Many useful R function come in packages, free libraries of code written by R's active user community. To install an R package, open an R session and type at the command line
install.packages("<the package's name>")
R will download the package from CRAN, so you'll need to be connected to the internet. Once you have a package installed, you can make its contents available to use in your current R session by running
library("<the package's name>")
There are thousands of helpful R packages for you to use, but navigating them all can be a challenge. To help you out, we've compiled this guide to some of the best. We've used each of these, and found them to be outstanding – we've even written some of them. But you don't have to take our word for it, these packages are also some of the top most downloaded R packages.
To load data
DBI - The standard for for communication between R and relational database management systems. Packages that connect R to databases depend on the DBI package.
foreign - Want to read a SAS data set into R? Or an SPSS data set? Foreign provides functions that help you load data files from other programs into R.
haven - Enables R to read and write data from SAS, SPSS, and Stata.
R can handle plain text files – no package required. Just use the functions read.csv, read.table, and read.fwf. If you have even more exotic data, consult the CRAN guide to data import and export.
For more information about using R with databases see db.rstudio.com.
To manipulate data
tidyverse - An opinionated collection of R packages designed for data science that share an underlying design philosophy, grammar, and data structures. This collection includes all the packages in this section, plus many more for data import, tidying, and visualization listed here.
dplyr - Essential shortcuts for subsetting, summarizing, rearranging, and joining together data sets. dplyr is our go to package for fast data manipulation.
stringr - Easy to learn tools for regular expressions and character strings.
lubridate - Tools that make working with dates and times easier.
To visualize data
ggvis - Interactive, web based graphics built with the grammar of graphics.
rgl - Interactive 3D visualizations with R
- leaflet (maps)
- dygraphs (time series)
- DT (tables)
- diagrammeR (diagrams)
- network3D (network graphs)
- threeJS (3D scatterplots and globes).
googleVis - Let's you use Google Chart tools to visualize data in R. Google Chart tools used to be called Gapminder, the graphing software Hans Rosling made famous in hie TED talk.
To model data
tidymodels - A collection of packages for modeling and machine learning using tidyverse principles. This collection includes rsample, parsnip, recipes, broom, and many other general and specialized packages listed here.
mgcv - Generalized Additive Models
randomForest - Random forest methods from machine learning
multcomp - Tools for multiple comparison testing
vcd - Visualization tools and tests for categorical data
glmnet - Lasso and elastic-net regression methods with cross validation
survival - Tools for survival analysis
caret - Tools for training regression and classification models
To report results
shiny - Easily make interactive, web apps with R. A perfect way to explore data and share findings with non-programmers.
R Markdown - The perfect workflow for reproducible reporting. Write R code in your markdown reports. When you run render, R Markdown will replace the code with its results and then export your report as an HTML, pdf, or MS Word document, or a HTML or pdf slideshow. The result? Automated reporting. R Markdown is integrated straight into RStudio.
xtable - The xtable function takes an R object (like a data frame) and returns the latex or HTML code you need to paste a pretty version of the object into your documents. Copy and paste, or pair up with R Markdown.
For Spatial data
maps - Easy to use map polygons for plots.
ggmap - Download street maps straight from Google maps and use them as a background in your ggplots.
For Time Series and Financial data
zoo - Provides the most popular format for saving time series objects in R.
xts - Very flexible tools for manipulating time series data sets.
quantmod - Tools for downloading financial data, plotting common charts, and doing technical analysis.
To write high performance R code
Rcpp - Write R functions that call C++ code for lightning fast speed.
data.table - An alternative way to organize data sets for very, very fast operations. Useful for big data.
parallel - Use parallel processing in R to speed up your code or to crunch large data sets.
To work with the web
XML - Read and create XML documents with R
jsonlite - Read and create JSON data tables with R
httr - A set of useful tools for working with http connections
To write your own R packages
devtools - An essential suite of tools for turning your code into an R package.
testthat - testthat provides an easy way to write unit tests for your code projects.
roxygen2 - A quick way to document your R packages. roxygen2 turns inline code comments into documentation pages and builds a package namespace.
You can also read about the entire package development process online in Hadley Wickham's R Packages book
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You may also ask for help from R and RStudio users on community.rstudio.com. Be sure to include a reproducible example of your issue. Click here to start a new community discussion.