Revolutions

Accelerating analytics at MSU with Revolution R Enterprise

Erik Sigur, Information Technologist for the Department of Statistics and Probability at Michigan State University, writes at ReadWriteWeb about using Revolution R Enterprise to provide high-performance computation in R to the researchers in his department:

Our search for a more effective version of R ultimately brought us to a product called Revolution R Enterprise by Revolution Analytics, which provides commercial support and software for open source R. It takes advantage of multiple processor cores by using optimized assembly code and efficient multi-threaded algorithms that use all of the processor cores simultaneously.

The department at MSU provides high-performance computing facilities via their Statistical Computing Cluster: a network of high-performance PCs running under the Microsoft HPC Server environment. Revolution R Enterprise 5 provides features to distribute R jobs amongst the nodes of a cluster, and to use the power of distributed computing to reduce the time required to process big-data statistical analyses. Says Erik:

Once the department could schedule R jobs in an HPC environment, the demand began to drastically increase. The HPC cluster is now scheduling more than four times the amount of jobs that were scheduled in previous semesters, from 200 jobs over a year ago to over 800 jobs this past semester. Jobs that were taking over three months to complete on open source R were completed in less than a few days with Revolution R. Computational jobs are now run multiple times with significantly higher levels of accuracy than ever before.

Learn more about Erik's experiences with Revolution R Enterprise at the link below. You can also read the case study on the Revolution Analytics website, or learn more about the HPC features of Revolution R Enterprise in our archived webinar.

ReadWriteEnterprise: [Case Study] Lessons in High Performance Computing with Open Source

Forbes: Top 20 influencers in Big Data

Haydn Shaughnessy at The Forbes blog provides a list of the "Top 20 Influencers in Big Data", and I'm humbled to report that yours truly is listed there at #2. It's an instantaneous ranking based on the social-media tracking tool Traakr, but it's still great to be listed alongside writers for SiliconAngle, GigaOM, and KDNuggets (and even Mashable!). I firmly believe that the R language has a strong future in the world of Big Data, so I'm glad to hear the message is getting through.

Forbes: Who Are The Top 20 Influencers in Big Data? 

New R User Groups in Austin, Adelaide

It's awesome to see so many local R user groups kicking off in 2011! Yet another is the Austin R User Group in Austin, Texas. They've already held their first informal get-together, and the first formal meeting on February 23 will be devoted to data management techniques in R. Props to Sandy Donlon for organizing the group!

And I'm so pleased to report that a local R user group has started in my old hometown of Adelaide, South Australia. Organized by Jonathan Tuke at the University of Adelaide (my alma mater), the Adelaide R-users Group has already had several successful meetups and their next meeting is on February 28. Australia now has an R user group in every major city except Perth, which I'd venture makes it the country with the most R groups on a per-capita basis. (And yet, ironically, still no groups in New Zealand -- c'mon Kiwis!)

MeetUp: Austin R User Group

R Chart featured in Facebook IPO

Page 7 of Facebook's 213-page S-1 filing for their record-breaking IPO includes the following chart, under the headline: "Our Mission: To make the world more open and connected".

This chart was created using the R language and Hadoop by Facebook intern Paul Butler. (Thanks to the blog IOER Tools for first noticing the inclusion of the chart.)

And speaking of R charts appearing in widely-read places, the latest issue of the prestigious science journal Nature includes several charts created in R in an article on the evolution of the nematode C. elegans. Hadley Wickham, author of the ggplot2 package, noted that his package was used to create one of the charts.

Analytic applications are built by data scientists

Ventana Research analyst David Menninger was on the judging panel for the Applications of R in Business contest. In a post on the Ventana research blog, he offers his perspectives on the contest, noting that

R, as a statistical package, includes many algorithms for predictive analytics, including regression, clustering, classification, text mining and other techniques. The contest submissions supported a variety of business cases, including, among others, predicting order amounts to optimize manufacturing processes,  predicting marketing campaign effectiveness to optimize marketing spending, predicting liquid steel temperatures to optimize steel plant processes and performing sentiment analysis of Twitter data.

(Incidentally, David also has a great riff on the terminology of "predictive analytics" and "big data" out today.) He also notes that these applications are compelling precisely because of the close relationship between the contest entrants and the business problems they demonstrated how to solve:

The entries also demonstrated a best practice: close alignment between the analyst and the underlying business objectives. Predictive analytics is not magic. It requires an understanding of business processes and an understanding of statistical techniques. The judging criteria reflected this requirement as well. One of the three categories we were asked to score was applicability of the submission to business. I think it’s clear how the analyses in the winning entries could provide significant business value.

As David notes, however, the counterpoint to this is that the analyst must combine *both* the . "How many people in your organization could perform those types of analyses", he rightly asks. A combination of statistical tools along with domain expertise (plus the technical skills to implement the solution) is the hallmark of a good data scientist, which exactly why many organizations are looking to build effective data science teams.

By the way, while the concept of "data scientist" is relatively new, this idea of combining statistical analysts with domain expertise is not. Bill Cleveland (yes, that Bill Cleveland) made similar suggestions in a prescient paper back in 2001: "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics". (ISI Review, 69)

David Menninger: Revolution Analytics Hosts Contest on Business Predicting the Future

New R User Group in Cambridge, UK

Yet another new local R user group has launched this month, this time in Cambridge, UK. Cambridge RUG was created by data analyst Andrew Caines to promote the use of R in the Cambridge area. 

The group aims to encourage people try the R language, act as an advice centre to help people get where they want to with R and host an annual gathering for R-based talks and workshops. The group's just getting started, if you're interested you can join the group email list to be notified of upcoming events.

RUG Cambridge: About

Pre-conference tutorials announced for useR! 2012

The worldwide R user conference, useR! 2012, will take place this year June 12-15 in Nashville, Tennessee. In addition to the regular program of invited and contributed talks, a highlight of every useR! is the pre-conference tutorials: hand-on, half-day in-depth expert-led sessions with a focused on a specific area of methodology with R. This year's roster of tutorials has been announced, with topics such as reproducible research, parallel programming, medical image analysis, Hadoop, predictive modeling, and many more. 

And as an extra-special bonus, Bill Venables (of Introduction to R fame) will present a one-day course on June 11. This session will be a "gentle survey of modern data analysis, graphics, and statistical modeling in the style of Modern Applied Statistics with S, Venables' classic textbook with co-author Brian D. Ripley, including developments both in R and statistics since the latest edition." Bill is one of the greatest tutors on statistics and R (he taught me most everything I know), so this is definitely not to be missed.

Tutorials often sell out, and early-bird registration for the conference is now open, so be sure to register early to secure your place. 

useR! 2012: Tutorials

 

 

New R User Group in Cleveland

Another new local R user group has just started up, this time in Cleveland, OH. The Cleveland R User Group is the brainchild of R user Nicholas Hermez, and their first meeting on February 22 is a get-together to plan future topics, presenters and venues. If you're in the Cleveland area why not drop by and contribute your ideas?

By the way, don't forget that both existing R user groups and new R user groups like the Cleveland group are eligible for sponsor that sponsorships from Revolution Analytics.

MeetUp: Greater Cleveland useR Group

Announcing the winners of the Applications of R in Business contest

It's with great pleasure that the team at Revolution Analytics announces the winners of the inaugural "Applications of R in Business" contest:

Grand Prize ($10,000): Shannon Terry and Ben Ogorek, Nationwide Insurance for 'A Direct Marketing In-flight Forecasting System'

Runner Up Prize ($5,000): Jeffrey Breen, Atmosphere Research Group for 'Mining Twitter for Airline Consumer Sentiment'

Congratulations to both winners, who were selected by a judging panel of industry experts, analysts, and R users. (The competition was also very close: there was only a coupe of points in the judges' aggregate scores separating the top two entries.) 

In addition, the following applications were selected for $1,000 Honorable Mention prizes by Revolution Analytics:

• Hakan Koç and Bengt Maas, Salzgitter Mannesmann Forschung GmbH: Towards an ideal steel plant - Online liquid steel temperature prediction using R
• Imre Kocsis, Kristóf Gáti, András Pataricza, and Gábor Horváth, Budapest University of Technology and Economics: Time Series Analysis and Order Prediction with R
• Stephen D. Simon, P.Mean Consulting / University of Missouri-Kansas City: Predict the duration of your clinical trial
• Shannon Terry and Ben Ogorek, Nationwide Insurance: Quantifying Uncertainty in IT Estimates
• Yihui Xie, PhD Candidate at Department of Statistics, Iowa State University: knitr: Elegant, flexible and fast dynamic report generation with R

You can learn more about all of the winning applications in the slideshow on the Applications of R in Business Contest Winners page, in the press release and in the reports in Read Write Web, Enterprise Apps Today and Information Management.

Thanks to everyone who participated in the contest. A special mention is due to Shannon Terry and Ben Ogorek, both from Customer Analytics within Nationwide Insurance. They have been gracious enough to donate both their grand prize and honorable mention prizes to charity Mid-Ohio Foodbank, a 501(c)(3) organization based in Columbus, OH and a close community partner with Nationwide Insurance, will receive $11,000 to support their efforts in hunger relief. What a wonderful contribution!

Revolution Analytics: Applications of R in Business Contest: 2012 Winners 

BBBT Podcast with David Smith

Last Friday I was fortunate enough to have the opportunity to travel to Boulder, CO and present R and Revolution Analytics to about 20 analysts as part of the Boulder BI Brain Trust (BBBT). It was a great pleasure to describe the vision for Revolution R Enterprise and got some great feedback from the particpants.

During the event, I had a great 10-minute conversation with BBBT president and founder Claudia Imhoff about the R project, how Revolution R Enterprise is used, and the future of Revolution Analytics. The podcast is now live on the web, and you can listen to it at the link below.

BBBT Podcasts: Revolution Analytics