Expand comma separated columns on two columns while adding NA to repeated values in R

I have the following dataframe:

structure(list(A = c("1,2,3", "1,2", "1,4"), B = c("X,Y", "X", 
"X"), ID = c(1, 2, 3), D = c(1, 2, 3)), class = "data.frame", row.names = c(NA, 
-3L))

I’m trying to expand A and B but creating NAs so no repeated values are found for columns. So columns A and B should expand "independently", as if the values of A and B are not directly correlated, they are only correlated to ID and D

structure(list(A = c("1", "2", "3", "NA", "NA", "1", "2", "NA", 
"1", "4", "NA"), B = c("NA", "NA", "NA", "X", "Y", "NA", "NA", 
"X", "NA", "NA", "X"), ID = c(1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3
), D = c(1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3)), class = "data.frame", row.names = c(NA, 
-11L))

None of previous questions about expanding multiple columns that I could find did something similar!

>Solution :

This is a very unusual expansion. You could tackle each column separately and then join them. For example

library(dplyr)
library(tidyr)
bind_rows(
  separate_longer_delim(select(dd, -B), A, ","), 
  separate_longer_delim(select(dd, -A), B, ",")
) |>
  select(A,B,ID, D) |>
  arrange(ID, D)
#       A    B ID D
# 1     1 <NA>  1 1
# 2     2 <NA>  1 1
# 3     3 <NA>  1 1
# 4  <NA>    X  1 1
# 5  <NA>    Y  1 1
# 6     1 <NA>  2 2
# 7     2 <NA>  2 2
# 8  <NA>    X  2 2
# 9     1 <NA>  3 3
# 10    4 <NA>  3 3
# 11 <NA>    X  3 3