step_sample creates a specification of a recipe step that will sample rows using dplyr::sample_n() or dplyr::sample_frac().

## Usage

step_sample(
recipe,
...,
role = NA,
trained = FALSE,
size = NULL,
replace = FALSE,
skip = TRUE,
id = rand_id("sample")
)

## Arguments

recipe

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

...

Argument ignored; included for consistency with other step specification functions.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

size

An integer or fraction. If the value is within (0, 1), dplyr::sample_frac() is applied to the data. If an integer value of 1 or greater is used, dplyr::sample_n() is applied. The default of NULL uses dplyr::sample_n() with the size of the training set (or smaller for smaller new_data).

replace

Sample with or without replacement?

skip

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

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.

## Row Filtering

This step can entirely remove observations (rows of data), which can have unintended and/or problematic consequences when applying the step to new data later via bake(). Consider whether skip = TRUE or skip = FALSE is more appropriate in any given use case. In most instances that affect the rows of the data being predicted, this step probably should not be applied at all; instead, execute operations like this outside and before starting a preprocessing recipe().

## Tidying

When you tidy() this step, a tibble with columns size, replace, and id is returned.

## Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Other row operation steps: step_arrange(), step_filter(), step_impute_roll(), step_lag(), step_naomit(), step_shuffle(), step_slice()

Other dplyr steps: step_arrange(), step_filter(), step_mutate_at(), step_mutate(), step_rename_at(), step_rename(), step_select(), step_slice()

## Examples


# Uses sample_n
recipe(~., data = mtcars) %>%
step_sample(size = 1) %>%
prep(training = mtcars) %>%
bake(new_data = NULL) %>%
nrow()
#> [1] 1

# Uses sample_frac
recipe(~., data = mtcars) %>%
step_sample(size = 0.9999) %>%
prep(training = mtcars) %>%
bake(new_data = NULL) %>%
nrow()
#> [1] 32

# Uses sample_n and returns _at maximum_ 20 samples.
smaller_cars <-
recipe(~., data = mtcars) %>%
step_sample() %>%
prep(training = mtcars %>% slice(1:20))

bake(smaller_cars, new_data = NULL) %>% nrow()
#> [1] 20
bake(smaller_cars, new_data = mtcars %>% slice(21:32)) %>% nrow()
#> [1] 12