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#' Mutate multiple columns
#'
#' `step_mutate_at` creates a *specification* of a recipe step that will modify
#' the selected variables using a common function.
#'
#' @inheritParams step_center
#' @param fn A function fun, a quosure style lambda `~ fun(.)`` or a list of
#' either form. (see [dplyr::mutate_at()]). **Note that this argument must be
#' named**.
#' @param role For model terms created by this step, what analysis role should
#' they be assigned? By default, the function assumes that the new dimension
#' columns created by the original variables will be used as predictors in a
#' model.
#' @param inputs A vector of column names populated by `prep()`.
#' @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` which contains the columns being transformed.
#' @keywords datagen
#' @concept preprocessing
#' @concept transformation_methods
#' @export
#' @examples
#' library(dplyr)
#' recipe(~ ., data = iris) %>%
#' step_mutate_at(contains("Length"), fn = ~ 1/.) %>%
#' prep() %>%
#' bake(new_data = NULL) %>%
#' slice(1:10)
#'
#' recipe(~ ., data = iris) %>%
#' # leads to more columns being created.
#' step_mutate_at(contains("Length"), fn = list(log = log, sqrt = sqrt)) %>%
#' prep() %>%
#' bake(new_data = NULL) %>%
#' slice(1:10)
#' @export
step_mutate_at <- function(
recipe, ...,
fn,
role = "predictor",
trained = FALSE,
inputs = NULL,
skip = FALSE,
id = rand_id("mutate_at")
) {
add_step(
recipe,
step_mutate_at_new(
terms = ellipse_check(...),
fn = fn,
trained = trained,
role = role,
inputs = inputs,
skip = skip,
id = id
)
)
}
step_mutate_at_new <-
function(terms, fn, role, trained, inputs, skip, id) {
step(
subclass = "mutate_at",
terms = terms,
fn = fn,
role = role,
trained = trained,
inputs = inputs,
skip = skip,
id = id
)
}
#' @export
prep.step_mutate_at <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
step_mutate_at_new(
terms = x$terms,
fn = x$fn,
trained = TRUE,
role = x$role,
inputs = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_mutate_at <- function(object, new_data, ...) {
dplyr::mutate_at(new_data, .vars = object$inputs, .funs = object$fn)
}
print.step_mutate_at <-
function(x, width = max(20, options()$width - 35), ...) {
cat("Variable mutation for ", sep = "")
printer(x$inputs, x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_mutate
#' @export
tidy.step_mutate_at <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = x$inputs)
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names)
}
res$id <- x$id
res
}
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