As a side note on julia. The only reasons I haven't started using it on the daily - and I've tried it and liked much of what I've seen - is that the module system doesn't let you build modular production code and apps like Python does and R is just so good for what I use it for there's no reason to look for a replacement.

Can you elaborate on this?

In my opinion this is where the difference lies: with python you can easily build bigger systems with more flexibility, where your data science code is an important piece. R shines in statistics and you can also produce an end-to-end system, but given that you fit well the R ecosystem and its constraints.

It is relatively easy if your app fit the whole loop. I would recommend with some Shiny tutorial to get you started: http://shiny.rstudio.com/tutorial/

But if you need e.g. some stream processing or more complex guis, then R might be not enough I guess.

I mostly use MATLAB (ugh).