glmnet, plyr, knitr, and ggplot2 were all developed by academic statisticians (some as grad students who have subsequently moved out of academia.) They didn't c

glmnet, plyr, knitr, and ggplot2 were all developed by academic statisticians (some as grad students who have subsequently moved out of academia.) They didn't choose R because of the reasons you listed, but because it was already popular among statisticians, along with S-plus. (This is a bit of an educated guess, I haven't directly asked any of these people why they chose R. Maybe one of them will chime in!) ggplot2 and knitr are extremely well-executed extensions of ideas that had existed in R before: lattice and sweave; and lattice and ggplot2 are both fairly direct implementations of ideas developed in quasi-academic statistics by Cleveland and Wilkinson. I don't think I'm selling anyone's contributions short, just pointing out the packages you're talking about came out of academia and built on ideas and implementations that existed for at least a decade before your timeline. But the bigger point I was making is that CRAN (and, as pointed out by bbgm, bioconductor) and R's package system have been around a long time before that. And, unfortunately, was built with _very_ little input from software engineers. :) edit: incidentally, this is a case where the opensource ideas really help a project. SAS and Stata don't exactly encourage any motivated grad student to rewrite their graphics interface or report generation tools.