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https://github.com/tidyverse/tidyverse

Easily install and load packages from the tidyverse
https://github.com/tidyverse/tidyverse

data-science r tidyverse

Last synced: 4 days ago
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Easily install and load packages from the tidyverse

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

options(cli.hyperlink = FALSE)

```



# tidyverse 



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

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

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



## Overview



The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The __tidyverse__ package is designed to make it easy to install and load core packages from the tidyverse in a single command.



If you'd like to learn how to use the tidyverse effectively, the best place to start is [R for Data Science (2e)](https://r4ds.hadley.nz/).



## Installation



::: .pkgdown-release

```{r eval = FALSE}

# Install from CRAN

install.packages("pak")

```



If you have any problems installing tidyverse, we recommend trying with [pak](https://pak.r-lib.org) since it is likely to help diagnose the problem:



```{r eval = FALSE}

# install.packages("pak")

pak::pak("tidyverse")

```



:::



::: .pkgdown-devel

```{r eval = FALSE}

# Install the development version from GitHub

# install.packages("pak")

pak::pak("tidyverse/tidyverse")

```

:::



If you're compiling from source, you can run `pak::pkg_system_requirements("tidyverse")`, to see the complete set of system packages needed on your machine.



## Usage



`library(tidyverse)` will load the core tidyverse packages:



* [ggplot2](https://ggplot2.tidyverse.org), for data visualisation.

* [dplyr](https://dplyr.tidyverse.org), for data manipulation.

* [tidyr](https://tidyr.tidyverse.org), for data tidying.

* [readr](https://readr.tidyverse.org), for data import.

* [purrr](https://purrr.tidyverse.org), for functional programming.

* [tibble](https://tibble.tidyverse.org), for tibbles, a modern re-imagining of data frames.

* [stringr](https://github.com/tidyverse/stringr), for strings.

* [forcats](https://github.com/tidyverse/forcats), for factors.

* [lubridate](https://github.com/tidyverse/lubridate), for date/times.



You also get a condensed summary of conflicts with other packages you have loaded:



```{r example}

library(tidyverse)

```



You can see conflicts created later with `tidyverse_conflicts()`:



```{r conflicts}

library(MASS)

tidyverse_conflicts()

```



And you can check that all tidyverse packages are up-to-date with `tidyverse_update()`:



```{r update, eval = FALSE}

tidyverse_update()

#> The following packages are out of date:

#>  * broom (0.4.0 -> 0.4.1)

#>  * DBI   (0.4.1 -> 0.5)

#>  * Rcpp  (0.12.6 -> 0.12.7)

#>

#> Start a clean R session then run:

#> install.packages(c("broom", "DBI", "Rcpp"))

```



## Packages



As well as the core tidyverse, installing this package also installs a selection of other packages that you're likely to use frequently, but probably not in every analysis. This includes packages for:



*   Working with specific types of vectors:



    * [hms](https://github.com/tidyverse/hms), for times.



*   Importing other types of data:



    * [haven](https://github.com/tidyverse/haven), for SPSS, SAS and Stata files.

    * [httr](https://github.com/r-lib/httr), for web apis.

    * [jsonlite](https://github.com/jeroen/jsonlite) for JSON.

    * [readxl](https://github.com/tidyverse/readxl), for `.xls` and `.xlsx` files.

    * [rvest](https://github.com/tidyverse/rvest), for web scraping.

    * [xml2](https://github.com/r-lib/xml2), for XML.



*   Modelling



    * [broom](https://github.com/tidymodels/broom), for turning models into

      tidy data



## Code of Conduct



Please note that the tidyverse project is released with a [Contributor Code of Conduct](https://tidyverse.tidyverse.org/CODE_OF_CONDUCT.html).

By contributing to this project, you agree to abide by its terms.