There is a lot of noise around the "R versus Contender X" for Data Science. I think the two main competitors right now that I hear about are Python and Julia. I'm not going to weigh into the debates because I go by the motto: "Why not just use something that works?"
R offers a lot of benefits if you are interested in statistical or predictive modeling. It is basically unrivaled in terms of the breadth of packages for applied statistics. But I think sometimes it isn't obvious that R can handle some tasks that you used to have to do with other languages. This misconception is particularly common among people who regularly code in a different language and are moving to R. So I thought I'd point out a few cool things that R can do. Please add to the list in the comments if I've missed things that R can do people don't expect.
- R can do regular expressions/text processing: Check out stringr, tm, and a large number of other natural language processing packages.
- R can get data out of a database: Check out RMySQL, RMongoDB, rhdf5, ROracle, MonetDB.R (via Anthony D.).
- R can process nasty data: Check out plyr, reshape2, Hmisc
- R can process images: EBImage is a good general purpose tool, but there are also packages for various file types like jpeg.
- R can handle different data formats: XML and RJSONIO handle two common types, but you can also read from Excel files with xlsx or handle pretty much every common data storage type (you'll have to search R + data type) to find the package.
- R can interact with APIs: Check out RCurl and httr for general purpose software, or you could try some specific examples like twitteR. You can create an api from R code using yhat.
- R can build apps/interactive graphics: Some pretty cool things have already been built with shiny, rCharts interfaces with a ton of interactive graphics packages.
- R can create dynamic documents: Try out knitr or slidify.
- R can play with Hadoop: Check out the rhadoop wiki.
- R can create interactive teaching modules: You can do it in the console with swirl or on the web with Datamind.
- R interfaces very nicely with C if you need to be hardcore (also maybe? interfaces with Python): Rcpp, enough said. Also read the tutorial. I haven't tried the rPython library, but it looks like a great idea.