Editors as they apply to data science adoption is certainly relevant, 
particularly as relates to ease of adoption for beginners. It's easy for an 
experienced developer to dismiss the difference of ease in adopting 
something like RStudio vs R by itself; Those with experience already have 
workflows they're used to (vim/emacs + tmux / whatever), but getting to 
that point is not trivial. And there are certainly those who come to R and 
python looking to do data science who have little programming experience. 
I've seen a lot of this among biologists in particular.

The Gorilla REPL does certainly take us a good way there, for those 
interested in the notebook model. But the RStudio/MATLAB workbench model is 
also something worth considering. Some easy to install packages gluing 
together Incanter, core.matrix, Gorilla REPL, Quil, and perhaps 
tools/interfaces that don't exist yet, with excellent documentation and 
guidance, could make a huge difference in adoption.

As for broader thoughts coming to mind: My experience has been that R is 
great for exploration, but is terrible for scaling into bigger systems from 
an architectural standpoint (but other's might disagree with me). It can 
also feel rather cumbersome when developing algorithms. Python feels much 
better along these lines, but also has its own warts as a language 
(concurrency for example). It's my opinion that the shortcomings of Clojure 
for data science are much more easily addressable than those of R or 
python, as they're less about the language itself than things missing from 
ecosystem which can be added. And I think the value of a language which 
scales from exploration to production naturally is not something to be 
undervalued.

Chris

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