The recipes package is an alternative method for creating and
preprocessing design matrices that can be used for modeling or
visualization. From
wikipedia:
In statistics, a design matrix (also known as regressor matrix or model matrix) is a matrix of values of explanatory variables of a set of objects, often denoted by X. Each row represents an individual object, with the successive columns corresponding to the variables and their specific values for that object.
While R already has long-standing methods for creating these matrices
(e.g.
formulas
and model.matrix), there are some limitations to what the existing
infrastructure can
do.
The idea of the recipes package is to define a recipe or blueprint
that can be used to sequentially define the encodings and preprocessing
of the data (i.e. “feature engineering”). For example, to create a
simple recipe containing only an outcome and predictors and have the
predictors centered and scaled:
library(recipes)
library(mlbench)
data(Sonar)
sonar_rec <- recipe(Class ~ ., data = Sonar) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors())To install it, use:
install.packages("recipes")
## for development version:
require("devtools")
install_github("tidymodels/recipes")