Visit DataCamp or Try R to learn how to write basic R code. Both sites provide interactive lessons that will get you writing real code in minutes. They are a great place to make mistakes and test out new skills. You are told immediately when you go wrong and given a chance to fix your code.
Or try Leada. Leada gets you programming in your own environment with videos and exercises. The first few courses are free and cover how to install R and RStudio.
Broaden your skills
Work through The Beginner’s Guide to R by Computerworld Magazine. This 30 page guide will show you how to install R, load data, run analyses, make graphs, and more.
Or try swirl, an R package designed to teach you R straight from the command line. Swirl provides exercises and feedback from within your R session to help you learn in a structured, interactive way.
Or attend an RStudio webinar. These free events teach you how do do useful things in R, and we’re always making more. Our previous webinars are archived into several tracks for your viewing convenience:
Read the R Style Guide for advice on how to write readable, maintainable code. This is how other R users will expect your code to look when you share it.
Look up help
When you need to learn more about an R function or package, visit Rdocumentation.org, a searchable database of R documentation. You can search for R packages and functions, look at package download statistics, and leave and read comments about R functions.
If you need a quick reminder about how to wrangle data, make a graph, or do some other common task in R download one of our free R cheat sheets. These make handy reference guides to keep next to your work station.
Seek help at StackOverflow, a searchable forum of questions and answers about computer programming. StackOverflow has answered (and archived) over 40,000 questions related to R programming.
If you a have question that is more about statistical methodology, there are also plenty of R users active on the the CrossValidated Q&A community.
Attend a course
If you prefer a structured learning environment, attend one of theses MOOC’s for an excellent introduction to the R language.
You can arrange a private or onsite course by contacting one of our training partners.
Keep tabs on the R community
Follow the RStudio blog to hear about our latest features, packages, and workshops. Our blog is a good place to find short tutorials about the packages we make.
Read R bloggers, a blog aggregator that reposts R related articles from across the web. R bloggers is a good place to find R tutorials, announcements, and other random happenings.
Deepen your expertise
To attain the ultimate R expertise, read Hadley Wickham’s Advanced R Programming book, which is available for free online at the link. Hadley explains in clear prose how R works from a computer science perspective.
Then, when you are ready, read Hadley’s R Packages book to learn how to share your R tools with others. R Packages is also available free online at the link.
Got R down? Then give Shiny a try.
Shiny lets you share your results as interactive, eye-catching web apps that are friendly to non-programmers.
Reactive by Design
We’ve designed a free curriculum of tutorials to help you learn Shiny at the Shiny Dev Center. There, you can also read articles, look up documentation, and learn from example Shiny apps.
R Markdown is way to write quick attractive reports that use R output. You write the reports in a markdown document, inserting R code where you like it (left).
R then generates a final document that replaces the R code with its results (right).
You can automatically update an R Markdown document whenever your data or R code changes, which creates one of the most reproducible — and efficient — workflows possible. You can use R Markdown to create attractive, fully customizable, HTML, PDF, and MS Word documents as well as Beamer slides.
Visit rmarkdown.rstudio.com to get started using R markdown right away. The site provides a quick tour of the R markdown syntax, as well as in depth articles and examples.
For an easy introduction to data science, read Garrett Grolemund’s book in progress, Data Science with R, which is available for free online at the link. Garrett explains how data science works in an easy to understand way. He then shows the best ways to do data science with a suite of R packages that have become known as the “Hadley-verse.” These include tidyr, dplyr, ggvis and more.
We’ll post tutorials here in the future about how to use R tools do data science. In the mean time, we’re proud to provide the ggvis package, the next iteration of the popular ggplot2 graphics package. ggvis creates dynamic, interactive data visualizations.
Check out the getting started guide at ggvis.rstudio.com. There you can find demonstrations of ggvis’s many features and descriptions of the advanced aspects of the package.