Can I mutate many columns according to many other columns with mutate() and across()?

Question

I have a dataset where loads of columns have NA characters because they depend on another column response. For example :

    df <- data.frame(X_home = c("Yes", "Yes", "No"), 
                     X_school = c("No", "Yes", "No"), 
                     Y_home = c("A", "B", NA), 
                     Y_school = c(NA, "A", NA))

If I use mutate(), I can do :

df %>% mutate(Y_home = if_else(X_home == "No", "C", Y_home),
              Y_school = if_else(X_school == "No", "C", Y_school))

to obtain what I want. But the problem is that I have many X_something and Y_something. So I'd like to make it with something like that :

df %>% mutate(across(starts_with("Y_"), ~ if_else(...))

Is it possible ?

Thanks a lot.

Answer 1

We can get the value of the corresponding column after replacing the substring of column name (cur_column()) using str_replace

library(dplyr)
library(stringr)
df %>%
   mutate(across(starts_with("Y_"),
    ~ case_when(get(str_replace(cur_column(), "Y_", "X_")) == "No" ~
       "C", TRUE ~ .x)))

-output

 X_home X_school Y_home Y_school
1    Yes       No      A        C
2    Yes      Yes      B        A
3     No       No      C        C

Answer 2

Here is one possible approach with pivoting:

We identify groups of two in long format to imitate the corresponding column pairs.

Within these groups we mutate across school and home and re-pivot back:

library(tidyr)
library(dplyr)

df %>%
  mutate(rn = row_number()) %>%
  pivot_longer(cols = -rn, names_to = c( "grp", '.value'), names_sep = "\\_") %>%
  group_by(rn) %>% 
  mutate(across(c(home, school), ~ if_else(is.na(.), "C",.))) %>% 
  pivot_wider(names_from = grp, 
              values_from = c(home, school),
              names_glue = "{grp}_{.value}") %>% 
  ungroup() %>% 
  select(-rn)

 X_home Y_home X_school Y_school
  <chr>  <chr>  <chr>    <chr>   
1 Yes    A      No       C       
2 Yes    B      Yes      A       
3 No     C      No       C