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#' Convert Ordered Factors to Unordered Factors
#'
#' `step_unorder` creates a *specification* of a recipe
#' step that will transform the data.
#'
#' @inheritParams step_center
#' @param ... One or more selector functions to choose which
#' variables are affected by the step. 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 columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @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
#' columns that will be affected).
#' @keywords datagen
#' @concept preprocessing
#' @concept ordinal_data
#' @export
#' @details The factors level order is preserved during the transformation.
#' @examples
#' lmh <- c("Low", "Med", "High")
#'
#' examples <- data.frame(X1 = factor(rep(letters[1:4], each = 3)),
#' X2 = ordered(rep(lmh, each = 4),
#' levels = lmh))
#'
#' rec <- recipe(~ X1 + X2, data = examples)
#'
#' factor_trans <- rec %>%
#' step_unorder(all_predictors())
#'
#' factor_obj <- prep(factor_trans, training = examples)
#'
#' transformed_te <- bake(factor_obj, examples)
#' table(transformed_te$X2, examples$X2)
#'
#' tidy(factor_trans, number = 1)
#' tidy(factor_obj, number = 1)
#' @seealso [step_ordinalscore()] [recipe()]
#' [prep.recipe()] [bake.recipe()]
step_unorder <-
function(recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("unorder")) {
add_step(recipe,
step_unorder_new(
terms = ellipse_check(...),
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
))
}
step_unorder_new <-
function(terms, role, trained, columns, skip, id) {
step(
subclass = "unorder",
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_unorder <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
order_check <- vapply(training[, col_names],
is.ordered,
logical(1L))
if(all(!order_check)) {
rlang::abort("`step_unorder` required ordered factors.")
} else {
if(any(!order_check)) {
bad_cols <- names(order_check)[!order_check]
bad_cols <- paste0(bad_cols, collapse = ", ")
rlang::warn(
paste0(
"`step_unorder` requires ordered factors. Variables ",
bad_cols,
" will be ignored."
)
)
col_names <- names(order_check)[order_check]
}
}
step_unorder_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_unorder <- function(object, new_data, ...) {
for (i in seq_along(object$columns))
new_data[, object$columns[i]] <-
factor(as.character(getElement(new_data, object$columns[i])),
levels = levels(getElement(new_data, object$columns[i])))
as_tibble(new_data)
}
print.step_unorder <-
function(x, width = max(20, options()$width - 33), ...) {
cat("Unordered variables ", sep = "")
printer(x$columns, x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_unorder
#' @param x A `step_unorder` object.
#' @export
tidy.step_unorder <- function(x, ...) {
res <- simple_terms(x, ...)
res$id <- x$id
res
}
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