Some R Highlights from the Bay Area Data Science Camp and Unconference

October 30, 2014
By

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

by Joseph Rickert

The San Francisco Bay Area Chapter of the Association of Computing Machinery (ACM) has been holding an annual Data Mining Camp and "unconference" since 2009. This year, to reflect the times, the group held a Data Science Camp and unconference, and we at Revolution Analytics were, once again, very happy to be a sponsor for the event and pleased to be able to participate. 

In an ACM unconference, except for prearranged tutorials and the keynote address, there are no scheduled talks. Instead, anyone with the passion to speak gets two minutes to pitch a session.  A show of hands determines what flys, the organizers allocate rooms and group talks by theme on-the-fly, and then off you go. The photo below shows how all of this sorted out on Saturday.

ACM_DSC_2014

As you might expect, there was a lot of interest in Big Data, NoSQL, NLP etc., but there was also quite a bit of interest in R, enough to run fill a large room for two back-to-back sessions. I was very happy to reprise some of the material from a recent webinar I presented on an introduction to  Machine Learning and Data Science with R, and Ram Narasimhan (a longtime member of the Bay Area useR Group) gave a high energy and very informative tutorial on the dplyr package that, judging from the audience reaction, inspired quite a few new R programmers.

But the real R highlight came early in the day. Irina Kukuyeva presented a tutorial on Principal Component Analysis with Applications in R and Python that was well worth getting up for early Saturday morning. Not only did irina put together a very nice introduction to PCA starting with the the basic math and illustrating how PCA is used through case studies, but in a laudable effort to be as inclusive as possible, she also took the trouble to write both Python and R code for all of her examples! The following slide shows what PCA looks like in both languages.

  PCA_R_Python

 

 This next slide shows what a good bit of statistics looks like in both languages.

Block_clustering_R_Python

 

 For more presentations and tutorials by Irina that feature R, have a look at her Tutorial page.

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

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