Looking to learn R or improve your R skills? You've come to the right place. Whether you're a beginner, an intermediate user or an advanced R programmer, these how-to guides and links to online tutorials, videos and more will help you do more with R.
Just starting out? You'll want to check out this beginner's guide that gives a brief R overview and then takes you step-by-step through tasks like data import, basic analysis and visualization:
After you've gotten your feet wet, learning a few additional skills will help you get a lot more out of R.
From working with dates to reshaping data to if-then-else statements, see how to perform common data munging tasks. You can also download these R tips & tricks as a PDF (free Insider registration required).
Basic visualizations in R are static, but if you want a graph where you can mouse over (or tap) to get more info -- or click to turn items on and off -- you'll want to check out rCharts. This step-by-step slideshow walks you through getting your data into the proper format and the few simple lines of code needed to generate your interactive dataviz.
Learn how to use R to bypass the Google Analytics Web interface, and automate creation of customized reports.
This curated list includes resources for every skill level, from statistics and R newbies to expert coders. It features a searchable, sortable chart so you can easily find resources of interest to you, and short write-ups with each link so you know what to expect before clicking through.
This post looks at "quality of opponent" for both 2014 Super Bowl teams by how each team's opponents finished the regular season. Includes R code.
The use of R is growing more quickly than that of SAS, SPSS and MATLAB, according to one researcher.
See how high R skills rank in Dice.com's 2013-14 salary survey. (Hint: quite high.)
For under $795 a year, Revolution Analytics will answer all R-related questions.
There have been several claims circulating on social media and the Web that Python, being a general-purpose language, has become more compelling for data work than a domain-specific language such as R. Claims, but not data. This installment of the Data Avenger blog series takes a look.