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Examples#> 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 -9.995436449 51 30 39 61 92 45 -#> 2 0.250660638 64 51 54 63 73 47 -#> 3 3.748859294 70 68 69 76 86 48 -#> 4 -0.999039138 63 45 47 54 84 35 -#> 5 7.746457501 78 56 66 71 83 47 -#> 6 -12.996637345 55 49 44 54 49 34 -#> 7 -7.000240034 67 42 56 66 68 35 -#> 8 -0.002641826 75 50 55 70 66 41 -#> 9 -4.254743395 82 72 67 71 83 31 -#> 10 6.501561311 61 45 47 62 80 41 -#> 11 9.503963103 53 53 58 58 67 34 -#> 12 7.251861535 60 47 39 59 74 41 -#> 13 7.751261086 62 57 42 55 63 25 -#> 14 -9.005043619 83 83 45 59 77 35 -#> 15 4.496757726 77 54 72 79 77 46 -#> 16 -1.257145187 90 50 72 60 54 36 -#> 17 -4.505644067 85 64 69 79 79 63 -#> 18 5.251861535 60 65 75 55 80 60 -#> 19 -2.251140706 70 46 57 75 85 46 -#> 20 -8.247538017 58 68 54 64 78 52 -#> 21 5.257866016 40 33 34 43 64 33 -#> 22 3.501561311 61 52 62 66 80 41 -#> 23 -11.249939810 66 52 50 63 80 37 -#> 24 -2.491233312 37 42 58 50 57 49 -#> 25 7.753662879 54 42 48 66 75 33 -#> 26 -6.503242274 77 66 63 88 76 72 -#> 27 6.997358174 75 58 74 80 78 49 -#> 28 -9.497237793 57 44 45 51 83 38 -#> 29 6.494355933 85 71 71 77 74 55 -#> 30 5.745256605 82 39 59 64 78 39 +#> rating complaints privileges learning raises critical advance +#> 1 -9.91476152 51 30 39 61 92 45 +#> 2 0.31153539 64 51 54 63 73 47 +#> 3 3.80059551 70 68 69 76 86 48 +#> 4 -0.93664129 63 45 47 54 84 35 +#> 5 7.78600899 78 56 66 71 83 47 +#> 6 -12.92205478 55 49 44 54 49 34 +#> 7 -6.94393455 67 42 56 66 68 35 +#> 8 0.04147893 75 50 55 70 66 41 +#> 9 -4.22128427 82 72 67 71 83 31 +#> 10 6.56700533 61 45 47 62 80 41 +#> 11 9.58159185 53 53 58 58 67 34 +#> 12 7.31882865 60 47 39 59 74 41 +#> 13 7.81518202 62 57 42 55 63 25 +#> 14 -8.97310758 83 83 45 59 77 35 +#> 15 4.53783230 77 54 72 79 77 46 +#> 16 -1.23587078 90 50 72 60 54 36 +#> 17 -4.47675421 85 64 69 79 79 63 +#> 18 5.31882865 60 65 75 55 80 60 +#> 19 -2.19940449 70 46 57 75 85 46 +#> 20 -8.17752472 58 68 54 64 78 52 +#> 21 5.35529494 40 33 34 43 64 33 +#> 22 3.56700533 61 52 62 66 80 41 +#> 23 -11.19211124 66 52 50 63 80 37 +#> 24 -2.38923512 37 42 58 50 57 49 +#> 25 7.82976854 54 42 48 66 75 33 +#> 26 -6.46216770 77 66 63 88 76 72 +#> 27 7.04147893 75 58 74 80 78 49 +#> 28 -9.42570141 57 44 45 51 83 38 +#> 29 6.52324579 85 71 71 77 74 55 +#> 30 5.77871573 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 diff --git a/search.json b/search.json index 0d6c11f48..ee849e053 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 #> #> 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 #> #> 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 ## ## 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 , species , films , ## # vehicles , starships ## # A tibble: 7 × 14 ## name height mass hair_color skin_color eye_color birth_year sex gender ## ## 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 , species , films , ## # vehicles , starships "},{"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 ## ## 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 ## ## 1 Luke Sk… 172 77 19 male mascu… Tatooine Human ## 2 C-3PO 167 75 112 none mascu… Tatooine Droid ## 3 R2-D2 96 32 33 none mascu… Naboo Droid ## 4 Darth V… 202 136 41.9 male mascu… Tatooine Human ## 5 Leia Or… 150 49 19 fema… femin… Alderaan Human ## 6 Owen La… 178 120 52 male mascu… Tatooine Human ## # ℹ 1 more variable: starships # ---------- 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 ## ## 1 Luke Sk… 172 77 19 male mascu… Tatooine Human ## 2 C-3PO 167 75 112 none mascu… Tatooine Droid ## 3 R2-D2 96 32 33 none mascu… Naboo Droid ## 4 Darth V… 202 136 41.9 male mascu… Tatooine Human ## 5 Leia Or… 150 49 19 fema… femin… Alderaan Human ## 6 Owen La… 178 120 52 male mascu… Tatooine Human ## # ℹ 1 more variable: starships # ---------- 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 ## ## 1 Luke Sk… 172 77 19 male mascu… Tatooine Human ## 2 C-3PO 167 75 112 none mascu… Tatooine Droid ## 3 R2-D2 96 32 33 none mascu… Naboo Droid ## 4 Darth V… 202 136 41.9 male mascu… Tatooine Human ## 5 Leia Or… 150 49 19 fema… femin… Alderaan Human ## 6 Owen La… 178 120 52 male mascu… Tatooine Human ## # ℹ 1 more variable: starships # ---------- datawizard ----------- starwars %>% data_select(select = is.numeric) # ---------- tidyverse ----------- starwars %>% select(where(is.numeric)) ## # A tibble: 6 × 3 ## height mass birth_year ## ## 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 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 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 ## ## 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 , species , films , ## # vehicles , starships # ---------- 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 ## ## 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 , species , films , ## # vehicles , starships "},{"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 ## ## 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 ## ## 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 , species , films , ## # vehicles , starships 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 ## ## 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 , Homeworld , Species , ## # Films , Vehicles , Starships 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 ## ## 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 , sex.NEW , gender.NEW , ## # homeworld.NEW , species.NEW , films.NEW , ## # vehicles.NEW , starships.NEW "},{"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 ## ## 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 , species , films , ## # vehicles , starships # ---------- 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 ## ## 1 Luke Sk… 172 77 blond fair blue 19 Human ## 2 C-3PO 167 75 NA gold yellow 112 Droid ## 3 R2-D2 96 32 NA white, bl… red 33 Droid ## 4 Darth V… 202 136 none white yellow 41.9 Human ## 5 Leia Or… 150 49 brown light brown 19 Human ## 6 Owen La… 178 120 brown, gr… light blue 52 Human ## # ℹ 5 more variables: vehicles , starships , sex , ## # gender , homeworld "},{"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` ## ## 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` , `>150k` , ## # `Don't know/refused` # ---------- 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 ## ## 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 ## ## 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 , wk10 , wk11 , wk12 , ## # wk13 , wk14 , wk15 , wk16 , wk17 , wk18 , ## # wk19 , wk20 , wk21 , wk22 , wk23 , wk24 , ## # wk25 , wk26 , wk27 , wk28 , wk29 , wk30 , ## # wk31 , wk32 , wk33 , wk34 , wk35 , wk36 , ## # wk37 , wk38 , wk39 , wk40 , wk41 , wk42 , … # ---------- 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 ## ## 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 ## ## 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 ## ## 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 ## ## 1 Mick Stones ## 2 John Beatles ## 3 Paul Beatles band_instruments ## # A tibble: 3 × 2 ## name plays ## ## 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 ## * ## 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 ## * ## 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 ## * ## 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 ## * ## 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 ## ## 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 ## ## 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 ## 2 Title Title2 ## 3 1 4 ## 4 2 5 ## 5 3 6 x2 <- x %>% row_to_colnames(row = 2) x2 ## Title Title2 ## 1 ## 3 1 4 ## 4 2 5 ## 5 3 6 x2 %>% colnames_to_row() ## x1 x2 ## 1 Title Title2 ## 11 ## 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 #> 1 2 b 6 2 f 100 #> 2 3 c 7 3 g 101 #> 4 NA 8 4 h 102 data_merge(x, y, join = \"left\") #> a b c id d e #> 3 1 a 5 1 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 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 #> 2 2 b 6 2 NA #> 3 3 c 7 3 NA #> 4 NA 6 2 f 100 #> 5 NA 7 3 g 101 #> 6 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 -9.995436449 51 30 39 61 92 45 #> 2 0.250660638 64 51 54 63 73 47 #> 3 3.748859294 70 68 69 76 86 48 #> 4 -0.999039138 63 45 47 54 84 35 #> 5 7.746457501 78 56 66 71 83 47 #> 6 -12.996637345 55 49 44 54 49 34 #> 7 -7.000240034 67 42 56 66 68 35 #> 8 -0.002641826 75 50 55 70 66 41 #> 9 -4.254743395 82 72 67 71 83 31 #> 10 6.501561311 61 45 47 62 80 41 #> 11 9.503963103 53 53 58 58 67 34 #> 12 7.251861535 60 47 39 59 74 41 #> 13 7.751261086 62 57 42 55 63 25 #> 14 -9.005043619 83 83 45 59 77 35 #> 15 4.496757726 77 54 72 79 77 46 #> 16 -1.257145187 90 50 72 60 54 36 #> 17 -4.505644067 85 64 69 79 79 63 #> 18 5.251861535 60 65 75 55 80 60 #> 19 -2.251140706 70 46 57 75 85 46 #> 20 -8.247538017 58 68 54 64 78 52 #> 21 5.257866016 40 33 34 43 64 33 #> 22 3.501561311 61 52 62 66 80 41 #> 23 -11.249939810 66 52 50 63 80 37 #> 24 -2.491233312 37 42 58 50 57 49 #> 25 7.753662879 54 42 48 66 75 33 #> 26 -6.503242274 77 66 63 88 76 72 #> 27 6.997358174 75 58 74 80 78 49 #> 28 -9.497237793 57 44 45 51 83 38 #> 29 6.494355933 85 71 71 77 74 55 #> 30 5.745256605 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) { # !identical(Sys.getenv(\"IN_PKGDOWN\"), \"true\") # \\donttest{ 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 # 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 # 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 "},{"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 99 #> 8 5 c 99 #> 9 4 d 33 #> 10 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 99 #> 8 5 c 99 #> 9 4 d 33 #> 10 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 #> #> 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 = ). 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 #> 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 #> 2 2 b 6 2 NA #> 3 3 c 7 3 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 5 NA #> 3 2 b 2 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 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 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 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 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 #> #> 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 #> 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 #> 3 2 b 6 #> 4 3 c #> 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 #> 2 #> 3 2 b 6 7 #> 4 3 c #> 5 x y z # 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 #> 3 b 6 7 #> 4 3 c #> 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 #> 3 2 b 6 7 #> 4 3 c #> 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 #> 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 #> 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 #> 4 5 j "},{"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) #> # 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 #> | 10 | 10.00 | | # data frame data_tabulate(efc, c(\"e42dep\", \"c172code\")) #> elder's dependency (e42dep) #> # 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 #> | 3 | 3.00 | | #> #> carer's level of education (c172code) #> # 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 #> | 10 | 10.00 | | # grouped data frame suppressPackageStartupMessages(library(poorman, quietly = TRUE)) efc %>% group_by(c172code) %>% data_tabulate(\"e16sex\") #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | #> #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | #> #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | #> #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | # 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 #> | | | 0 | 0.00 | | #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex | c172code (2) | 1 | 32 | 48.48 | 48.48 | 48.48 #> | | 2 | 34 | 51.52 | 51.52 | 100.00 #> | | | 0 | 0.00 | | #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex | c172code (3) | 1 | 4 | 25.00 | 25.00 | 25.00 #> | | 2 | 12 | 75.00 | 75.00 | 100.00 #> | | | 0 | 0.00 | | #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex | c172code (NA) | 1 | 5 | 50.00 | 50.00 | 50.00 #> | | 2 | 5 | 50.00 | 50.00 | 100.00 #> | | | 0 | 0.00 | | #> ---------------------------------------------------------------------- # 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) #> # 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 #> | 0 | 0.00 | | # to remove the big mark, use \"print(..., big_mark = \"\")\" print(data_tabulate(x), big_mark = \"\") #> x #> # 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 #> | 0 | 0.00 | | "},{"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 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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 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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 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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 #> 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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 #> #> 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 #> # 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 #> | 10 | 10.00 | | x <- labels_to_levels(x) levels(x) #> [1] \"low\" \"mid\" \"high\" data_tabulate(x) #> x #> # 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 #> | 10 | 10.00 | | "},{"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 #> 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 #> 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 #> 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 #> 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 #> 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 #> 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 #> 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":[]},{"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 issues data_write() writing labelled data SPSS format character vectors missing value labels, existing variable 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":""}] +[{"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 #> #> 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 #> #> 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 ## ## 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 , species , films , ## # vehicles , starships ## # A tibble: 7 × 14 ## name height mass hair_color skin_color eye_color birth_year sex gender ## ## 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 , species , films , ## # vehicles , starships "},{"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 ## ## 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 ## ## 1 Luke Sk… 172 77 19 male mascu… Tatooine Human ## 2 C-3PO 167 75 112 none mascu… Tatooine Droid ## 3 R2-D2 96 32 33 none mascu… Naboo Droid ## 4 Darth V… 202 136 41.9 male mascu… Tatooine Human ## 5 Leia Or… 150 49 19 fema… femin… Alderaan Human ## 6 Owen La… 178 120 52 male mascu… Tatooine Human ## # ℹ 1 more variable: starships # ---------- 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 ## ## 1 Luke Sk… 172 77 19 male mascu… Tatooine Human ## 2 C-3PO 167 75 112 none mascu… Tatooine Droid ## 3 R2-D2 96 32 33 none mascu… Naboo Droid ## 4 Darth V… 202 136 41.9 male mascu… Tatooine Human ## 5 Leia Or… 150 49 19 fema… femin… Alderaan Human ## 6 Owen La… 178 120 52 male mascu… Tatooine Human ## # ℹ 1 more variable: starships # ---------- 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 ## ## 1 Luke Sk… 172 77 19 male mascu… Tatooine Human ## 2 C-3PO 167 75 112 none mascu… Tatooine Droid ## 3 R2-D2 96 32 33 none mascu… Naboo Droid ## 4 Darth V… 202 136 41.9 male mascu… Tatooine Human ## 5 Leia Or… 150 49 19 fema… femin… Alderaan Human ## 6 Owen La… 178 120 52 male mascu… Tatooine Human ## # ℹ 1 more variable: starships # ---------- datawizard ----------- starwars %>% data_select(select = is.numeric) # ---------- tidyverse ----------- starwars %>% select(where(is.numeric)) ## # A tibble: 6 × 3 ## height mass birth_year ## ## 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 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 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 ## ## 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 , species , films , ## # vehicles , starships # ---------- 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 ## ## 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 , species , films , ## # vehicles , starships "},{"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 ## ## 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 ## ## 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 , species , films , ## # vehicles , starships 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 ## ## 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 , Homeworld , Species , ## # Films , Vehicles , Starships 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 ## ## 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 , sex.NEW , gender.NEW , ## # homeworld.NEW , species.NEW , films.NEW , ## # vehicles.NEW , starships.NEW "},{"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 ## ## 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 , species , films , ## # vehicles , starships # ---------- 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 ## ## 1 Luke Sk… 172 77 blond fair blue 19 Human ## 2 C-3PO 167 75 NA gold yellow 112 Droid ## 3 R2-D2 96 32 NA white, bl… red 33 Droid ## 4 Darth V… 202 136 none white yellow 41.9 Human ## 5 Leia Or… 150 49 brown light brown 19 Human ## 6 Owen La… 178 120 brown, gr… light blue 52 Human ## # ℹ 5 more variables: vehicles , starships , sex , ## # gender , homeworld "},{"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` ## ## 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` , `>150k` , ## # `Don't know/refused` # ---------- 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 ## ## 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 ## ## 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 , wk10 , wk11 , wk12 , ## # wk13 , wk14 , wk15 , wk16 , wk17 , wk18 , ## # wk19 , wk20 , wk21 , wk22 , wk23 , wk24 , ## # wk25 , wk26 , wk27 , wk28 , wk29 , wk30 , ## # wk31 , wk32 , wk33 , wk34 , wk35 , wk36 , ## # wk37 , wk38 , wk39 , wk40 , wk41 , wk42 , … # ---------- 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 ## ## 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 ## ## 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 ## ## 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 ## ## 1 Mick Stones ## 2 John Beatles ## 3 Paul Beatles band_instruments ## # A tibble: 3 × 2 ## name plays ## ## 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 ## * ## 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 ## * ## 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 ## * ## 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 ## * ## 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 ## ## 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 ## ## 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 ## 2 Title Title2 ## 3 1 4 ## 4 2 5 ## 5 3 6 x2 <- x %>% row_to_colnames(row = 2) x2 ## Title Title2 ## 1 ## 3 1 4 ## 4 2 5 ## 5 3 6 x2 %>% colnames_to_row() ## x1 x2 ## 1 Title Title2 ## 11 ## 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 #> 1 2 b 6 2 f 100 #> 2 3 c 7 3 g 101 #> 4 NA 8 4 h 102 data_merge(x, y, join = \"left\") #> a b c id d e #> 3 1 a 5 1 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 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 #> 2 2 b 6 2 NA #> 3 3 c 7 3 NA #> 4 NA 6 2 f 100 #> 5 NA 7 3 g 101 #> 6 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 -9.91476152 51 30 39 61 92 45 #> 2 0.31153539 64 51 54 63 73 47 #> 3 3.80059551 70 68 69 76 86 48 #> 4 -0.93664129 63 45 47 54 84 35 #> 5 7.78600899 78 56 66 71 83 47 #> 6 -12.92205478 55 49 44 54 49 34 #> 7 -6.94393455 67 42 56 66 68 35 #> 8 0.04147893 75 50 55 70 66 41 #> 9 -4.22128427 82 72 67 71 83 31 #> 10 6.56700533 61 45 47 62 80 41 #> 11 9.58159185 53 53 58 58 67 34 #> 12 7.31882865 60 47 39 59 74 41 #> 13 7.81518202 62 57 42 55 63 25 #> 14 -8.97310758 83 83 45 59 77 35 #> 15 4.53783230 77 54 72 79 77 46 #> 16 -1.23587078 90 50 72 60 54 36 #> 17 -4.47675421 85 64 69 79 79 63 #> 18 5.31882865 60 65 75 55 80 60 #> 19 -2.19940449 70 46 57 75 85 46 #> 20 -8.17752472 58 68 54 64 78 52 #> 21 5.35529494 40 33 34 43 64 33 #> 22 3.56700533 61 52 62 66 80 41 #> 23 -11.19211124 66 52 50 63 80 37 #> 24 -2.38923512 37 42 58 50 57 49 #> 25 7.82976854 54 42 48 66 75 33 #> 26 -6.46216770 77 66 63 88 76 72 #> 27 7.04147893 75 58 74 80 78 49 #> 28 -9.42570141 57 44 45 51 83 38 #> 29 6.52324579 85 71 71 77 74 55 #> 30 5.77871573 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) { # !identical(Sys.getenv(\"IN_PKGDOWN\"), \"true\") # \\donttest{ 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 # 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 # 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 "},{"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 99 #> 8 5 c 99 #> 9 4 d 33 #> 10 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 99 #> 8 5 c 99 #> 9 4 d 33 #> 10 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 #> #> 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 = ). 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 #> 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 #> 2 2 b 6 2 NA #> 3 3 c 7 3 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 5 NA #> 3 2 b 2 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 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 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 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 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 #> #> 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 #> 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 #> 3 2 b 6 #> 4 3 c #> 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 #> 2 #> 3 2 b 6 7 #> 4 3 c #> 5 x y z # 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 #> 3 b 6 7 #> 4 3 c #> 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 #> 3 2 b 6 7 #> 4 3 c #> 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 #> 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 #> 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 #> 4 5 j "},{"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) #> # 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 #> | 10 | 10.00 | | # data frame data_tabulate(efc, c(\"e42dep\", \"c172code\")) #> elder's dependency (e42dep) #> # 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 #> | 3 | 3.00 | | #> #> carer's level of education (c172code) #> # 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 #> | 10 | 10.00 | | # grouped data frame suppressPackageStartupMessages(library(poorman, quietly = TRUE)) efc %>% group_by(c172code) %>% data_tabulate(\"e16sex\") #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | #> #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | #> #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | #> #> elder's gender (e16sex) #> 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 #> | 0 | 0.00 | | # 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 #> | | | 0 | 0.00 | | #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex | c172code (2) | 1 | 32 | 48.48 | 48.48 | 48.48 #> | | 2 | 34 | 51.52 | 51.52 | 100.00 #> | | | 0 | 0.00 | | #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex | c172code (3) | 1 | 4 | 25.00 | 25.00 | 25.00 #> | | 2 | 12 | 75.00 | 75.00 | 100.00 #> | | | 0 | 0.00 | | #> ---------+---------------+-------+----+-------+---------+------------- #> e16sex | c172code (NA) | 1 | 5 | 50.00 | 50.00 | 50.00 #> | | 2 | 5 | 50.00 | 50.00 | 100.00 #> | | | 0 | 0.00 | | #> ---------------------------------------------------------------------- # 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) #> # 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 #> | 0 | 0.00 | | # to remove the big mark, use \"print(..., big_mark = \"\")\" print(data_tabulate(x), big_mark = \"\") #> x #> # 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 #> | 0 | 0.00 | | "},{"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 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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 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#> 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 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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 #> #> 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 #> # 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 #> | 10 | 10.00 | | x <- labels_to_levels(x) levels(x) #> [1] \"low\" \"mid\" \"high\" data_tabulate(x) #> x #> # 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 #> | 10 | 10.00 | | "},{"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 #> 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 #> 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 #> 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 #> 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 #> 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 #> 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 #> 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":[]},{"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 issues data_write() writing labelled data SPSS format character vectors missing value labels, existing variable 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":""}]