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.
The best place to learn about all the packages in the tidyverse and how they fit together is R for Data Science. Expect to hear more about the tidyverse in the coming months as I work on improved package websites, making citation easier, and providing a common home for discussions about data analysis with the tidyverse.
You can install tidyverse with
This will install the core tidyverse packages that you are likely to use in almost every analysis:
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.
It also installs a selection of other tidyverse packages that you’re likely to use frequently, but probably not in every analysis. This includes packages for data manipulation:
DBI, for databases.
haven, for SPSS, SAS and Stata files.
httr, for web apis.
jsonlite for JSON.
rvest, for web scraping.
xml2, for XML.
These packages will be installed along with tidyverse, but you’ll load them explicitly with
library(tidyverse) will load the core tidyverse packages: ggplot2, tibble, tidyr, readr, purrr, and dplyr. 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
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() #> The following packages are out of date: #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
Welcome to the rstudio::glimpse() newsletter. Get a glimpse into our tools and how to use them.
We’d like to highlight and answer some of the great audience questions asked during the "MLOps with vetiver in Python and R" webinar.