There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It’s also a dynamic language and rapidly changing, so it’s important to keep up with the latest tools and technologies.

That’s why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. It’s a mix of materials ranging from documentation, online courses, books, and more.

Just like R, this learning path is a dynamic resource. We want to continually evolve and improve the resources to provide the best possible learning experience. So if you have suggestions for improvement please email tal.galili@gmail.com with your feedback.

Getting started:  The basics of R

Setting up your machine

R packages

Importing your data into R

Data Manipulation

Data Visualization

Data Science & Machine Learning with R

Reporting Results in R

Learning advanced R topics in (paid) online courses

Next steps

The best way to learn R is by doing. In case you are just getting started with R, this free introduction to R tutorial by DataCamp is a great resource as well the successorIntermediate R programming (subscription required). Both courses teach you R programming and data science interactively, at your own pace, in the comfort of your browser. You get immediate feedback during exercises with helpful hints along the way so you don’t get stuck.

Another free online interactive learning tutorial for R is available by O’reilly’s code school website called try R. An offline interactive learning resource is swirl, an R package that makes if fun and easy to become an R programmer. You can take a swirl course by (i)installing the package in R, and (ii) selecting a course from the course library. If you want to start right away without needing to install anything you can also choose for the online version of Swirl.

There are also some very good MOOC’s available on edX and Coursera that teach you the basics of R programming. On edX you can find Introduction to R Programming by Microsoft, an 8 hour course that focuses on the fundamentals and basic syntax of R. At Coursera there is the very popular R Programming course by Johns Hopkins. Both are highly recommended!

If you instead prefer to learn R via a written tutorial or book there is plenty of choice. There is the introduction to R manual by CRAN, as well as some very accessible books like Jared Lander’s R for Everyone or R in Action by Robert Kabacoff.

You can download a copy of R from the Comprehensive R Archive Network (CRAN). There are binaries available for Linux, Mac and Windows.

Once R is installed you can choose to either work with the basic R console, or with an integrated development environment (IDE).RStudio is by far the most popular IDE for R and supports debugging, workspace management, plotting and much more (make sure to check out the RStudio shortcuts).

Next to RStudio you also have Architect, and Eclipse-based IDE for R. If you prefer to work with a graphical user interface you can have a look at R-commander  (aka as Rcmdr), or Deducer.

R packages are the fuel that drive the growth and popularity of R. R packages are bundles of code, data, documentation, and tests that are easy to share with others. Before you can use a package, you will first have to install it. Some packages, like the base package, are automatically installed when you install R. Other packages, like for example the ggplot2 package, won’t come with the bundled R installation but need to be installed.

Many (but not all) R packages are organized and available fromCRAN, a network of servers around the world that store identical, up-to-date, versions of code and documentation for R. You can easily install these package from inside R, using the install.packages function. CRAN also maintains a set ofTask Views that identify all the packages associated with a particular task such as for exampleTimeSeries.

Next to CRAN you also have bioconductor which has packages for the analysis of high-throughput genomic data, as well as for example the github and bitbucket repositories of R package developers. You can easily install packages from these repositories using the devtools package.

Finding a package can be hard, but luckily you can easily search packages from CRAN, github and bioconductor usingRdocumentation, inside-R, or you can have a look at this quick list of useful R packages.

To end, once you start working with R, you’ll quickly find out that R package dependencies can cause a lot of headaches. Once you get confronted with that issue, make sure to check out packrat (seevideo tutorial) or checkpoint. When you’d need to update R, if you are using Windows, you can use the updateR() function from theinstallr package.

The data you want to import into R can come in all sorts for formats: flat files, statistical software files, databases and web data.