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).
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.
library(tidyverse)
will load the core tidyverse packages:
You also get a condensed summary of conflicts with other packages you have loaded:
library(tidyverse)
#> ── Attaching core tidyverse packages ─────────────────── tidyverse 2.0.0.9000 ──
#> ✔ dplyr 1.1.3 ✔ readr 2.1.4
#> ✔ forcats 1.0.0 ✔ stringr 1.5.0
#> ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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 ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ MASS::select() masks dplyr::select()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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 The following packages are out of date: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)
#>
#> Start a clean R session then run:
#> install.packages(c("broom", "DBI", "Rcpp"))
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, for times.
Importing other types of data:
feather, for sharing with Python and other languages.
.xls
and
.xlsx
files.xml2, for XML.
Modelling
modelr, for modelling within a pipeline
Please note that the tidyverse project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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