recipes: step_indicate_na – R documentation

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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 class '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

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())

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release