by Joseph Rickert
There is something about R user group meetings that both encourages, and nourshies a certain kind of "after hours" creativity. Maybe it is the pressure of having to make a presentation about stuff you do at work interesting to a general audience, or maybe it is just the desire to reach a high level of play. But, R user group presentations often manage to make some obscure area of computational statistics seem to be not only accessible, but also relevant and fun. Here are a couple of examples of what I mean.
Recently Xiaocun Sun conducted an Image processing workshop for KRUG, the Knoxville R User's Group. As the folowing slide indicates, he used the EBImage Bioconductor package, a package that I imagine few people who don't do medical imaging for a living would be likely to stubmle upon by accident, to illustrate the basics of image processing.
Xiacuns's presentation along with R code is available for download from the KRUG site.
As a second example, consider the presentation that Antonio Piccolboni recently made to the Bay Area useR Group (BARUG): 10 Eigenmaps of the United States of America. Inspired by an article in the New York Times, Antonio decided to undertake his own idiosyncratic tour through the Census data and look at socio-economic trends in the United States. His analysis is both thought provoking and visually compelling. For example, concerning the following map Antonio writes:
This map shows a very interesting ring pattern around some cities, including Atlanta, Dallas an Minneapolis. The red areas show strong population increase, including migration, and increase in available housing and high median income. The blue areas have a higher death rate, Federal Government payments to individuals, more widows, single person households and older people receiving social security.
Antonio's presentation might well illustrate the theme: "Data Scientist reads the Sunday paper and finds data to begin a conversation about what he read with his quantitative, R-literate friends".
This kind of active reading fits nicely with ideas about responsible, quantitative journalism that Chris Wiggins expresses in a presentation he recently made to the New York Open Statistical Programming Meetup. Here, Chris provides some insight into the role of Data Science at the New York Times and offers advice on using data to study relevant issues and clearly communicate findings. One major point in Chris' presentation is that data science plus clear communication can have a very positive influence on shaping our culture.
It is not an exaggeration to say that the kind of work that Xiaocun, Antonio and other R user group presenters undertake in their spare time "for fun" is valuable and important beyond the immediate goals of learning and teaching R.