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The biggest boon for R was the rise of "data science". Data scientists have actually existed for decades, but they had different titles (quants, actuaries, quan
by rm999 12y ago
The biggest boon for R was the rise of "data science". Data scientists have actually existed for decades, but they had different titles (quants, actuaries, quantitative researchers, etc) and came from fields that preferred enterprise software for various reasons.
Data scientists, on the other hand, largely rose from tech companies that hired software engineers and preferred open-source software. When I started in the field ~10 years ago there were very few reasonable open-source options outside of R. Matlab and SAS were prohibitively expensive, Octave was too immature, Python didn't have basic functionality like data frames. In short, it was our only viable option, so we used it. A lot of people started using it for the same reasons, so R's library support became the best. This is what keeps me semi-locked into R - I'll probably eventually move to Python or Julia, but R hit the critical mass first and in a huge way.
- grayclhn 12y agoR's library support predates your timeline.
- rm999 12y agoThe most impactful, important packages I use were all developed after 2005: ggplot2, plyr, glmnet, gbm, reshape, knitr. Out of curiosity, which ones are you referring to? edit: I may be using the word library imprecisely here, which may be causing miscommunication. I mean 'packages', to be clear.
- Blahah 12y agoPrior to those packages, R was the language in which most new statistical methods would be published. So it has had very good library support for statistics for a long time. Only more recently has it also gained excellent data manipulation support.
- disgruntledphd2 12y agoWhich kinda violates the principles expressed by the writers of said open source project, but I'm all for it on the basis that if I never again have to use base reshape, stack or gsub then it would be a good thing. I believe An Introduction to R specifically notes that one should use other software to provide R with appropriate data.
- grayclhn 12y agoglmnet, 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.
- hadley 12y agoAt the time I wrote ggplot2, R really was the only game in town. But I strongly believe that R has excellent features to support data science: non-standard evaluation, NAs at a very low level, data frames, first class functions, ...
- grayclhn 12y agoThanks for sharing that. Much more informative than my speculation :)
- bbgm 12y agoIn the world of Bioinformatics, R has been the tool of choice for a long time due to packages like Bioconductor [1], which has been around since 2001. ---- 1. http://www.bioconductor.org/ http://www.bioconductor.org/