R or Python: Which Should You Learn in 2020?

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It's that time of year to revisit the most famous rivalry of data science. R vs. Python.
(Disclaimer: These languages both have very dedicated user communities and do not operate in a mutually exclusive relationship. They can be used together to very powerful end results; so it's not important not to get hung up on the rivalry term!)

We look at three things: how easy they are to pick up, their capabilities compared with one another in the year 2020, and their relative popularity.

Ease of use
Python is thought by many to be easier to pick up, but I would argue this depends on your personality and background. I had an easier time at first with R than with Python, stemming from the fact R was built for statisticians while Python was built for programmers.

Relative capabilities
R has the amazing ggplot2, ggvis, and htmlwidgets packages, which make it a force to be reckoned with in the data visualization department. It makes statistical modeling and tests a breeze, all behind the beautifully laid out RStudio. It also features the amazing Shiny package for making interactive visualizations and applications, and the RMarkdown framework for creating reports accessible to any audience.

Python was built as a general purpose programming languages with packages NumPy and Pandas forming the foundation of data analysis and data science. Compared to R it will typically perform complex tasks faster, and it is designed for deploying applications to production. It has an arsenal of deep learning packages and the incredible Scikit-Learn package for machine learning. Additionally it excels for tasks like web scraping and natural language processing.

Popularity
Based on the Tiobe index, R has fallen in its share of search results it constitutes while Python has risen massively over the same time period. Both languages enjoyed a massive boom from 2014 onward, but Python's popularity has skyrocketed while R's has been flat or fallen since 2018. Additionally the number of R jobs and Python jobs are both growing, but Python jobs are growing at a considerably faster rate.

Overall though, they're both excellent programming languages and I highly encourage you to learn one, master it, and then get good at the other. You'll be an unstoppable data force.

KDNuggets poll: https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html
KDNuggets jobs report: https://www.kdnuggets.com/2019/06/data-science-jobs-report.html
Tiobe index: https://www.tiobe.com/tiobe-index/

#RvsPython #ROrPython #DataScience

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