Taking R to Eleven

July 28, 2010
By

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

R, the open-source statistical software system, is certainly a hot topic these days. It's been the subject of increasing media interest over the last year or so, and the user community is expanding rapidly: there are now about 40 R user groups around the world, and last week's worldwide R User Conference was the most successful ever.

So why the sudden attention for R? Steve Miller at Information Management posted last week an insightful analysis of why the time is right for R, and Revolution's role in its commercial success. (Steve also had some kind words to say about this very blog -- thanks, Steve! By the way, if you'd like to receive the monthly Revolution newsletter that Steve mentions in his article, you can sign up here.). He charts R's ascendance in 10 steps:

  1. The "Data Deluge", the rapid rise in the size of data sets, is a critical issue for businesses, because:
  2. Advanced analytics are more accurate than "traditional" (backward-looking) data analysis methods or "expert opinion", and as a result:
  3. Statistical analysis software is a hot topic for Business Intelligence community.
  4. The legacy statistical platfoms, SAS and SPSS, are based on dated technology, but:
  5. S is a modern statistical language platform (and, I'd add, a winner of the ACM Software Systems award, an honor it shares with Java, Apache and the Web itself), and:
  6. R, the open-source descendent of S, has taken the academic world by storm and is now the lingua franca of statistical research in academia. Academics trained in R are now moving into the commercial world.
  7. Revolution Analytics was formed 3 years ago with a mission to commercialize open-source R, and:
  8. Revolution Analytics is headed by statistics pioneer and SPSS founder Norman Nie, bringing a wealth of experience to the mission.
  9. The challenge is to build upon the open-source R platform to entice current users of SPSS and SAS. An immediate hurdle is to enhance R's support for ever-expanding data set sizes.
  10. Revolution must work with the open-source R community, who may be leery of a commercial venture related to R. 

It's a great summary, and I agree with all of it. Let me expand on the last two points:

Regarding the challenges of building upon R, Revolution has laid out its plan not just to bring scalable, high-performance analysis of large data sets to R, but also to provide a modern Web Services integration platform for R analytics, and to create an easy-to-use GUI for the more casual user. You'll be hearing much more about those initiatives in the coming weeks.

And finally, supporting the open-source R community is a critical part of Revolution's mission. And not just because it's the right thing to do: after all, Revolution is building a business on decades of collective work by volunteers to the R Project, starting with the foundation created Robert Gentleman and Ross Ihaka, realized by the dedication of the R Core Group, and expanded by the thousands of contributors of R packages. But also because the R community itself is a key part of the value of R: its innovation, its adaptiveness to new applications, and the resources and help from community members themselves.

That's why Revolution is supporting the R Community in a number of ways. Not just by contributing code to the R project (like the foreach and iterators packages), but also in supporting local user groups, sponsoring R conferences, funding students to do research and development for R, and evangelizing the benefits of R to the media and analyst community. And just last week, we launched inside-R.org, a portal for the open-source R community, to make it easier to find the wealth of R resources around the Web.

We'll keep working on points 9 and 10 in Steve's list above, and we look forward to continuing the R story ... to 11 and beyond.

Information Management: The Revolution Analytics Blog

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