I have a dataframe where I would like to separate each column into two columns. Each column follow the same pattern "x_y"

test <- structure(list(A = c("511686_0.112", "503316_0.105", "476729_0.148", 
"229348_0.181", "385774_0.178", "209277_0.029", "299921_0.124", 
"486771_0.123", "524146_0.07", "496030_0.119"), B = c("363323_0.103", 
"260709_0.105", "361361_0.148", "731426_0.181", "222799_0.178", 
"140296_0.029", "388191_0.124", "500136_0.123", "487344_0.07", 
"267303_0.119"), C = c("362981_0.103", "260261_0.105", "360912_0.148", 
"730423_0.181", "222351_0.178", "139847_0.029", "379717_0.124", 
"499662_0.123", "486869_0.07", "266907_0.119")), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -10L))

Using the separate functions seems to work well for one column. How can I apply this same function to each of the columns?

# works

test2 <- test %>%
  separate_wider_delim(A, delim = "_", names_sep = "_")


> test2
# A tibble: 10 × 4
   A_1    A_2   B            C           
   <chr>  <chr> <chr>        <chr>       
 1 511686 0.112 363323_0.103 362981_0.103
 2 503316 0.105 260709_0.105 260261_0.105
 3 476729 0.148 361361_0.148 360912_0.148
 4 229348 0.181 731426_0.181 730423_0.181
 5 385774 0.178 222799_0.178 222351_0.178
 6 209277 0.029 140296_0.029 139847_0.029
 7 299921 0.124 388191_0.124 379717_0.124
 8 486771 0.123 500136_0.123 499662_0.123
 9 524146 0.07  487344_0.07  486869_0.07 
10 496030 0.119 267303_0.119 266907_0.119



# doesn't work


test3 <- test %>%
  mutate(across(everything(), separate_wider_delim, delim = "_", names_sep = "_"))


Error in `mutate()`:
ℹ In argument: `across(everything(), separate_wider_delim, delim = "_", names_sep = "_")`.
Caused by error in `across()`:
! Can't compute column `A`.
Caused by error in `fn()`:
! `data` must be a data frame, not a character vector.
Run `rlang::last_error()` to see where the error occurred.

>Solution :

For specific columns why not simply:

test2 <- test %>%
  separate_wider_delim(cols = A:C,delim = '_', names_sep = '_')
test2

Or to expand to all columns in a dataframe:

test2 <- test %>%
  separate_wider_delim(cols = everything(),delim = '_', names_sep = '_')
test2