step_impute_median: Impute numeric data using the median

In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

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(). bake() 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.

See Also

Other imputation steps: step_impute_bag(), step_impute_knn(), step_impute_linear(), step_impute_lower(), step_impute_mean(), step_impute_mode(), step_impute_roll()

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)

tidymodels/recipes documentation built on April 17, 2025, 6:17 p.m.