Get new columns based on data from other columns

Question

My data:

data <- structure(list(col1 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), col2 = c(0L, 1L, 1L, 0L, 0L, 
1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L)), class = "data.frame", row.names = c(NA, 
-18L))

I want to get 2 new columns based on col1 and col2.

• column 3 is obtained: We leave units if there is zero in the second column, 2 are simply transferred.
• column 4 will turn out: We leave units if there is one in the second column, 2 are simply transferred.

What I want to get:

data <- structure(list(col1 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), col2 = c(0L, 1L, 1L, 0L, 0L, 
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), group1 = c(1L, 
NA, NA, 1L, 1L, NA, 1L, NA, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L), group2 = c(NA, 1L, 1L, NA, NA, 1L, NA, 1L, NA, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L)), class = "data.frame", row.names = c(NA, 
-18L))

Answer 1

A solution that uses tidyr::pivot_wider():

library(dplyr)

data %>%
  mutate(id = 1:n(), name = paste0("group", col2 + 1), value = 1) %>%
  tidyr::pivot_wider() %>%
  mutate(col2 = replace(col2, col1 == 2, 0),
         across(starts_with("group"), replace, col1 == 2, 2)) %>%
  select(-id)

# A tibble: 18 x 4
    col1  col2 group1 group2
   <int> <dbl>  <dbl>  <dbl>
 1     1     0      1     NA
 2     1     1     NA      1
 3     1     1     NA      1
 4     1     0      1     NA
 5     1     0      1     NA
 6     1     1     NA      1
 7     1     0      1     NA
 8     1     1     NA      1
 9     1     0      1     NA
10     2     0      2      2
11     2     0      2      2
12     2     0      2      2
13     2     0      2      2
14     2     0      2      2
15     2     0      2      2
16     2     0      2      2
17     2     0      2      2
18     2     0      2      2

Answer 2

You can use ifelse to get group1 and group2.

transform(data
        , group1 = ifelse(col1==2, 2, ifelse(col2==0, 1, NA))
        , group2 = ifelse(col1==2, 2, ifelse(col2==1, 1, NA))
          )
#   col1 col2 group1 group2
#1     1    0      1     NA
#2     1    1     NA      1
#3     1    1     NA      1
#4     1    0      1     NA
#5     1    0      1     NA
#6     1    1     NA      1
#7     1    0      1     NA
#8     1    1     NA      1
#9     1    0      1     NA
#10    2    0      2      2
#11    2    1      2      2
#12    2    1      2      2
#13    2    0      2      2
#14    2    0      2      2
#15    2    1      2      2
#16    2    0      2      2
#17    2    1      2      2
#18    2    0      2      2