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#' Convert Numbers to Factors
#'
#' `step_num2factor` will convert one or more numeric vectors to factors
#' (ordered or unordered). This can be useful when categories are encoded as
#' integers.
#'
#' @inheritParams step_center
#' @inherit step_center return
#' @param ... One or more selector functions to choose which variables will be
#' converted to factors. See [selections()] for more details. For the `tidy`
#' method, these are not currently used.
#' @param role Not used by this step since no new variables are created.
#' @param transform A function taking a single argument `x` that can be used
#' to modify the numeric values prior to determining the levels (perhaps using
#' [base::as.integer()]). The output of a function should be an integer that
#' corresponds to the value of `levels` that should be assigned. If not an
#' integer, the value will be converted to an integer during `bake()`.
#' @param levels A character vector of values that will be used as the levels.
#' These are the numeric data converted to character and ordered. This is
#' modified once [prep.recipe()] is executed.
#' @param ordered A single logical value; should the factor(s) be ordered?
#' @return An updated version of `recipe` with the new step added to the
#' sequence of existing steps (if any). For the `tidy` method, a tibble with
#' columns `terms` (the selectors or variables selected) and `ordered`.
#' @keywords datagen
#' @concept preprocessing
#' @concept variable_encodings
#' @concept factors
#' @export
#' @seealso [step_factor2string()], [step_string2factor()], [step_dummy()]
#' @examples
#' library(dplyr)
#' library(modeldata)
#' data(attrition)
#'
#' attrition %>%
#' group_by(StockOptionLevel) %>%
#' count()
#'
#' amnt <- c("nothin", "meh", "some", "copious")
#'
#' rec <-
#' recipe(Attrition ~ StockOptionLevel, data = attrition) %>%
#' step_num2factor(
#' StockOptionLevel,
#' transform = function(x) x + 1,
#' levels = amnt
#' )
#'
#' encoded <- rec %>% prep() %>% bake(new_data = NULL)
#'
#' table(encoded$StockOptionLevel, attrition$StockOptionLevel)
#'
#'
#' # an example for binning
#'
#' binner <- function(x) {
#' x <- cut(x, breaks = 1000 * c(0, 5, 10, 20), include.lowest = TRUE)
#' # now return the group number
#' as.numeric(x)
#' }
#'
#' inc <- c("low", "med", "high")
#'
#' rec <-
#' recipe(Attrition ~ MonthlyIncome, data = attrition) %>%
#' step_num2factor(
#' MonthlyIncome,
#' transform = binner,
#' levels = inc,
#' ordered = TRUE
#' ) %>%
#' prep()
#'
#' encoded <- bake(rec, new_data = NULL)
#'
#' table(encoded$MonthlyIncome, binner(attrition$MonthlyIncome))
#'
#' # What happens when a value is out of range?
#' ceo <- attrition %>% slice(1) %>% mutate(MonthlyIncome = 10^10)
#'
#' bake(rec, ceo)
step_num2factor <-
function(recipe,
...,
role = NA,
transform = function(x) x,
trained = FALSE,
levels,
ordered = FALSE,
skip = FALSE,
id = rand_id("num2factor")) {
if (!is_tune(ordered) & !is_varying(ordered)) {
if (!is.logical(ordered) || length(ordered) != 1)
rlang::abort("`ordered` should be a single logical variable")
}
if (rlang::is_missing(levels) || !is.character(levels)) {
rlang::abort("Please provide a character vector of appropriate length for `levels`.")
}
add_step(
recipe,
step_num2factor_new(
terms = ellipse_check(...),
role = role,
transform = transform,
trained = trained,
levels = levels,
ordered = ordered,
skip = skip,
id = id
)
)
}
step_num2factor_new <-
function(terms, role, transform, trained, levels, ordered, skip, id) {
step(
subclass = "num2factor",
terms = terms,
role = role,
transform = transform,
trained = trained,
levels = levels,
ordered = ordered,
skip = skip,
id = id
)
}
get_ord_lvls_num <- function(x, foo)
sort(unique(as.character(foo(x))))
#' @export
prep.step_num2factor <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
res <- lapply(training[, col_names], get_ord_lvls_num, foo = x$transform)
res <- c(res, ..levels = list(x$levels))
ord <- rep(x$ordered, length(col_names))
names(ord) <- col_names
step_num2factor_new(
terms = x$terms,
role = x$role,
transform = x$transform,
trained = TRUE,
levels = res,
ordered = ord,
skip = x$skip,
id = x$id
)
}
make_factor_num <- function(x, lvl, ord, foo) {
y <- foo(x)
if (!is.integer(y)) {
y <- as.integer(y)
}
factor(lvl[y], levels = lvl, ordered = ord)
}
#' @export
bake.step_num2factor <- function(object, new_data, ...) {
col_names <- names(object$ordered)
lvls <- object$levels[names(object$levels) == "..levels"]
object$levels <- object$levels[names(object$levels) != "..levels"]
new_data[, col_names] <-
map_df(new_data[, col_names],
make_factor_num,
lvl = lvls[[1]],
ord = object$ordered[1],
foo = object$transform)
if (!is_tibble(new_data))
new_data <- as_tibble(new_data)
new_data
}
print.step_num2factor <-
function(x, width = max(20, options()$width - 30), ...) {
cat("Factor variables from ")
printer(names(x$ordered), x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_num2factor
#' @param x A `step_num2factor` object.
#' @export
tidy.step_num2factor <- function(x, ...) {
term_names <- sel2char(x$terms)
p <- length(term_names)
if (is_trained(x)) {
res <- tibble(terms = term_names,
ordered = rep(x$ordered, p))
} else {
res <- tibble(terms = term_names,
ordered = rep(x$ordered, p))
}
res$id <- x$id
res
}
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