dplyr add rows (based on condition) that reproduce values (strings) in remaining cols

Question

I've reviewed many 'do X using dplyr based on condition' SO questions, and I believe only this one comes close to what I'm doing. I've tried but cannot manage to adapt @divibisan's answer there.

My data looks like:

df <- tibble(
  Group_ID = c(1,2,3,4,5,6),
  statusA = c("NEW", "NEW", "OLD","NEW", "OLD","OLD"),
  statusB = c("BONUS", NA, "BONUS", NA, NA,"ROLLBACK"),
  someVar = c("Thanks", "for", "your", "help","!","!"))

When statusB is any non-NA value, I want to 'split' (as it were) that row such that for the Group_ID there exists a distinct row for its statusA and its statusB. When rows are 'split', the row with a non-NA value for statusA should show NA for statusB, and the row with a non-NA value for statusB should show NA for statusA. All remaining variables (i.e., other than statusA and statusB) should appear, per each Group_ID, on each line.

desired_df <- tibble(
  Group_ID = c(1,1,2,3,3,4,5,6,6),
  statusA = c("NEW",NA,"NEW","OLD",NA,"NEW","OLD","OLD",NA),
  statusB = c(NA,"BONUS",NA,NA,"BONUS",NA,NA,NA,"ROLLBACK"),
  someVar = c("Thanks","Thanks", "for", "your","your","help","!","!","!"))

I strongly wish to avoid using a loop. Can this be accomplished using dplyr?

Answer 1

You can try:

library(dplyr)
library(tidyr)

df |> 
  reframe(statusA = c(statusA, NA[!is.na(statusB)]),
          statusB = c(NA[!is.na(statusB)], statusB), .by = -c(statusA, statusB)) |> 
  relocate(c(statusA, statusB), .after = Group_ID)


# A tibble: 9 × 4
  Group_ID statusA statusB  someVar
     <dbl> <chr>   <chr>    <chr>  
1        1 NEW     NA       Thanks 
2        1 NA      BONUS    Thanks 
3        2 NEW     NA       for    
4        3 OLD     NA       your   
5        3 NA      BONUS    your   
6        4 NEW     NA       help   
7        5 OLD     NA       !      
8        6 OLD     NA       !      
9        6 NA      ROLLBACK !