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")
```