Learn R Online

If you like interactive tutorials, be sure to check Swirl out. You'll have to install R first, however, as the tutorial is actually an installable package that will be run on your R console. They have a comprehensive collection of courses you can download and install, so after you are finished with them all, you should have a pretty good understanding of how to use R for data analysis.

This 76-page online PDF aims to introduce total beginners to essential concepts in R programming. The author strives to make the explanations as easy as possible to understand for non-specialists, and perhaps you won't find R as complex as it initially seems after reading this tutorial.

If you want to see what R's about right away, you can learn R from your browser through this interactive course. There are 8 levels for you to go through in order to get started on R programming where you'll learn how to graph with R vectors, organize data values, and finally how to work with real-world data.

This online tutorial will teach you step-by-step the basic knowledge of R, and aims to teach you how to solve common data analysis tasks and problems with Rlang, such as data manipulation, statistical analysis, graphing, and more. It's best to follow along this tutorial with your R command window open to see the code in action.

This comprehensive quickstart guide will help beginners to R understand how the R interface works, R data input & management, and how to use R for statsitics and graphing. The tutorial also dives into some more advanced functionalities of the R as well as introduce some packages you can use for them.

Tooting our horns here, but if you're interested in learning about Spark & R or how to use R for data analysis, Codementor also has several tutorials on the subject, including how to build interactive web apps with Shiny.

Naturally, DataCamp is the first to come in mind if talking about courses to learn R. You don't have to install R or anything and simply try R out in your browser. The courses are interactive and come with video lessons as well as coding challenges to let you get an idea of whether you've learned what was taught. Several basic courses are free, so this is a great place to get started with R.

Although you can purchase each course individually, this extensive program will usually ned 3~6 months of committment in order to complete, where you'll learn R programming first and then get into data analysis techniques as well as machine learning. Upon finishing the 9 courses, you can also choose to work on applying what you learned on a project. By the end of the program you should be able to develop simple data products and have a solid understanding of data science in general.

Based on Google's R Style Guide, this short but useful guide will teach you what are the recommended practices that will make your code easier to read. If you plan on working with others in the future, this is a good guide to adhere to for consistency.

As a blog feed aggregator With over 500 blog contributors, this is an excellent site to find daily news and tutorials about the R programming language. If you want to keep updated about R tips and tricks as well as job opportunities, this is a good place to keep your eyes on.

After going through some of the tutorials or courses listed in this guide to learning R, you'd probably want to start building your own project. Here's where RDocumentation.org will come in handy, as you can easily use it to search for packages on CRAN (the Comprehensive R Archive Network)

Born from the IPython project, Jupyter is now a widely used interpreter in the data science community, currently in use at big enterprises such as Google, Microsoft, and IBM. The Jupyter Notebook supports interactive data science and scientific computing, able to produce visualized data that can be manipulated in real time. You can use the Notebook with Apache Spark, Scala, and more to analyze big data as well, so you should definitely try jupyter out in your own browser.

R Studio is a popular IDE for R that includes a console and a syntax-highlighting editor that supports direct code execution, so this IDE goes great with tutorials. Additional features include the ability to quickly jump to function definitions, integrated R documentation as well as an interactive debugger to boost your productivity. Needless to say, many in the R community seem to love this IDE, so check it out to understand why.

For a more comprehensive list of R frameworks, packages, and software you can use, be sure to check out this epic list of developer tools for data manipulation, data management, machine learning, natural language processing, and more.