step_indicate_na {recipes} | R Documentation |
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 data set to indicate which
observations are missing.
Usage
step_indicate_na(
recipe,
...,
role = "predictor",
trained = FALSE,
columns = NULL,
prefix = "na_ind",
keep_original_cols = TRUE,
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 |
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 the selected variable names. This field
is a placeholder and will be populated once |
prefix |
A character string that will be the prefix to the resulting new variables. Defaults to "na_ind". |
keep_original_cols |
A logical to keep the original variables in the
output. Defaults to |
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. |
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
and id
:
- terms
character, the selectors or variables selected
- id
character, id of this step
Case weights
The underlying operation does not allow for case weights.
See Also
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy()
,
step_dummy_extract()
,
step_dummy_multi_choice()
,
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()
Examples
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)