filter_all: Filter within a selection of variables
In javifar/TIDYVERSE-DPLYR: A Grammar of Data Manipulation

Description Usage Arguments Grouping variables Examples

\Sexpr[results=rd, stage=render]{lifecycle::badge("superseded")}

Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb.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

filter_all(.tbl, .vars_predicate, .preserve = FALSE)

filter_if(.tbl, .predicate, .vars_predicate, .preserve = FALSE)

filter_at(.tbl, .vars, .vars_predicate, .preserve = FALSE)

`.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`.

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

# 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))