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#' Convert Strings to Factors
#'
#' `step_string2factor` will convert one or more character
#' vectors to factors (ordered or unordered).
#'
#' @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 levels An options specification of the levels to be used
#' for the new factor. If left `NULL`, the sorted unique
#' values present when `bake` is called will be used.
#' @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
#' @details If `levels` is given, `step_string2factor` will
#' convert all variables affected by this step to have the same
#' levels.
#'
#' Also, note that `prep` has an option `strings_as_factors` that
#' defaults to `TRUE`. This should be changed so that raw character
#' data will be applied to `step_string2factor`. However, this step
#' can also take existing factors (but will leave them as-is).
#' @seealso [step_factor2string()] [step_dummy()] [step_other()]
#' [step_novel()]
#' @examples
#' library(modeldata)
#' data(okc)
#'
#' rec <- recipe(~ diet + location, data = okc)
#'
#' make_factor <- rec %>%
#' step_string2factor(diet)
#' make_factor <- prep(make_factor,
#' training = okc,
#' strings_as_factors = FALSE)
#'
#' # note that `diet` is a factor
#' bake(make_factor, new_data = NULL) %>% head
#' okc %>% head
#' tidy(make_factor, number = 1)
step_string2factor <-
function(recipe,
...,
role = NA,
trained = FALSE,
levels = NULL,
ordered = FALSE,
skip = FALSE,
id = rand_id("string2factor")) {
if (!is_tune(ordered) & !is_varying(ordered)) {
if (!is.logical(ordered) || length(ordered) != 1) {
rlang::abort("`ordered` should be a single logical variable")
}
}
if ((!is.null(levels) & !is.character(levels)) | is.list(levels)) {
rlang::abort("`levels` should be NULL or a single character vector")
}
add_step(
recipe,
step_string2factor_new(
terms = ellipse_check(...),
role = role,
trained = trained,
levels = levels,
ordered = ordered,
skip = skip,
id = id
)
)
}
step_string2factor_new <-
function(terms, role, trained, levels, ordered, skip, id) {
step(
subclass = "string2factor",
terms = terms,
role = role,
trained = trained,
levels = levels,
ordered = ordered,
skip = skip,
id = id
)
}
get_ord_lvls <- function(x)
sort(unique(x))
#' @export
prep.step_string2factor <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
str_check <-
vapply(
training[, col_names],
function(x) is.character(x) | is.factor(x),
logical(1)
)
if (any(!str_check))
rlang::abort(
paste0(
"The following variables are not character vectors: ",
paste0("`", names(str_check)[!str_check], "`", collapse = ", ")
)
)
if (is.null(x$levels)) {
res <- lapply(training[, col_names], get_ord_lvls)
} else
res <- x$levels
ord <- rep(x$ordered, length(col_names))
names(ord) <- col_names
step_string2factor_new(
terms = x$terms,
role = x$role,
trained = TRUE,
levels = res,
ordered = ord,
skip = x$skip,
id = x$id
)
}
make_factor <- function(x, lvl, ord) {
if (is.factor(x))
return(x)
factor(x, levels = lvl, ordered = ord)
}
#' @export
bake.step_string2factor <- function(object, new_data, ...) {
col_names <- names(object$ordered)
if (is.list(object$levels)) {
new_data[, col_names] <-
map2_df(new_data[, col_names],
object$levels,
make_factor,
ord = object$ordered[1])
} else {
new_data[, col_names] <-
map_df(new_data[, col_names],
make_factor,
lvl = object$levels,
ord = object$ordered[1])
}
if (!is_tibble(new_data))
new_data <- as_tibble(new_data)
new_data
}
print.step_string2factor <-
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_string2factor
#' @param x A `step_string2factor` object.
#' @export
tidy.step_string2factor <- 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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