step_indicate_na {recipes} | R Documentation |
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") )
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 |
role |
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. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
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 step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with columns
terms
(the selectors or variables selected) and model
(the
median value) is returned.
The underlying operation does not allow for case weights.
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy_extract()
,
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_time()
,
step_unknown()
,
step_unorder()
data("credit_data", package = "modeldata") ## 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())