https://github.com/tidyverse/purrr

A functional programming toolkit for R
https://github.com/tidyverse/purrr

functional-programming r

Last synced: 29 days ago
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A functional programming toolkit for R

• Host: GitHub
• URL: https://github.com/tidyverse/purrr
• Owner: tidyverse
• License: other
• Created: 2014-11-29T17:33:40.000Z (almost 10 years ago)
• Default Branch: main
• Last Pushed: 2024-08-21T21:15:45.000Z (3 months ago)
• Last Synced: 2024-09-30T03:55:07.775Z (about 1 month ago)
• Topics: functional-programming, r
• Language: R
• Homepage: https://purrr.tidyverse.org/
• Size: 9.42 MB
• Stars: 1,267
• Watchers: 62
• Forks: 271
• Open Issues: 19
• 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
  • Support: .github/SUPPORT.md

Awesome Lists containing this project

• awesome-R - purrr - A functional programming toolkit for R  (2017)
• awesome-starred - purrr - A functional programming toolkit for R (R)
• jimsghstars - tidyverse/purrr - A functional programming toolkit for R (R)

README

        
---

output: github_document

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# purrr 

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/purrr)](https://cran.r-project.org/package=purrr)

[![Codecov test coverage](https://codecov.io/gh/tidyverse/purrr/branch/master/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/purrr?branch=master)

[![R-CMD-check](https://github.com/tidyverse/purrr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidyverse/purrr/actions/workflows/R-CMD-check.yaml)

## Overview

purrr enhances R's functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors. If you've never heard of FP before, the best place to start is the family of `map()` functions which allow you to replace many for loops with code that is both more succinct and easier to read. The best place to learn about the `map()` functions is the [iteration chapter](https://r4ds.hadley.nz/iteration) in R for Data Science.

## Installation

```{r, eval = FALSE}

# The easiest way to get purrr is to install the whole tidyverse:

install.packages("tidyverse")

# Alternatively, install just purrr:

install.packages("purrr")

# Or the the development version from GitHub:

# install.packages("pak")

pak::pak("tidyverse/purrr")

```

## Cheatsheet

  

## Usage

The following example uses purrr to solve a fairly realistic problem: split a data frame into pieces, fit a model to each piece, compute the summary, then extract the R^2^.

```{r, eval = getRversion() >= "4.1"}

library(purrr)

mtcars |> 

  split(mtcars$cyl) |>  # from base R

  map(\(df) lm(mpg ~ wt, data = df)) |> 

  map(summary) |>

  map_dbl("r.squared")

```

This example illustrates some of the advantages of purrr functions over the equivalents in base R:

* The first argument is always the data, so purrr works naturally with the pipe.

* All purrr functions are type-stable. They always return the advertised output

  type (`map()` returns lists; `map_dbl()` returns double vectors), or they 

  throw an error.

  

* All `map()` functions accept functions (named, anonymous, and lambda), 

  character vector (used to extract components by name), or numeric vectors 

  (used to extract by position).