dplyr (version 1.0.10)

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Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb.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))