R: The Good and The Bad

3. Effectively the lingua franca of data analysis and statistical computing Free and open source As a statistical language, it’s generally considered to be very easy to code in (vs. SAS, JSL, SPSS, etc.) The Good

4. Native cross-platform and 64-bit support Typically easy to install and configure Community of millions of users; brilliant minds Rapidly growing number of packages (2800+ on CRAN, 950+ projects on R-Forge) http://cran.r-project.org/web/packages/ and http://r-forge.r-project.org/ The Good

5. Great free, open soruce IDEs and GUIs (e.g., StatET for Eclipse, RStudio just released in late February, Emacs Speaks Statistics, JGR, Tinn-R, lots more) See “Editors and IDEs” and “Graphical User Interfaces” sections of http://en.wikipedia.org/wiki/R_(programming_language). Also see http://sciviews.org/_rgui/ and http://stackoverflow.com/questions/1097367/what-ides-are-available-for-r-in-linux The Good

6. Active mailing lists, trolled by the gurus, very easy to get your questions answered On a humorous note: http://yihui.name/en/2010/04/rules-of-thumb-to-meet-r-gurus-in-the-help-list/ CRAN Task Views http://cran.r-project.org/web/views/ The Good

7. Growing coverage on Stack Exchange, also on “CrossValidated” statistical analysis Stack Exchange site http://stackoverflow.com/questions/tagged/r and http://stats.stackexchange.com/ #rstatshashtag on Twitter http://twitter.com/search/%23rstats Blogger community dedicated to covering R http://www.r-bloggers.com/ Growing list of print books and ebooks The Good

8. Commercial and open source data analysis/mining/analytics/visualization software increasingly integrating with R (Spotfire, SPSS, Netezza, JMP, SAS/IML, RapidMiner) http://decisionstats.com/2010/05/04/commercial-r-integration-in-software/ Revolution Analytics (products, blog, community site) http://www.revolutionanalytics.com/, http://blog.revolutionanalytics.com/, and http://www.inside-r.org/ The Good

9. The Bad… ? =

10. Command prompt, lack of GUI is intimidating Slow (especially looping) Poor parallelization Syntactical curiosities, annoyances, design flaws; little chance of them being remedied E.g., http://radfordneal.wordpress.com/2008/09/21/design-flaws-in-r-3-%E2%80%94-zero-subscripts/ Indices start at 1! The Bad

11. Subtle problems with scoping http://stackoverflow.com/questions/3840769/scoping-and-functions-in-r-2-11-1-whats-going-wrong Poor memory performance, difficulty handing big data Can be difficult to compile base R and R packages from source Requires compilers for Fortran, Perl, C/C++, Tcl The Bad

12. Onerous termsof AGPL Has been proposed that the R community start over and build something better from scratch Estimated that a total rewrite could improve speed by 2 orders of magnitude http://stackoverflow.com/questions/3706990/is-r-that-bad-that-it-should-be-rewritten-from-scratch Increasingly attractive alternatives (e.g. Python) The Bad

13. The Verdict ?

14. Join the Raleigh-Durham-Chapel Hill R Users Group at:http://www.meetup.com/Triangle-useR/