knitr::opts_chunk$set( message = FALSE, digits = 3, collapse = TRUE, comment = "#>" ) options(digits = 3) library(recipes)
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() recipe( ~ Species, data = iris) %>% summary() recipe(Sepal.Length + Sepal.Width ~ ., data = iris) %>% summary()
These 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()
When you want to get rid of a role for a column, use
recipe(HHV ~ ., data = biomass) %>% remove_role(sample, old_role = "predictor") %>% summary()
It 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_)
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()
If 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()
Additional 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
multi_role %>% add_role(HHV, new_role = "nocenter") %>% step_center(all_predictors(), -has_role("nocenter")) %>% prep(training = biomass, retain = TRUE) %>% juice() %>% head()
You can start a recipe without any roles:
recipe(biomass) %>% summary()
and 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()
All 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,
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() %>% juice(all_predictors()) %>% dplyr::select(starts_with("Species")) %>% names() # or something else recipe( ~ ., data = iris) %>% step_dummy(Species, role = "trousers") %>% prep() %>% juice(has_role("trousers")) %>% names()
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