more about across(.fns = NULL) (#6027)

* more about .fns = NULL (the default)

still need to find a decent example

* complete.cases(across()) combo to show that result of across() may be used as another function input.

* use `x_complete` instead of  `z`

R/across.R

@@ -15,17 +15,19 @@
 #' `across()` supersedes the family of "scoped variants" like
 #' `summarise_at()`, `summarise_if()`, and `summarise_all()`.
 #'
-#' @param cols,.cols <[`tidy-select`][dplyr_tidy_select]> Columns to transform.
+#' @param .cols,cols <[`tidy-select`][dplyr_tidy_select]> Columns to transform.
 #'   Because `across()` is used within functions like `summarise()` and
 #'   `mutate()`, you can't select or compute upon grouping variables.
 #' @param .fns Functions to apply to each of the selected columns.
 #'   Possible values are:
 #'
-#'   - `NULL`, to returns the columns untransformed.
 #'   - A function, e.g. `mean`.
 #'   - A purrr-style lambda, e.g. `~ mean(.x, na.rm = TRUE)`
 #'   - A list of functions/lambdas, e.g.
 #'     `list(mean = mean, n_miss = ~ sum(is.na(.x))`
+#'   - `NULL`: the default value, returns the selected columns in a data
+#'   frame without applying a transformation. This is useful for when you want to
+#'   use a function that takes a data frame.
 #'
 #'   Within these functions you can use [cur_column()] and [cur_group()]
 #'   to access the current column and grouping keys respectively.
@@ -106,6 +108,17 @@
 #'   group_by(Species) %>%
 #'   summarise(across(starts_with("Sepal"), list(mean, sd), .names = "{.col}.fn{.fn}"))
 #'
+#' # across() returns a data frame, which can be used as input of another function
+#' df <- data.frame(
+#'   x1  = c(1, 2, NA),
+#'   x2  = c(4, NA, 6),
+#'   y   = c("a", "b", "c")
+#' )
+#' df %>%
+#'   mutate(x_complete = complete.cases(across(starts_with("x"))))
+#' df %>%
+#'   filter(complete.cases(across(starts_with("x"))))
+#'
 #' # if_any() and if_all() ----------------------------------------------------
 #' iris %>%
 #'   filter(if_any(ends_with("Width"), ~ . > 4))