Column-wise operations

It’s often useful to perform the same operation on multiple columns, but copying and pasting is both tedious and error prone:

df %>% 
  group_by(g1, g2) %>% 
  summarise(a = mean(a), b = mean(b), c = mean(c), d = mean(d))

(If you’re trying to compute mean(a, b, c, d) for each row, instead see vignette("rowwise"))

This vignette will introduce you to the across() function, which lets you rewrite the previous code more succinctly:

df %>% 
  group_by(g1, g2) %>% 
  summarise(across(a:d, mean))

We’ll start by discussing the basic usage of across(), particularly as it applies to summarise(), and show how to use it with multiple functions. We’ll then show a few uses with other verbs. We’ll finish off with a bit of history, showing why we prefer across() to our last approach (the _if(), _at() and _all() functions) and how to translate your old code to the new syntax.

library(dplyr, warn.conflicts = FALSE)

Basic usage

across() has two primary arguments:

The first argument, .cols, selects the columns you want to operate on. It uses tidy selection (like select()) so you can pick variables by position, name, and type.
The second argument, .fns, is a function or list of functions to apply to each column. This can also be a purrr style formula (or list of formulas) like ~ .x / 2. (This argument is optional, and you can omit it if you just want to get the underlying data; you’ll see that technique used in vignette("rowwise").)

Here are a couple of examples of across() in conjunction with its favourite verb, summarise(). But you can use across() with any dplyr verb, as you’ll see a little later.

starwars %>% 
  summarise(across(where(is.character), n_distinct))
#> # A tibble: 1 × 8
#>    name hair_color skin_color eye_color   sex gender homeworld species
#>   <int>      <int>      <int>     <int> <int>  <int>     <int>   <int>
#> 1    87         12         31        15     5      3        49      38

starwars %>% 
  group_by(species) %>% 
  filter(n() > 1) %>% 
  summarise(across(c(sex, gender, homeworld), n_distinct))
#> # A tibble: 9 × 4
#>   species    sex gender homeworld
#>   <chr>    <int>  <int>     <int>
#> 1 Droid        1      2         3
#> 2 Gungan       1      1         1
#> 3 Human        2      2        15
#> 4 Kaminoan     2      2         1
#> # ℹ 5 more rows

starwars %>% 
  group_by(homeworld) %>% 
  filter(n() > 1) %>% 
  summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
#> # A tibble: 10 × 4
#>   homeworld height  mass birth_year
#>   <chr>      <dbl> <dbl>      <dbl>
#> 1 Alderaan    176.  64         43  
#> 2 Corellia    175   78.5       25  
#> 3 Coruscant   174.  50         91  
#> 4 Kamino      208.  83.1       31.5
#> # ℹ 6 more rows

Because across() is usually used in combination with summarise() and mutate(), it doesn’t select grouping variables in order to avoid accidentally modifying them:

df <- data.frame(g = c(1, 1, 2), x = c(-1, 1, 3), y = c(-1, -4, -9))
df %>% 
  group_by(g) %>% 
  summarise(across(where(is.numeric), sum))
#> # A tibble: 2 × 3
#>       g     x     y
#>   <dbl> <dbl> <dbl>
#> 1     1     0    -5
#> 2     2     3    -9

Multiple functions

You can transform each variable with more than one function by supplying a named list of functions or lambda functions in the second argument:

min_max <- list(
  min = ~min(.x, na.rm = TRUE), 
  max = ~max(.x, na.rm = TRUE)
)
starwars %>% summarise(across(where(is.numeric), min_max))
#> # A tibble: 1 × 6
#>   height_min height_max mass_min mass_max birth_year_min birth_year_max
#>        <int>      <int>    <dbl>    <dbl>          <dbl>          <dbl>
#> 1         66        264       15     1358              8            896
starwars %>% summarise(across(c(height, mass, birth_year), min_max))
#> # A tibble: 1 × 6
#>   height_min height_max mass_min mass_max birth_year_min birth_year_max
#>        <int>      <int>    <dbl>    <dbl>          <dbl>          <dbl>
#> 1         66        264       15     1358              8            896

Control how the names are created with the .names argument which takes a glue spec:

starwars %>% summarise(across(where(is.numeric), min_max, .names = "{.fn}.{.col}"))
#> # A tibble: 1 × 6
#>   min.height max.height min.mass max.mass min.birth_year max.birth_year
#>        <int>      <int>    <dbl>    <dbl>          <dbl>          <dbl>
#> 1         66        264       15     1358              8            896
starwars %>% summarise(across(c(height, mass, birth_year), min_max, .names = "{.fn}.{.col}"))
#> # A tibble: 1 × 6
#>   min.height max.height min.mass max.mass min.birth_year max.birth_year
#>        <int>      <int>    <dbl>    <dbl>          <dbl>          <dbl>
#> 1         66        264       15     1358              8            896

If you’d prefer all summaries with the same function to be grouped together, you’ll have to expand the calls yourself:

starwars %>% summarise(
  across(c(height, mass, birth_year), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
  across(c(height, mass, birth_year), ~max(.x, na.rm = TRUE), .names = "max_{.col}")
)
#> # A tibble: 1 × 6
#>   min_height min_mass min_birth_year max_height max_mass max_birth_year
#>        <int>    <dbl>          <dbl>      <int>    <dbl>          <dbl>
#> 1         66       15              8        264     1358            896

(One day this might become an argument to across() but we’re not yet sure how it would work.)

We cannot however use where(is.numeric) in that last case because the second across() would pick up the variables that were newly created (“min_height”, “min_mass” and “min_birth_year”).

We can work around this by combining both calls to across() into a single expression that returns a tibble:

starwars %>% summarise(
  tibble(
    across(where(is.numeric), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
    across(where(is.numeric), ~max(.x, na.rm = TRUE), .names = "max_{.col}")  
  )
)
#> # A tibble: 1 × 6
#>   min_height min_mass min_birth_year max_height max_mass max_birth_year
#>        <int>    <dbl>          <dbl>      <int>    <dbl>          <dbl>
#> 1         66       15              8        264     1358            896

Alternatively we could reorganize results with relocate():

starwars %>% 
  summarise(across(where(is.numeric), min_max, .names = "{.fn}.{.col}")) %>% 
  relocate(starts_with("min"))
#> # A tibble: 1 × 6
#>   min.height min.mass min.birth_year max.height max.mass max.birth_year
#>        <int>    <dbl>          <dbl>      <int>    <dbl>          <dbl>
#> 1         66       15              8        264     1358            896

Current column

If you need to, you can access the name of the “current” column inside by calling cur_column(). This can be useful if you want to perform some sort of context dependent transformation that’s already encoded in a vector:

df <- tibble(x = 1:3, y = 3:5, z = 5:7)
mult <- list(x = 1, y = 10, z = 100)

df %>% mutate(across(all_of(names(mult)), ~ .x * mult[[cur_column()]]))
#> # A tibble: 3 × 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1     1    30   500
#> 2     2    40   600
#> 3     3    50   700

Gotchas

Be careful when combining numeric summaries with where(is.numeric):

df <- data.frame(x = c(1, 2, 3), y = c(1, 4, 9))

df %>% 
  summarise(n = n(), across(where(is.numeric), sd))
#>    n x        y
#> 1 NA 1 4.041452

Here n becomes NA because n is numeric, so the across() computes its standard deviation, and the standard deviation of 3 (a constant) is NA. You probably want to compute n() last to avoid this problem:

df %>% 
  summarise(across(where(is.numeric), sd), n = n())
#>   x        y n
#> 1 1 4.041452 3

Alternatively, you could explicitly exclude n from the columns to operate on:

df %>% 
  summarise(n = n(), across(where(is.numeric) & !n, sd))
#>   n x        y
#> 1 3 1 4.041452

Another approach is to combine both the call to n() and across() in a single expression that returns a tibble:

df %>% 
  summarise(
    tibble(n = n(), across(where(is.numeric), sd))
  )
#>   n x        y
#> 1 3 1 4.041452

Other verbs

So far we’ve focused on the use of across() with summarise(), but it works with any other dplyr verb that uses data masking:

Rescale all numeric variables to range 0-1:

rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}
df <- tibble(x = 1:4, y = rnorm(4))
df %>% mutate(across(where(is.numeric), rescale01))
#> # A tibble: 4 × 2
#>       x     y
#>   <dbl> <dbl>
#> 1 0     0.385
#> 2 0.333 1    
#> 3 0.667 0    
#> 4 1     0.903

For some verbs, like group_by(), count() and distinct(), you don’t need to supply a summary function, but it can be useful to use tidy-selection to dynamically select a set of columns. In those cases, we recommend using the complement to across(), pick(), which works like across() but doesn’t apply any functions and instead returns a data frame containing the selected columns.

Find all distinct

starwars %>% distinct(pick(contains("color")))
#> # A tibble: 67 × 3
#>   hair_color skin_color  eye_color
#>   <chr>      <chr>       <chr>    
#> 1 blond      fair        blue     
#> 2 NA         gold        yellow   
#> 3 NA         white, blue red      
#> 4 none       white       yellow   
#> # ℹ 63 more rows

Count all combinations of variables with a given pattern:

starwars %>% count(pick(contains("color")), sort = TRUE)
#> # A tibble: 67 × 4
#>   hair_color skin_color eye_color     n
#>   <chr>      <chr>      <chr>     <int>
#> 1 brown      light      brown         6
#> 2 brown      fair       blue          4
#> 3 none       grey       black         4
#> 4 black      dark       brown         3
#> # ℹ 63 more rows

across() doesn’t work with select() or rename() because they already use tidy select syntax; if you want to transform column names with a function, you can use rename_with().

filter()

We cannot directly use across() in filter() because we need an extra step to combine the results. To that end, filter() has two special purpose companion functions:

if_any() keeps the rows where the predicate is true for at least one selected column:

starwars %>% 
  filter(if_any(everything(), ~ !is.na(.x)))
#> # A tibble: 87 × 14
#>   name       height  mass hair_color skin_color eye_color birth_year sex  
#>   <chr>       <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>
#> 1 Luke Skyw…    172    77 blond      fair       blue            19   male 
#> 2 C-3PO         167    75 NA         gold       yellow         112   none 
#> 3 R2-D2          96    32 NA         white, bl… red             33   none 
#> 4 Darth Vad…    202   136 none       white      yellow          41.9 male 
#> # ℹ 83 more rows
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

if_all() keeps the rows where the predicate is true for all selected columns:

starwars %>% 
  filter(if_all(everything(), ~ !is.na(.x)))
#> # A tibble: 29 × 14
#>   name       height  mass hair_color skin_color eye_color birth_year sex  
#>   <chr>       <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>
#> 1 Luke Skyw…    172    77 blond      fair       blue            19   male 
#> 2 Darth Vad…    202   136 none       white      yellow          41.9 male 
#> 3 Leia Orga…    150    49 brown      light      brown           19   fema…
#> 4 Owen Lars     178   120 brown, gr… light      blue            52   male 
#> # ℹ 25 more rows
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

`_if`, `_at`, `_all`

Prior versions of dplyr allowed you to apply a function to multiple columns in a different way: using functions with _if, _at, and _all() suffixes. These functions solved a pressing need and are used by many people, but are now superseded. That means that they’ll stay around, but won’t receive any new features and will only get critical bug fixes.

Why do we like `across()`?

Why did we decide to move away from these functions in favour of across()?

across() makes it possible to express useful summaries that were previously impossible:

df %>%
  group_by(g1, g2) %>% 
  summarise(
    across(where(is.numeric), mean), 
    across(where(is.factor), nlevels),
    n = n(), 
  )

across() reduces the number of functions that dplyr needs to provide. This makes dplyr easier for you to use (because there are fewer functions to remember) and easier for us to implement new verbs (since we only need to implement one function, not four).
across() unifies _if and _at semantics so that you can select by position, name, and type, and you can now create compound selections that were previously impossible. For example, you can now transform all numeric columns whose name begins with “x”: across(where(is.numeric) & starts_with("x")).
across() doesn’t need to use vars(). The _at() functions are the only place in dplyr where you have to manually quote variable names, which makes them a little weird and hence harder to remember.

Why did it take so long to discover `across()`?

It’s disappointing that we didn’t discover across() earlier, and instead worked through several false starts (first not realising that it was a common problem, then with the _each() functions, and most recently with the _if()/_at()/_all() functions). But across() couldn’t work without three recent discoveries:

You can have a column of a data frame that is itself a data frame. This is something provided by base R, but it’s not very well documented, and it took a while to see that it was useful, not just a theoretical curiosity.
We can use data frames to allow summary functions to return multiple columns.
We can use the absence of an outer name as a convention that you want to unpack a data frame column into individual columns.

How do you convert existing code?

Fortunately, it’s generally straightforward to translate your existing code to use across():

Strip the _if(), _at() and _all() suffix off the function.
Call across(). The first argument will be:
1. For _if(), the old second argument wrapped in where().
2. For _at(), the old second argument, with the call to vars() removed.
3. For _all(), everything().
The subsequent arguments can be copied as is.

For example:

df %>% mutate_if(is.numeric, ~mean(.x, na.rm = TRUE))
# ->
df %>% mutate(across(where(is.numeric), ~mean(.x, na.rm = TRUE)))

df %>% mutate_at(vars(c(x, starts_with("y"))), mean)
# ->
df %>% mutate(across(c(x, starts_with("y")), mean))

df %>% mutate_all(mean)
# ->
df %>% mutate(across(everything(), mean))

There are a few exceptions to this rule:

rename_*() and select_*() follow a different pattern. They already have select semantics, so are generally used in a different way that doesn’t have a direct equivalent with across(); use the new rename_with() instead.

Previously, filter_*() were paired with the all_vars() and any_vars() helpers. The new helpers if_any() and if_all() can be used inside filter() to keep rows for which the predicate is true for at least one, or all selected columns:

df <- tibble(x = c("a", "b"), y = c(1, 1), z = c(-1, 1))

# Find all rows where EVERY numeric variable is greater than zero
df %>% filter(if_all(where(is.numeric), ~ .x > 0))
#> # A tibble: 1 × 3
#>   x         y     z
#>   <chr> <dbl> <dbl>
#> 1 b         1     1

# Find all rows where ANY numeric variable is greater than zero
df %>% filter(if_any(where(is.numeric), ~ .x > 0))
#> # A tibble: 2 × 3
#>   x         y     z
#>   <chr> <dbl> <dbl>
#> 1 a         1    -1
#> 2 b         1     1

When used in a mutate(), all transformations performed by an across() are applied at once. This is different to the behaviour of mutate_if(), mutate_at(), and mutate_all(), which apply the transformations one at a time. We expect that you’ll generally find the new behaviour less surprising:

df <- tibble(x = 2, y = 4, z = 8)
df %>% mutate_all(~ .x / y)
#> # A tibble: 1 × 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1   0.5     1     8

df %>% mutate(across(everything(), ~ .x / y))
#> # A tibble: 1 × 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1   0.5     1     2