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#' Impute numeric data using the median
#'
#' `step_impute_median` creates a *specification* of a recipe step that will
#' substitute missing values of numeric variables by the training set median of
#' those variables.
#'
#' @inheritParams step_center
#' @param medians A named numeric vector of medians. This is `NULL` until
#' computed by [prep()]. Note that, if the original data are integers,
#' the median will be converted to an integer to maintain the same data type.
#' @template step-return
#' @family imputation steps
#' @export
#' @details `step_impute_median` estimates the variable medians from the data
#' used in the `training` argument of `prep.recipe`. `bake.recipe` then applies
#' the new values to new data sets using these medians.
#'
#' As of `recipes` 0.1.16, this function name changed from
#' `step_medianimpute()` to `step_impute_median()`.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with
#' columns `terms` (the selectors or variables selected) and `model`
#' (themedian value) is returned.
#'
#' @template case-weights-unsupervised
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data("credit_data", package = "modeldata")
#'
#' ## missing data per column
#' vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))
#'
#' set.seed(342)
#' in_training <- sample(1:nrow(credit_data), 2000)
#'
#' credit_tr <- credit_data[in_training, ]
#' credit_te <- credit_data[-in_training, ]
#' missing_examples <- c(14, 394, 565)
#'
#' rec <- recipe(Price ~ ., data = credit_tr)
#'
#' impute_rec <- rec %>%
#' step_impute_median(Income, Assets, Debt)
#'
#' imp_models <- prep(impute_rec, training = credit_tr)
#'
#' imputed_te <- bake(imp_models, new_data = credit_te, everything())
#'
#' credit_te[missing_examples, ]
#' imputed_te[missing_examples, names(credit_te)]
#'
#' tidy(impute_rec, number = 1)
#' tidy(imp_models, number = 1)
step_impute_median <-
function(recipe,
...,
role = NA,
trained = FALSE,
medians = NULL,
skip = FALSE,
id = rand_id("impute_median")) {
add_step(
recipe,
step_impute_median_new(
terms = enquos(...),
role = role,
trained = trained,
medians = medians,
skip = skip,
id = id,
case_weights = NULL
)
)
}
#' @rdname step_impute_median
#' @export
step_medianimpute <-
function(recipe,
...,
role = NA,
trained = FALSE,
medians = NULL,
skip = FALSE,
id = rand_id("impute_median")) {
lifecycle::deprecate_stop(
when = "0.1.16",
what = "recipes::step_medianimpute()",
with = "recipes::step_impute_median()"
)
step_impute_median(
recipe,
...,
role = role,
trained = trained,
medians = medians,
skip = skip,
id = id
)
}
step_impute_median_new <-
function(terms, role, trained, medians, skip, id, case_weights) {
step(
subclass = "impute_median",
terms = terms,
role = role,
trained = trained,
medians = medians,
skip = skip,
id = id,
case_weights = case_weights
)
}
#' @export
prep.step_impute_median <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
if (isFALSE(were_weights_used)) {
wts <- NULL
}
medians <- medians(training[, col_names], wts = wts)
medians <- purrr::map2(medians, training[, col_names], cast)
step_impute_median_new(
terms = x$terms,
role = x$role,
trained = TRUE,
medians = medians,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
#' @export
#' @keywords internal
prep.step_medianimpute <- prep.step_impute_median
#' @export
bake.step_impute_median <- function(object, new_data, ...) {
check_new_data(names(object$medians), object, new_data)
for (i in names(object$medians)) {
if (any(is.na(new_data[[i]]))) {
new_data[[i]] <- vec_cast(new_data[[i]], object$medians[[i]])
}
new_data[is.na(new_data[[i]]), i] <- object$medians[[i]]
}
new_data
}
#' @export
#' @keywords internal
bake.step_medianimpute <- bake.step_impute_median
#' @export
print.step_impute_median <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Median imputation for "
print_step(names(x$medians), x$terms, x$trained, title, width,
case_weights = x$case_weights)
invisible(x)
}
#' @export
#' @keywords internal
print.step_medianimpute <- print.step_impute_median
#' @rdname tidy.recipe
#' @export
tidy.step_impute_median <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = names(x$medians),
model = vctrs::list_unchop(unname(x$medians), ptype = double())
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names, model = na_dbl)
}
res$id <- x$id
res
}
#' @export
#' @keywords internal
tidy.step_medianimpute <- tidy.step_impute_median
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