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ordinalscore.R
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ordinalscore.R
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#' Convert ordinal factors to numeric scores
#'
#' `step_ordinalscore()` creates a *specification* of a recipe step that will
#' convert ordinal factor variables into numeric scores.
#'
#' @inheritParams step_center
#' @inheritParams step_pca
#' @param convert A function that takes an ordinal factor vector
#' as an input and outputs a single numeric variable.
#' @template step-return
#' @family dummy variable and encoding steps
#' @export
#' @details Dummy variables from ordered factors with `C`
#' levels will create polynomial basis functions with `C-1`
#' terms. As an alternative, this step can be used to translate the
#' ordered levels into a single numeric vector of values that
#' represent (subjective) scores. By default, the translation uses
#' a linear scale (1, 2, 3, ... `C`) but custom score
#' functions can also be used (see the example below).
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms` and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @examples
#' fail_lvls <- c("meh", "annoying", "really_bad")
#'
#' ord_data <-
#' data.frame(
#' item = c("paperclip", "twitter", "airbag"),
#' fail_severity = factor(fail_lvls,
#' levels = fail_lvls,
#' ordered = TRUE
#' )
#' )
#'
#' model.matrix(~fail_severity, data = ord_data)
#'
#' linear_values <- recipe(~ item + fail_severity, data = ord_data) %>%
#' step_dummy(item) %>%
#' step_ordinalscore(fail_severity)
#'
#' linear_values <- prep(linear_values, training = ord_data)
#'
#' bake(linear_values, new_data = NULL)
#'
#' custom <- function(x) {
#' new_values <- c(1, 3, 7)
#' new_values[as.numeric(x)]
#' }
#'
#' nonlin_scores <- recipe(~ item + fail_severity, data = ord_data) %>%
#' step_dummy(item) %>%
#' step_ordinalscore(fail_severity, convert = custom)
#'
#' tidy(nonlin_scores, number = 2)
#'
#' nonlin_scores <- prep(nonlin_scores, training = ord_data)
#'
#' bake(nonlin_scores, new_data = NULL)
#'
#' tidy(nonlin_scores, number = 2)
step_ordinalscore <-
function(recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
convert = as.numeric,
skip = FALSE,
id = rand_id("ordinalscore")) {
add_step(
recipe,
step_ordinalscore_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
convert = convert,
skip = skip,
id = id
)
)
}
step_ordinalscore_new <-
function(terms, role, trained, columns, convert, skip, id) {
step(
subclass = "ordinalscore",
terms = terms,
role = role,
trained = trained,
columns = columns,
convert = convert,
skip = skip,
id = id
)
}
#' @export
prep.step_ordinalscore <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = "ordered")
step_ordinalscore_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
convert = x$convert,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_ordinalscore <- function(object, new_data, ...) {
col_names <- object$columns
check_new_data(col_names, object, new_data)
for (col_name in col_names) {
score <- object$convert(new_data[[col_name]])
score <- vctrs::vec_cast(score, integer())
new_data[[col_name]] <- score
}
new_data
}
#' @export
print.step_ordinalscore <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Scoring for "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_ordinalscore <- function(x, ...) {
res <- simple_terms(x, ...)
res$id <- x$id
res
}