dplyr (version 1.0.10)

filter_all: Filter within 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Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb.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 filtering verbs apply a predicate expression to a selection of variables. The predicate expression should be quoted with all_vars() or any_vars() and should mention the pronoun . to refer to variables.

Usage

filter_all(.tbl, .vars_predicate, .preserve = FALSE)
filter_if(.tbl, .predicate, .vars_predicate, .preserve = FALSE)
filter_at(.tbl, .vars, .vars_predicate, .preserve = FALSE)

Arguments

.tbl

A tbl object.

.vars_predicate

A quoted predicate expression as returned by all_vars() or any_vars().

Can also be a function or purrr-like formula. In this case, the intersection of the results is taken by default and there's currently no way to request the union.

.preserve

when FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise it is kept as is.

.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.

.vars

A list of columns generated by vars(), a character vector of column names, a numeric vector of column positions, or NULL.

Grouping variables

The grouping variables that are part of the selection are taken into account to determine filtered rows.

Examples

# While filter() accepts expressions with specific variables, the
# scoped filter verbs take an expression with the pronoun `.` and
# replicate it over all variables. This expression should be quoted
# with all_vars() or any_vars():
all_vars(is.na(.))
any_vars(is.na(.))


# You can take the intersection of the replicated expressions:
filter_all(mtcars, all_vars(. > 150))
# ->
filter(mtcars, if_all(everything(), ~ .x > 150))

# Or the union:
filter_all(mtcars, any_vars(. > 150))
# ->
filter(mtcars, if_any(everything(), ~ . > 150))


# You can vary the selection of columns on which to apply the
# predicate. filter_at() takes a vars() specification:
filter_at(mtcars, vars(starts_with("d")), any_vars((. %% 2) == 0))
# ->
filter(mtcars, if_any(starts_with("d"), ~ (.x %% 2) == 0))

# And filter_if() selects variables with a predicate function:
filter_if(mtcars, ~ all(floor(.) == .), all_vars(. != 0))
# ->
is_int <- function(x) all(floor(x) == x)
filter(mtcars, if_all(where(is_int), ~ .x != 0))