Creating multiple NEW columns using across() in R

The difference between my question and existing questions is that I want to create new columns with mutate that do not depend on existing columns.

Some dummy data:

library(dplyr)
dat <- tibble(
    a = 1:5,
    b = LETTERS[1:5]
)

I know I can create new columns one-by-one like so

dat <- dat %>%
    mutate(foo = NA, bar = NA, bar2 = NA)

And I can modify columns more conveniently using across, e.g. :

new_vars <- c("foo", "bar", "bar2")
dat <- dat %>%
    mutate(across(all_of(new_vars), ~ replace(., is.na(.), 0)))

But how do I create new columns without referencing existing columns in a similar manner? E.g. adding new columns filled with NA:

tibble(
    a = 1:5,
    b = LETTERS[1:5]
) %>% 
    # mutate(across(all_of(new_vars), ~ function(.x) NA))  # Error
    mutate(across(all_of(new_vars), NA))                   # Error

Open to any tidyverse alternatives.

Similar to this answer buried in the popular question here, you can use:

new_vars <- c("foo", "bar", "bar2")

tibble(
  a = 1:5,
  b = LETTERS[1:5]
) %>% 
  mutate(!!!setNames(rep(NA, length(new_vars)), new_vars))
# or (thanks @joran)
# tibble::add_column(!!!setNames(rep(NA, length(new_vars)), new_vars))

output

     a b     foo   bar   bar2 
  <int> <chr> <lgl> <lgl> <lgl>
1     1 A     NA    NA    NA   
2     2 B     NA    NA    NA   
3     3 C     NA    NA    NA   
4     4 D     NA    NA    NA   
5     5 E     NA    NA    NA   

I use tidyverse stuff as much as the next fellow, but the lengths we're going to to avoid doing things the simple way is getting a little silly, imho.

Here. Pipe friendly.

library(dplyr)
dat <- tibble(
  a = 1:5,
  b = LETTERS[1:5]
)

new_vars <- c("foo", "bar", "bar2")

# ?
# dat[new_vars] <- NA

add_vars <- function(df,vars,val){
  df[vars] <- val
  df
}

dat |>
  add_vars(df = _,vars = new_vars,val = NA)

You could even use an anonymous function (but only with the magrittr pipe):

dat %>%
  (\(x) {x[new_vars] <- NA; x})

This also works (with the magrittr pipe) with the function(x) syntax.

Using dplyr::bind_cols() and pipes:

library(dplyr)

tibble(a = 1:5,
       b = LETTERS[1:5]) %>% 
bind_cols(., setNames(lapply(new_vars, function(x) x = NA), new_vars))

Result:

# A tibble: 5 × 5
      a b     foo   bar   bar2 
  <int> <chr> <lgl> <lgl> <lgl>
1     1 A     NA    NA    NA   
2     2 B     NA    NA    NA   
3     3 C     NA    NA    NA   
4     4 D     NA    NA    NA   
5     5 E     NA    NA    NA

Although I think the second answer to this question, on which this is based, is just as good.

If you really want mutate, Ritchie's answer in the comments works.

Maybe this is the style you're looking for:

library(dplyr)

dat <- tibble(
	a = 1:5,
	b = LETTERS[1:5]
)

new_vars <- c("foo", "bar", "bar2")

dat %>% 
	purrr::reduce(new_vars, ~mutate(.x, {{.y}} := 0), .init = .)

Instead of using across() we use purrr::reduce() which will loop over the new_vars. We apply the mutate function to the output of the previous iteration. We want to start with .init = dat, but we pipe it in.

You could even use reduce2 if you wanted to have different values for each of the new_vars.