Apply a function (or functions) across multiple columns

across() makes it easy to apply the same transformation to multiple columns, allowing you to use select() semantics inside in "data-masking" functions like summarise() and mutate(). See vignette("colwise") for more details.

if_any() and if_all() apply the same predicate function to a selection of columns and combine the results into a single logical vector.

across() supersedes the family of "scoped variants" like summarise_at(), summarise_if(), and summarise_all().

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

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

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

Arguments

.fns	Functions to apply to each of the selected columns. Possible values are: `NULL`, to returns the columns untransformed. A function, e.g. `mean`. A purrr-style lambda, e.g. `~ mean(.x, na.rm = TRUE)` A list of functions/lambdas, e.g. `list(mean = mean, n_miss = ~ sum(is.na(.x))` Within these functions you can use `cur_column()` and `cur_group()` to access the current column and grouping keys respectively.
...	Additional arguments for the function calls in `.fns`.
.names	A glue specification that describes how to name the output columns. This can use `{.col}` to stand for the selected column name, and `{.fn}` to stand for the name of the function being applied. The default (`NULL`) is equivalent to `"{.col}"` for the single function case and `"{.col}_{.fn}"` for the case where a list is used for `.fns`.
cols, .cols	<`tidy-select`> Columns to transform. Because `across()` is used within functions like `summarise()` and `mutate()`, you can't select or compute upon grouping variables.

Value

across() returns a tibble with one column for each column in .cols and each function in .fns.

if_any() and if_all() return a logical vector.

Examples

# across() -----------------------------------------------------------------
# Different ways to select the same set of columns
# See <https://tidyselect.r-lib.org/articles/syntax.html> for details
iris %>%
  as_tibble() %>%
  mutate(across(c(Sepal.Length, Sepal.Width), round))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1            5           4          1.4         0.2 setosa 
#>  2            5           3          1.4         0.2 setosa 
#>  3            5           3          1.3         0.2 setosa 
#>  4            5           3          1.5         0.2 setosa 
#>  5            5           4          1.4         0.2 setosa 
#>  6            5           4          1.7         0.4 setosa 
#>  7            5           3          1.4         0.3 setosa 
#>  8            5           3          1.5         0.2 setosa 
#>  9            4           3          1.4         0.2 setosa 
#> 10            5           3          1.5         0.1 setosa 
#> # … with 140 more rows
iris %>%
  as_tibble() %>%
  mutate(across(c(1, 2), round))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1            5           4          1.4         0.2 setosa 
#>  2            5           3          1.4         0.2 setosa 
#>  3            5           3          1.3         0.2 setosa 
#>  4            5           3          1.5         0.2 setosa 
#>  5            5           4          1.4         0.2 setosa 
#>  6            5           4          1.7         0.4 setosa 
#>  7            5           3          1.4         0.3 setosa 
#>  8            5           3          1.5         0.2 setosa 
#>  9            4           3          1.4         0.2 setosa 
#> 10            5           3          1.5         0.1 setosa 
#> # … with 140 more rows
iris %>%
  as_tibble() %>%
  mutate(across(1:Sepal.Width, round))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1            5           4          1.4         0.2 setosa 
#>  2            5           3          1.4         0.2 setosa 
#>  3            5           3          1.3         0.2 setosa 
#>  4            5           3          1.5         0.2 setosa 
#>  5            5           4          1.4         0.2 setosa 
#>  6            5           4          1.7         0.4 setosa 
#>  7            5           3          1.4         0.3 setosa 
#>  8            5           3          1.5         0.2 setosa 
#>  9            4           3          1.4         0.2 setosa 
#> 10            5           3          1.5         0.1 setosa 
#> # … with 140 more rows
iris %>%
  as_tibble() %>%
  mutate(across(where(is.double) & !c(Petal.Length, Petal.Width), round))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1            5           4          1.4         0.2 setosa 
#>  2            5           3          1.4         0.2 setosa 
#>  3            5           3          1.3         0.2 setosa 
#>  4            5           3          1.5         0.2 setosa 
#>  5            5           4          1.4         0.2 setosa 
#>  6            5           4          1.7         0.4 setosa 
#>  7            5           3          1.4         0.3 setosa 
#>  8            5           3          1.5         0.2 setosa 
#>  9            4           3          1.4         0.2 setosa 
#> 10            5           3          1.5         0.1 setosa 
#> # … with 140 more rows

# A purrr-style formula
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), ~mean(.x, na.rm = TRUE)))
#> # A tibble: 3 x 3
#>   Species    Sepal.Length Sepal.Width
#>   <fct>             <dbl>       <dbl>
#> 1 setosa             5.01        3.43
#> 2 versicolor         5.94        2.77
#> 3 virginica          6.59        2.97

# A named list of functions
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))
#> # A tibble: 3 x 5
#>   Species    Sepal.Length_mean Sepal.Length_sd Sepal.Width_mean Sepal.Width_sd
#>   <fct>                  <dbl>           <dbl>            <dbl>          <dbl>
#> 1 setosa                  5.01           0.352             3.43          0.379
#> 2 versicolor              5.94           0.516             2.77          0.314
#> 3 virginica               6.59           0.636             2.97          0.322

# Use the .names argument to control the output names
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), mean, .names = "mean_{.col}"))
#> # A tibble: 3 x 3
#>   Species    mean_Sepal.Length mean_Sepal.Width
#>   <fct>                  <dbl>            <dbl>
#> 1 setosa                  5.01             3.43
#> 2 versicolor              5.94             2.77
#> 3 virginica               6.59             2.97
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd), .names = "{.col}.{.fn}"))
#> # A tibble: 3 x 5
#>   Species    Sepal.Length.mean Sepal.Length.sd Sepal.Width.mean Sepal.Width.sd
#>   <fct>                  <dbl>           <dbl>            <dbl>          <dbl>
#> 1 setosa                  5.01           0.352             3.43          0.379
#> 2 versicolor              5.94           0.516             2.77          0.314
#> 3 virginica               6.59           0.636             2.97          0.322

# When the list is not named, .fn is replaced by the function's position
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean, sd), .names = "{.col}.fn{.fn}"))
#> # A tibble: 3 x 5
#>   Species    Sepal.Length.fn1 Sepal.Length.fn2 Sepal.Width.fn1 Sepal.Width.fn2
#>   <fct>                 <dbl>            <dbl>           <dbl>           <dbl>
#> 1 setosa                 5.01            0.352            3.43           0.379
#> 2 versicolor             5.94            0.516            2.77           0.314
#> 3 virginica              6.59            0.636            2.97           0.322

# if_any() and if_all() ----------------------------------------------------
iris %>%
  filter(if_any(ends_with("Width"), ~ . > 4))
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.7         4.4          1.5         0.4  setosa
#> 2          5.2         4.1          1.5         0.1  setosa
#> 3          5.5         4.2          1.4         0.2  setosa
iris %>%
  filter(if_all(ends_with("Width"), ~ . > 2))
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
#> 1           6.3         3.3          6.0         2.5 virginica
#> 2           7.1         3.0          5.9         2.1 virginica
#> 3           6.5         3.0          5.8         2.2 virginica
#> 4           7.6         3.0          6.6         2.1 virginica
#> 5           7.2         3.6          6.1         2.5 virginica
#> 6           6.8         3.0          5.5         2.1 virginica
#> 7           5.8         2.8          5.1         2.4 virginica
#> 8           6.4         3.2          5.3         2.3 virginica
#> 9           7.7         3.8          6.7         2.2 virginica
#> 10          7.7         2.6          6.9         2.3 virginica
#> 11          6.9         3.2          5.7         2.3 virginica
#> 12          6.7         3.3          5.7         2.1 virginica
#> 13          6.4         2.8          5.6         2.1 virginica
#> 14          6.4         2.8          5.6         2.2 virginica
#> 15          7.7         3.0          6.1         2.3 virginica
#> 16          6.3         3.4          5.6         2.4 virginica
#> 17          6.9         3.1          5.4         2.1 virginica
#> 18          6.7         3.1          5.6         2.4 virginica
#> 19          6.9         3.1          5.1         2.3 virginica
#> 20          6.8         3.2          5.9         2.3 virginica
#> 21          6.7         3.3          5.7         2.5 virginica
#> 22          6.7         3.0          5.2         2.3 virginica
#> 23          6.2         3.4          5.4         2.3 virginica

Apply a function (or functions) across multiple columns

Arguments

Value

See also

Examples