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#' Relevel factors to a desired level
#'
#' `step_relevel()` creates a *specification* of a recipe step that will reorder
#' the provided factor columns so that the level specified by `ref_level` is
#' first. This is useful for [contr.treatment()] contrasts which take the first
#' level as the reference.
#'
#' @inheritParams step_center
#' @param ref_level A single character value that will be used to
#' relevel the factor column(s) (if the level is present).
#' @param objects A list of objects that contain the information
#' on factor levels that will be determined by [prep()].
#' @template step-return
#' @family dummy variable and encoding steps
#' @export
#' @details
#'
#' The selected variables are releveled to a level
#' (given by `ref_level`), placing the `ref_level` in the first
#' position.
#'
#' Note that if the original columns are character, they will be
#' converted to factors by this step.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `value` , and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{value}{character, the value of `ref_level`}
#' \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(Sacramento, package = "modeldata")
#' rec <- recipe(~ city + zip, data = Sacramento) %>%
#' step_unknown(city, new_level = "UNKNOWN") %>%
#' step_relevel(city, ref_level = "UNKNOWN") %>%
#' prep()
#'
#' data <- bake(rec, Sacramento)
#' levels(data$city)
step_relevel <-
function(recipe,
...,
role = NA,
trained = FALSE,
ref_level,
objects = NULL,
skip = FALSE,
id = rand_id("relevel")) {
add_step(
recipe,
step_relevel_new(
terms = enquos(...),
role = role,
trained = trained,
ref_level = ref_level,
objects = objects,
skip = skip,
id = id
)
)
}
step_relevel_new <-
function(terms, role, trained, ref_level, objects, skip, id) {
step(
subclass = "relevel",
terms = terms,
role = role,
trained = trained,
ref_level = ref_level,
objects = objects,
skip = skip,
id = id
)
}
#' @export
prep.step_relevel <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("string", "factor", "ordered"))
# Get existing levels and their factor type (i.e. ordered)
objects <- lapply(training[, col_names], get_existing_values)
# Check to make sure that no ordered levels are provided
order_check <- map_lgl(objects, attr, "is_ordered")
if (any(order_check)) {
offenders <- names(order_check)[order_check]
cli::cli_abort(
"Columns contain ordered factors (which cannot be releveled) \\
{.val {x$ref_level}}: {offenders}."
)
}
# Check to make sure that the reference level exists in the factor
ref_check <- map_lgl(objects, function(x, y) !y %in% x,
y = x$ref_level
)
if (any(ref_check)) {
offenders <- names(order_check)[!order_check]
cli::cli_abort(
"{cli::qty(length(offenders))}The following column{?s} doesn't include \\
required reference level {.val {x$ref_level}}: {.var {offenders}}."
)
}
step_relevel_new(
terms = x$terms,
role = x$role,
trained = TRUE,
ref_level = x$ref_level,
objects = objects,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_relevel <- function(object, new_data, ...) {
col_names <- names(object$objects)
check_new_data(col_names, object, new_data)
for (col_name in col_names) {
new_data[[col_name]] <- stats::relevel(
as.factor(new_data[[col_name]]),
ref = object$ref_level
)
}
new_data
}
#' @export
print.step_relevel <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Re-order factor level to ref_level for "
print_step(names(x$objects), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_relevel <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = names(x$objects),
value = x$ref_level
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
value = x$ref_level
)
}
res$id <- x$id
res
}
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