From f34e7842c63cd753b0c2b67d11a6f426a742aa30 Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Mon, 18 Dec 2023 12:40:16 +0100
Subject: [PATCH] lintr

---
 R/data_reverse.R | 30 +++++++++++++++---------------
 R/utils_labels.R | 14 +++++++-------
 2 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/R/data_reverse.R b/R/data_reverse.R
index 6e1ef414b..2fc9ef493 100644
--- a/R/data_reverse.R
+++ b/R/data_reverse.R
@@ -93,21 +93,21 @@ reverse.numeric <- function(x,
   }
 
   # old minimum and maximum
-  min <- min(range)
-  max <- max(range)
+  min_value <- min(range)
+  max_value <- max(range)
 
   # check if a valid range (i.e. vector of length 2) is provided
   if (length(range) > 2) {
     insight::format_error(
       "`range` must be a numeric vector of length two, indicating lowest and highest value of the required range.",
-      sprintf("Did you want to provide `range = c(%g, %g)`?", min, max)
+      sprintf("Did you want to provide `range = c(%g, %g)`?", min_value, max_value)
     )
   }
 
-  new_min <- max
-  new_max <- min
+  new_min <- max_value
+  new_max <- min_value
 
-  out <- as.vector((new_max - new_min) / (max - min) * (x - min) + new_min)
+  out <- as.vector((new_max - new_min) / (max_value - min_value) * (x - min_value) + new_min)
 
   # labelled data?
   out <- .set_back_labels(out, x, reverse_values = TRUE)
@@ -134,7 +134,9 @@ reverse.factor <- function(x, range = NULL, verbose = TRUE, ...) {
   # save for later use
   original_x <- x
 
-  if (!is.null(range)) {
+  if (is.null(range)) {
+    old_levels <- levels(x)
+  } else {
     # no missing values allowed
     if (anyNA(range)) {
       insight::format_error("`range` is not allowed to have missing values.")
@@ -180,8 +182,6 @@ reverse.factor <- function(x, range = NULL, verbose = TRUE, ...) {
     }
     old_levels <- range
     x <- factor(x, levels = range)
-  } else {
-    old_levels <- levels(x)
   }
 
   int_x <- as.integer(x)
@@ -225,7 +225,7 @@ reverse.grouped_df <- function(x,
   # create the new variables and updates "select", so new variables are processed
   if (!isFALSE(append)) {
     # process arguments
-    args <- .process_append(
+    arguments <- .process_append(
       x,
       select,
       append,
@@ -233,8 +233,8 @@ reverse.grouped_df <- function(x,
       preserve_value_labels = TRUE
     )
     # update processed arguments
-    x <- args$x
-    select <- args$select
+    x <- arguments$x
+    select <- arguments$select
   }
 
   x <- as.data.frame(x)
@@ -279,7 +279,7 @@ reverse.data.frame <- function(x,
   # create the new variables and updates "select", so new variables are processed
   if (!isFALSE(append)) {
     # process arguments
-    args <- .process_append(
+    arguments <- .process_append(
       x,
       select,
       append,
@@ -287,8 +287,8 @@ reverse.data.frame <- function(x,
       preserve_value_labels = TRUE
     )
     # update processed arguments
-    x <- args$x
-    select <- args$select
+    x <- arguments$x
+    select <- arguments$select
   }
 
   # Transform the range so that it is a list now
diff --git a/R/utils_labels.R b/R/utils_labels.R
index 4b28ac778..64b517086 100644
--- a/R/utils_labels.R
+++ b/R/utils_labels.R
@@ -6,24 +6,24 @@
 .set_back_labels <- function(new, old, include_values = TRUE, reverse_values = FALSE) {
   # labelled data?
   attr(new, "label") <- attr(old, "label", exact = TRUE)
-  labels <- attr(old, "labels", exact = TRUE)
+  value_labels <- attr(old, "labels", exact = TRUE)
   # "include_values" is used to preserve value labels
-  if (isTRUE(include_values) && !is.null(labels)) {
+  if (isTRUE(include_values) && !is.null(value_labels)) {
     if (reverse_values) {
       # reverse values? Used for "reverse_scale()"
-      attr(new, "labels") <- stats::setNames(rev(labels), names(labels))
+      attr(new, "labels") <- stats::setNames(rev(value_labels), names(value_labels))
     } else {
       # keep value oder? Used for "to_numeric()"
       if (is.numeric(new)) {
-        if (any(grepl("[^0-9]", labels))) {
+        if (any(grepl("[^0-9]", value_labels))) {
           # if we have any non-numeric characters, convert to numeric
-          attr(new, "labels") <- stats::setNames(as.numeric(as.factor(labels)), names(labels))
+          attr(new, "labels") <- stats::setNames(as.numeric(as.factor(value_labels)), names(value_labels))
         } else {
           # if we have numeric, or "numeric character" (like "1", "2", "3" etc.)
-          attr(new, "labels") <- stats::setNames(as.numeric(labels), names(labels))
+          attr(new, "labels") <- stats::setNames(as.numeric(value_labels), names(value_labels))
         }
       } else {
-        attr(new, "labels") <- stats::setNames(labels, names(labels))
+        attr(new, "labels") <- stats::setNames(value_labels, names(value_labels))
       }
     }
   } else if (isFALSE(include_values)) {