distinct_all: Select distinct rows by 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\Sexpr[results=rd, stage=render]{lifecycle::badge("superseded")}

Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb.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 variants of distinct() extract distinct rows by a selection of variables. Like distinct(), you can modify the variables before ordering with the .funs argument.

distinct_all(.tbl, .funs = list(), ..., .keep_all = FALSE)

distinct_at(.tbl, .vars, .funs = list(), ..., .keep_all = FALSE)

distinct_if(.tbl, .predicate, .funs = list(), ..., .keep_all = FALSE)

`.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.
`.keep_all`	If `TRUE`, keep all variables in `.data`. If a combination of `...` is not distinct, this keeps the first row of values.
`.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.

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

df <- tibble(x = rep(2:5, each = 2) / 2, y = rep(2:3, each = 4) / 2)

distinct_all(df)
# ->
distinct(df, across())

distinct_at(df, vars(x,y))
# ->
distinct(df, across(c(x, y)))

distinct_if(df, is.numeric)
# ->
distinct(df, across(where(is.numeric)))

# You can supply a function that will be applied before extracting the distinct values
# The variables of the sorted tibble keep their original values.
distinct_all(df, round)
# ->
distinct(df, across(everything(), round))