CODEWITHSUNDEEP
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NewBee
NewBee

Reputation: 1040

Re-value selection of columns by group

I have data such as this:

data_in <- read_table2("condition   Q11_1   Q11_2   Q11_3   Q11_4   Q11_5   Q11_6   Q11_7   Q11_8   Q11_9   Q11_10  Q11_11  Q11_12  Q11_13
1   2   5   5   2   5   5   5   5   2   5   5   2   2
0   1   1   1   2   2   5   5   5   1   2   2   2   1
1   6   5   6   6   6   6   5   6   5   6   6   6   6
0   6   6   6   6   6   6   6   6   6   6   6   6   6
1   5   6   6   6   6   6   6   6   6   6   6   6   5
1   6   6   6   6   6   6   6   6   5   5   6   6   5
")

I have a series of variables (from a survey) with the values (1,2,5,6). I want to change the values of a specific set of variables from 5 to 3 and the 6 to 4. In this example I've only included the variables Q11_1:Q11_13 that I am interested in changing. There about 100 other variables I have not included in the example. I also want this to apply when condition==1.

I know I can do this to to change all the columns that contain Q11:

survey %>% 
  mutate(across(contains("Q11"),.fns = ~ case_when(.x == 5 ~ 3,
                                                   .x == 6 ~ 4,
                                                   TRUE ~ .x)))

Now, I want to re-value all the columns that contain Q11 AND have the condition==1. I don't want those columns to be changed if the condition of that row==0.

Upvotes: 1

Views: 38

Answers (1)

akrun
akrun

Reputation: 887108

We can use the & within case_when

library(dplyr)
data_in %>% 
  mutate(across(starts_with('Q11'), ~ 
     case_when(. == 5 & condition == 1 ~ 3, 
               . == 6 & condition == 6 ~ 4, TRUE ~ .)))
# A tibble: 6 x 14
#  condition Q11_1 Q11_2 Q11_3 Q11_4 Q11_5 Q11_6 Q11_7 Q11_8 Q11_9 Q11_10 Q11_11 Q11_12 Q11_13
#      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#1         1     2     3     3     2     3     3     3     3     2      3      3      2      2
#2         0     1     1     1     2     2     5     5     5     1      2      2      2      1
#3         1     6     3     6     6     6     6     3     6     3      6      6      6      6
#4         0     6     6     6     6     6     6     6     6     6      6      6      6      6
#5         1     3     6     6     6     6     6     6     6     6      6      6      6      3
#6         1     6     6     6     6     6     6     6     6     3      3      6      6      3

Upvotes: 1

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