recipes (version 0.1.7)

step_filter: Filter rows using dplyr

Description

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

Usage

step_filter(recipe, ..., role = NA, trained = FALSE, inputs = NULL,
  skip = FALSE, id = rand_id("filter"))
# S3 method for step_filter
tidy(x, ...)

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. For the tidy method, these are not currently used.

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 = TRUE as it may affect the computations for subsequent operations

id

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

x

A step_filter object

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which contains the conditional statements. These expressions are text representations and are not parsable.

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.

Examples

# NOT RUN {
rec <- recipe( ~ ., data = iris) %>%
  step_filter(Sepal.Length > 4.5, Species == "setosa")

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

library(dplyr)

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

rec_train <- juice(prepped)
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)
# }