recipes can assign one or more roles to each column in the data. The roles are not restricted to a predefined set; they can be anything. For most conventional situations, they are typically “predictor” and/or “outcome”. Additional roles enable targeted step operations on specific variables or groups of variables.
When a recipe is created using the formula interface, this defines the roles for all columns of the data set. summary() can be used to view a tibble containing information regarding the roles.
library(recipes)
recipe(Species ~ ., data = iris) %>% summary()
#> # A tibble: 6 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric predictor original
#> 2 Sepal.Width numeric predictor original
#> 3 Petal.Length numeric predictor original
#> 4 Petal.Width numeric predictor original
#> 5 original nominal predictor original
#> 6 Species nominal outcome original
recipe( ~ Species, data = iris) %>% summary()
#> # A tibble: 1 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Species nominal predictor original
recipe(Sepal.Length + Sepal.Width ~ ., data = iris) %>% summary()
#> # A tibble: 6 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Petal.Length numeric predictor original
#> 2 Petal.Width numeric predictor original
#> 3 Species nominal predictor original
#> 4 original nominal predictor original
#> 5 Sepal.Length numeric outcome original
#> 6 Sepal.Width numeric outcome originalThese roles can be updated despite this initial assignment. update_role() can modify a single existing role:
library(modeldata)
data(biomass)
recipe(HHV ~ ., data = biomass) %>%
update_role(dataset, new_role = "dataset split variable") %>%
update_role(sample, new_role = "sample ID") %>%
summary()
#> # A tibble: 8 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 sample nominal sample ID original
#> 2 dataset nominal dataset split variable original
#> 3 carbon numeric predictor original
#> 4 hydrogen numeric predictor original
#> 5 oxygen numeric predictor original
#> 6 nitrogen numeric predictor original
#> 7 sulfur numeric predictor original
#> 8 HHV numeric outcome originalWhen you want to get rid of a role for a column, use remove_role().
recipe(HHV ~ ., data = biomass) %>%
remove_role(sample, old_role = "predictor") %>%
summary()
#> # A tibble: 8 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 sample nominal <NA> original
#> 2 dataset nominal predictor original
#> 3 carbon numeric predictor original
#> 4 hydrogen numeric predictor original
#> 5 oxygen numeric predictor original
#> 6 nitrogen numeric predictor original
#> 7 sulfur numeric predictor original
#> 8 HHV numeric outcome originalIt represents the lack of a role as NA, which means that the variable is used in the recipe, but does not yet have a declared role. Setting the role manually to NA is not allowed:
recipe(HHV ~ ., data = biomass) %>%
update_role(sample, new_role = NA_character_)
#> Error in `single_chr()`:
#> ! `new_role` must not be `NA`.When there are cases when a column will be used in more than one context, add_role() can create additional roles:
multi_role <- recipe(HHV ~ ., data = biomass) %>%
update_role(dataset, new_role = "dataset split variable") %>%
update_role(sample, new_role = "sample ID") %>%
# Roles below from https://wordcounter.net/random-word-generator
add_role(sample, new_role = "jellyfish")
multi_role %>%
summary()
#> # A tibble: 9 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 sample nominal sample ID original
#> 2 sample nominal jellyfish original
#> 3 dataset nominal dataset split variable original
#> 4 carbon numeric predictor original
#> 5 hydrogen numeric predictor original
#> 6 oxygen numeric predictor original
#> 7 nitrogen numeric predictor original
#> 8 sulfur numeric predictor original
#> 9 HHV numeric outcome originalIf a variable has multiple existing roles and you want to update one of them, the additional old_role argument to update_role() must be used to resolve any ambiguity.
multi_role %>%
update_role(sample, new_role = "flounder", old_role = "jellyfish") %>%
summary()
#> # A tibble: 9 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 sample nominal sample ID original
#> 2 sample nominal flounder original
#> 3 dataset nominal dataset split variable original
#> 4 carbon numeric predictor original
#> 5 hydrogen numeric predictor original
#> 6 oxygen numeric predictor original
#> 7 nitrogen numeric predictor original
#> 8 sulfur numeric predictor original
#> 9 HHV numeric outcome originalAdditional variable roles allow you to use has_role() in combination with other selection methods (see ?selections) to target specific variables in subsequent processing steps. For example, in the following recipe, by adding the role "nocenter" to the HHV predictor, you can use -has_role("nocenter") to exclude HHV when centering all_predictors().
multi_role %>%
add_role(HHV, new_role = "nocenter") %>%
step_center(all_predictors(), -has_role("nocenter")) %>%
prep(training = biomass, retain = TRUE) %>%
bake(new_data = NULL) %>%
head()
#> # A tibble: 6 × 8
#> sample dataset carbon hydrogen oxygen nitrogen sulfur HHV
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Akhrot Shell Training 1.52 0.181 4.37 -0.667 -0.234 20.0
#> 2 Alabama Oak Wood Waste Training 1.21 0.241 2.73 -0.877 -0.234 19.2
#> 3 Alder Training -0.475 0.341 7.68 -0.967 -0.214 18.3
#> 4 Alfalfa Training -3.19 -0.489 -2.97 2.22 -0.0736 18.2
#> 5 Alfalfa Seed Straw Training -1.53 -0.0586 2.15 -0.0772 -0.214 18.4
#> 6 Alfalfa Stalks Training -2.89 0.291 1.63 0.963 -0.134 18.5The selector all_numeric_predictors() can also be used in place of the compound specification above.
You can start a recipe without any roles:
recipe(biomass) %>%
summary()
#> # A tibble: 8 × 4
#> variable type role source
#> <chr> <chr> <lgl> <chr>
#> 1 sample nominal NA original
#> 2 dataset nominal NA original
#> 3 carbon numeric NA original
#> 4 hydrogen numeric NA original
#> 5 oxygen numeric NA original
#> 6 nitrogen numeric NA original
#> 7 sulfur numeric NA original
#> 8 HHV numeric NA originaland roles can be added in bulk as needed:
recipe(biomass) %>%
update_role(contains("gen"), new_role = "lunchroom") %>%
update_role(sample, HHV, new_role = "snail") %>%
summary()
#> # A tibble: 8 × 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 sample nominal snail original
#> 2 dataset nominal <NA> original
#> 3 carbon numeric <NA> original
#> 4 hydrogen numeric lunchroom original
#> 5 oxygen numeric lunchroom original
#> 6 nitrogen numeric lunchroom original
#> 7 sulfur numeric <NA> original
#> 8 HHV numeric snail originalAll recipes steps have a role argument that lets you set the role of new columns generated by the step. When a recipe modifies a column in-place, the role is never modified. For example, ?step_center has the documentation:
role: Not used by this step since no new variables are created
In other cases, the roles are defaulted to a relevant value based the context. For example, ?step_dummy has
role: For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the binary dummy variable columns created by the original variables will be used as predictors in a model.
So, by default, they are predictors but don’t have to be:
recipe( ~ ., data = iris) %>%
step_dummy(Species) %>%
prep() %>%
bake(new_data = NULL, all_predictors()) %>%
dplyr::select(starts_with("Species")) %>%
names()
#> [1] "Species_X1" "Species_X2"
# or something else
recipe( ~ ., data = iris) %>%
step_dummy(Species, role = "trousers") %>%
prep() %>%
bake(new_data = NULL, has_role("trousers")) %>%
names()
#> [1] "Species_X1" "Species_X2"