Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tidymodels/recipes
Pipeable steps for feature engineering and data preprocessing to prepare for modeling
https://github.com/tidymodels/recipes
Last synced: 5 days ago
JSON representation
Pipeable steps for feature engineering and data preprocessing to prepare for modeling
- Host: GitHub
- URL: https://github.com/tidymodels/recipes
- Owner: tidymodels
- License: other
- Created: 2016-12-16T02:40:24.000Z (about 8 years ago)
- Default Branch: main
- Last Pushed: 2025-01-22T19:23:59.000Z (30 days ago)
- Last Synced: 2025-02-09T08:00:47.810Z (12 days ago)
- Language: R
- Homepage: https://recipes.tidymodels.org
- Size: 70.6 MB
- Stars: 580
- Watchers: 25
- Forks: 112
- Open Issues: 98
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- jimsghstars - tidymodels/recipes - Pipeable steps for feature engineering and data preprocessing to prepare for modeling (R)
README
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# recipes 
[](https://github.com/tidymodels/recipes/actions)
[](https://app.codecov.io/gh/tidymodels/recipes?branch=main)
[](https://CRAN.R-project.org/package=recipes)
[](https://CRAN.R-project.org/package=recipes)
[](https://lifecycle.r-lib.org/articles/stages.html)
## Introduction
With recipes, you can use [dplyr](https://dplyr.tidyverse.org/)-like pipeable sequences of feature engineering steps to get your data ready for modeling. For example, to create a recipe containing an outcome plus two numeric predictors and then center and scale ("normalize") the predictors:
```{r simple, message=FALSE}
library(recipes)
data(ad_data, package = "modeldata")
ad_rec <- recipe(Class ~ tau + VEGF, data = ad_data) %>%
step_normalize(all_numeric_predictors())
ad_rec
```
More information on recipes can be found at the [_Get Started_](https://www.tidymodels.org/start/recipes/) page of [tidymodels.org](https://www.tidymodels.org).
You may consider recipes as an alternative method for creating and preprocessing design matrices (also known as model matrices) that can be used for modeling or visualization. While R already has long-standing methods for creating such matrices (e.g. [formulas](https://rviews.rstudio.com/2017/02/01/the-r-formula-method-the-good-parts/) and `model.matrix`), there are some [limitations to what the existing infrastructure can do](https://rviews.rstudio.com/2017/03/01/the-r-formula-method-the-bad-parts/).
## Installation
There are several ways to install recipes:
```{r, eval = FALSE}
# The easiest way to get recipes is to install all of tidymodels:
install.packages("tidymodels")
# Alternatively, install just recipes:
install.packages("recipes")
# Or the development version from GitHub:
# install.packages("pak")
pak::pak("tidymodels/recipes")
```
## Contributing
- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://forum.posit.co/c/ml/15).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/recipes/issues).
- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.
- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).