diff --git a/R/data_rotate.R b/R/data_rotate.R
index f9a3389d3..2bb39a85d 100644
--- a/R/data_rotate.R
+++ b/R/data_rotate.R
@@ -56,7 +56,7 @@ data_rotate <- function(data, rownames = NULL, colnames = FALSE, verbose = TRUE)
 
   # warning after possible removal of columns
   if (verbose && insight::n_unique(vapply(data, typeof, FUN.VALUE = character(1L))) > 1L) {
-    insight::format_warning("Your data frame contains mixed types of data. After transposition, all variables will be transformed into characters.")
+    insight::format_warning("Your data frame contains mixed types of data. After transposition, all variables will be transformed into characters.") # nolint
   }
 
   # rotate data frame by 90 degrees
diff --git a/R/data_seek.R b/R/data_seek.R
index c0a56ab8f..17463878d 100644
--- a/R/data_seek.R
+++ b/R/data_seek.R
@@ -63,7 +63,7 @@ data_seek <- function(data, pattern, seek = c("names", "labels"), fuzzy = FALSE)
   # check valid args
   seek <- intersect(seek, c("names", "labels", "values", "levels", "column_names", "columns", "all"))
   if (is.null(seek) || !length(seek)) {
-    insight::format_error("`seek` must be one of \"names\", \"labels\", \"values\", a combination of these options, or \"all\".")
+    insight::format_error("`seek` must be one of \"names\", \"labels\", \"values\", a combination of these options, or \"all\".") # nolint
   }
 
   pos1 <- pos2 <- pos3 <- NULL
@@ -71,7 +71,7 @@ data_seek <- function(data, pattern, seek = c("names", "labels"), fuzzy = FALSE)
   pos <- unlist(lapply(pattern, function(search_pattern) {
     # search in variable names?
     if (any(seek %in% c("names", "columns", "column_names", "all"))) {
-      pos1 <- which(grepl(search_pattern, colnames(data)))
+      pos1 <- grep(search_pattern, colnames(data))
       # find in near distance?
       if (fuzzy) {
         pos1 <- c(pos1, .fuzzy_grep(x = colnames(data), pattern = search_pattern))
@@ -80,15 +80,15 @@ data_seek <- function(data, pattern, seek = c("names", "labels"), fuzzy = FALSE)
 
     # search in variable labels?
     if (any(seek %in% c("labels", "all"))) {
-      labels <- insight::compact_character(unlist(lapply(data, attr, which = "label", exact = TRUE)))
-      if (!is.null(labels) && length(labels)) {
-        found <- grepl(search_pattern, labels)
-        pos2 <- match(names(labels)[found], colnames(data))
+      var_labels <- insight::compact_character(unlist(lapply(data, attr, which = "label", exact = TRUE)))
+      if (!is.null(var_labels) && length(var_labels)) {
+        found <- grepl(search_pattern, var_labels)
+        pos2 <- match(names(var_labels)[found], colnames(data))
         # find in near distanc?
         if (fuzzy) {
-          found <- .fuzzy_grep(x = labels, pattern = search_pattern)
+          found <- .fuzzy_grep(x = var_labels, pattern = search_pattern)
           if (length(found)) {
-            pos2 <- c(pos2, match(names(labels)[found], colnames(data)))
+            pos2 <- c(pos2, match(names(var_labels)[found], colnames(data)))
           }
         }
       }
@@ -129,7 +129,7 @@ data_seek <- function(data, pattern, seek = c("names", "labels"), fuzzy = FALSE)
   pos <- unique(pos)
 
   # variable labels of matching variables
-  labels <- vapply(
+  var_labels <- vapply(
     colnames(data[pos]),
     function(i) {
       l <- attr(data[[i]], "label", exact = TRUE)
@@ -145,7 +145,7 @@ data_seek <- function(data, pattern, seek = c("names", "labels"), fuzzy = FALSE)
   out <- data.frame(
     index = pos,
     column = colnames(data)[pos],
-    labels = labels,
+    labels = var_labels,
     stringsAsFactors = FALSE
   )
   # no row names
diff --git a/R/data_separate.R b/R/data_separate.R
index c5e83183d..53243fb33 100644
--- a/R/data_separate.R
+++ b/R/data_separate.R
@@ -254,7 +254,7 @@ data_separate <- function(data,
     # catch error
     if (is.null(separated_columns)) {
       insight::format_error(
-        "Something went wrong. Probably the number of provided column names did not match number of newly created columns?"
+        "Something went wrong. Probably the number of provided column names did not match number of newly created columns?" # nolint
       )
     }
 
@@ -264,14 +264,12 @@ data_separate <- function(data,
     # if no column names provided, use standard names
     if (is.null(new_columns[[sep_column]])) {
       new_column_names <- paste0(sep_column, "_", seq_along(out))
-    } else {
+    } else if (make_unique_colnames) {
       # if we have multiple columns that were separated, we avoid duplicated
       # column names of created variables by appending name of original column
-      if (make_unique_colnames) {
-        new_column_names <- paste0(sep_column, "_", new_columns[[sep_column]])
-      } else {
-        new_column_names <- new_columns[[sep_column]]
-      }
+      new_column_names <- paste0(sep_column, "_", new_columns[[sep_column]])
+    } else {
+      new_column_names <- new_columns[[sep_column]]
     }
 
     colnames(out) <- new_column_names
@@ -338,29 +336,27 @@ data_separate <- function(data,
       out <- rep(NA_character_, times = n_cols)
     } else if (n_values > n_cols) {
       # we have more values than required - drop extra columns
-      if (extra == "drop_left") {
-        out <- i[(n_values - n_cols + 1):n_values]
-      } else if (extra == "drop_right") {
-        out <- i[1:n_cols]
-      } else if (extra == "merge_left") {
-        out <- paste(i[1:(n_values - n_cols + 1)], collapse = " ")
-        out <- c(out, i[(n_values - n_cols + 2):n_values])
-      } else {
-        out <- i[1:(n_cols - 1)]
-        out <- c(out, paste(i[n_cols:n_values], collapse = " "))
-      }
+      out <- switch(extra,
+        drop_left = i[(n_values - n_cols + 1):n_values],
+        drop_right = i[1:n_cols],
+        merge_left = {
+          tmp <- paste(i[1:(n_values - n_cols + 1)], collapse = " ")
+          c(tmp, i[(n_values - n_cols + 2):n_values])
+        },
+        {
+          tmp <- i[1:(n_cols - 1)]
+          c(tmp, paste(i[n_cols:n_values], collapse = " "))
+        }
+      )
       warn_extra <- TRUE
     } else if (n_values < n_cols) {
       # we have fewer values than required - fill columns
-      if (fill == "left") {
-        out <- c(rep(NA_character_, times = n_cols - n_values), i)
-      } else if (fill == "right") {
-        out <- c(i, rep(NA_character_, times = n_cols - n_values))
-      } else if (fill == "value_left") {
-        out <- c(rep(i[1], times = n_cols - n_values), i)
-      } else {
-        out <- c(i, rep(i[length(i)], times = n_cols - n_values))
-      }
+      out <- switch(fill,
+        left = c(rep(NA_character_, times = n_cols - n_values), i),
+        right = c(i, rep(NA_character_, times = n_cols - n_values)),
+        value_left = c(rep(i[1], times = n_cols - n_values), i),
+        c(i, rep(i[length(i)], times = n_cols - n_values))
+      )
       warn_fill <- TRUE
     } else {
       out <- i
diff --git a/R/describe_distribution.R b/R/describe_distribution.R
index 00a8a2475..37850299a 100644
--- a/R/describe_distribution.R
+++ b/R/describe_distribution.R
@@ -82,18 +82,18 @@ describe_distribution.list <- function(x,
   num_el <- which(vapply(x, is.numeric, FUN.VALUE = logical(1L)))
 
   # get elements names as is
-  # ex: list(mtcars$mpg, mtcars$cyl) -> c("mtcars$mpg", "mtcars$cyl")
+  # ex: `list(mtcars$mpg, mtcars$cyl) -> c("mtcars$mpg", "mtcars$cyl")`
   nm <- vapply(sys.call()[[2]], insight::safe_deparse, FUN.VALUE = character(1L))[-1]
 
-  if (!isTRUE(include_factors)) {
-    x <- x[num_el]
+  if (isTRUE(include_factors)) {
+    x <- x[c(num_el, factor_el)]
     if (length(nm) != 0) {
-      nm <- nm[num_el]
+      nm <- nm[c(num_el, factor_el)]
     }
   } else {
-    x <- x[c(num_el, factor_el)]
+    x <- x[num_el]
     if (length(nm) != 0) {
-      nm <- nm[c(num_el, factor_el)]
+      nm <- nm[num_el]
     }
   }
 
@@ -123,12 +123,12 @@ describe_distribution.list <- function(x,
   }))
 
 
-  if (!is.null(names(x))) {
-    empty_names <- which(names(x) == "")
+  if (is.null(names(x))) {
+    new_names <- nm
+  } else {
+    empty_names <- which(!nzchar(names(x), keepNA = TRUE))
     new_names <- names(x)
     new_names[empty_names] <- nm[empty_names]
-  } else {
-    new_names <- nm
   }
 
   out$Variable <- new_names
diff --git a/R/labels_to_levels.R b/R/labels_to_levels.R
index ed63e0583..c1ff97a16 100644
--- a/R/labels_to_levels.R
+++ b/R/labels_to_levels.R
@@ -79,7 +79,7 @@ labels_to_levels.data.frame <- function(x,
   # create the new variables and updates "select", so new variables are processed
   if (!isFALSE(append)) {
     # process arguments
-    args <- .process_append(
+    arguments <- .process_append(
       x,
       select,
       append,
@@ -89,8 +89,8 @@ labels_to_levels.data.frame <- function(x,
       keep_character = FALSE
     )
     # update processed arguments
-    x <- args$x
-    select <- args$select
+    x <- arguments$x
+    select <- arguments$select
   }
 
   x[select] <- lapply(
diff --git a/R/to_numeric.R b/R/to_numeric.R
index 8bfcac6bc..c43956399 100644
--- a/R/to_numeric.R
+++ b/R/to_numeric.R
@@ -17,6 +17,12 @@
 #' @inheritParams find_columns
 #' @inheritParams categorize
 #'
+#' @note By default, `to_numeric()` converts factors into "binary" dummies, i.e.
+#' each factor level is converted into a separate column filled with a binary
+#' 0-1 value. If only one column is required, use `dummy_factors = FALSE`. If
+#' you want to preserve the original factor levels (in case these represent
+#' numeric values), use `preserve_levels = TRUE`.
+#'
 #' @section Selection of variables - `select` argument:
 #' For most functions that have a `select` argument the complete input data
 #' frame is returned, even when `select` only selects a range of variables.
@@ -34,6 +40,8 @@
 #' x <- as.factor(mtcars$gear)
 #' to_numeric(x, dummy_factors = FALSE)
 #' to_numeric(x, dummy_factors = FALSE, preserve_levels = TRUE)
+#' # same as:
+#' coerce_to_numeric(x)
 #'
 #' @return A data frame of numeric variables.
 #'
@@ -211,15 +219,13 @@ to_numeric.factor <- function(x,
         # if the first observation was missing, add NA row and bind data frame
         if (i == 1 && na_values[i] == 1) {
           out <- rbind(NA, out)
-        } else {
+        } else if (na_values[i] == rows_x) {
           # if the last observation was NA, add NA row to data frame
-          if (na_values[i] == rows_x) {
-            out <- rbind(out, NA)
-          } else {
-            # else, pick rows from beginning to current NA value, add NA,
-            # and rbind the remaining rows
-            out <- rbind(out[1:(na_values[i] - 1), ], NA, out[na_values[i]:nrow(out), ])
-          }
+          out <- rbind(out, NA)
+        } else {
+          # else, pick rows from beginning to current NA value, add NA,
+          # and rbind the remaining rows
+          out <- rbind(out[1:(na_values[i] - 1), ], NA, out[na_values[i]:nrow(out), ])
         }
       }
       rownames(out) <- NULL
diff --git a/man/to_numeric.Rd b/man/to_numeric.Rd
index 540f4f65a..7c78b1ba6 100644
--- a/man/to_numeric.Rd
+++ b/man/to_numeric.Rd
@@ -106,6 +106,13 @@ Convert data to numeric by converting characters to factors and factors to
 either numeric levels or dummy variables. The "counterpart" to convert
 variables into factors is \code{to_factor()}.
 }
+\note{
+By default, \code{to_numeric()} converts factors into "binary" dummies, i.e.
+each factor level is converted into a separate column filled with a binary
+0-1 value. If only one column is required, use \code{dummy_factors = FALSE}. If
+you want to preserve the original factor levels (in case these represent
+numeric values), use \code{preserve_levels = TRUE}.
+}
 \section{Selection of variables - \code{select} argument}{
 
 For most functions that have a \code{select} argument the complete input data
@@ -125,5 +132,7 @@ to_numeric(head(ToothGrowth), dummy_factors = FALSE)
 x <- as.factor(mtcars$gear)
 to_numeric(x, dummy_factors = FALSE)
 to_numeric(x, dummy_factors = FALSE, preserve_levels = TRUE)
+# same as:
+coerce_to_numeric(x)
 
 }
diff --git a/tests/testthat/_snaps/describe_distribution.md b/tests/testthat/_snaps/describe_distribution.md
index 5c582859d..0261ab9b1 100644
--- a/tests/testthat/_snaps/describe_distribution.md
+++ b/tests/testthat/_snaps/describe_distribution.md
@@ -16,3 +16,12 @@
       -----------------------------------------------------------
            |    | [VC, OJ] |        0 |    -2.07 | 60 |         0
 
+# describe_distribution formatting
+
+    Code
+      format(x)
+    Output
+      Mean |   SD |  IQR |        Range |  Quartiles | Skewness | Kurtosis |   n | n_Missing
+      --------------------------------------------------------------------------------------
+      3.06 | 0.44 | 0.52 | [2.00, 4.40] | 2.80, 3.30 |     0.32 |     0.23 | 150 |         0
+
diff --git a/tests/testthat/test-describe_distribution.R b/tests/testthat/test-describe_distribution.R
index 5976286ae..83d2abb33 100644
--- a/tests/testthat/test-describe_distribution.R
+++ b/tests/testthat/test-describe_distribution.R
@@ -4,8 +4,8 @@ test_that("describe_distribution - numeric: works with basic numeric vector", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(mtcars$mpg)
-  expect_equal(dim(x), c(1, 9))
-  expect_equal(round(x$Mean), 20)
+  expect_identical(dim(x), c(1L, 9L))
+  expect_identical(round(x$Mean), 20)
 })
 
 test_that("describe_distribution - numeric: correctly handles missing values", {
@@ -15,8 +15,8 @@ test_that("describe_distribution - numeric: correctly handles missing values", {
   test <- mtcars$mpg
   test[1] <- NA
   with_missing <- describe_distribution(test)
-  expect_equal(with_missing$n, 31)
-  expect_equal(with_missing$n_Missing, 1)
+  expect_identical(with_missing$n, 31L)
+  expect_identical(with_missing$n_Missing, 1L)
   expect_false(with_missing$Mean == no_missing$Mean)
 })
 
@@ -24,7 +24,7 @@ test_that("describe_distribution - numeric: works with quartiles", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(mtcars$mpg, quartiles = TRUE)
-  expect_equal(dim(x), c(1, 11))
+  expect_identical(dim(x), c(1L, 11L))
   expect_true("Q1" %in% names(x))
   expect_true("Q3" %in% names(x))
 })
@@ -33,7 +33,7 @@ test_that("describe_distribution - numeric: works with range", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(mtcars$mpg, range = FALSE)
-  expect_equal(dim(x), c(1, 7))
+  expect_identical(dim(x), c(1L, 7L))
   expect_false("min" %in% names(x))
   expect_false("max" %in% names(x))
 })
@@ -53,8 +53,8 @@ test_that("describe_distribution - data frame: works with basic data frame", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(mtcars)
-  expect_equal(dim(x), c(11, 10))
-  expect_equal(round(x[1, "Mean"]), 20)
+  expect_identical(dim(x), c(11L, 10L))
+  expect_identical(round(x[1, "Mean"]), 20)
 })
 
 test_that("describe_distribution - data frame: correctly handles missing values", {
@@ -64,8 +64,8 @@ test_that("describe_distribution - data frame: correctly handles missing values"
   test <- mtcars
   test[1, ] <- NA
   with_missing <- describe_distribution(test)
-  expect_equal(unique(with_missing$n), 31)
-  expect_equal(unique(with_missing$n_Missing), 1)
+  expect_identical(unique(with_missing$n), 31L)
+  expect_identical(unique(with_missing$n_Missing), 1L)
   expect_false(unique(with_missing$Mean == no_missing$Mean))
 })
 
@@ -73,7 +73,7 @@ test_that("describe_distribution - data frame: works with quartiles", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(mtcars, quartiles = TRUE)
-  expect_equal(dim(x), c(11, 12))
+  expect_identical(dim(x), c(11L, 12L))
   expect_true("Q1" %in% names(x))
   expect_true("Q3" %in% names(x))
 })
@@ -82,7 +82,7 @@ test_that("describe_distribution - data frame: works with range", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(mtcars, range = FALSE)
-  expect_equal(dim(x), c(11, 8))
+  expect_identical(dim(x), c(11L, 8L))
   expect_false("min" %in% names(x))
   expect_false("max" %in% names(x))
 })
@@ -120,14 +120,14 @@ test_that("describe_distribution - list: works with basic list", {
   named <- describe_distribution(list(foo = mtcars$mpg, foo2 = mtcars$cyl))
   mix <- describe_distribution(list(foo = mtcars$mpg, mtcars$cyl))
 
-  expect_equal(dim(stored), c(2, 10))
-  expect_equal(round(stored$Mean), c(20, 6))
-  expect_equal(dim(unnamed), c(2, 10))
-  expect_equal(round(unnamed$Mean), c(20, 6))
-  expect_equal(dim(named), c(2, 10))
-  expect_equal(round(named$Mean), c(20, 6))
-  expect_equal(dim(mix), c(2, 10))
-  expect_equal(round(mix$Mean), c(20, 6))
+  expect_identical(dim(stored), c(2L, 10L))
+  expect_identical(round(stored$Mean), c(20, 6))
+  expect_identical(dim(unnamed), c(2L, 10L))
+  expect_identical(round(unnamed$Mean), c(20, 6))
+  expect_identical(dim(named), c(2L, 10L))
+  expect_identical(round(named$Mean), c(20, 6))
+  expect_identical(dim(mix), c(2L, 10L))
+  expect_identical(round(mix$Mean), c(20, 6))
 })
 
 test_that("describe_distribution - list: works with include_factors", {
@@ -140,14 +140,14 @@ test_that("describe_distribution - list: works with include_factors", {
   x2 <- describe_distribution(list(mtcars$mpg, factor(mtcars$cyl)),
     include_factors = TRUE
   )
-  expect_equal(dim(x2), c(2, 10))
-  expect_equal(x2$Variable, c("mtcars$mpg", "factor(mtcars$cyl)"))
+  expect_identical(dim(x2), c(2L, 10L))
+  expect_identical(x2$Variable, c("mtcars$mpg", "factor(mtcars$cyl)"))
 
   x3 <- describe_distribution(list(mtcars$mpg, foo = factor(mtcars$cyl)),
     include_factors = TRUE
   )
-  expect_equal(dim(x3), c(2, 10))
-  expect_equal(x3$Variable, c("mtcars$mpg", "foo"))
+  expect_identical(dim(x3), c(2L, 10L))
+  expect_identical(x3$Variable, c("mtcars$mpg", "foo"))
 })
 
 test_that("describe_distribution - list: correctly removes character elements", {
@@ -167,10 +167,10 @@ test_that("describe_distribution - list: correctly handles variable names", {
   named <- describe_distribution(list(foo = mtcars$mpg, foo2 = mtcars$cyl))
   mix <- describe_distribution(list(foo = mtcars$mpg, mtcars$cyl))
 
-  expect_equal(stored$Variable, c("Var_1", "Var_2"))
-  expect_equal(unnamed$Variable, c("mtcars$mpg", "mtcars$cyl"))
-  expect_equal(named$Variable, c("foo", "foo2"))
-  expect_equal(mix$Variable, c("foo", "mtcars$cyl"))
+  expect_identical(stored$Variable, c("Var_1", "Var_2"))
+  expect_identical(unnamed$Variable, c("mtcars$mpg", "mtcars$cyl"))
+  expect_identical(named$Variable, c("foo", "foo2"))
+  expect_identical(mix$Variable, c("foo", "mtcars$cyl"))
 })
 
 test_that("describe_distribution - list: correctly handles missing values", {
@@ -182,8 +182,8 @@ test_that("describe_distribution - list: correctly handles missing values", {
   test[1] <- NA
   test2[1] <- NA
   with_missing <- describe_distribution(list(test, test2))
-  expect_equal(unique(with_missing$n), 31)
-  expect_equal(unique(with_missing$n_Missing), 1)
+  expect_identical(unique(with_missing$n), 31L)
+  expect_identical(unique(with_missing$n_Missing), 1L)
   expect_false(unique(with_missing$Mean == no_missing$Mean))
 })
 
@@ -191,7 +191,7 @@ test_that("describe_distribution - list: works with quartiles", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(list(mtcars$mpg, mtcars$cyl), quartiles = TRUE)
-  expect_equal(dim(x), c(2, 12))
+  expect_identical(dim(x), c(2L, 12L))
   expect_true("Q1" %in% names(x))
   expect_true("Q3" %in% names(x))
 })
@@ -200,7 +200,7 @@ test_that("describe_distribution - list: works with range", {
   skip_if_not_installed("bayestestR")
 
   x <- describe_distribution(list(mtcars$mpg, mtcars$cyl), range = FALSE)
-  expect_equal(dim(x), c(2, 8))
+  expect_identical(dim(x), c(2L, 8L))
   expect_false("min" %in% names(x))
   expect_false("max" %in% names(x))
 })
@@ -215,7 +215,7 @@ test_that("describe_distribution - select", {
   data(iris)
   out <- describe_distribution(iris, select = starts_with("Petal"))
 
-  expect_equal(out$Variable, c("Petal.Length", "Petal.Width"))
+  expect_identical(out$Variable, c("Petal.Length", "Petal.Width"))
   expect_equal(out$Mean, c(3.758000, 1.199333), tolerance = 1e-3)
 
   expect_null(describe_distribution(iris, select = "Species"))
@@ -235,12 +235,12 @@ test_that("describe_distribution - grouped df", {
   x <- data_group(iris, Species)
   out <- describe_distribution(x, select = starts_with("Petal"))
 
-  expect_equal(out$.group, c(
+  expect_identical(out$.group, c(
     "Species=setosa", "Species=setosa",
     "Species=versicolor", "Species=versicolor",
     "Species=virginica", "Species=virginica"
   ))
-  expect_equal(out$Variable, c(
+  expect_identical(out$Variable, c(
     "Petal.Length", "Petal.Width",
     "Petal.Length", "Petal.Width",
     "Petal.Length", "Petal.Width"
@@ -250,23 +250,22 @@ test_that("describe_distribution - grouped df", {
 
 
 # distribution_mode --------------------------
-
 test_that("distribution_mode works as expected", {
   skip_if_not_installed("bayestestR")
 
   # atomic vector
-  expect_equal(distribution_mode(c(1, 2, 3, 3, 4, 5)), 3)
-  expect_equal(distribution_mode(c(1, 2, 3, 3, 4, 4, 5)), 3)
-  expect_equal(distribution_mode(c(1.5, 2.3, 3.7, 3.7, 4.0, 5)), 3.7)
+  expect_identical(distribution_mode(c(1, 2, 3, 3, 4, 5)), 3)
+  expect_identical(distribution_mode(c(1, 2, 3, 3, 4, 4, 5)), 3)
+  expect_identical(distribution_mode(c(1.5, 2.3, 3.7, 3.7, 4.0, 5)), 3.7)
 
   # list
-  expect_equal(distribution_mode(list(1, 2, 3, 3, 4, 5)), list(3))
+  expect_identical(distribution_mode(list(1, 2, 3, 3, 4, 5)), list(3))
 
   # scalar
-  expect_equal(distribution_mode("a"), "a")
+  expect_identical(distribution_mode("a"), "a")
 
   # empty
-  expect_null(distribution_mode(c()))
+  expect_null(distribution_mode(NULL))
 })
 
 # select helpers ------------------------------
@@ -275,6 +274,15 @@ test_that("describe_distribution regex", {
 
   expect_equal(
     describe_distribution(mtcars, select = "pg", regex = TRUE),
-    describe_distribution(mtcars, select = "mpg")
+    describe_distribution(mtcars, select = "mpg"),
+    ignore_attr = TRUE
   )
 })
+
+# formatting ------------------------------
+test_that("describe_distribution formatting", {
+  skip_if_not_installed("bayestestR")
+  data(iris)
+  x <- describe_distribution(iris$Sepal.Width, quartiles = TRUE)
+  expect_snapshot(format(x))
+})
diff --git a/tests/testthat/test-labels_to_levels.R b/tests/testthat/test-labels_to_levels.R
index 866154c8f..87a0418b9 100644
--- a/tests/testthat/test-labels_to_levels.R
+++ b/tests/testthat/test-labels_to_levels.R
@@ -28,6 +28,18 @@ test_that("labels_to_levels, factor, error on no labels", {
   expect_error(labels_to_levels(iris), regex = "Could not change factor")
 })
 
+test_that("labels_to_levels, data frame, append", {
+  data(efc)
+  out <- labels_to_levels(efc, append = "_ll")
+  expect_named(out, c("c12hour", "e16sex", "e42dep", "c172code", "neg_c_7", "e42dep_ll"))
+})
+
+test_that("labels_to_levels, data frame, append", {
+  data(iris)
+  d <- as.data.frame(lapply(iris, as.factor))
+  expect_identical(labels_to_levels(d), d)
+})
+
 test_that("labels_to_levels, factor, data frame", {
   data(efc)
   out <- labels_to_levels(efc)