R U Ready for Open Source Computation?

Perhaps it is completely analogous to the ascendancy of the blogosphere in news reporting, but it is nonetheless shockingly satisfying to see this article on the ascendancy of the R statistical programming language. As a former user of its commercial competitors, I became disenchanted with their licensing policies, not least of which was the hassle of reconfiguring the license manager every time I had to re-install the software on a new laptop. But when I jumped ship, it also quickly became apparent that through the enthusiastic, almost cult-like, devotion of legion programmers and statisticians across the globe, the R software code base provides a far richer set of functionality than any other. Particularly when it comes to rapidly evolving areas of quantitative science such as genomics, R software (especially the huge set of R modules collectively called Bioconductor) have provided the best and most up-to-date functionality. Moreover, the educational programs that have grown around R have been equally first rate. I have recommended this book on learning R for introductory statistics to many colleagues who have then confessed that it was the first time that they had truly understood basic statistics.

I am quite sure that anybody presenting the R "business model" eight years ago would have been scoffed at by most experts in software distribution and dissemination. Yet it is but one example of what can happen if the the producers and consumers of a knowledge or information product are the very same academics. Perhaps we can one day achieve the same efficiencies in disseminating our scholarly publications.

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