# workflows

## What is a workflow?

A workflow is an object that can bundle together your pre-processing, modeling, and post-processing requests. For example, if you have a `recipe`

and `parsnip`

model, these can be combined into a workflow. The advantages are:

You don’t have to keep track of separate objects in your workspace.

The recipe prepping and model fitting can be executed using a single call to `fit()`

.

If you have custom tuning parameter settings, these can be defined using a simpler interface when combined with tune.

In the future, workflows will be able to add post-processing operations, such as modifying the probability cutoff for two-class models.

## Installation

You can install workflows from CRAN with:

`install.packages("workflows")`

You can install the development version from GitHub with:

```
# install.packages("devtools")
devtools::install_github("tidymodels/workflows")
```

## Example

Suppose you were modeling data on cars. Say…the fuel efficiency of 32 cars. You know that the relationship between engine displacement and miles-per-gallon is nonlinear, and you would like to model that as a spline before adding it to a Bayesian linear regression model. You might have a recipe to specify the spline:

```
library(recipes)
library(parsnip)
library(workflows)
spline_cars <- recipe(mpg ~ ., data = mtcars) %>%
step_ns(disp, deg_free = 10)
```

and a model object:

```
bayes_lm <- linear_reg() %>%
set_engine("stan")
```

To use these, you would generally run:

```
spline_cars_prepped <- prep(spline_cars, mtcars)
bayes_lm_fit <- fit(bayes_lm, mpg ~ ., data = juice(spline_cars_prepped))
```

You can’t predict on new samples using `bayes_lm_fit`

without the prepped version of `spline_cars`

around. You also might have other models and recipes in your workspace. This might lead to getting them mixed-up or forgetting to save the model/recipe pair that you are most interested in.

workflows makes this easier by combining these objects together:

```
car_wflow <- workflow() %>%
add_recipe(spline_cars) %>%
add_model(Bayes_lm)
```

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

:

`car_wflow_fit <- fit(car_wflow, data = mtcars)`

You can alter existing workflows using `update_recipe()`

/ `update_model()`

and `remove_recipe()`

/ `remove_model()`

.