step_filter: Filter rows using dplyr

Description Usage Arguments Details Value Row Filtering See Also Examples

View source: R/filter.R

Description

step_filter creates a specification of a recipe step that will remove rows using dplyr::filter().

Usage

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step_filter(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  inputs = NULL,
  skip = TRUE,
  id = rand_id("filter")
)

Arguments

recipe

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

...

Logical predicates defined in terms of the variables in the data. Multiple conditions are combined with &. Only rows where the condition evaluates to TRUE are kept. See dplyr::filter() for more details.

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.

inputs

Quosure of values given by ....

skip

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 = FALSE.

id

A character string that is unique to this step to identify it.

Details

When an object in the user's global environment is referenced in the expression defining the new variable(s), it is a good idea to use quasiquotation (e.g. !!) to embed the value of the object in the expression (to be portable between sessions). See the examples.

When you tidy() this step, a tibble with column terms which contains the conditional statements is returned. These expressions are text representations and are not parsable.

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.recipe(). 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().

See Also

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

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

Examples

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rec <- recipe( ~ ., data = iris) %>%
  step_filter(Sepal.Length > 4.5, Species == "setosa")

prepped <- prep(rec, training = iris %>% slice(1:75))

library(dplyr)

dplyr_train <-
  iris %>%
  as_tibble() %>%
  slice(1:75) %>%
  dplyr::filter(Sepal.Length > 4.5, Species == "setosa")

rec_train <- bake(prepped, new_data = NULL)
all.equal(dplyr_train, rec_train)

dplyr_test <-
  iris %>%
  as_tibble() %>%
  slice(76:150) %>%
  dplyr::filter(Sepal.Length > 4.5, Species != "setosa")
rec_test <- bake(prepped, iris %>% slice(76:150))
all.equal(dplyr_test, rec_test)

values <- c("versicolor", "virginica")

qq_rec <-
  recipe( ~ ., data = iris) %>%
  # Embed the `values` object in the call using !!
  step_filter(Sepal.Length > 4.5, Species  %in% !!values)

tidy(qq_rec, number = 1)

recipes documentation built on Sept. 27, 2021, 5:09 p.m.