Using functions of multiple columns in a dplyr mutate_at call

Question

I'd like to use dplyr's mutate_at function to apply a function to several columns in a dataframe, where the function inputs the column to which it is directly applied as well as another column in the dataframe.

As a concrete example, I'd look to mutate the following dataframe

# Example input dataframe df <- data.frame(     x = c(TRUE, TRUE, FALSE),     y = c("Hello", "Hola", "Ciao"),     z = c("World", "ao", "HaOlam") ) 

with a mutate_at call that looks similar to this

df %>% mutate_at(.vars = vars(y, z),           .funs = ifelse(x, ., NA)) 

to return a dataframe that looks something like this

# Desired output dataframe df2 <- data.frame(x = c(TRUE, TRUE, FALSE),                   y_1 = c("Hello", "Hola", NA),                   z_1 = c("World", "ao", NA)) 

The desired mutate_at call would be similar to the following call to mutate:

df %>%    mutate(y_1 = ifelse(x, y, NA),           z_1 = ifelse(x, z, NA)) 

I know that this can be done in base R in several ways, but I would specifically like to accomplish this goal using dplyr's mutate_at function for the sake of readability, interfacing with databases, etc.

Below are some similar questions asked on stackoverflow which do not address the question I posed here:

adding multiple columns in a dplyr mutate call

dplyr::mutate to add multiple values

Use of column inside sum() function using dplyr's mutate() function

Answer 1

This was answered by @eipi10 in @eipi10's comment on the question, but I'm writing it here for posterity.

The solution here is to use:

df %>%    mutate_at(.vars = vars(y, z),              .funs = list(~ ifelse(x, ., NA))) 

You can also use the new across() function with mutate(), like so:

df %>%    mutate(across(c(y, z), ~ ifelse(x, ., NA))) 

The use of the formula operator (as in ~ ifelse(...)) here indicates that ifelse(x, ., NA) is an anonymous function that is being defined within the call to mutate_at().

This works similarly to defining the function outside of the call to mutate_at(), like so:

temp_fn <- function(input) ifelse(test = df[["x"]],                                   yes = input,                                   no = NA)  df %>%    mutate_at(.vars = vars(y, z),              .funs = temp_fn) 

Note on syntax changes in dplyr: Prior to dplyr version 0.8.0, you would simply write .funs = funs(ifelse(x, . , NA)), but the funs() function is being deprecated and will soon be removed from dplyr.

Answer 2

To supplement the previous response, if you wanted mutate_at() to add new variables (instead of replacing), with names such as z_1 and y_1 as in the original question, you just need to:

• dplyr >=1 with across(): add .names="{.col}_1", or alternatively use list('1'=~ifelse(x, ., NA) (back ticks!)
• dplyr [0.8, 1[: use list('1'=~ifelse(x, ., NA)
• dplyr <0.8: use funs('1'=ifelse(x, ., NA)

library(tidyverse)  df <- data.frame(   x = c(TRUE, TRUE, FALSE),   y = c("Hello", "Hola", "Ciao"),   z = c("World", "ao", "HaOlam") )  ## Version >=1 df %>%   mutate(across(c(y, z),                  list(~ifelse(x, ., NA)),                 .names="{.col}_1")) #>       x     y      z   y_1   z_1 #> 1  TRUE Hello  World Hello World #> 2  TRUE  Hola     ao  Hola    ao #> 3 FALSE  Ciao HaOlam  <NA>  <NA>   ## 0.8 - <1 df %>%   mutate_at(.vars = vars(y, z),             .funs = list(`1`=~ifelse(x, ., NA))) #>       x     y      z   y_1   z_1 #> 1  TRUE Hello  World Hello World #> 2  TRUE  Hola     ao  Hola    ao #> 3 FALSE  Ciao HaOlam  <NA>  <NA>  ## Before 0.8 df %>%   mutate_at(.vars = vars(y, z),             .funs = funs(`1`=ifelse(x, ., NA))) #> Warning: `funs()` is deprecated as of dplyr 0.8.0. #> Please use a list of either functions or lambdas:  #>  #>   # Simple named list:  #>   list(mean = mean, median = median) #>  #>   # Auto named with `tibble::lst()`:  #>   tibble::lst(mean, median) #>  #>   # Using lambdas #>   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE)) #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_warnings()` to see where this warning was generated. #>       x     y      z   y_1   z_1 #> 1  TRUE Hello  World Hello World #> 2  TRUE  Hola     ao  Hola    ao #> 3 FALSE  Ciao HaOlam  <NA>  <NA> 

^{Created on 2020-10-03 by the reprex package (v0.3.0)}

For more details and tricks, see: Create new variables with mutate_at while keeping the original ones