has_role {recipes}

Role Selection

Description

has_role(), all_predictors(), and all_outcomes() can be used to select variables in a formula that have certain roles.

In most cases, the right approach for users will be use to use the predictor-specific selectors such as all_numeric_predictors() and all_nominal_predictors(). In general you should be careful about using -all_outcomes() if a ⁠*_predictors()⁠ selector would do what you want.

Similarly, has_type(), all_numeric(), all_integer(), all_double(), all_nominal(), all_ordered(), all_unordered(), all_factor(), all_string(), all_date() and all_datetime() are used to select columns based on their data type.

all_factor() captures ordered and unordered factors, all_string() captures characters, all_unordered() captures unordered factors and characters, all_ordered() captures ordered factors, all_nominal() captures characters, unordered and ordered factors.

all_integer() captures integers, all_double() captures doubles, all_numeric() captures all kinds of numeric.

all_date() captures Date() variables, all_datetime() captures POSIXct() variables.

See selections for more details.

current_info() is an internal function.

All of these functions have have limited utility outside of column selection in step functions.

Usage

has_role(match = "predictor")

has_type(match = "numeric")

all_outcomes()

all_predictors()

all_date()

all_date_predictors()

all_datetime()

all_datetime_predictors()

all_double()

all_double_predictors()

all_factor()

all_factor_predictors()

all_integer()

all_integer_predictors()

all_logical()

all_logical_predictors()

all_nominal()

all_nominal_predictors()

all_numeric()

all_numeric_predictors()

all_ordered()

all_ordered_predictors()

all_string()

all_string_predictors()

all_unordered()

all_unordered_predictors()

current_info()

Arguments

match

A single character string for the query. Exact matching is used (i.e. regular expressions won't work).

Value

Selector functions return an integer vector.

current_info() returns an environment with objects vars and data.

Examples

data(biomass, package = "modeldata")

rec <- recipe(biomass) %>%
  update_role(
    carbon, hydrogen, oxygen, nitrogen, sulfur,
    new_role = "predictor"
  ) %>%
  update_role(HHV, new_role = "outcome") %>%
  update_role(sample, new_role = "id variable") %>%
  update_role(dataset, new_role = "splitting indicator")

recipe_info <- summary(rec)
recipe_info

# Centering on all predictors except carbon
rec %>%
  step_center(all_predictors(), -carbon) %>%
  prep(training = biomass) %>%
  bake(new_data = NULL)