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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.
#'
#' @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)) {
rlang::abort(
"Columns contain ordered factors (which cannot be releveled) '",
x$ref_level, "': ",
paste0(names(order_check)[order_check], collapse = ", ")
)
}
# Check to make sure that the reference level exists in the factor
ref_check <- map_lgl(objects, function(x, y) !y %in% x,
y = x$ref_level
)
if (any(ref_check)) {
rlang::abort(
paste0(
"Columns must contain the reference level '",
x$ref_level, "': ",
paste0(names(ref_check)[ref_check], collapse = ", ")
)
)
}
step_relevel_new(
terms = x$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, ...) {
check_new_data(names(object$objects), object, new_data)
for (i in names(object$objects)) {
new_data[[i]] <- stats::relevel(as.factor(new_data[[i]]), ref = object$ref_level)
}
new_data
}
print.step_relevel <-
function(x, width = max(20, options()$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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