I think open-source eventually replaces commercial products, in the same way that proprietary products become commoditized. The response for commercial products is also the same: continual differentiation, adding new features, benefits, support, documentation etc. Exceptions are also the same: natural monopolies (e.g. strong network effects).<p>Open-source is great at hill-climbing, where there are clear directions for improvement and especially for features that are obviously needed by users (provided the structure of the project is sufficiently modular to facilitate it), by tapping the collective intelligence of users.<p>It's not great at "hill-hopping": originating radically different products.
Anecdotally, NumPy (Python) has some traction. Similarly they don't consider SQL libraries. And I'm sure there are statistical analysis libraries for Java. According to the bar chart below R is mentioned by 45%, SQL by 32%, Python by 25%, Java by 24%. This seems a more reasonable comparison to me than the graphs earlier (higher up) in the post.<p><a href="https://sites.google.com/site/r4statistics/_/rsrc/1318535062528/popularity/Fig_6_KDnuggetsPollLanguages.PNG" rel="nofollow">https://sites.google.com/site/r4statistics/_/rsrc/1318535062...</a>
I use R as my primary data-analysis tool for almost all of my work, with occasional recourse to SAS for certain specialized models (e.g., PROC GLIMMIX for generalized mixed models).<p>My only complaint is the awful default IDE, which can be mitigated to a large extent by scripting elsewhere and source()ing the script, and some odd edge behaviors including the mystifying row names of dataframes, the difficulty of dropping unused factor levels from aggregated or sliced data (another dataframe issue), and the perhaps unnecessary obscurity of some of the plotting functions (although holding R responsible for the lattice library is unfair).<p>All that said, for a free tool, it's extraordinary, and the authors of the base language and the many packages that I use have my gratitude.
Probably worth noting about the author:<p>> Robert A. Muenchen is the author of R for SAS and SPSS Users and, with Joseph M. Hilbe, R for Stata Users. He is also the creator of r4stats.com, a popular web site devoted to helping people learn R. Bob is a consulting statistician with 30 years of experience<p>Disclaimer: I hate R's syntax, but my company's analytics group uses R for just about everything.
Unfortunately, it's almost impossible to work with a very large datasets in R, because of the speed limitations. Many researchers I know use Matlab because of this.