https://github.com/easystats/easystats/issues/404

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: datawizard
 Title: Easy Data Wrangling and Statistical Transformations
-Version: 0.10.0.3
+Version: 0.10.0.4
 Authors@R: c(
     person("Indrajeet", "Patil", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0003-1995-6531", Twitter = "@patilindrajeets")),

NEWS.md

@@ -1,5 +1,12 @@
 # datawizard 0.10.1
 
+BREAKING CHANGES
+
+* Arguments named `group` or `group_by` will be deprecated in a future release.
+  Please use `by` instead. This affects following functions in *datawizard*.
+
+  * `data_partition()`
+
 CHANGES
 
 * `recode_into()` is more relaxed regarding checking the type of `NA` values.

R/data_partition.R

@@ -2,19 +2,20 @@
 #'
 #' Creates data partitions (for instance, a training and a test set) based on a
 #' data frame that can also be stratified (i.e., evenly spread a given factor)
-#' using the `group` argument.
+#' using the `by` argument.
 #'
 #' @inheritParams data_rename
 #' @param proportion Scalar (between 0 and 1) or numeric vector, indicating the
 #'   proportion(s) of the training set(s). The sum of `proportion` must not be
 #'   greater than 1. The remaining part will be used for the test set.
-#' @param group A character vector indicating the name(s) of the column(s) used
+#' @param by A character vector indicating the name(s) of the column(s) used
 #'   for stratified partitioning.
 #' @param seed A random number generator seed. Enter an integer (e.g. 123) so
 #'   that the random sampling will be the same each time you run the function.
 #' @param row_id Character string, indicating the name of the column that
 #'   contains the row-id's.
 #' @param verbose Toggle messages and warnings.
+#' @param group Deprecated. Use `by` instead.
 #'
 #' @return A list of data frames. The list includes one training set per given
 #'   proportion and the remaining data as test set. List elements of training
@@ -28,7 +29,7 @@
 #' nrow(out$p_0.9)
 #'
 #' # Stratify by group (equal proportions of each species)
-#' out <- data_partition(iris, proportion = 0.9, group = "Species")
+#' out <- data_partition(iris, proportion = 0.9, by = "Species")
 #' out$test
 #'
 #' # Create multiple partitions
@@ -38,21 +39,27 @@
 #' # 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")
+#' out <- data_partition(iris, proportion = c(0.3, 0.5), by = "Species")
 #' lapply(out, function(i) table(i$Species))
 #'
 #' @inherit data_rename seealso
 #' @export
 data_partition <- function(data,
                            proportion = 0.7,
-                           group = NULL,
+                           by = NULL,
                            seed = NULL,
                            row_id = ".row_id",
                            verbose = TRUE,
+                           group = NULL,
                            ...) {
   # validation checks
   data <- .coerce_to_dataframe(data)
 
+  ## TODO: deprecate later
+  if (!is.null(group)) {
+    by <- group
+  }
+
   if (sum(proportion) > 1) {
     insight::format_error("Sum of `proportion` cannot be higher than 1.")
   }
@@ -91,12 +98,12 @@ data_partition <- function(data,
 
   # Create list of data groups. We generally lapply over list of
   # sampled row-id's by group, thus, we even create a list if not grouped.
-  if (is.null(group)) {
+  if (is.null(by)) {
     indices_list <- list(seq_len(nrow(data)))
   } else {
     # else, split by group(s) and extract row-ids per group
     indices_list <- lapply(
-      split(data, data[group]),
+      split(data, data[by]),
       data_extract,
       select = row_id,
       as_data_frame = FALSE
@@ -130,7 +137,7 @@ data_partition <- function(data,
   })
 
   # we need to move all list elements one level higher.
-  if (is.null(group)) {
+  if (is.null(by)) {
     training_sets <- training_sets[[1]]
   } else {
     # for grouped training sets, we need to row-bind all sampled training

tests/testthat/test-data_partition.R

@@ -53,8 +53,8 @@ test_that("data_partition works as expected", {
   data(iris)
   expect_snapshot(str(data_partition(iris, proportion = 0.7, seed = 123)))
   expect_snapshot(str(data_partition(iris, proportion = c(0.2, 0.5), seed = 123)))
-  expect_snapshot(str(data_partition(iris, proportion = 0.7, group = "Species", seed = 123)))
-  expect_snapshot(str(data_partition(iris, proportion = c(0.2, 0.5), group = "Species", seed = 123)))
+  expect_snapshot(str(data_partition(iris, proportion = 0.7, by = "Species", seed = 123)))
+  expect_snapshot(str(data_partition(iris, proportion = c(0.2, 0.5), by = "Species", seed = 123)))
 })
 
 test_that("data_partition warns if no testing set", {