`group_by` in `cb_summarize_numeric()` fails with numeric grouping variable

[ccsarapas]

Reprex:

dat <- data.frame(
  grp = c(1, 2, 1, 2),
  num = c(1, 2, 3, 4),
  cat = factor(c("a", "a", "b", "b"))
)

cb <- cb_create(dat)

cb_summarize_numeric(cb, group_by = grp)
# Error in `dplyr::summarize()`:
# ℹ In argument: `dplyr::across(.cols = c("grp", "num"), .fns = .fns, .names = "{.col}__SEP__{.fn}")`.
# Caused by error in `across()`:
# ! Can't select columns that don't exist.
# ✖ Column `grp` doesn't exist.

Works fine for cb_summarize_categorical():

cb_summarize_categorical(cb, group_by = grp)
# # A tibble: 4 × 6
#   grp   name  value     n pct_of_all pct_of_valid
#   <fct> <chr> <chr> <int>      <dbl>        <dbl>
# 1 1     cat   a         1        0.5          0.5
# 2 1     cat   b         1        0.5          0.5
# 3 2     cat   a         1        0.5          0.5
# 4 2     cat   b         1        0.5          0.5

And works for both if grp isn't numeric:

cb2 <- dat |>
  transform(grp = factor(grp)) |>
  cb_create()

cb_summarize_numeric(cb2, group_by = grp)
# # A tibble: 2 × 13
#   grp   name  valid_n valid_pct  mean    SD median   MAD   min   max range  skew
#   <fct> <chr>   <int>     <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1     num         2         1     2  1.41      2  1.48     1     3     2    NA
# 2 2     num         2         1     3  1.41      3  1.48     2     4     2    NA
# # ℹ 1 more variable: kurt <dbl>

cb_summarize_categorical(cb2, group_by = grp)
# # A tibble: 4 × 6
#   grp   name  value     n pct_of_all pct_of_valid
#   <fct> <chr> <chr> <int>      <dbl>        <dbl>
# 1 1     cat   a         1        0.5          0.5
# 2 1     cat   b         1        0.5          0.5
# 3 2     cat   a         1        0.5          0.5
# 4 2     cat   b         1        0.5          0.5