---
title: "Introduction to recipes"
output: rmarkdown::html_vignette
description: |
  Start here if this is your first time using recipes! You will learn about 
  basic usage, steps, selectors, and checks.
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Introduction to recipes}
  %\VignetteEncoding{UTF-8}
---

```{r}
#| label: ex_setup
#| include: false
knitr::opts_chunk$set(
  message = FALSE,
  digits = 3,
  collapse = TRUE,
  comment = "#>",
  eval = requireNamespace("modeldata", quietly = TRUE) && requireNamespace("rsample", quietly = TRUE)
  )
options(digits = 3)
```

This document demonstrates some basic uses of recipes. First, some definitions are required: 

 * __variables__ are the original (raw) data columns in a data frame or tibble. For example, in a traditional formula `Y ~ A + B + A:B`, the variables are `A`, `B`, and `Y`. 
 * __roles__ define how variables will be used in the model. Examples are: `predictor` (independent variables), `response`, and `case weight`. This is meant to be open-ended and extensible. 
 * __terms__ are columns in a design matrix such as `A`, `B`, and `A:B`. These can be other derived entities that are grouped, such as a set of principal components or a set of columns, that define a basis function for a variable. These are synonymous with features in machine learning. Variables that have `predictor` roles would automatically be main effect terms.

## An Example

The packages contains a data set used to predict whether a person will pay back a bank loan. It has 13 predictor columns and a factor variable `Status` (the outcome). We will first separate the data into a training and test set:

```{r}
#| label: data
library(recipes)
library(rsample)
library(modeldata)

data("credit_data")

set.seed(55)
train_test_split <- initial_split(credit_data)

credit_train <- training(train_test_split)
credit_test <- testing(train_test_split)
```

Note that there are some missing values in these data:

```{r}
#| label: missing
vapply(credit_train, function(x) mean(!is.na(x)), numeric(1))
```

Rather than remove these, their values will be imputed. 

The idea is that the preprocessing operations will all be created using the training set and then these steps will be applied to both the training and test set. 

## An Initial Recipe

First, we will create a recipe object from the original data and then specify the processing steps. 

Recipes can be created manually by sequentially adding roles to variables in a data set. 

If the analysis only requires **outcomes** and **predictors**, the easiest way to create the initial recipe is to use the standard formula method:

```{r}
#| label: first_rec
rec_obj <- recipe(Status ~ ., data = credit_train)
rec_obj
```

The data contained in the `data` argument need not be the training set; this data is only used to catalog the names of the variables and their types (e.g. numeric, etc.).  

(Note that the formula method is used here to declare the variables, their roles and nothing else. If you use inline functions (e.g. `log`) it will complain. These types of operations can be added later.)

## Preprocessing Steps

From here, preprocessing steps for some step _X_ can be added sequentially in one of two ways:

```{r}
#| label: step_code
#| eval: false
rec_obj <- step_{X}(rec_obj, arguments)    ## or
rec_obj <- rec_obj |> step_{X}(arguments)
```

`step_dummy` and the other functions will always return updated recipes. 

One other important facet of the code is the method for specifying which variables should be used in different steps. The manual page `?selections` has more details but [`dplyr`](https://cran.r-project.org/package=dplyr)-like selector functions can be used: 

 * use basic variable names (e.g. `x1, x2`),
 *  [`dplyr`](https://cran.r-project.org/package=dplyr) functions for selecting variables: `contains()`, `ends_with()`, `everything()`, `matches()`, `num_range()`, and `starts_with()`,
 * functions that subset on the role of the variables that have been specified so far: `all_outcomes()`, `all_predictors()`, `has_role()`,  
 * similar functions for the type of data: `all_nominal()`, `all_numeric()`, and `has_type()`, or 
 * compound selectors such as `all_nominal_predictors()` or `all_numeric_predictors()`.


Note that the methods listed above are the only ones that can be used to select variables inside the steps. Also, minus signs can be used to deselect variables. 

For our data, we can add an operation to impute the predictors. There are many ways to do this and `recipes` includes a few steps for this purpose:

```{r}
#| label: imp-steps
grep("impute_", ls("package:recipes"), value = TRUE)
```

Here, _K_-nearest neighbor imputation will be used. This works for both numeric and non-numeric predictors and defaults _K_ to five To do this, it selects all predictors and then removes those that are numeric:

```{r}
#| label: dummy
imputed <- rec_obj |>
  step_impute_knn(all_predictors()) 
imputed
```

It is important to realize that the _specific_ variables have not been declared yet (as shown when the recipe is printed above). In some preprocessing steps, variables will be added or removed from the current list of possible variables. 

Since some predictors are categorical in nature (i.e. nominal), it would make sense to convert these factor predictors into numeric dummy variables (aka indicator variables) using `step_dummy()`. To do this, the step selects all non-numeric predictors:

```{r}
#| label: imputing
ind_vars <- imputed |>
  step_dummy(all_nominal_predictors()) 
ind_vars
```

At this point in the recipe, all of the predictor should be encoded as numeric, we can further add more steps to center and scale them:

```{r}
#| label: center_scale
standardized <- ind_vars |>
  step_center(all_numeric_predictors()) |>
  step_scale(all_numeric_predictors()) 
standardized
```


If these are the only preprocessing steps for the predictors, we can now estimate the means and standard deviations from the training set. The `prep` function is used with a recipe and a data set:

```{r}
#| label: trained
trained_rec <- prep(standardized, training = credit_train)
trained_rec
```

Note that the real variables are listed (e.g. `Home` etc.) instead of the selectors (`all_numeric_predictors()`). 

Now that the statistics have been estimated, the preprocessing can be _applied_ to the training and test set:

```{r}
#| label: apply
train_data <- bake(trained_rec, new_data = credit_train)
test_data  <- bake(trained_rec, new_data = credit_test)
```
`bake` returns a tibble that, by default, includes all of the variables: 
```{r}
#| label: tibbles
class(test_data)
test_data
vapply(test_data, function(x) mean(!is.na(x)), numeric(1))
```

Selectors can also be used. For example, if only the predictors are needed, you can use `bake(object, new_data, all_predictors())`. 

There are a number of other steps included in the package:

```{r}
#| label: step_list
#| echo: false
grep("^step_", ls("package:recipes"), value = TRUE)
```

## Checks

Another type of operation that can be added to a recipes is a _check_. Checks conduct some sort of data validation and, if no issue is found, returns the data as-is; otherwise, an error is thrown. 

For example, `check_missing` will fail if any of the variables selected for validation have missing values. This check is done when the recipe is prepared as well as when any data are baked. Checks are added in the same way as steps:

```{r}
#| label: check
#| eval: false
trained_rec <- trained_rec |>
  check_missing(contains("Marital"))
```

Currently, `recipes` includes:

```{r}
#| label: check_list
#| echo: false
grep("^check_", ls("package:recipes"), value = TRUE)
```
