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#' Filter rows using dplyr
#'
#' `step_filter` creates a *specification* of a recipe step
#' that will remove rows using [dplyr::filter()].
#'
#' @template row-ops
#' @inheritParams step_center
#' @param ... Logical predicates defined in terms of the variables
#' in the data. Multiple conditions are combined with `&`. Only
#' rows where the condition evaluates to `TRUE` are kept. See
#' [dplyr::filter()] for more details.
#' @param inputs Quosure of values given by `...`.
#' @template step-return
#' @details When an object in the user's global environment is
#' referenced in the expression defining the new variable(s),
#' it is a good idea to use quasiquotation (e.g. `!!`) to embed
#' the value of the object in the expression (to be portable
#' between sessions). See the examples.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with column
#' `terms` which contains the conditional statements is returned.
#' These expressions are text representations and are not parsable.
#'
#' @template case-weights-not-supported
#'
#' @family row operation steps
#' @family dplyr steps
#' @export
#' @examples
#' rec <- recipe(~., data = iris) %>%
#' step_filter(Sepal.Length > 4.5, Species == "setosa")
#'
#' prepped <- prep(rec, training = iris %>% slice(1:75))
#'
#' library(dplyr)
#'
#' dplyr_train <-
#' iris %>%
#' as_tibble() %>%
#' slice(1:75) %>%
#' dplyr::filter(Sepal.Length > 4.5, Species == "setosa")
#'
#' rec_train <- bake(prepped, new_data = NULL)
#' all.equal(dplyr_train, rec_train)
#'
#' dplyr_test <-
#' iris %>%
#' as_tibble() %>%
#' slice(76:150) %>%
#' dplyr::filter(Sepal.Length > 4.5, Species != "setosa")
#' rec_test <- bake(prepped, iris %>% slice(76:150))
#' all.equal(dplyr_test, rec_test)
#'
#' values <- c("versicolor", "virginica")
#'
#' qq_rec <-
#' recipe(~., data = iris) %>%
#' # Embed the `values` object in the call using !!
#' step_filter(Sepal.Length > 4.5, Species %in% !!values)
#'
#' tidy(qq_rec, number = 1)
step_filter <- function(recipe, ...,
role = NA,
trained = FALSE,
inputs = NULL,
skip = TRUE,
id = rand_id("filter")) {
inputs <- enquos(...)
add_step(
recipe,
step_filter_new(
terms = terms,
trained = trained,
role = role,
inputs = inputs,
skip = skip,
id = id
)
)
}
step_filter_new <-
function(terms, role, trained, inputs, skip, id) {
step(
subclass = "filter",
terms = terms,
role = role,
trained = trained,
inputs = inputs,
skip = skip,
id = id
)
}
#' @export
prep.step_filter <- function(x, training, info = NULL, ...) {
step_filter_new(
terms = x$terms,
trained = TRUE,
role = x$role,
inputs = x$inputs,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_filter <- function(object, new_data, ...) {
dplyr::filter(new_data, !!!object$inputs)
}
print.step_filter <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Row filtering using "
print_step(x$inputs, x$inputs, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_filter <- function(x, ...) {
cond_expr <- map(unname(x$inputs), quo_get_expr)
cond_expr <- map_chr(cond_expr, quo_text, width = options()$width, nlines = 1)
tibble(
terms = cond_expr,
id = rep(x$id, length(x$inputs))
)
}
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