recipes (version 0.1.16)

step_indicate_na: Create Missing Data Column Indicators

Description

step_indicate_na creates a specification of a recipe step that will create and append additional binary columns to the dataset to indicate which observations are missing.

Usage

step_indicate_na(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  prefix = "na_ind",
  skip = FALSE,
  id = rand_id("indicate_na")
)
# S3 method for step_indicate_na
tidy(x, ...)

Arguments

recipe

A recipe object. The check will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See selections() for more details. For the tidy method, these are not currently used.

role

For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new na indicator columns created from the original variables will be used as predictors in a model.

trained

A logical for whether the selectors in ... have been resolved by prep().

columns

A character string of variable names that will be populated (eventually) by the terms argument.

prefix

A character string that will be the prefix to the resulting new variables. Defaults to "na_ind".

skip

A logical. Should the check be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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.

x

A step_indicate_na object.

Value

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 model (the median value).

Examples

# NOT RUN {
library(modeldata)
data("credit_data")

## missing data per column
purrr::map_dbl(credit_data, function(x) mean(is.na(x)))

set.seed(342)
in_training <- sample(1:nrow(credit_data), 2000)

credit_tr <- credit_data[ in_training, ]
credit_te <- credit_data[-in_training, ]

rec <- recipe(Price ~ ., data = credit_tr)

impute_rec <- rec %>%
  step_indicate_na(Income, Assets, Debt)

imp_models <- prep(impute_rec, training = credit_tr)

imputed_te <- bake(imp_models, new_data = credit_te, everything())
# }