#' Clean Categorical Levels
#'
#' `step_clean_levels()` creates a *specification* of a recipe step that will
#' clean nominal data (character or factor) so the levels consist only of
#' letters, numbers, and the underscore.
#'
#' @template args-recipe
#' @template args-dots
#' @template args-role_no-new
#' @template args-trained
#' @param clean A named character vector to clean and recode categorical levels.
#' This is `NULL` until computed by [recipes::prep.recipe()]. Note that if the
#' original variable is a character vector, it will be converted to a factor.
#' @template args-skip
#' @template args-id
#'
#' @template returns
#'
#' @details
#'
#' The new levels are cleaned and then reset with [dplyr::recode_factor()]. When
#' data to be processed contains novel levels (i.e., not contained in the
#' training set), they are converted to missing.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `orginal`, `value`, and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{original}{character, the original levels}
#' \item{value}{character, the cleaned levels}
#' \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @seealso [step_clean_names()], [recipes::step_factor2string()],
#' [recipes::step_string2factor()], [recipes::step_regex()],
#' [recipes::step_unknown()], [recipes::step_novel()], [recipes::step_other()]
#' @family Steps for Text Cleaning
#'
#' @examplesIf rlang::is_installed("janitor")
#' library(recipes)
#' library(modeldata)
#' data(Smithsonian)
#'
#' smith_tr <- Smithsonian[1:15, ]
#' smith_te <- Smithsonian[16:20, ]
#'
#' rec <- recipe(~., data = smith_tr)
#'
#' rec <- rec %>%
#' step_clean_levels(name)
#' rec <- prep(rec, training = smith_tr)
#'
#' cleaned <- bake(rec, smith_tr)
#'
#' tidy(rec, number = 1)
#'
#' # novel levels are replaced with missing
#' bake(rec, smith_te)
#' @export
step_clean_levels <-
function(recipe,
...,
role = NA,
trained = FALSE,
clean = NULL,
skip = FALSE,
id = rand_id("clean_levels")) {
add_step(
recipe,
step_clean_levels_new(
terms = enquos(...),
role = role,
trained = trained,
clean = clean,
skip = skip,
id = id
)
)
}
step_clean_levels_new <-
function(terms, role, trained, clean, skip, id) {
step(
subclass = "clean_levels",
terms = terms,
role = role,
trained = trained,
clean = clean,
skip = skip,
id = id
)
}
#' @export
prep.step_clean_levels <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("string", "factor", "ordered"))
if (length(col_names) > 0) {
orig <- purrr::map(training[, col_names], levels)
cleaned <- purrr::map(orig, janitor::make_clean_names)
clean <- purrr::map2(cleaned, orig, rlang::set_names)
} else {
clean <- NULL
}
step_clean_levels_new(
terms = x$terms,
role = x$role,
trained = TRUE,
clean = clean,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_clean_levels <- function(object, new_data, ...) {
col_names <- names(object$clean)
check_new_data(names(object$clean), object, new_data)
if (is.null(names(object$clean))) {
# Backwards compatibility with 1.0.3 (#230)
names(object$clean) <- col_names
}
for (col_name in col_names) {
new_data[[col_name]] <- dplyr::recode_factor(
new_data[[col_name]], !!!object$clean[[col_name]]
)
}
new_data
}
#' @export
print.step_clean_levels <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Cleaning factor levels for "
print_step(names(x$clean), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname step_clean_levels
#' @usage NULL
#' @export
tidy.step_clean_levels <- function(x, ...) {
if (is_trained(x)) {
if (is.null(x$clean)) {
res <- tibble(terms = character())
} else {
res <- purrr::map_dfr(
x$clean,
tibble::enframe,
name = "original",
.id = "terms"
)
}
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.step
#' @export
required_pkgs.step_clean_levels <- function(x, ...) {
c("textrecipes", "janitor")
}
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