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.

Installation

# Install from CRAN
install.packages("tidyverse")

# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("hadley/tidyverse")

Usage

library(tidyverse) will load the core tidyverse packages:

  • ggplot2, for data visualisation.
  • dplyr, for data manipulation.
  • tidyr, for data tidying.
  • readr, for data import.
  • purrr, for functional programming.
  • tibble, for tibbles, a modern re-imagining of data frames.

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

library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag():    dplyr, stats

You can see conflicts created later with tidyverse_conflicts():

library(MASS)
#> 
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#> 
#>     select
tidyverse_conflicts()
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag():    dplyr, stats
#> select(): dplyr, MASS

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

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 The following packages are out of date: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)
#> Update now?
#> 
#> 1: Yes
#> 2: No

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:

  • Importing other types of data:

    • feather, for sharing with Python and other languages.
    • haven, for SPSS, SAS and Stata files.
    • httr, for web apis.
    • jsonlite for JSON.
    • readxl, for .xls and .xlsx files.
    • rvest, for web scraping.
    • xml2, for XML.
  • Modelling

    • modelr, for modelling within a pipeline
    • broom, for turning models into tidy data