Dplyr: add multiple columns with mutate/across from character vector-CodePudding

I want to add several columns (filled with NA) to a data.frame using dplyr. I've defined the names of the columns in a character vector. Usually, with only one new column, you can use the following pattern:

test %>% 
  mutate(!!new_column := NA)

However, I don't get it to work with across:

library(dplyr)

test <- data.frame(a = 1:3)
add_cols <- c("col_1", "col_2")

test %>% 
  mutate(across(!!add_cols, ~ NA))
#> Error: Problem with `mutate()` input `..1`.
#> x Can't subset columns that don't exist.
#> x Columns `col_1` and `col_2` don't exist.
#> ℹ Input `..1` is `across(c("col_1", "col_2"), ~NA)`.

test %>% 
  mutate(!!add_cols := NA)
#> Error: The LHS of `:=` must be a string or a symbol

expected_output <- data.frame(
  a = 1:3,
  col_1 = rep(NA, 3),
  col_2 = rep(NA, 3)
)
expected_output
#>   a col_1 col_2
#> 1 1    NA    NA
#> 2 2    NA    NA
#> 3 3    NA    NA

^{Created on 2021-10-05 by the reprex package (v1.0.0)}

With the first approach, the column names are correctly created, but then it directly tries to find it in the existing column names. In the second approach, I can't use anything other than a single string.

Is there a tidyverse solution or do I need to resort to the good old for loop?

CodePudding user response：

The !! works for a single element

for(nm in add_cols) test <- test %>% 
         mutate(!! nm := NA)

-output

> test
  a col_1 col_2
1 1    NA    NA
2 2    NA    NA
3 3    NA    NA

Or another option is

test %>% 
   bind_cols(setNames(rep(list(NA), length(add_cols)), add_cols))
  a col_1 col_2
1 1    NA    NA
2 2    NA    NA
3 3    NA    NA

In base R, this is easier

test[add_cols] <- NA

Which can be used in a pipe

test %>%
  `[<-`(., add_cols, value = NA)
  a col_1 col_2
1 1    NA    NA
2 2    NA    NA
3 3    NA    NA

across works only if the columns are already present i.e. it is suggesting to loop across the columns present in the data and do some modification/create new columns with .names modification

We could make use add_column from tibble

library(tibble)
library(janitor)
add_column(test, !!! add_cols) %>% 
   clean_names %>% 
   mutate(across(all_of(add_cols), ~ NA))
  a col_1 col_2
1 1    NA    NA
2 2    NA    NA
3 3    NA    NA

CodePudding user response：

Another approach:

library(tidyverse)
f <- function(x) df$x = NA
mutate(test, map_dfc(add_cols,~ f(.x)))