step_impute_median: Impute numeric data using the median
In recipes: Preprocessing and Feature Engineering Steps for Modeling

step_impute_median

R Documentation

Impute numeric data using the median

Description

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.

Usage

step_impute_median(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  medians = NULL,
  skip = FALSE,
  id = rand_id("impute_median")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose variables for this step. See `selections()` for more details.
`role`	Not used by this step since no new variables are created.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`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.
`skip`	A logical. Should the step be skipped when the recipe is baked by `bake()`? While all operations are baked when `prep()` is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations.
`id`	A character string that is unique to this step to identify it.

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().

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value , and id:

terms: character, the selectors or variables selected
value: numeric, the median value
id: character, id of this step

Sparse data

This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Examples


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)

credit_te[missing_examples, ]
imputed_te[missing_examples, names(credit_te)]

tidy(impute_rec, number = 1)
tidy(imp_models, number = 1)

recipes documentation built on April 13, 2025, 5:10 p.m.