recipes

Introduction

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())

More information on recipes can be found at the Get Started page of tidymodels.org.

Installation

To install this package, use:

install.packages("recipes")

## for development version:
require("devtools")
install_github("tidymodels/recipes")

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

• For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.

• If you think you have encountered a bug, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.