Nothing
#' Detect a regular expression
#'
#' `step_regex()` creates a *specification* of a recipe step that will create a
#' new dummy variable based on a regular expression.
#'
#' @inheritParams step_classdist
#' @inheritParams step_pca
#' @inheritParams step_center
#' @param ... A single selector function to choose which variable
#' will be searched for the regex pattern. The selector should resolve
#' to a single variable. See [selections()] for more details.
#' @param pattern A character string containing a regular
#' expression (or character string for `fixed = TRUE`) to be
#' matched in the given character vector. Coerced by
#' `as.character` to a character string if possible.
#' @param options A list of options to [grepl()] that
#' should not include `x` or `pattern`.
#' @param result A single character value for the name of the new
#' variable. It should be a valid column name.
#' @param input A single character value for the name of the
#' variable being searched. This is `NULL` until computed by
#' [prep()].
#' @template step-return
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `result` , and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{result}{character, new column name}
#' \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @family dummy variable and encoding steps
#' @export
#' @examplesIf rlang::is_installed("modeldata")
#' data(covers, package = "modeldata")
#'
#' rec <- recipe(~description, covers) %>%
#' step_regex(description, pattern = "(rock|stony)", result = "rocks") %>%
#' step_regex(description, pattern = "ratake families")
#'
#' rec2 <- prep(rec, training = covers)
#' rec2
#'
#' with_dummies <- bake(rec2, new_data = covers)
#' with_dummies
#' tidy(rec, number = 1)
#' tidy(rec2, number = 1)
step_regex <- function(recipe,
...,
role = "predictor",
trained = FALSE,
pattern = ".",
options = list(),
result = make.names(pattern),
input = NULL,
keep_original_cols = TRUE,
skip = FALSE,
id = rand_id("regex")) {
if (!is_tune(pattern)) {
check_string(pattern)
}
valid_args <- names(formals(grepl))[-(1:2)]
if (any(!(names(options) %in% valid_args))) {
cli::cli_abort(c(
"x" = "The following elements of {.arg options} are not allowed:",
"*" = "{.val {setdiff(names(options), valid_args)}}.",
"i" = "Valid options are: {.val {valid_args}}."
))
}
terms <- enquos(...)
if (length(terms) > 1) {
cli::cli_abort(c(
x = "For this step, only a single selector can be used.",
i = "The following {length(terms)} selectors were used: \\
{.var {as.character(terms)}}."
))
}
add_step(
recipe,
step_regex_new(
terms = terms,
role = role,
trained = trained,
pattern = pattern,
options = options,
result = result,
input = input,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
)
}
step_regex_new <-
function(terms, role, trained, pattern, options, result, input,
keep_original_cols, skip, id) {
step(
subclass = "regex",
terms = terms,
role = role,
trained = trained,
pattern = pattern,
options = options,
result = result,
input = input,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
}
#' @export
prep.step_regex <- function(x, training, info = NULL, ...) {
col_name <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_name], types = c("string", "factor", "ordered"))
if (length(col_name) > 1) {
cli::cli_abort(c(
x = "The selector should select at most a single variable.",
i = "The following {length(col_name)} were selected: \\
{.and {.var {col_name}}}."
))
}
step_regex_new(
terms = x$terms,
role = x$role,
trained = TRUE,
pattern = x$pattern,
options = x$options,
input = col_name,
result = x$result,
keep_original_cols = get_keep_original_cols(x),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_regex <- function(object, new_data, ...) {
col_name <- names(object$input)
if (length(col_name) == 0) {
return(new_data)
}
check_new_data(col_name, object, new_data)
## sub in options
regex <- expr(
grepl(
x = new_data[[col_name]],
pattern = object$pattern,
ignore.case = FALSE,
perl = FALSE,
fixed = FALSE,
useBytes = FALSE
)
)
if (length(object$options) > 0) {
regex <- rlang::call_modify(regex, !!!object$options)
}
new_values <- tibble::tibble(!!object$result := ifelse(eval(regex), 1L, 0L))
new_values <- check_name(new_values, new_data, object, object$result)
new_data <- vec_cbind(new_data, new_values)
new_data <- remove_original_cols(new_data, object, col_name)
new_data
}
#' @export
print.step_regex <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Regular expression dummy variable using "
pattern <- glue("\"{x$pattern}\"")
untrained_terms <- rlang::parse_quos(pattern, rlang::current_env())
print_step(pattern, untrained_terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_regex <- function(x, ...) {
term_names <- sel2char(x$terms)
p <- length(term_names)
if (is_trained(x)) {
res <- tibble(
terms = term_names,
result = rep(unname(x$result), p)
)
} else {
res <- tibble(
terms = term_names,
result = rep(na_chr, p)
)
}
res$id <- x$id
res
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.