dplyr: across – R documentation

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across

Apply a function (or functions) across multiple columns

Description

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().

Usage

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.

Timing of evaluation

R code in dplyr verbs is generally evaluated once per group. Inside across() however, code is evaluated once for each combination of columns and groups. If the evaluation timing is important, for example if you're generating random variables, think about when it should happen and place your code in consequence.

gdf <-
  tibble(g = c(1, 1, 2, 3), v1 = 10:13, v2 = 20:23) %>%
  group_by(g)

set.seed(1)

# Outside: 1 normal variate
n <- rnorm(1)
gdf %>% mutate(across(v1:v2, ~ .x + n))

## # A tibble: 4 x 3
## # Groups:   g [3]
##       g    v1    v2
##   <dbl> <dbl> <dbl>
## 1     1  9.37  19.4
## 2     1 10.4   20.4
## 3     2 11.4   21.4
## 4     3 12.4   22.4

# Inside a verb: 3 normal variates (ngroup)
gdf %>% mutate(n = rnorm(1), across(v1:v2, ~ .x + n))

## # A tibble: 4 x 4
## # Groups:   g [3]
##       g    v1    v2      n
##   <dbl> <dbl> <dbl>  <dbl>
## 1     1  10.2  20.2  0.184
## 2     1  11.2  21.2  0.184
## 3     2  11.2  21.2 -0.836
## 4     3  14.6  24.6  1.60

# Inside `across()`: 6 normal variates (ncol * ngroup)
gdf %>% mutate(across(v1:v2, ~ .x + rnorm(1)))

## # A tibble: 4 x 3
## # Groups:   g [3]
##       g    v1    v2
##   <dbl> <dbl> <dbl>
## 1     1  10.3  20.7
## 2     1  11.3  21.7
## 3     2  11.2  22.6
## 4     3  13.5  22.7

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))
iris %>%
  as_tibble() %>%
  mutate(across(c(1, 2), round))
iris %>%
  as_tibble() %>%
  mutate(across(1:Sepal.Width, round))
iris %>%
  as_tibble() %>%
  mutate(across(where(is.double) & !c(Petal.Length, Petal.Width), round))

# A purrr-style formula
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), ~ mean(.x, na.rm = TRUE)))

# A named list of functions
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))

# Use the .names argument to control the output names
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), mean, .names = "mean_{.col}"))
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd), .names = "{.col}.{.fn}"))

# 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}"))

# if_any() and if_all() ----------------------------------------------------
iris %>%
  filter(if_any(ends_with("Width"), ~ . > 4))
iris %>%
  filter(if_all(ends_with("Width"), ~ . > 2))

dplyr

A Grammar of Data Manipulation

v1.0.6

MIT + file LICENSE

Authors

Hadley Wickham [aut, cre] (<https://orcid.org/0000-0003-4757-117X>), Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>), Lionel Henry [aut], Kirill Müller [aut] (<https://orcid.org/0000-0002-1416-3412>), RStudio [cph, fnd]

Initial release