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#' Create Dummy Variables using Regular Expressions
#'
#' `step_regex` creates a *specification* of a recipe step that will
#' create a new dummy variable based on a regular expression.
#'
#' @inheritParams step_center
#' @inherit step_center return
#' @param ... A single selector functions to choose which variable
#' will be searched for the pattern. The selector should resolve
#' into a single variable. See [selections()] for more
#' details. For the `tidy` method, these are not currently
#' used.
#' @param role For a variable created by this step, what analysis
#' role should they be assigned?. By default, the function assumes
#' that the new dummy variable column created by the original
#' variable will be used as a predictor in a model.
#' @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.recipe()].
#' @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 `result` (the
#' new column name).
#' @keywords datagen
#' @concept preprocessing
#' @concept dummy_variables
#' @concept regular_expressions
#' @export
#' @examples
#' library(modeldata)
#' data(covers)
#'
#' 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,
skip = FALSE,
id = rand_id("regex")) {
if (!is_tune(pattern) & !is_varying(pattern)) {
if (!is.character(pattern)) {
rlang::abort("`pattern` should be a character string")
}
if (length(pattern) != 1) {
rlang::abort("`pattern` should be a single pattern")
}
}
valid_args <- names(formals(grepl))[-(1:2)]
if (any(!(names(options) %in% valid_args))) {
rlang::abort(paste0("Valid options are: ",
paste0(valid_args, collapse = ", ")))
}
terms <- ellipse_check(...)
if (length(terms) > 1)
rlang::abort("For this step, only a single selector can be used.")
add_step(
recipe,
step_regex_new(
terms = terms,
role = role,
trained = trained,
pattern = pattern,
options = options,
result = result,
input = input,
skip = skip,
id = id
)
)
}
step_regex_new <-
function(terms, role, trained, pattern, options, result, input, skip, id) {
step(
subclass = "regex",
terms = terms,
role = role,
trained = trained,
pattern = pattern,
options = options,
result = result,
input = input,
skip = skip,
id = id
)
}
#' @export
prep.step_regex <- function(x, training, info = NULL, ...) {
col_name <- eval_select_recipes(x$terms, training, info)
if (length(col_name) != 1)
rlang::abort("The selector should only select a single variable")
if (any(info$type[info$variable %in% col_name] != "nominal"))
rlang::abort("The regular expression input should be character or factor")
step_regex_new(
terms = x$terms,
role = x$role,
trained = TRUE,
pattern = x$pattern,
options = x$options,
input = col_name,
result = x$result,
skip = x$skip,
id = x$id
)
}
bake.step_regex <- function(object, new_data, ...) {
## sub in options
regex <- expr(
grepl(
x = getElement(new_data, object$input),
pattern = object$pattern,
ignore.case = FALSE,
perl = FALSE,
fixed = FALSE,
useBytes = FALSE
)
)
if (length(object$options) > 0)
regex <- mod_call_args(regex, args = object$options)
new_data[, object$result] <- ifelse(eval(regex), 1, 0)
new_data
}
print.step_regex <-
function(x, width = max(20, options()$width - 30), ...) {
cat("Regular expression dummy variable using `",
x$pattern,
"`",
sep = "")
if (x$trained)
cat(" [trained]\n")
else
cat("\n")
invisible(x)
}
#' @rdname step_regex
#' @param x A `step_regex` object.
#' @export
tidy.step_regex <- function(x, ...) {
term_names <- sel2char(x$terms)
p <- length(term_names)
if (is_trained(x)) {
res <- tibble(terms = term_names,
result = rep(x$result, p))
} else {
res <- tibble(terms = term_names,
result = rep(na_chr, p))
}
res$id <- x$id
res
}
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