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Python or R for data science?

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The two most popular programming tools for data science work are Python and R at the moment (take a look at this Data Science Survey conducted by O’Reilly). It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and and open source, and were developed in the early 1990s — R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.

With the massive growth in the importance of Big Data, machine learning, and data science in the software industry or software service companies, two languages have emerged as the most favourable ones for the developers. R and Python have become the two most popular and favourite languages for the data scientists and data analysts. Both of these are similar, yet, different in their ways which makes it difficult for the developers to pick one out of the two.

R is considered to be the best programming language for any statistician as it possesses an extensive catalogue of statistical and graphical methods. On the other hand, Python does pretty much the same work as R, but data scientists or data analysts prefer it because of its simplicity and high performance. Now both the programming languages are free and open source and were developed in the early 90s.

R is a powerful scripting language, and highly flexible with a vibrant community and resource back whereas Python is a widely used object-oriented language which is easy to learn and debug.

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The graph above shows how Python and R have trended over time based on the use of their tags since 2008 (Stack Overflow was founded).

While both languages are competing to be the data scientist’s language of choice, let’s look at their platform share and compare 2016 with 2017.


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