filter_all function

Filter within a selection of variables

Scoped verbs (_if, _at, _all) have been superseded by the use of if_all() or if_any() in an existing verb.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

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))