---
title: "Tidymodels with {equatiomatic}"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Tidymodels with {equatiomatic}}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
library(equatiomatic)
library(recipes)
library(parsnip)
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

To paraphrase its web site, [Tidymodels](https://www.tidymodels.org) provides a series of packages for modeling and machine learning using the [tidyverse](https://www.tidyverse.org) principles. {equatiomatic} is now partly compatible with it, meaning that it can extract the equation of certain models.

Here is an example (adapted from the {workflows} main page):

```{r cars}
# Preparation of the dataset using {recipes}
spline_cars <- recipe(mpg ~ ., data = mtcars) |>
  step_ns(disp, deg_free = 10)
spline_cars_prepped <- prep(spline_cars, mtcars)
```

Here is a simple (tidy)model:

```{r lm}
# Fitting of a least-square linear model
lm_fit <- linear_reg() |>
  fit(mpg ~ ., data = juice(spline_cars_prepped))
```

We can extract the equation of this model with `extract_eq()`:

```{r lm_equation}
extract_eq(lm_fit, wrap = TRUE)
```

## Working with workflows

The {equatiomatic} `extract_eq()` also works with models fitted using the {workflows} package.

```{r wflow}
library(workflows)

# A model compatible with {equatiomatic}
linear_lm <- linear_reg()

# A workflow object
car_wflow <- workflow() |>
  add_recipe(spline_cars) |>
  add_model(linear_lm)
```

Now you can prepare the recipe and estimate the model via a single call to `fit()`:

```{r wflow_fit}
wflow_fit <- fit(car_wflow, data = mtcars)
```

You can also extract the equation from `wflow_fit`:

```{r wflow_equation}
extract_eq(wflow_fit, wrap = TRUE)
```

You notice that the original name of the dependent variable is lost, but you can reset it manually using `swapt_var_names=`:

```{r wflow_equation2}
extract_eq(wflow_fit, wrap = TRUE, swap_var_names = c(..y = "mpg"))
```

## Models requiring {broom.mixed}

For some models, {broom} is not enough. You need also to `library(broom.mixed)` before you can extract the equation. This is the case of a Bayes linear model using `"stan"`. Note: this code is not run in the vignette to avoid heavy extra-dependencies, but you can run this code in your R process.

```{r bayes_fit, eval=FALSE}
library(broom.mixed) # Required for some models, or extract_eq() will choke!

bayes_fit <- linear_reg() |>
  set_engine("stan") |>
  fit(mpg ~ hp + drat, data = mtcars)
```

And the equation would be obtained with:

```{r bayes_equation, eval=FALSE}
extract_eq(bayes_fit)
```

```{r bayes_equation2, echo=FALSE}
equation("E( \\operatorname{mpg} ) = \\alpha + \\beta_{1}(\\operatorname{hp}) + \\beta_{2}(\\operatorname{drat})")
```