Merge pull request #1435 from tidymodels/cleanup

Author: EmilHvitfeldt

.Rbuildignore

@@ -21,3 +21,4 @@ tests/testthat/NMF*
 ^LICENSE\.md$
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^\.vscode$

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+  "[r]": {
+      "editor.formatOnSave": true
+  }
+}

NAMESPACE

@@ -14,18 +14,6 @@ S3method(.get_data_types,logical)
 S3method(.get_data_types,numeric)
 S3method(.get_data_types,ordered)
 S3method(.get_data_types,textrecipes_tokenlist)
-S3method(.recipes_destroy_sparsity,default)
-S3method(.recipes_destroy_sparsity,step_arrange)
-S3method(.recipes_destroy_sparsity,step_filter)
-S3method(.recipes_destroy_sparsity,step_impute_mean)
-S3method(.recipes_destroy_sparsity,step_impute_median)
-S3method(.recipes_destroy_sparsity,step_rename)
-S3method(.recipes_destroy_sparsity,step_rename_at)
-S3method(.recipes_destroy_sparsity,step_sample)
-S3method(.recipes_destroy_sparsity,step_select)
-S3method(.recipes_destroy_sparsity,step_shuffle)
-S3method(.recipes_destroy_sparsity,step_slice)
-S3method(.recipes_destroy_sparsity,step_sqrt)
 S3method(.recipes_estimate_sparsity,default)
 S3method(.recipes_estimate_sparsity,recipe)
 S3method(.recipes_estimate_sparsity,step_count)
@@ -35,6 +23,18 @@ S3method(.recipes_estimate_sparsity,step_dummy_multi_choice)
 S3method(.recipes_estimate_sparsity,step_holiday)
 S3method(.recipes_estimate_sparsity,step_indicate_na)
 S3method(.recipes_estimate_sparsity,step_regex)
+S3method(.recipes_preserve_sparsity,default)
+S3method(.recipes_preserve_sparsity,step_arrange)
+S3method(.recipes_preserve_sparsity,step_filter)
+S3method(.recipes_preserve_sparsity,step_impute_mean)
+S3method(.recipes_preserve_sparsity,step_impute_median)
+S3method(.recipes_preserve_sparsity,step_rename)
+S3method(.recipes_preserve_sparsity,step_rename_at)
+S3method(.recipes_preserve_sparsity,step_sample)
+S3method(.recipes_preserve_sparsity,step_select)
+S3method(.recipes_preserve_sparsity,step_shuffle)
+S3method(.recipes_preserve_sparsity,step_slice)
+S3method(.recipes_preserve_sparsity,step_sqrt)
 S3method(bake,check_class)
 S3method(bake,check_cols)
 S3method(bake,check_missing)
@@ -542,8 +542,8 @@ S3method(tune_args,step)
 S3method(update,step)
 export("%>%")
 export(.get_data_types)
-export(.recipes_destroy_sparsity)
 export(.recipes_estimate_sparsity)
+export(.recipes_preserve_sparsity)
 export(.recipes_toggle_sparse_args)
 export(add_check)
 export(add_role)

R/BoxCox.R

@@ -115,7 +115,7 @@ prep.step_BoxCox <- function(x, training, info = NULL, ...) {
     limits = x$limits,
     num_unique = x$num_unique
   )
-  if (any(is.na(values))) {
+  if (anyNA(values)) {
     var_names <- names(values[is.na(values)])
     cli::cli_warn(
       "No Box-Cox transformation could be estimated for: {.var {var_names}}."

R/YeoJohnson.R

@@ -118,7 +118,7 @@ prep.step_YeoJohnson <- function(x, training, info = NULL, ...) {
   check_type(training[, col_names], types = c("double", "integer"))
   check_number_whole(x$num_unique, args = "num_unique")
   check_bool(x$na_rm, arg = "na_rm")
-  if (!is.numeric(x$limits) || any(is.na(x$limits)) || length(x$limits) != 2) {
+  if (!is.numeric(x$limits) || anyNA(x$limits) || length(x$limits) != 2) {
     cli::cli_abort(
       "{.arg limits} should be a numeric vector with two values,
                     not {.obj_type_friendly {x$limits}}"

R/arrange.R

@@ -144,6 +144,6 @@ tidy.step_arrange <- function(x, ...) {
 }
 
 #' @export
-.recipes_destroy_sparsity.step_arrange <- function(x, ...) {
-  FALSE
+.recipes_preserve_sparsity.step_arrange <- function(x, ...) {
+  TRUE
 }

R/case_weights.R

@@ -168,7 +168,7 @@ averages <- function(x, wts = NULL, na_rm = TRUE) {
     res <- purrr::map_dbl(x, ~wt_calcs(.x, wts))
   }
   if (!na_rm) {
-    res[map_lgl(x, ~any(is.na(.x)))] <- NA
+    res[map_lgl(x, ~anyNA(.x))] <- NA
   }
   res
 }
@@ -211,7 +211,7 @@ variances <- function(x, wts = NULL, na_rm = TRUE) {
     wts <- as.double(wts)
     res <- purrr::map_dbl(x, ~wt_calcs(.x, wts, statistic = "var"))
     if (!na_rm) {
-      res[map_lgl(x, ~any(is.na(.x)))] <- NA
+      res[map_lgl(x, ~anyNA(.x))] <- NA
     }
   }
   res

R/corr.R

@@ -237,7 +237,7 @@ corr_filter <-
           )
         }
       }
-      if (any(is.na(x))) {
+      if (anyNA(x)) {
         cli::cli_warn(
           "The correlation matrix has sporadic missing values. \\
           Some columns were excluded from the filter."

R/cut.R

@@ -155,7 +155,7 @@ create_full_breaks <- function(var, breaks, call = rlang::caller_env()) {
     )
   }
 
-  if (any(is.na(var))) {
+  if (anyNA(var)) {
     cli::cli_warn(
       "{.arg var} contains missing values. These will be ignored in break
        calculations.",

R/discretize.R

@@ -185,7 +185,7 @@ predict.discretize <- function(object, new_data, ...) {
     if (object$keep_na) {
       out_levels <- levels(out)
       out <- as.character(out)
-      if (any(is.na(new_data))) {
+      if (anyNA(new_data)) {
         missing_label <- object$labels[1] %||% "[missing]"
 
         out[is.na(new_data)] <- missing_label