters find. pattern Character vector. data_rename(), indicates columns selected renaming. Can NULL (case columns selected). data_addprefix() data_addsuffix(), character string, added prefix suffix column names.","code":""},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convenient text formatting functionalities — text_format","text":"character string.","code":""},{"path":"https://easystats.github.io/datawizard/reference/text_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convenient text formatting functionalities — text_format","text":"","code":"# Add full stop if missing text_fullstop(c(\"something\", \"something else.\")) #> [1] \"something.\"      \"something else.\"  # Find last characters text_lastchar(c(\"ABC\", \"DEF\"), n = 2) #>  ABC  DEF  #> \"BC\" \"EF\"   # Smart concatenation text_concatenate(c(\"First\", \"Second\", \"Last\")) #> [1] \"First, Second and Last\" text_concatenate(c(\"First\", \"Second\", \"Last\"), last = \" or \", enclose = \"`\") #> [1] \"`First`, `Second` or `Last`\"  # Remove parts of string text_remove(c(\"one!\", \"two\", \"three!\"), \"!\") #> [1] \"one\"   \"two\"   \"three\"  # Wrap text long_text <- paste(rep(\"abc \", 100), collapse = \"\") cat(text_wrap(long_text, width = 50)) #>  abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc abc abc abc abc abc abc abc abc #> abc abc abc abc  # Paste with optional separator text_paste(c(\"A\", \"\", \"B\"), c(\"42\", \"42\", \"42\")) #> [1] \"A, 42\" \"42\"    \"B, 42\""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data to factors — to_factor","title":"Convert data to factors — to_factor","text":"Convert data factors","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data to factors — to_factor","text":"","code":"to_factor(x, ...)  # S3 method for numeric to_factor(x, labels_to_levels = TRUE, verbose = TRUE, ...)  # S3 method for data.frame to_factor(   x,   select = NULL,   exclude = NULL,   ignore_case = FALSE,   append = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data to factors — to_factor","text":"x data frame vector. ... Arguments passed methods. labels_to_levels Logical, TRUE, value labels used factor levels x converted factor. Else, factor levels based values x (.e. using .factor()). verbose Toggle warnings. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data to factors — to_factor","text":"factor, data frame factors.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert data to factors — to_factor","text":"Convert variables data factors. data labelled, value labels used factor levels. counterpart convert variables numeric to_numeric().","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Convert data to factors — to_factor","text":"Factors ignored returned . want use value labels levels factors, use labels_to_levels() instead.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"selection-of-variables-the-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - the select argument","title":"Convert data to factors — to_factor","text":"functions select argument (including function), complete input data frame returned, even select selects range variables. , function applied variables match select, variables remain unchanged. words: function, select omit non-included variables, returned data frame include variables input data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_factor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data to factors — to_factor","text":"","code":"str(to_factor(iris)) #> 'data.frame':\t150 obs. of  5 variables: #>  $ Sepal.Length: Factor w/ 35 levels \"4.3\",\"4.4\",\"4.5\",..: 9 7 5 4 8 12 4 8 2 7 ... #>  $ Sepal.Width : Factor w/ 23 levels \"2\",\"2.2\",\"2.3\",..: 15 10 12 11 16 19 14 14 9 11 ... #>  $ Petal.Length: Factor w/ 43 levels \"1\",\"1.1\",\"1.2\",..: 5 5 4 6 5 8 5 6 5 6 ... #>  $ Petal.Width : Factor w/ 22 levels \"0.1\",\"0.2\",\"0.3\",..: 2 2 2 2 2 4 3 2 2 1 ... #>  $ Species     : Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ...  # use labels as levels data(efc) str(efc$c172code) #>  num [1:100] 2 2 1 2 2 2 2 2 NA 2 ... #>  - attr(*, \"label\")= chr \"carer's level of education\" #>  - attr(*, \"labels\")= Named num [1:3] 1 2 3 #>   ..- attr(*, \"names\")= chr [1:3] \"low level of education\" \"intermediate level of education\" \"high level of education\" head(to_factor(efc$c172code)) #> [1] intermediate level of education intermediate level of education #> [3] low level of education          intermediate level of education #> [5] intermediate level of education intermediate level of education #> 3 Levels: low level of education ... high level of education"},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data to numeric — to_numeric","title":"Convert data to numeric — to_numeric","text":"Convert data numeric converting characters factors factors either numeric levels dummy variables. \"counterpart\" convert variables factors to_factor().","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data to numeric — to_numeric","text":"","code":"to_numeric(x, ...)  # S3 method for data.frame to_numeric(   x,   select = NULL,   exclude = NULL,   dummy_factors = TRUE,   preserve_levels = FALSE,   lowest = NULL,   append = FALSE,   ignore_case = FALSE,   regex = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data to numeric — to_numeric","text":"x data frame, factor vector. ... Arguments passed methods. select Variables included performing required tasks. Can either variable specified literal variable name (e.g., column_name), string variable name (e.g., \"column_name\"), character vector variable names (e.g., c(\"col1\", \"col2\", \"col3\")), formula variable names (e.g., ~column_1 + column_2), vector positive integers, giving positions counting left (e.g. 1 c(1, 3, 5)), vector negative integers, giving positions counting right (e.g., -1 -1:-3), one following select-helpers: starts_with(), ends_with(), contains(), range using : regex(\"\"). starts_with(), ends_with(),  contains() accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). function testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3), ranges specified via literal variable names, select-helpers (except regex()) (user-defined) functions can negated, .e. return non-matching elements, prefixed -, e.g. -ends_with(\"\"), -.numeric -(Sepal.Width:Petal.Length). Note: Negation means matches excluded, thus, exclude argument can used alternatively. instance, select=-ends_with(\"Length\") (-) equivalent exclude=ends_with(\"Length\") (-). case negation work expected, use exclude argument instead. NULL, selects columns. Patterns found matches silently ignored, e.g. find_columns(iris, select = c(\"Species\", \"Test\")) just return \"Species\". exclude See select, however, column names matched pattern exclude excluded instead selected. NULL (default), excludes columns. dummy_factors Transform factors dummy factors (factor levels different columns filled binary 0-1 value). preserve_levels Logical, applies x factor. TRUE, x numeric factor levels, converted related numeric values. possible, converted numeric values start 1 number levels. lowest Numeric, indicating lowest (minimum) value converting factors character vectors numeric values. append Logical string. TRUE, recoded converted variables get new column names appended (column bind) x, thus returning original recoded variables. new columns get suffix, based calling function: \"_r\" recode functions, \"_n\" to_numeric(), \"_f\" to_factor(), \"_s\" slide(). append=FALSE, original variables x overwritten recoded versions. character value, recoded variables appended new column names (using defined suffix) original data frame. ignore_case Logical, TRUE one select-helpers regular expression used select, ignores lower/upper case search pattern matching variable names. regex Logical, TRUE, search pattern select treated regular expression. regex = TRUE, select must character string (variable containing character string) allowed one supported select-helpers character vector length > 1. regex = TRUE comparable using one two select-helpers, select = contains(\"\") select = regex(\"\"), however, since select-helpers may work called inside functions (see 'Details'), argument may used workaround. verbose Toggle warnings.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data to numeric — to_numeric","text":"data frame numeric variables.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"selection-of-variables-select-argument","dir":"Reference","previous_headings":"","what":"Selection of variables - select argument","title":"Convert data to numeric — to_numeric","text":"functions select argument complete input data frame returned, even select selects range variables. However, to_numeric(), factors might converted dummies, thus, number variables returned data frame longer match input data frame. Hence, select used, variables (dummies) specified select returned. Use append=TRUE also include original variables returned data frame.","code":""},{"path":"https://easystats.github.io/datawizard/reference/to_numeric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data to numeric — to_numeric","text":"","code":"to_numeric(head(ToothGrowth)) #>    len supp.OJ supp.VC dose #> 1  4.2       0       1  0.5 #> 2 11.5       0       1  0.5 #> 3  7.3       0       1  0.5 #> 4  5.8       0       1  0.5 #> 5  6.4       0       1  0.5 #> 6 10.0       0       1  0.5 to_numeric(head(ToothGrowth), dummy_factors = FALSE) #>    len supp dose #> 1  4.2    2  0.5 #> 2 11.5    2  0.5 #> 3  7.3    2  0.5 #> 4  5.8    2  0.5 #> 5  6.4    2  0.5 #> 6 10.0    2  0.5  # factors x <- as.factor(mtcars$gear) to_numeric(x, dummy_factors = FALSE) #>  [1] 2 2 2 1 1 1 1 2 2 2 2 1 1 1 1 1 1 2 2 2 1 1 1 1 1 2 3 3 3 3 3 2 to_numeric(x, dummy_factors = FALSE, preserve_levels = TRUE) #>  [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4"},{"path":"https://easystats.github.io/datawizard/reference/visualisation_recipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare objects for visualisation — visualisation_recipe","title":"Prepare objects for visualisation — visualisation_recipe","text":"function prepares objects visualisation returning list layers data geoms can easily plotted using instance ggplot2. see package installed, call visualization_recipe() can replaced plot(), internally call former plot using ggplot. resulting plot can customized ad-hoc (adding ggplot's geoms, theme specifications), via arguments visualisation_recipe() control aesthetic parameters. See specific documentation page object's class: modelbased: https://easystats.github.io/modelbased/reference/visualisation_recipe.estimate_predicted.html correlation: https://easystats.github.io/correlation/reference/visualisation_recipe.easycormatrix.html","code":""},{"path":"https://easystats.github.io/datawizard/reference/visualisation_recipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare objects for visualisation — visualisation_recipe","text":"","code":"visualisation_recipe(x, ...)"},{"path":"https://easystats.github.io/datawizard/reference/visualisation_recipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare objects for visualisation — visualisation_recipe","text":"x easystats object. ... arguments passed functions.","code":""},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted Mean, Median, SD, and MAD — weighted_mean","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"Weighted Mean, Median, SD, MAD","code":""},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"","code":"weighted_mean(x, weights = NULL, remove_na = TRUE, verbose = TRUE, ...)  weighted_median(x, weights = NULL, remove_na = TRUE, verbose = TRUE, ...)  weighted_sd(x, weights = NULL, remove_na = TRUE, verbose = TRUE, ...)  weighted_mad(   x,   weights = NULL,   constant = 1.4826,   remove_na = TRUE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"x object containing values whose weighted mean     computed. weights numerical vector weights length x giving weights use elements x. weights = NULL, x passed non-weighted function. remove_na Logical, TRUE (default), removes missing (NA) infinite values x weights. verbose Show warning weights negative? ... arguments passed methods. constant scale factor.","code":""},{"path":"https://easystats.github.io/datawizard/reference/weighted_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weighted Mean, Median, SD, and MAD — weighted_mean","text":"","code":"## GPA from Siegel 1994 x <- c(3.7, 3.3, 3.5, 2.8) wt <- c(5, 5, 4, 1) / 15  weighted_mean(x, wt) #> [1] 3.453333 weighted_median(x, wt) #> [1] 3.5  weighted_sd(x, wt) #> [1] 0.2852935 weighted_mad(x, wt) #> [1] 0.29652"},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Winsorize data — winsorize","title":"Winsorize data — winsorize","text":"Winsorize data","code":""},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Winsorize data — winsorize","text":"","code":"winsorize(data, ...)  # S3 method for numeric winsorize(   data,   threshold = 0.2,   method = \"percentile\",   robust = FALSE,   verbose = TRUE,   ... )"},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Winsorize data — winsorize","text":"data data frame vector. ... Currently used. threshold amount winsorization, depends value method: method = \"percentile\": amount winsorize tail. value threshold must 0 0.5 length 1. method = \"zscore\": number SD/MAD-deviations mean/median (see robust). value threshold must greater 0 length 1. method = \"raw\": vector length 2 lower upper bound winsorization. method One \"percentile\" (default), \"zscore\", \"raw\". robust Logical, TRUE, winsorizing \"zscore\" method done via median median absolute deviation (MAD); FALSE, via mean standard deviation. verbose used anymore since datawizard 0.6.6.","code":""},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Winsorize data — winsorize","text":"data frame winsorized columns winsorized vector.","code":""},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Winsorize data — winsorize","text":"Winsorizing winsorization transformation statistics limiting extreme values statistical data reduce effect possibly spurious outliers. distribution many statistics can heavily influenced outliers. typical strategy set outliers (values beyond certain threshold) specified percentile data; example, 90% winsorization see data 5th percentile set 5th percentile, data 95th percentile set 95th percentile. Winsorized estimators usually robust outliers standard forms.","code":""},{"path":[]},{"path":"https://easystats.github.io/datawizard/reference/winsorize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Winsorize data — winsorize","text":"","code":"hist(iris$Sepal.Length, main = \"Original data\")   hist(winsorize(iris$Sepal.Length, threshold = 0.2),   xlim = c(4, 8), main = \"Percentile Winsorization\" )   hist(winsorize(iris$Sepal.Length, threshold = 1.5, method = \"zscore\"),   xlim = c(4, 8), main = \"Mean (+/- SD) Winsorization\" )   hist(winsorize(iris$Sepal.Length, threshold = 1.5, method = \"zscore\", robust = TRUE),   xlim = c(4, 8), main = \"Median (+/- MAD) Winsorization\" )   hist(winsorize(iris$Sepal.Length, threshold = c(5, 7.5), method = \"raw\"),   xlim = c(4, 8), main = \"Raw Thresholds\" )   # Also works on a data frame: winsorize(iris, threshold = 0.2) #>     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species #> 1            5.1         3.4          1.5         0.2     setosa #> 2            5.0         3.0          1.5         0.2     setosa #> 3            5.0         3.2          1.5         0.2     setosa #> 4            5.0         3.1          1.5         0.2     setosa #> 5            5.0         3.4          1.5         0.2     setosa #> 6            5.4         3.4          1.7         0.4     setosa #> 7            5.0         3.4          1.5         0.3     setosa #> 8            5.0         3.4          1.5         0.2     setosa #> 9            5.0         2.9          1.5         0.2     setosa #> 10           5.0         3.1          1.5         0.2     setosa #> 11           5.4         3.4          1.5         0.2     setosa #> 12           5.0         3.4          1.6         0.2     setosa #> 13           5.0         3.0          1.5         0.2     setosa #> 14           5.0         3.0          1.5         0.2     setosa #> 15           5.8         3.4          1.5         0.2     setosa #> 16           5.7         3.4          1.5         0.4     setosa #> 17           5.4         3.4          1.5         0.4     setosa #> 18           5.1         3.4          1.5         0.3     setosa #> 19           5.7         3.4          1.7         0.3     setosa #> 20           5.1         3.4          1.5         0.3     setosa #> 21           5.4         3.4          1.7         0.2     setosa #> 22           5.1         3.4          1.5         0.4     setosa #> 23           5.0         3.4          1.5         0.2     setosa #> 24           5.1         3.3          1.7         0.5     setosa #> 25           5.0         3.4          1.9         0.2     setosa #> 26           5.0         3.0          1.6         0.2     setosa #> 27           5.0         3.4          1.6         0.4     setosa #> 28           5.2         3.4          1.5         0.2     setosa #> 29           5.2         3.4          1.5         0.2     setosa #> 30           5.0         3.2          1.6         0.2     setosa #> 31           5.0         3.1          1.6         0.2     setosa #> 32           5.4         3.4          1.5         0.4     setosa #> 33           5.2         3.4          1.5         0.2     setosa #> 34           5.5         3.4          1.5         0.2     setosa #> 35           5.0         3.1          1.5         0.2     setosa #> 36           5.0         3.2          1.5         0.2     setosa #> 37           5.5         3.4          1.5         0.2     setosa #> 38           5.0         3.4          1.5         0.2     setosa #> 39           5.0         3.0          1.5         0.2     setosa #> 40           5.1         3.4          1.5         0.2     setosa #> 41           5.0         3.4          1.5         0.3     setosa #> 42           5.0         2.7          1.5         0.3     setosa #> 43           5.0         3.2          1.5         0.2     setosa #> 44           5.0         3.4          1.6         0.6     setosa #> 45           5.1         3.4          1.9         0.4     setosa #> 46           5.0         3.0          1.5         0.3     setosa #> 47           5.1         3.4          1.6         0.2     setosa #> 48           5.0         3.2          1.5         0.2     setosa #> 49           5.3         3.4          1.5         0.2     setosa #> 50           5.0         3.3          1.5         0.2     setosa #> 51           6.5         3.2          4.7         1.4 versicolor #> 52           6.4         3.2          4.5         1.5 versicolor #> 53           6.5         3.1          4.9         1.5 versicolor #> 54           5.5         2.7          4.0         1.3 versicolor #> 55           6.5         2.8          4.6         1.5 versicolor #> 56           5.7         2.8          4.5         1.3 versicolor #> 57           6.3         3.3          4.7         1.6 versicolor #> 58           5.0         2.7          3.3         1.0 versicolor #> 59           6.5         2.9          4.6         1.3 versicolor #> 60           5.2         2.7          3.9         1.4 versicolor #> 61           5.0         2.7          3.5         1.0 versicolor #> 62           5.9         3.0          4.2         1.5 versicolor #> 63           6.0         2.7          4.0         1.0 versicolor #> 64           6.1         2.9          4.7         1.4 versicolor #> 65           5.6         2.9          3.6         1.3 versicolor #> 66           6.5         3.1          4.4         1.4 versicolor #> 67           5.6         3.0          4.5         1.5 versicolor #> 68           5.8         2.7          4.1         1.0 versicolor #> 69           6.2         2.7          4.5         1.5 versicolor #> 70           5.6         2.7          3.9         1.1 versicolor #> 71           5.9         3.2          4.8         1.8 versicolor #> 72           6.1         2.8          4.0         1.3 versicolor #> 73           6.3         2.7          4.9         1.5 versicolor #> 74           6.1         2.8          4.7         1.2 versicolor #> 75           6.4         2.9          4.3         1.3 versicolor #> 76           6.5         3.0          4.4         1.4 versicolor #> 77           6.5         2.8          4.8         1.4 versicolor #> 78           6.5         3.0          5.0         1.7 versicolor #> 79           6.0         2.9          4.5         1.5 versicolor #> 80           5.7         2.7          3.5         1.0 versicolor #> 81           5.5         2.7          3.8         1.1 versicolor #> 82           5.5         2.7          3.7         1.0 versicolor #> 83           5.8         2.7          3.9         1.2 versicolor #> 84           6.0         2.7          5.1         1.6 versicolor #> 85           5.4         3.0          4.5         1.5 versicolor #> 86           6.0         3.4          4.5         1.6 versicolor #> 87           6.5         3.1          4.7         1.5 versicolor #> 88           6.3         2.7          4.4         1.3 versicolor #> 89           5.6         3.0          4.1         1.3 versicolor #> 90           5.5         2.7          4.0         1.3 versicolor #> 91           5.5         2.7          4.4         1.2 versicolor #> 92           6.1         3.0          4.6         1.4 versicolor #> 93           5.8         2.7          4.0         1.2 versicolor #> 94           5.0         2.7          3.3         1.0 versicolor #> 95           5.6         2.7          4.2         1.3 versicolor #> 96           5.7         3.0          4.2         1.2 versicolor #> 97           5.7         2.9          4.2         1.3 versicolor #> 98           6.2         2.9          4.3         1.3 versicolor #> 99           5.1         2.7          3.0         1.1 versicolor #> 100          5.7         2.8          4.1         1.3 versicolor #> 101          6.3         3.3          5.3         1.9  virginica #> 102          5.8         2.7          5.1         1.9  virginica #> 103          6.5         3.0          5.3         1.9  virginica #> 104          6.3         2.9          5.3         1.8  virginica #> 105          6.5         3.0          5.3         1.9  virginica #> 106          6.5         3.0          5.3         1.9  virginica #> 107          5.0         2.7          4.5         1.7  virginica #> 108          6.5         2.9          5.3         1.8  virginica #> 109          6.5         2.7          5.3         1.8  virginica #> 110          6.5         3.4          5.3         1.9  virginica #> 111          6.5         3.2          5.1         1.9  virginica #> 112          6.4         2.7          5.3         1.9  virginica #> 113          6.5         3.0          5.3         1.9  virginica #> 114          5.7         2.7          5.0         1.9  virginica #> 115          5.8         2.8          5.1         1.9  virginica #> 116          6.4         3.2          5.3         1.9  virginica #> 117          6.5         3.0          5.3         1.8  virginica #> 118          6.5         3.4          5.3         1.9  virginica #> 119          6.5         2.7          5.3         1.9  virginica #> 120          6.0         2.7          5.0         1.5  virginica #> 121          6.5         3.2          5.3         1.9  virginica #> 122          5.6         2.8          4.9         1.9  virginica #> 123          6.5         2.8          5.3         1.9  virginica #> 124          6.3         2.7          4.9         1.8  virginica #> 125          6.5         3.3          5.3         1.9  virginica #> 126          6.5         3.2          5.3         1.8  virginica #> 127          6.2         2.8          4.8         1.8  virginica #> 128          6.1         3.0          4.9         1.8  virginica #> 129          6.4         2.8          5.3         1.9  virginica #> 130          6.5         3.0          5.3         1.6  virginica #> 131          6.5         2.8          5.3         1.9  virginica #> 132          6.5         3.4          5.3         1.9  virginica #> 133          6.4         2.8          5.3         1.9  virginica #> 134          6.3         2.8          5.1         1.5  virginica #> 135          6.1         2.7          5.3         1.4  virginica #> 136          6.5         3.0          5.3         1.9  virginica #> 137          6.3         3.4          5.3         1.9  virginica #> 138          6.4         3.1          5.3         1.8  virginica #> 139          6.0         3.0          4.8         1.8  virginica #> 140          6.5         3.1          5.3         1.9  virginica #> 141          6.5         3.1          5.3         1.9  virginica #> 142          6.5         3.1          5.1         1.9  virginica #> 143          5.8         2.7          5.1         1.9  virginica #> 144          6.5         3.2          5.3         1.9  virginica #> 145          6.5         3.3          5.3         1.9  virginica #> 146          6.5         3.0          5.2         1.9  virginica #> 147          6.3         2.7          5.0         1.9  virginica #> 148          6.5         3.0          5.2         1.9  virginica #> 149          6.2         3.4          5.3         1.9  virginica #> 150          5.9         3.0          5.1         1.8  virginica"},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-090","dir":"Changelog","previous_headings":"","what":"datawizard 0.9.0","title":"datawizard 0.9.0","text":"CRAN release: 2023-09-15 NEW FUNCTIONS row_means(), compute row means, optionally rows least min_valid non-missing values. contr.deviation() sum-deviation contrast coding factors. means_by_group(), compute mean values variables, grouped levels specified factors. data_seek(), seek variables data frame, based column names, variables labels, value labels factor levels. Searching labels works “labelled” data, .e. variables label labels attribute. CHANGES recode_into() gains overwrite argument skip overwriting already recoded cases multiple recode patterns apply case. recode_into() gains preserve_na argument preserve NA values recoding. data_read() now passes encoding argument data.table::fread(). allows read files non-ASCII characters. datawizard moves GPL-3 license MIT license. unnormalize() unstandardize() now work grouped data (#415). unnormalize() now errors instead emitting warning doesn’t necessary info (#415). BUG FIXES Fixed issue labels_to_levels() values labels sorted order values sequentially numbered. Fixed issues data_write() writing labelled data SPSS format vectors different type value labels. Fixed issue recode_into() probably wrong case number printed warning several recode patterns match one case. Fixed issue recode_into() original data contained NA values NA included recode pattern. Fixed issue data_filter() functions containing = (e.g. naming arguments, like grepl(pattern, x = )) mistakenly seen faulty syntax. Fixed issue empty_column() strings invalid multibyte strings. data frames files, empty_column() data_read() longer fails.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-080","dir":"Changelog","previous_headings":"","what":"datawizard 0.8.0","title":"datawizard 0.8.0","text":"CRAN release: 2023-06-16 BREAKING CHANGES following re-exported functions insight now removed: object_has_names(), object_has_rownames(), is_empty_object(), compact_list(), compact_character(). Argument na.rm renamed remove_na throughout datawizard functions. na.rm kept backward compatibility, deprecated later removed future updates. way expressions defined data_filter() revised. filter argument replaced ..., allowing separate multiple expression comma (combined &). Furthermore, expressions can now also defined strings, provided character vectors, allow string-friendly programming. CHANGES Weighted-functions (weighted_sd(), weighted_mean(), …) gain remove_na argument, remove keep missing infinite values. default, remove_na = TRUE, .e. missing infinite values removed default. reverse_scale(), normalize() rescale() gain append argument (similar data frame methods transformation functions), append recoded variables input data frame instead overwriting existing variables. NEW FUNCTIONS rowid_as_column() complement rownames_as_column() (mimic tibble::rowid_to_column()). Note behavior different tibble::rowid_to_column() grouped data. See Details section docs. data_unite(), merge values multiple variables one new variable. data_separate(), counterpart data_unite(), separate single variable multiple new variables. data_modify(), create new variables, modify remove existing variables data frame. MINOR CHANGES to_numeric() variables type Date, POSIXct POSIXlt now includes class name warning message. Added print() method center(), standardize(), normalize() rescale(). BUG FIXES standardize_parameters() now works package namespace model formula (#401). data_merge() longer yields warning tibbles join = \"bind\". center() standardize() work grouped data frames (class grouped_df) force = TRUE. data.frame method describe_distribution() returns NULL instead error valid variable passed (example factor variable include_factors = FALSE) (#421).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-071","dir":"Changelog","previous_headings":"","what":"datawizard 0.7.1","title":"datawizard 0.7.1","text":"CRAN release: 2023-04-03 BREAKING CHANGES add_labs() renamed assign_labels(). Since add_labs() existed days, alias backwards compatibility. NEW FUNCTIONS labels_to_levels(), use value labels factors levels. MINOR CHANGES data_read() now checks imported object actually data frame (coercible data frame), , longer errors, gives informative warning type object imported. BUG FIXES Fix test CRAN check Mac OS arm64","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-070","dir":"Changelog","previous_headings":"","what":"datawizard 0.7.0","title":"datawizard 0.7.0","text":"CRAN release: 2023-03-22 BREAKING CHANGES selection patterns, expressions like -var1:var3 exclude variables var1 var3 longer accepted. correct expression -(var1:var3). 2 reasons: consistent behavior numerics (-1:2 accepted -(1:2) ); consistent dplyr::select(), throws warning uses first variable first expression. NEW FUNCTIONS recode_into(), similar dplyr::case_when(), recode values one variables new variable. mean_sd() median_mad() summarizing vectors mean (median) range one SD (MAD) . data_write() counterpart data_read(), write data frames CSV, SPSS, SAS, Stata files many file types. One advantage existing functions write data packages labelled (numeric) data can converted factors (values labels used factor levels) even text formats like CSV similar. allows exporting “labelled” data file formats, . add_labs(), manually add value variable labels attributes variables. attributes stored \"label\" \"labels\" attributes, similar labelled class haven package. MINOR CHANGES data_rename() gets verbose argument. winsorize() now errors threshold incorrect (previously, provided warning returned unchanged data). argument verbose now useless kept backward compatibility. documentation now contains details valid values threshold (#357). functions arguments select /exclude, now one warning per misspelled variable. previous behavior one warning. Fixed inconsistent behaviour standardize() one arguments center scale provided (#365). unstandardize() replace_nan_inf() now work select helpers (#376). Added informative warning error messages reverse(). Furthermore, docs now describe range argument clearly (#380). unnormalize() errors unexpected inputs (#383). BUG FIXES empty_columns() (therefore remove_empty_columns()) now correctly detects columns containing NA_character_ (#349). Select helpers now work custom functions argument called select (#356). Fix unexpected warning convert_na_to() select list (#352). Fixed issue correct labelling numeric variables nine unique values associated value labels.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-065","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.5","title":"datawizard 0.6.5","text":"CRAN release: 2022-12-14 MAJOR CHANGES Etienne Bacher new maintainer. MINOR CHANGES standardize(), center(), normalize() rescale() can used model formulas, similar base::scale(). data_codebook() now includes proportion category/value, addition counts. Furthermore, data contains tagged NA values, included frequency table. BUG FIXES center(x) now works correctly x single value either reference center specified (#324). Fixed issue data_codebook(), failed labelled vectors values labels sorted order.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-064","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.4","title":"datawizard 0.6.4","text":"CRAN release: 2022-11-19 NEW FUNCTIONS data_codebook(): generate codebooks data frames. New functions deal duplicates: data_duplicated() (keep duplicates, including first occurrence) data_unique() (returns data, excluding duplicates except one instance , based selected method). MINOR CHANGES .data.frame methods now preserve custom attributes. include_bounds argument normalize() can now also numeric value, defining limit upper lower bound (.e. distance 1 0). data_filter() now works grouped data. BUG FIXES data_read() longer prints message empty columns data actually empty columns. data_to_wide() now drops columns id_cols (specified), names_from, values_from. behaviour observed tidyr::pivot_wider().","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-063","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.3","title":"datawizard 0.6.3","text":"CRAN release: 2022-10-22 MAJOR CHANGES new publication datawizard package: https://joss.theoj.org/papers/10.21105/joss.04684 Fixes failing tests due changes R-devel. data_to_long() data_to_wide() significant performance improvements, sometimes high ten-fold speedup. MINOR CHANGES column names misspelled, functions now suggest existing columns possibly meant. Miscellaneous performance gains. convert_to_na() now requires argument na class ‘Date’ convert specific dates NA. example, convert_to_na(x, na = \"2022-10-17\") must changed convert_to_na(x, na = .Date(\"2022-10-17\")). BUG FIXES data_to_long() data_to_wide() now correctly keep date format.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-062","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.2","title":"datawizard 0.6.2","text":"CRAN release: 2022-10-04 BREAKING CHANGES Methods grouped data frames (.grouped_df) longer support dplyr::group_by() dplyr version 0.8.0. empty_columns() remove_empty_columns() now also remove columns contain empty characters. Likewise, empty_rows() remove_empty_rows() remove observations completely missing empty character values. MINOR CHANGES data_read() gains convert_factors argument, turn automatic conversion numeric variables factors. BUG FIXES data_arrange() now works data frames grouped using data_group() (#274).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-061","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.1","title":"datawizard 0.6.1","text":"CRAN release: 2022-09-25 Updates tests upcoming changes tidyselect package (#267).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-060","dir":"Changelog","previous_headings":"","what":"datawizard 0.6.0","title":"datawizard 0.6.0","text":"CRAN release: 2022-09-15 BREAKING CHANGES minimum needed R version bumped 3.6. Following deprecated functions removed: data_cut(), data_recode(), data_shift(), data_reverse(), data_rescale(), data_to_factor(), data_to_numeric() New text_format() alias introduced format_text(), latter removed next release. New recode_values() alias introduced change_code(), latter removed next release. data_merge() now errors columns specified datasets. Using negative values arguments select exclude now removes columns selection/exclusion. previous behavior start selection/exclusion end dataset, inconsistent use “-” selecting possibilities. NEW FUNCTIONS data_peek(): peek values type variables data frame. coef_var(): compute coefficient variation. CHANGES data_filter() give informative messages malformed syntax filter argument. now possible use curly brackets pass variable names data_filter(), like following example. See examples section documentation data_filter(). regex argument added functions use select-helpers already argument. Select helpers starts_with(), ends_with(), contains() now accept several patterns, e.g starts_with(\"Sep\", \"Petal\"). Arguments select exclude present functions improved work loops custom functions. example, following code now works: now vignette summarizing various ways select exclude variables datawizard functions.","code":"foo <- function(data) {   i <- \"Sep\"   find_columns(data, select = starts_with(i)) } foo(iris)  for (i in c(\"Sepal\", \"Sp\")) {   head(iris) |>     find_columns(select = starts_with(i)) |>     print() }"},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-051","dir":"Changelog","previous_headings":"","what":"datawizard 0.5.1","title":"datawizard 0.5.1","text":"CRAN release: 2022-08-17 Fixes failing tests due poorman update.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-050","dir":"Changelog","previous_headings":"","what":"datawizard 0.5.0","title":"datawizard 0.5.0","text":"CRAN release: 2022-08-07 MAJOR CHANGES Following statistical transformation functions renamed data_*() prefix, since work exclusively data frames, typically first used vectors, therefore misleading names: data_cut() -> categorize() data_recode() -> change_code() data_shift() -> slide() data_reverse() -> reverse() data_rescale() -> rescale() data_to_factor() -> to_factor() data_to_numeric() -> to_numeric() Note functions also .data.frame() methods still work data frames well. Former function names still available aliases, deprecated removed future release. Bumps needed minimum R version 3.5. Removed deprecated function data_findcols(). Please use replacement, data_find(). Removed alias extract() data_extract() function since collided tidyr::extract(). Argument training_proportion data_partition() deprecated. Please use proportion now. Given continued significant contributions package, Etienne Bacher (@etiennebacher) now included author. unstandardise() now works center(x) unnormalize() now works change_scale(x) reshape_wider() now follows consistently tidyr::pivot_wider() syntax. Arguments colnames_from, sep, rows_from deprecated replaced names_from, names_sep, id_cols respectively. reshape_wider() also gains argument names_glue (#182, #198). Similarly, reshape_longer() now follows consistently tidyr::pivot_longer() syntax. Argument colnames_to deprecated replaced names_to. reshape_longer() also gains new arguments: names_prefix, names_sep, names_pattern, values_drop_na (#189). CHANGES text formatting helpers (like text_concatenate()) gain enclose argument, wrap text elements surrounding characters. winsorize now accepts “raw” “zscore” methods (addition “percentile”). Additionally, robust set TRUE together method = \"zscore\", winsorizes via median median absolute deviation (MAD); else via mean standard deviation. (@rempsyc, #177, #49, #47). convert_na_to now accepts numeric replacements character vectors single replacement multiple vector classes. (@rempsyc, #214). data_partition() now allows create multiple partitions data, returning multiple training remaining test set. Functions like center(), normalize() standardize() longer fail data contains infinite values (Inf). NEW FUNCTIONS row_to_colnames() colnames_to_row() move row column names, column names row (@etiennebacher, #169). data_arrange() sort rows dataframe according values selected columns. BUG FIXES Fixed wrong column names data_to_wide() (#173).","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-041","dir":"Changelog","previous_headings":"","what":"datawizard 0.4.1","title":"datawizard 0.4.1","text":"CRAN release: 2022-05-16 BREAKING Added standardize.default() method (moved package effectsize), consistent default-method now package generic. standardize.default() behaves exactly like effectsize particularly works regression model objects. effectsize now re-exports standardize() datawizard. NEW FUNCTIONS data_shift() shift value range numeric variables. data_recode() recode old new values. data_to_factor() counterpart data_to_numeric(). data_tabulate() create frequency tables variables. data_read() read (import) data files (text, foreign statistical packages). unnormalize() counterpart normalize(). function works variables normalized normalize(). data_group() data_ungroup() create grouped data frames, remove grouping information grouped data frames. CHANGES data_find() added alias find_colums(), consistent name patterns datawizard functions. data_findcols() removed future update usage discouraged. select argument (thus, also exclude argument) now also accepts functions testing logical conditions, e.g. .numeric() (.numeric), user-defined function selects variables function returns TRUE (like: foo <- function(x) mean(x) > 3). Arguments select exclude now allow negation select-helpers, like -ends_with(\"\"), -.numeric -Sepal.Width:Petal.Length. Many functions now get .default method, capture unsupported classes. now yields message returns original input, hence, .data.frame methods won’t stop due error. filter argument data_filter() can also numeric vector, indicate row indices rows returned. convert_to_na() gets methods variables class logical Date. convert_to_na() factors (data frames) gains drop_levels argument, drop unused levels replaced NA. data_to_numeric() gains two arguments, preserve_levels lowest, give better control conversion factors. BUG FIXES logicals passed center() standardize() force = TRUE, properly converted numeric variables.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-040","dir":"Changelog","previous_headings":"","what":"datawizard 0.4.0","title":"datawizard 0.4.0","text":"CRAN release: 2022-03-30 MAJOR CHANGES data_match() now returns filtered data default. Old behavior (returning rows indices) can set setting return_indices = TRUE. following functions now re-exported insight package: object_has_names(), object_has_rownames(), is_empty_object(), compact_list(), compact_character() data_findcols() become deprecated future updates. Please use new replacements find_columns() get_columns(). vignette Analysing Longitudinal Panel Data now moved parameters package. NEW FUNCTIONS convert rownames column, vice versa: rownames_as_column() column_as_rownames() (@etiennebacher, #80). find_columns() get_columns() find column names retrieve subsets data frames, based various select-methods (including select-helpers). function supersede data_findcols() future. data_filter() complement data_match(), works logical expressions filtering rows data frames. computing weighted centrality measures dispersion: weighted_mean(), weighted_median(), weighted_sd() weighted_mad(). replace NA vectors dataframes: convert_na_to() (@etiennebacher, #111). MINOR CHANGES select argument several functions (like data_remove(), reshape_longer(), data_extract()) now allows use select-helpers selecting variables based specific patterns. data_extract() gains new arguments allow type-safe return values, .e. always return vector data frame. Thus, data_extract() can now used select multiple variables pull single variable data frames. data_match() gains match argument, indicate logical operation matching results combined. Improved support labelled data many functions, .e. returned data frame preserve value variable label attributes, possible applicable. describe_distribution() now works lists (@etiennebacher, #105). data_rename() doesn’t use pattern anymore rename columns replacement provided (@etiennebacher, #103). data_rename() now adds suffix duplicated names replacement (@etiennebacher, #103). BUG FIXES data_to_numeric() produced wrong results factors dummy_factors =   TRUE factor contained missing values. data_match() produced wrong results data contained missing values. Fixed CRAN check issues data_extract() one variable extracted data frame.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-030","dir":"Changelog","previous_headings":"","what":"datawizard 0.3.0","title":"datawizard 0.3.0","text":"CRAN release: 2022-03-02 NEW FUNCTIONS find remove empty rows columns data frame: empty_rows(), empty_columns(), remove_empty_rows(), remove_empty_columns(), remove_empty. check names: object_has_names() object_has_rownames(). rotate data frames: data_rotate(). reverse score variables: data_reverse(). merge/join multiple data frames: data_merge() (alias data_join()). cut (recode) data groups: data_cut(). replace specific values NAs: convert_to_na(). replace Inf NaN values NAs: replace_nan_inf(). Arguments cols, data_relocate() can now also numeric values, indicating position destination column.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-023","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.3","title":"datawizard 0.2.3","text":"CRAN release: 2022-01-26 New functions: work lists: is_empty_object() compact_list() work strings: compact_character()","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-022","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.2","title":"datawizard 0.2.2","text":"CRAN release: 2022-01-04 New function data_extract() (alias extract()) pull single variables data frame, possibly naming value row names data frame. reshape_ci() gains ci_type argument, reshape data frames CI-columns prefixes \"CI\". standardize() center() gain arguments center scale, define references centrality deviation used centering standardizing variables. center() gains arguments force reference, similar standardize(). functionality append argument center() standardize() revised. made suffix argument redundant, thus removed. Fixed issue standardize(). Fixed issue data_findcols().","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-021","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.1","title":"datawizard 0.2.1","text":"CRAN release: 2021-10-04 Exports plot method visualisation_recipe() objects see package. centre(), standardise(), unstandardise() exported aliases center(), standardize(), unstandardize(), respectively.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-0201","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.0.1","title":"datawizard 0.2.0.1","text":"CRAN release: 2021-09-02 mainly maintenance release addresses issues conflicting namespaces.","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-020","dir":"Changelog","previous_headings":"","what":"datawizard 0.2.0","title":"datawizard 0.2.0","text":"CRAN release: 2021-08-17 New function: visualisation_recipe(). following function now moved performance package: check_multimodal(). Minor updates documentation, including new vignette demean().","code":""},{"path":"https://easystats.github.io/datawizard/news/index.html","id":"datawizard-010","dir":"Changelog","previous_headings":"","what":"datawizard 0.1.0","title":"datawizard 0.1.0","text":"CRAN release: 2021-06-18 First release.","code":""}]