Remove arguments deprecated in 0.5.0

NEWS.md

@@ -1,5 +1,13 @@
 # datawizard 0.9.2
 
+BREAKING CHANGES
+
+* The following arguments were deprecated in 0.5.0 and are now removed:
+
+  * in `data_to_wide()`: `colnames_from`, `rows_from`, `sep` 
+  * in `data_to_long()`: `colnames_to` 
+  * in `data_partition()`: `training_proportion`
+
 NEW FUNCTIONS
 
 * `data_summary()`, to compute summary statistics of (grouped) data frames.
@@ -13,7 +21,7 @@ CHANGES
   argument, to compute weighted frequency tables. `include_na` allows to include
   or omit missing values from the table. Furthermore, a `by` argument was added,
   to compute crosstables (#479, #481).
-
+  
 # datawizard 0.9.1
 
 CHANGES
@@ -121,7 +129,7 @@ CHANGES
   (similar to other data frame methods of transformation functions), to append
   recoded variables to the input data frame instead of overwriting existing
   variables.
-  
+
 NEW FUNCTIONS
 
 * `rowid_as_column()` to complement `rownames_as_column()` (and to mimic 

R/data_partition.R

@@ -8,7 +8,6 @@
 #' @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 training_proportion Deprecated, please use `proportion`.
 #' @param group 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
@@ -50,7 +49,6 @@ data_partition <- function(data,
                            seed = NULL,
                            row_id = ".row_id",
                            verbose = TRUE,
-                           training_proportion = proportion,
                            ...) {
   # validation checks
   data <- .coerce_to_dataframe(data)
@@ -132,26 +130,26 @@ data_partition <- function(data,
   })
 
   # we need to move all list elements one level higher.
-  if (!is.null(group)) {
+  if (is.null(group)) {
+    training_sets <- training_sets[[1]]
+  } else {
     # for grouped training sets, we need to row-bind all sampled training
     # sets from each group. currently, we have a list of data frames,
     # grouped by "group"; but we want one data frame per proportion that
     # contains sampled rows from all groups.
     training_sets <- lapply(seq_along(proportion), function(p) {
       do.call(rbind, lapply(training_sets, function(i) i[[p]]))
     })
-  } else {
-    # else, just move first list element one level higher
-    training_sets <- training_sets[[1]]
   }
 
   # use probabilies as element names
   names(training_sets) <- sprintf("p_%g", proportion)
 
   # remove all training set id's from data, add remaining data (= test set)
+  all_ids <- lapply(training_sets, data_extract, select = row_id, as_data_frame = FALSE)
   out <- c(
     training_sets,
-    list(test = data[-unlist(lapply(training_sets, data_extract, select = row_id, as_data_frame = FALSE), use.names = FALSE), ])
+    list(test = data[-unlist(all_ids, use.names = FALSE), ])
   )
 
   lapply(out, `row.names<-`, NULL)

R/data_to_long.R

@@ -25,7 +25,6 @@
 #' @param cols Identical to `select`. This argument is here to ensure compatibility
 #'   with `tidyr::pivot_longer()`. If both `select` and `cols` are provided, `cols`
 #'   is used.
-#' @param colnames_to Deprecated. Use `names_to` instead.
 #'
 #' @return If a tibble was provided as input, `reshape_longer()` also returns a
 #' tibble. Otherwise, it returns a data frame.
@@ -78,17 +77,10 @@ data_to_long <- function(data,
                          ignore_case = FALSE,
                          regex = FALSE,
                          ...,
-                         cols,
-                         colnames_to) {
-  # Check args
-  if (!missing(colnames_to)) {
-    .is_deprecated("colnames_to", "names_to")
-    if (is.null(names_to)) {
-      names_to <- colnames_to
-    }
-  }
+                         cols) { # nolint
 
   # Prefer "cols" over "select" for compat with tidyr::pivot_longer
+  # nolint start
   if (!missing(cols)) {
     select <- substitute(cols)
     cols <- .select_nse(
@@ -115,6 +107,7 @@ data_to_long <- function(data,
       )
     }
   }
+  # nolint end
 
   # nothing to select?
   if (length(cols) == 0L) {
@@ -205,7 +198,7 @@ data_to_long <- function(data,
         header = FALSE
       )
       names(tmp) <- paste0("V", seq_len(ncol(tmp)))
-      tmp[tmp == ""] <- NA
+      tmp[tmp == ""] <- NA # nolint
 
       stacked_data$ind <- NULL
       stacked_data <- cbind(tmp, stacked_data)

R/data_to_wide.R

@@ -25,9 +25,6 @@
 #' missing values in the new columns created.
 #' @param verbose Toggle warnings.
 #' @param ... Not used for now.
-#' @param colnames_from Deprecated. Use `names_from` instead.
-#' @param rows_from Deprecated. Use `id_cols` instead.
-#' @param sep Deprecated. Use `names_sep` instead.
 #'
 #' @return If a tibble was provided as input, `reshape_wider()` also returns a
 #' tibble. Otherwise, it returns a data frame.
@@ -93,29 +90,8 @@ data_to_wide <- function(data,
                          names_glue = NULL,
                          values_fill = NULL,
                          verbose = TRUE,
-                         ...,
-                         colnames_from,
-                         rows_from,
-                         sep) {
-  if (!missing(colnames_from)) {
-    .is_deprecated("colnames_from", "names_from")
-    if (is.null(names_from)) {
-      names_from <- colnames_from
-    }
-  }
-  if (!missing(rows_from)) {
-    .is_deprecated("rows_from", "id_cols")
+                         ...) {
     if (is.null(id_cols)) {
-      id_cols <- rows_from
-    }
-  }
-  if (!missing(sep)) {
-    .is_deprecated("sep", "names_sep")
-    if (is.null(names_sep)) {
-      names_sep <- sep
-    }
-  }
-  if (is.null(id_cols)) {
     id_cols <- setdiff(names(data), c(names_from, values_from))
   }
 
@@ -218,28 +194,26 @@ data_to_wide <- function(data,
   if (!is.null(values_fill)) {
     if (length(values_fill) == 1L) {
       if (is.numeric(new_data[[values_from]])) {
-        if (!is.numeric(values_fill)) {
-          insight::format_error(paste0("`values_fill` must be of type numeric."))
-        } else {
+        if (is.numeric(values_fill)) {
           new_data <- convert_na_to(new_data, replace_num = values_fill)
+        } else {
+          insight::format_error(paste0("`values_fill` must be of type numeric."))
         }
       } else if (is.character(new_data[[values_from]])) {
-        if (!is.character(values_fill)) {
-          insight::format_error(paste0("`values_fill` must be of type character."))
-        } else {
+        if (is.character(values_fill)) {
           new_data <- convert_na_to(new_data, replace_char = values_fill)
+        } else {
+          insight::format_error(paste0("`values_fill` must be of type character."))
         }
       } else if (is.factor(new_data[[values_from]])) {
-        if (!is.factor(values_fill)) {
-          insight::format_error(paste0("`values_fill` must be of type factor."))
-        } else {
+        if (is.factor(values_fill)) {
           new_data <- convert_na_to(new_data, replace_fac = values_fill)
+        } else {
+          insight::format_error(paste0("`values_fill` must be of type factor."))
         }
       }
-    } else {
-      if (verbose) {
-        insight::format_error("`values_fill` must be of length 1.")
-      }
+    } else if (verbose) {
+      insight::format_error("`values_fill` must be of length 1.")
     }
   }