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.

step_indicate_na(
recipe,
...,
role = "predictor",
trained = FALSE,
columns = NULL,
prefix = "na_ind",
skip = FALSE,
id = rand_id("indicate_na")
)

## 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. For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model. A logical to indicate if the quantities for preprocessing have been estimated. A character string of variable names that will be populated (eventually) by the terms argument. A character string that will be the prefix to the resulting new variables. Defaults to "na_ind". A logical. Should the step 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. A character string that is unique to this step to identify it.

## Value

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

## Details

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and model (the median value) is returned.

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_date(), step_dummy_multi_choice(), step_dummy(), step_factor2string(), step_holiday(), step_integer(), step_novel(), step_num2factor(), step_ordinalscore(), step_other(), step_regex(), step_relevel(), step_string2factor(), step_unknown(), step_unorder()

## Examples

library(modeldata)
data("credit_data")

## missing data per column
purrr::map_dbl(credit_data, function(x) mean(is.na(x)))
#>       Status    Seniority         Home         Time          Age      Marital
#> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000 0.0002245173
#>      Records          Job     Expenses       Income       Assets         Debt
#> 0.0000000000 0.0004490346 0.0000000000 0.0855410867 0.0105523125 0.0040413112
#>       Amount        Price
#> 0.0000000000 0.0000000000

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())