I’m trying to mutate values across multiple columns to values from another column.

This is my dataset:

library(stringr)
library(dplyr)
library(fastDummies)

score <- sample(1:100,20,replace=TRUE)
df <- data.frame(score)

df <- df %>%
   mutate(grp = cut(score, breaks = c(-Inf, seq(0, 100, by = 20), Inf)), 
      grp = str_c("G", as.integer(droplevels(grp)), '_', 
      str_replace(grp, '\\((\\d+),(\\d+)\\]', 
     '\\1_\\2'))) %>% 
   dummy_cols("grp", remove_selected_columns = TRUE) %>% 
   rename_with(~ str_remove(.x, 'grp_'), starts_with('grp_'))

I want to mutate columns that start with the letter "G", so G1_0_20, G2_20_40, etc.

If columns that start with G (G1_0_20, G2_20_40,etc) has value of 1, then its value should match column "Score", otherwise NA.

I can’t quite figure out how to use mutate across with ifelse statement.

I would appreciate all the help there is! Thanks!!!

>Solution :

I think this is it:

df %>%
  mutate(across(starts_with("G"), ~ifelse(. == 1, score, NA)))
   score G1_0_20 G2_20_40 G3_40_60 G4_60_80 G5_80_100
1     52      NA       NA       52       NA        NA
2     90      NA       NA       NA       NA        90
3     73      NA       NA       NA       73        NA
4     11      11       NA       NA       NA        NA
5     16      16       NA       NA       NA        NA
6     47      NA       NA       47       NA        NA
7     42      NA       NA       42       NA        NA
8     62      NA       NA       NA       62        NA
9     64      NA       NA       NA       64        NA
10    25      NA       25       NA       NA        NA
11    47      NA       NA       47       NA        NA
12    63      NA       NA       NA       63        NA
13    96      NA       NA       NA       NA        96
14    95      NA       NA       NA       NA        95
15     3       3       NA       NA       NA        NA
16    25      NA       25       NA       NA        NA
17    78      NA       NA       NA       78        NA
18    10      10       NA       NA       NA        NA
19    51      NA       NA       51       NA        NA
20    12      12       NA       NA       NA        NA