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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 a
by haddr 10y ago
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.
- photoJ 10y agoI'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?
- haddr 10y agoBy 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/ 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.
- photoJ 10y agoCool! Yeah, I know of that workflow. I've seen some neat stuff done with that. Thanks for the follow up!
- gvd 10y agoUse 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.