Creating and Preprocessing a Design Matrix with Recipes

R has an excellent framework for specifying models using formulas. While elegant and useful, it was designed in a time when models had small numbers of terms and complex preprocessing of data was not commonplace. As such, it has some limitations. In this talk, a new package called recipes is shown where the specification of model terms and preprocessing steps can be enumerated sequentially. The recipe can be estimated and applied to any dataset. Current options include simple transformations (log, Box-Cox, interactions, dummy variables, …), signal extraction (PCA, ICA, MDS), basis functions (splines, polynomials), imputation methods, and others.