Subscribe to DSC Newsletter

Views: 30998


You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by Dragos Bandur on January 23, 2017 at 11:30am

I simply love R as statistical tool and also as programming tool (a component which is quite scarcely explored in media).
Although the mainstream to data science originates in computer science/engineering, I remain partial to data scientists that come from mathematics/statistics background; it takes years to master it as opposed to months to learn decent coding.  From this perspective, I feel that schemes and taxonomies like the one presented above are simply interesting and informative if not ephemeral.

I am not fully convinced by the "pluses" for Python, while most of the "minuses" for R have long since vanished.

Comment by Michael Thompson on June 1, 2016 at 10:24pm

Interesting article, but the graphic on movement of supporters for R / Python / Other could have been more helpful if all figures were on the same base. With a lot of calculations, I found that R had moved to 73%, Python 87%, and Other to only 18% (presumably of those who filled in a survey) IF I've got it right, perhaps someone could confirm or not. We're supposed to open up the data for others to understand, not to have to do data science (almost) to work out what has actually happened. But then, I'm sure I've done just the same to others plenty of times.

Comment by Nayyar Kazmi on March 2, 2016 at 11:13am

Very thorough analysis

Comment by Abhishek Srivastava on February 8, 2016 at 1:22pm

Through and Precise!

I like R, but my vote goes to python as it can be used to make 'intelligent' decisions on the fly and especially in scenarios when the ecosystem is user driven. Dynamic inputs and dynamic 'smart' outputs will change the definition of the web.

Comment by Joel Pulliam on July 15, 2015 at 10:47am

I wanted to like Python, but it does not handle categorical data well. With R, you can through continuous and categorical data at it with abandon and easily create a predictive model. With Python / sklearn, categorical data requires too many compromises and too much data munging. Ironically, I moved to Python because it's a real programming language that's great at data manipulation.

R's big limitation for me was that it was inefficient on a single computer. You have to run it on some serious hardware to do anything even moderately large.

I moved back to R with R Studio and the data.table package. I'm playing with running R in the cloud. Requires a little bit of setup, but now I can handle very large tasks without significant limitations.

So, I'd suggest R for anyone serious about data science.

Comment by Musleh Farid on June 22, 2015 at 9:14am

R seems to be gaining in popularity, according to the Popularity Rankings graph.

Comment by Nick Toscano on June 3, 2015 at 8:04am

Hmmm Never searched for a TLDR on an infographic before

Comment by Shajee Pulukkool on May 23, 2015 at 6:15am

Amazing comparison. Looks like a photo finish for a marathon !!! 

Comment by Jacqueline Silva Carvalho on May 15, 2015 at 10:05am

This is amazing !! Thank U very much :D

Comment by Loic COULET on May 14, 2015 at 7:25pm

Very nice !

IMO, the most important and impactive difference is not clearly explained: R is licensed under GNU GPL while Python is permissive license BSD-like.

It means that if you want to distribute software under copyright you can't bundle it with R since the GPL License applies (R is not a mere programming language but an execution environment) or all the produced code would become GPL.

This can be a problem, and it doesn't apply with Python.

Follow Us


  • Add Videos
  • View All


© 2017   Data Science Central   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service