A wealth of tutorials, articles, and examples exist to help you learn R and its extensions. Scroll down or click a link below for a curated guide to learning R and its extensions.
There are hundreds of websites that can help you learn R. Here’s how you can use some of the best to become a productive R programmer.
Learn the basics
Visit Try R or DataCamp 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.
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.
Practice good habits
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.
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.
- The Johns Hopkins Data Science Specialization on Coursera
- Udacity Exploratory Data Analysis
- Stanford University StatLearning: Statistical Learning
- Statistics.com – Using R
Keep tabs on the R community
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.
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.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.
The RStudio repository on github contains all of the tutorial decks that go with the Introduction to Data Science with R by Garrett Grolemund from RStudio – https://github.com/rstudio/Intro/tree/master/slides
This is the first course in the RStudio Datacamp Online Track This course covers dplyr, next iteration of plyr, that provides tools for working with data frames – https://www.datacamp.com/courses/dplyr