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The Latest R vs Python Dust Up

ZDNET just posted an article on the Tiobe rankings that shows R fell out of the top 20.

It is a great read. Looking at the rankings, it seems like there is a clear top 10 and then the second tier is harder to distinguish. From 11-22 looks like a tie so I wouldn’t read too much into a shift anywhere from 15-25 as dire. The article even mentions this point.

Developer analyst Redmonk also noted a two-spot fall in R’s ranking last August, but the company cautioned not to place too much importance on this move since R had dropped two spots previously and bounced back after that.

R’s specificity would keep it outside the top 10 languages, but also keep it relevant in the field it was built for, Redmonk analyst Stephen O’Grady argued.

As the industry starts to tool out the data science space, then Python will continue to grow in importance. It appears that Python is the data science developer tool.

However, for data analysis and exploration I don’t see such a lead or drop for R. In fact, I think R is still winning in this space with bright and dominant visualization tools, data manipulation standards, and easily implemented statistical tools. Notice in the links below how the use cases of R are for analytics and the answers that data can provide today versus data science tools that can answer data problems in general. R will continue to bow to Python for building pipeline tools. As we have seen in the past, when other languages make great pipeline tools, R just leverages that code. See the new rminiconda package from Ryan Hafen that allows R users to easily embed Python from R to use the reticulate package to leverage a python package for R users.

The debate continues.

Author

J. Hathaway

Data Scientist, Consultant, Teacher, Learner