Conditionally change values in a data frame to their column name

Question

I have a data frame that looks like this

set.seed(123)
test_data <- data.frame(id   = 1:6,
                        var1 = rbinom(n = 6, size = 1, prob = .5),
                        var2 = rbinom(n = 6, size = 1, prob = .5),
                        age  = sample(18:30, size = 6, replace = T))

I want to use dplyr or purrr to change the values that are equal to 1 in var1 and var2, to the name of their column and keep the 0's the way they are.

The result would look like this.

id    var1  var2    age
1     0     var2    26
2     var1  var2    25
3     0     var2    19
4     var1  0       29
5     var1  var2    21
6     0     0       18

I have tried using dplyr::mutate_at

mutate_at(test_data,
          vars(var1, var2), 
          function(var_x) { ifelse(var_x == 1, colnames(var_x), var_x) })

This returns the following error. So, probably not the best way to go.

Error in evalq(sys.calls(), ) : replacement has length zero In addition: Warning message: In rep(yes, length.out = length(ans)) : 'x' is NULL so the result will be NULL

And I have tried using purrr:map_at

map_at(test_data, 
       c("var1", "var2"), 
       function(var_x) { ifelse(var_x == 1, colnames(var_x), var_x) })

And this returns this error.

Error in ans[test & ok] <- rep(yes, length.out = length(ans))[test & ok] : replacement has length zero In addition: Warning message: In rep(yes, length.out = length(ans)) : 'x' is NULL so the result will be NULL

While I'd prefer to work with dplyr or purrr, I'm open to suggestions using other approaches.

Answer 1

Here is an idea via tidyverse. The trick here is to gather first, replace the values and then spread

library(tidyverse)

test_data %>% 
  gather(var, val, -c(id, age)) %>% 
  mutate(val = ifelse(val == 1, var, val)) %>% 
  spread(var, val)

#  id age var1 var2
#1  1  26    0 var2
#2  2  25 var1 var2
#3  3  19    0 var2
#4  4  29 var1    0
#5  5  21 var1 var2
#6  6  18    0    0