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DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: datawizard
 Title: Easy Data Wrangling and Statistical Transformations
-Version: 0.10.0.2
+Version: 0.10.0.3
 Authors@R: c(
     person("Indrajeet", "Patil", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0003-1995-6531", Twitter = "@patilindrajeets")),
@@ -42,18 +42,19 @@ Suggests:
     brms,
     curl,
     data.table,
-    dplyr (>= 1.0),
+    dplyr (>= 1.1),
     effectsize,
     emmeans,
     gamm4,
-    ggplot2,
+    ggplot2 (>= 3.5.0),
     gt,
     haven,
     htmltools,
     httr,
     knitr,
     lme4,
     mediation,
+    modelbased,
     parameters (>= 0.21.6),
     poorman (>= 0.2.7),
     psych,
@@ -67,6 +68,8 @@ Suggests:
     tibble,
     tidyr,
     withr
+Remotes:
+    easystats/modelbased
 VignetteBuilder: 
     knitr
 Encoding: UTF-8

R/adjust.R

@@ -30,7 +30,7 @@
 #'
 #' @return A data frame comparable to `data`, with adjusted variables.
 #'
 #' @examplesIf require("bayestestR", quietly = TRUE) && require("rstanarm", quietly = TRUE) && require("gamm4", quietly = TRUE)
 #' adjusted_all <- adjust(attitude)
 #' head(adjusted_all)
 #' adjusted_one <- adjust(attitude, effect = "complaints", select = "rating")
@@ -51,7 +51,8 @@
 #' adjusted_icpt <- adjust(data, effect = "V1", select = "V2", keep_intercept = TRUE)
 #'
 #' # Visualize
-#' plot(data$V1, data$V2,
+#' 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"

R/datawizard-package.R

@@ -7,12 +7,12 @@
 #' A lightweight package to assist in key steps involved in any data analysis
 #' workflow:
 #'
-#' - wrangling the raw data to get it in the needed form,
-#' - applying preprocessing steps and statistical transformations, and
-#' - compute statistical summaries of data properties and distributions.
+#'  - wrangling the raw data to get it in the needed form,
+#'  - applying preprocessing steps and statistical transformations, and
+#'  - compute statistical summaries of data properties and distributions.
 #'
 #' It is also the data wrangling backend for packages in 'easystats' ecosystem.
-#' References: Patil et al. (2022) \doi{10.21105/joss.04684}.
+#' Reference: Patil et al. (2022) \doi{10.21105/joss.04684}.
 #'
 #' @docType package
 #' @aliases datawizard datawizard-package

R/row_means.R

@@ -8,7 +8,7 @@
 #' @param min_valid Optional, a numeric value of length 1. May either be
 #' - a numeric value that indicates the amount of valid values per row to
 #'   calculate the row mean;
-#' - or a value between 0 and 1, indicating a proportion of valid values per
+#' - or a value between `0` and `1`, indicating a proportion of valid values per
 #'   row to calculate the row mean (see 'Details').
 #' - `NULL` (default), in which all cases are considered.
 #'
@@ -110,7 +110,7 @@
   }
 
   # proceed here if min_valid is not NULL
   if (!is.null(min_valid)) {
     # is 'min_valid' indicating a proportion?
     decimals <- min_valid %% 1
     if (decimals != 0) {

R/smoothness.R

@@ -1,9 +1,9 @@
 #' Quantify the smoothness of a vector
 #'
 #' @param x Numeric vector (similar to a time series).
-#' @param method Can be "diff" (the standard deviation of the standardized
-#'   differences) or "cor" (default, lag-one autocorrelation).
-#' @param lag An integer indicating which lag to use. If less than 1, will be
+#' @param method Can be `"diff"` (the standard deviation of the standardized
+#'   differences) or `"cor"` (default, lag-one autocorrelation).
+#' @param lag An integer indicating which lag to use. If less than `1`, will be
 #'   interpreted as expressed in percentage of the length of the vector.
 #' @inheritParams skewness
 #'
@@ -39,13 +39,13 @@
   }
 
   if (method == "cor") {
     smooth <- stats::cor(utils::head(x, length(x) - lag), utils::tail(x, length(x) - lag))
   } else {
     smooth <- stats::sd(diff(x, lag = lag)) / abs(mean(diff(x, lag = lag)))
   }
 
   if (!is.null(iterations)) {
     if (!requireNamespace("boot", quietly = TRUE)) {
       insight::format_warning("Package 'boot' needed for bootstrapping SEs.")
     } else {
       results <- boot::boot(
@@ -56,7 +56,7 @@
         lag = lag
       )
       out_se <- stats::sd(results$t, na.rm = TRUE)
       smooth <- data.frame(Smoothness = smooth, SE = out_se)
     }
   }
 

R/standardize.models.R

@@ -96,10 +96,10 @@
                                 weights = TRUE,
                                 verbose = TRUE,
                                 include_response = TRUE,
                                 update_expr,
                                 ...) {
   m_info <- .get_model_info(x, ...)
   data <- insight::get_data(x, source = "mf", verbose = FALSE)
 
   if (isTRUE(attr(data, "is_subset"))) {
     insight::format_error("Cannot standardize a model fit with a 'subset = '.")
@@ -195,8 +195,8 @@
 
 
 
-
   ## ---- STANDARDIZE! ----
+
   w <- insight::get_weights(x, na_rm = TRUE)
 
   data_std <- standardize(data[do_standardize],
@@ -365,7 +365,7 @@
 
 
   if (!is.null(covs)) {
     covs <- mapply(.rescale_fixed_values, covs, names(covs),
       SIMPLIFY = FALSE,
       MoreArgs = list(
         y_data = y_data, m_data = m_data,
@@ -391,7 +391,7 @@
   #
   #   control.value <- temp_vals[1]
   #   treat.value <- temp_vals[2]
   #   if (verbose) insight::format_alert("control and treatment values have been rescaled to their standardized scales.")
   # }
 
   if (verbose && !all(c(control.value, treat.value) %in% c(0, 1))) {

R/utils.R

@@ -48,7 +48,7 @@
 
 
 #' Fuzzy grep, matches pattern that are close, but not identical
-#' Example:
+#' @examples
 #' colnames(iris)
 #' p <- sprintf("(%s){~%i}", "Spela", 2)
 #' grep(pattern = p, x = colnames(iris), ignore.case = FALSE)

README.md

@@ -505,20 +505,7 @@ To rescale a numeric variable to a new range:
 ``` r
 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"
+#> (original range = -5 to 5)
 ```
 
 ### Rotate or transpose

inst/WORDLIST

@@ -5,6 +5,7 @@ Bafumi
 CMD
 Carle
 Catran
+Crosstables
 DOI
 De
 Dom
@@ -84,14 +85,13 @@ platykurtic
 poorman
 pth
 px
-quartile
-quartiles
 readr
 readxl
 recode
 recoded
 recodes
 recoding
+recodings
 relevel
 rempsyc
 reproducibility

tests/testthat/test-data_tabulate.R

@@ -173,7 +173,6 @@ test_that("data_tabulate big numbers", {
 
 
 test_that("data_tabulate print multiple, collapse", {
-  skip_if_not(packageVersion("insight") > "0.17.0", "insight must be >= 0.17.0")
   data(efc, package = "datawizard")
   expect_snapshot(data_tabulate(efc, c("c172code", "e16sex"), collapse = TRUE))
 })

tests/testthat/test-standardize_models.R

@@ -57,7 +57,6 @@ test_that("transformations", {
     ignore_attr = TRUE
   )
 
-  skip_if_not_installed("insight", minimum_version = "0.10.0")
   d <- data.frame(
     time = as.factor(c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5)),
     group = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2),
@@ -303,6 +302,7 @@ test_that("brms", {
   skip_on_cran()
   skip_on_os(c("windows", "mac"))
   skip_if_not_installed("brms")
+  skip_if_not_installed("RcppEigen")
 
   invisible(
     capture.output({