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