Regular readers of this blog may be familiar with our ongoing effort to benchmark Revolution R Enterprise (RRE) across a range of use cases and on different platforms. We take these benchmarks seriously at Revolution Analytics, and constantly seek to improve the performance of our software.
The Mountain View Voice is a weekly newspaper serving the Silicon Valley area, and is a familiar sight to anyone wandering the streets of Palo Alto or Menlo Park. Angela Hey writes for 'Hey Tech!', an online blog of the Voice, and has just published a feature on R and the local Bay Area User Group (BARUG).
A couple of weeks ago, I participated in a panel discussion for DM Radio: "Still Sexy? How's that Data Scientist Gig Working Out?". The title was provocative, but the discussion mostly revolved around the rise of data science and how advanced analytics (often implemented with R) is changing the way many companies do business today.
As a language for statistical computing, R has always had a bias towards linear algebra, and is optimized for operations dealing in complete vectors and matrixes. This can be surprising to programmers coming to R from lower-level languages, where iterative programming (looping over the elements of a vector or matrix) is more natural and often more efficient. That's not the case with R, though: Noam Ross explains why vectorized programming in R is a good idea:
A new Task View on CRAN will be of anyone who needs to connect R with Web-based applications. The Web Technologies and Services Task View lists R functions and pacakges for reading data from websites (via public APIs or by scraping data from HTML packegs); for interfacing with Cloud-based platforms (including AWS); for authenticating and accessing data from social media services (including Twitter and Facebook); and for integrating with Web frameworks for building your own Web-aware applications with R.