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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.

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I'd like to know more about end-to-end systems in R. I'm not familiar with serious production environments using such tools. Any pointers?

By end-to-end i mean that you can: (1) read data from SQL or NoSQL DB, excel file, csv, JSON through some REST service, etc. (2) process it in R (3) display it to the user through some interactive web interface e.g. using Shiny

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.

Cool! Yeah, I know of that workflow. I've seen some neat stuff done with that. Thanks for the follow up!

Use Scala or some other statically typed language for building bigger stuff. Python might be nice for the first 3 lines but you will be convinced once you need to do some serious refactoring.

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