Lighting Talks at Ruser Group Meetups

Recently there has been an increase in the number of lighting talks at Ruser Group Meetups. I first saw this type of talk at the R/Finance 2011 conference in Chicago at UIC. They were without a doubt the best group of talks from that conference. Lighting talks allow groups to cover a wide range of topics in a shallow dive for the listener to further investigate in the future if he or she wants to.

The lighting talks also provide a great format to present some of the more basic topics needed to develop the new R users and remind the more experienced user of good practice. I have become slightly concerned as of late that the R meetups were doing presentations of such a technical level that we were potentially alienating an important group in the R community, the newer user.

I am not sure if there were other people who agreed with me or if it is just a coincidence, but Lighting talks has swept through the Meetup world like a firestorm in the last month. The NYC Predictive Analytics Meetup did a series of Lighting talks as did the NYC Rusers Meetup. I believe they have already posted the presentations on the links that I have provided. The files and videos of presentations posted on these Group Meetup sites are a valuable resource that you should use even if you did not go to the presentation.

On July 13 the Greater Boston Area Ruser Group is doing as series of 5 lighting talks on Predictive Analytics in R, the GoogleViz package, R image cluster and 3D visualizations, RGraphViz and BiomaRt. On July 12 the Los Angeles Ruser Meetup is doing three quick talks on R for Multilevel Modeling, Unit testing and Debugging in R and calling C from R.

Another thing to note is a number of these lighting talks featured a presentation by the guys from RStudio. I think their IDE has quickly become the de facto user interface for R. It has shown itself to be useful to the experienced user, and an invaluable tool to speed up the learning curve for the new user. A friend of mine used RStudio in a class he taught in the spring, and he was astounded by how much faster his students become capable R users compared to when he taught this same class with RStudio in the past. That is a pretty strong endorsement from I guy who I know prefers to program from the command line.

What did I learn for R/Finance 2011

The R/finance 2011 meeting was a huge success! All the talks were just great. I do not have the time to go through each talk one by one but I do feel there were a couple of themes that ran through the entire conference. The opening speaker, Mebane Faber, and the keynote speaker,  John Bollinger, touched on two topics near to my heart. The first is that in many cases the simplified model does nearly as well as the more complex one and in some case with fewer pitfalls. The second is that models are our attempt to describe reality, but they are not reality. Therefore there is always the possibility that the model is a bad fit for the reality that it is trying to model or there exists a deviation from the  model to the reality it is describing. Both phenomenons can be exploited for advantage. Never get blindly enamored with a model and approach things with an opening mind. These ideas carried pretty consistently throughout the conference.

Parallel or High Performance Computing for R are becoming a more and more important factor in analytic computing. I am not sure if it is because to the continue growth of data in general, the enterance of HPC into general awareness through the "cloud",  or because the really cool problems seems to exist on the edge of our current capability. I believe with the exposure of more users to HPC tools for R it is time to update the various pros and cons of each approach and to benchmark them against each other with a set of set typical data set and models. I do wonder if the recent problems on Amazons EC2 could will slow down the growth of cloud computing? Lost time is one issue here but the users that lost their data could be much more reluctant to take that risk in the future.

I was also amazed at the traction that Rstudio had among this group of experienced R users. I have always held the belief that experienced users of any software package shy away for IDEs and GUIs and prefer the simple interaction of command line coding. I felt IDE were the tool for new or mid-level users. In this case, I was wrong. Rstudio appears to provide benefit to the very experienced R user to the point they are willing to change away from what they are currently doing and learn this model tool.

I thought JD Long's Dr Seuss inspired talk was the most entertaining of the confernece. It takes some talent to do that and even more to do it well. His Segue for R package is pretty cool too. Flash talks are a great format, and I wish they were used more often

doRedis: A parallel back end for R/foreach using Redis.

Bryan Lewis recently did a Vignette using RStudio to run some financial data using R/foreach and doRedis. Parallelizing R continues to become more important as users want to run computations on larger and larger quantities of data in reasonable amounts of time particularly on clusters or the cloud. doRedis includes the following features:

• Support for dynamic pools of parallel workers during running computations.
• Simple cross-platform parallel computing, including at least Windows, GNU/Linux and OS X.
• Fault-tolerant 

doRedis Vignette

Redis

What Operating Systems do R users use?

I went to the Greater Boston R meetup last night where the Rstudio guys did a presentation on their IDE. It was well attended, and Josh and JJ did a great job.

At the end of the talk JJ said that the breakdown of Operating Systems for the Rstudio downloads was roughly 60% windows, 30% mac and 10% linux. Frankly I was surprised by those numbers. I always felt that the majority of people who use open source R would run it on an open source Operating System. Rstudio's results would seem to strongly counter that thought. After the talk, I remembered a survey done by KDnuggets in September of 2007 on what Operating system did people use for their analytics work. It also showed about 60% Windows users but 30% linux and 10% Mac. I believe this suggests an increase in the number of people using Macs to do analytics in the last four years.

I am not sure the data from downloads of an IDE or a survey of Operating Systems by KDnuggets is a good indicator of what is the relative popularity of the various Operating Systems being used by the R community. I do believe it does show the importance of supporting Windows which is consistantly shown as the dominate platform among people surveyed and downloads at least Rstudio. It also showed that if you make an IDE that is not supported on the Mac you could be missing 30% of the potential users.

The recent KDnuggets survey on which R interfaces which was interesting. For example, I would not have expect so many people to respond that they use Tinn-R or R commander but they do.  I always enjoyed the KDnuggets surveys.

KDnuggets Operating System survey
KDnuggets R interface poll