Create new column based on existing columns whose names are stored in another column (dplyr)

Consider the following dataset:

df <- tibble(v1 = 1:5, v2= 101:105, v3 = c("v1", "v2", "v1", "v2", "v1"))

# A tibble: 5 × 3
     v1    v2 v3   
  <int> <int> <chr>
1     1   101 v1   
2     2   102 v2   
3     3   103 v1   
4     4   104 v2   
5     5   105 v1  

I would like to generate a new column that takes values from either v1 or v2, depending on which column is listed in v3.

    # A tibble: 5 × 4
     v1    v2 v3       v4
  <int> <int> <chr> <dbl>
1     1   101 v1        1
2     2   102 v2      102
3     3   103 v1        3
4     4   104 v2      104
5     5   105 v1        5

Normally, I would use if_else, or if I had more cases, case_when. However, I have a lot of columns, so I’d rather not have a case_when statement that’s many lines long. Is there a way to get R to interpret the values in v3 as column names? I’ve tried embracing the expression with {{ }} and using the .data[[ ]], but I can’t seem to figure out the correct syntax.

>Solution :

A tidyverse option would be rowwise with extraction using cur_data()

library(dplyr)
df %>% 
  rowwise %>%
  mutate(v4 = cur_data()[[v3]]) %>% 
  ungroup
# A tibble: 5 × 4
     v1    v2 v3       v4
  <int> <int> <chr> <int>
1     1   101 v1        1
2     2   102 v2      102
3     3   103 v1        3
4     4   104 v2      104
5     5   105 v1        5

Or a compact approach would be get after rowwise

df %>%
  rowwise %>%
  mutate(v4 = get(v3)) %>%
  ungroup

Or in base R, use row/column indexing for faster execution

df$v4 <- as.data.frame(df[1:2])[cbind(seq_len(nrow(df)), 
      match(df$v3, names(df)))]
df$v4
[1]   1 102   3 104   5