R: Apply a function (or a set of functions) to a set of columns

across {dplyr}

R Documentation

Apply a function (or a set of functions) to a set of columns

Description

across() makes it easy to apply the same transformation to multiple columns, allowing you to use select() semantics inside in summarise() and mutate(). across() supersedes the family of "scoped variants" like summarise_at(), summarise_if(), and summarise_all(). See vignette("colwise") for more details.

c_across() is designed to work with rowwise() to make it easy to perform row-wise aggregations. It has two differences from c():

It uses tidy select semantics so you can easily select multiple variables. See vignette("rowwise") for more details.
It uses vctrs::vec_c() in order to give safer outputs.

Usage

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

c_across(cols = everything())

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

A tibble with one column for each column in .cols and each function in .fns.

Examples

# across() -----------------------------------------------------------------
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), mean))
iris %>%
  as_tibble() %>%
  mutate(across(where(is.factor), as.character))

# 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}"))
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean, sd), .names = "{.col}.fn{.fn}"))

# c_across() ---------------------------------------------------------------
df <- tibble(id = 1:4, w = runif(4), x = runif(4), y = runif(4), z = runif(4))
df %>%
  rowwise() %>%
  mutate(
    sum = sum(c_across(w:z)),
    sd = sd(c_across(w:z))
 )

[Package dplyr version 1.0.2 Index]