Simple code update.

R/adorn_totals.R

@@ -143,8 +143,7 @@ adorn_totals <- function(dat, where = "row", fill = "-", na.rm = TRUE, name = "T
     if ("col" %in% where) {
       # Add totals col
       row_totals <- dat %>%
-        dplyr::select(dplyr::all_of(cols_to_total)) %>%
-        dplyr::select_if(is.numeric) %>%
+        dplyr::select(dplyr::all_of(cols_to_total) & dplyr::where(is.numeric)) %>%
         dplyr::transmute(Total = rowSums(., na.rm = na.rm))
 
       dat[[name[2]]] <- row_totals$Total

R/remove_empties.R

@@ -27,7 +27,7 @@
 #' dd %>% remove_empty("rows")
 #' # solution: preprocess to convert whitespace/empty strings to NA,
 #' # _then_ remove empty (all-NA) rows
-#' dd %>% mutate(across(is.character,~na_if(trimws(.),""))) %>%
+#' dd %>% mutate(across(where(is.character),~na_if(trimws(.),""))) %>%
 #'    remove_empty("rows")
 #' @export
 remove_empty <- function(dat, which = c("rows", "cols"), cutoff=1, quiet=TRUE) {
@@ -93,7 +93,7 @@ remove_empty <- function(dat, which = c("rows", "cols"), cutoff=1, quiet=TRUE) {
 #'
 #' # To find the columns that are constant
 #' data.frame(A=1, B=1:3) %>%
-#'   dplyr::select_at(setdiff(names(.), names(remove_constant(.)))) %>%
+#'   dplyr::select(!dplyr::all_of(names(remove_constant(.)))) %>%
 #'   unique()
 #' @importFrom stats na.omit
 #' @family remove functions

tests/testthat/test-clean-names.R

@@ -589,7 +589,7 @@ test_that("tbl_graph/tidygraph", {
     tidygraph::play_erdos_renyi(10, 0.5) %>%
     # create nodes wi
     tidygraph::bind_nodes(test_df) %>%
-    tidygraph::mutate_all(tidyr::replace_na, 1)
+    dplyr::mutate(dplyr::across(dplyr::where(is.numeric), ~ dplyr::coalesce(x, 1)))
 
   # create a graph with clean names
   # warning due to unhandled mu