dplyr: group_by_all – R documentation

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group_by_all

Group by a selection of variables

Description

Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb. See vignette("colwise") for details.

These scoped variants of group_by() group a data frame by a selection of variables. Like group_by(), they have optional mutate semantics.

Usage

group_by_all(
  .tbl,
  .funs = list(),
  ...,
  .add = FALSE,
  .drop = group_by_drop_default(.tbl)
)

group_by_at(
  .tbl,
  .vars,
  .funs = list(),
  ...,
  .add = FALSE,
  .drop = group_by_drop_default(.tbl)
)

group_by_if(
  .tbl,
  .predicate,
  .funs = list(),
  ...,
  .add = FALSE,
  .drop = group_by_drop_default(.tbl)
)

Arguments

`.tbl`	A `tbl` object.
`.funs`	A function `fun`, a quosure style lambda `~ fun(.)` or a list of either form.
`...`	Additional arguments for the function calls in `.funs`. These are evaluated only once, with tidy dots support.
`.add`	See `group_by()`
`.drop`	Drop groups formed by factor levels that don't appear in the data? The default is `TRUE` except when `.data` has been previously grouped with `.drop = FALSE`. See `group_by_drop_default()` for details.
`.vars`	A list of columns generated by `vars()`, a character vector of column names, a numeric vector of column positions, or `NULL`.
`.predicate`	A predicate function to be applied to the columns or a logical vector. The variables for which `.predicate` is or returns `TRUE` are selected. This argument is passed to `rlang::as_function()` and thus supports quosure-style lambda functions and strings representing function names.

Grouping variables

Existing grouping variables are maintained, even if not included in the selection.

Examples

# Group a data frame by all variables:
group_by_all(mtcars)
# ->
mtcars %>% group_by(across())

# Group by variables selected with a predicate:
group_by_if(iris, is.factor)
# ->
iris %>% group_by(across(where(is.factor)))

# Group by variables selected by name:
group_by_at(mtcars, vars(vs, am))
# ->
mtcars %>% group_by(across(c(vs, am)))

# Like group_by(), the scoped variants have optional mutate
# semantics. This provide a shortcut for group_by() + mutate():
d <- tibble(x=c(1,1,2,2), y=c(1,2,1,2))
group_by_all(d, as.factor)
# ->
d %>% group_by(across(everything(), as.factor))

group_by_if(iris, is.factor, as.character)
# ->
iris %>% group_by(across(where(is.factor), as.character))

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