dplyr::across()

across(.cols = everything(), .fns = NULL, ..., .names = NULL)

across() and where() are dplyr functions that enable column-wise transformations. across() applies the same function(s) to multiple columns simultaneously, replacing values in place. where() is a selection helper that picks columns based on their type or a condition, making it easy to target specific column types for transformation.

Syntax

across(.cols = everything(), .fns = NULL, ..., .names = NULL)

where(.predicate, ...)

Parameters

Parameter	Type	Default	Description
.cols	<tidy-select>	everything()	Columns to transform. Accepts column names, positions, or selection helpers.
.fns	<list> / <function>	NULL	Functions to apply. Can be a single function, named list, or unnamed list of functions.
.names	<string>	NULL	Formula for naming output columns. Uses {col} and {fn} glue syntax.
.predicate	<function>	—	A function that returns TRUE/FALSE for each column.
...	<arguments>	—	Additional arguments passed to .fns.

Examples

Basic usage with mutate

Apply mean() to all numeric columns to center them around zero.

library(dplyr)

df <- tibble(
  a = c(1, 3, 5),
  b = c(2, 4, 6),
  name = c("Alice", "Bob", "Carol")
)

df |> mutate(across(where(is.numeric), ~ .x - mean(.x)))
#> # A tibble: 3 × 3
#>       a     b name 
#>   <dbl> <dbl> <chr>
#> 1    -2    -2 Alice
#> 2     0     0 Bob   
#> 3     2     2 Carol

Applying multiple functions

Apply both mean() and sd() to numeric columns simultaneously.

df <- tibble(
  x = c(1, 2, 3, 4, 5),
  y = c(10, 20, 30, 40, 50),
  z = c("a", "b", "c", "d", "e")
)

df |> summarise(across(where(is.numeric), list(mean = mean, sd = sd)))
#> # A tibble: 1 × 4
#>     x_mean  x_sd  y_mean  y_sd
#>      <dbl> <dbl>   <dbl> <dbl>
#> 1        3   1.4      30    15

Custom naming with .names

Use the .names argument to control output column names.

df <- tibble(
  score1 = c(85, 92, 78),
  score2 = c(90, 88, 95),
  name = c("Alice", "Bob", "Carol")
)

df |> mutate(across(starts_with("score"), round, .names = "{col}_rounded"))
#> # A tibble: 3 × 5
#>   score1 score2 name  score1_rounded score2_rounded
#>    <dbl>  <dbl> <chr>          <dbl>          <dbl>
#> 1     85     90 Alice              85              90
#> 2     92     88 Bob                92              88
#> 3     78     95 Carol              78              95

Using where() with other verbs

where() works with any tidy-select verb like select(), mutate(), and summarise().

df <- tibble(
  id = 1:3,
  age = c(25, 30, 35),
  salary = c(50000, 60000, 70000),
  name = c("A", "B", "C")
)

# Select all numeric columns
df |> select(where(is.numeric))
#> # A tibble: 3 × 3
#>      id   age salary
#>   <int> <dbl>  <dbl>
#> 1     1    25  50000
#> 2     2    30  60000
#> 3     3    35  70000

# Summarise all numeric columns
df |> summarise(across(where(is.numeric), sum))
#> # A tibble: 1 × 3
#>      id   age salary
#>   <int> <dbl>  <dbl>
#> 1     6    90 180000

Common Patterns

Scaling all numeric columns

# Standardize (z-score) all numeric columns
df <- tibble(
  a = c(1, 2, 3, 4, 5),
  b = c(10, 20, 30, 40, 50)
)

df |> mutate(across(where(is.numeric), ~ (.x - mean(.x)) / sd(.x)))

Converting character columns to factors

df <- tibble(
  name = c("Alice", "Bob", "Carol"),
  score = c(85, 92, 78)
)

df |> mutate(across(where(is.character), factor))

Conditional transformations

Apply different functions to different column types.

df <- tibble(
  a = c(1.5, 2.5, 3.5),
  b = c(4.1, 5.2, 6.3),
  name = c("x", "y", "z")
)

df |> mutate(
  across(where(is.double), floor),
  across(where(is.character), toupper)
)

See Also

• dplyr::filter
• dplyr::select
• dplyr::mutate
• dplyr::summarise