Back in 1991, Guido Van Rossum introduced the world to his new programming language, Python. The language entered into mainstream usage quickly, but only several years later, creators Ross Ihaka and Robert Gentleman created another programming language, R. Since then, both languages have been used heavily in the data analysis field. But which language is better? Since 2013, Python is being used by nearly four times as many people as R. Python has the fourth most active usage on Github and Stackoverflow, while R lands at 15th. However, that does not necessarily mean that Python is a better language for data science.
R is completely centered around data and statistical analysis. Data can be analyzed in tables, and manipulated with simple strings of commands. R provides it’s users with a plethora of base functions to extract information from data sets, and by combining these simple functions it is easy to produce a more complex command. Typically, R is not taught as a first programming language because it is known to be more difficult than languages such as Python. However, once the basic syntax is understood, it is easy to dive into everything R can do.
Another advantage that R has over Python is it’s code repository. R has a massive availability of packages to install, all available at CRAN, the Comprehensive R Archive Network. Python has a similar repository called PyPi, but it is not as heavily contributed to. This wide selection of packages allows R to continue to grow, while Python does not focus as much on the usage of packages.
However, with all of these advantages, Python is beginning to raise in popularity, looking to overtake R. As seen in the diagram below, more people are switching over to Python than ever before.
The world is becoming a greater environment for engineering. It isn’t only computer scientists that know how to code now. People in all different fields use some sort of coding in their occupation. That is why the adaptability of Python is beginning to take precedence over the raw functionality of R. Python code is easy to read, which means that people in different parts of a business can understand the code, even with no real knowledge of computer science. Python can also combine data analysis with programming better than R can. Python is much more applicable to engineering purposes and development purposes than R is, and there is more development happening in the world than ever before. This is why Python is becoming dominant in the world of data science.
-Nick Bagley
Information Science 