Despite Rs apparent successas MongoDBs Matt Asays has arguedwhile R was once the language of choice for data scientists, it is quickly ceding ground to Python. One of the reasons for a perceived decrease in Rs popularityit is arguedis its complex programming environment that requires special training. According to Robert Muenchen at the University of Tennessee, even for data scientists who possess expertise in statistical tools such as SAS, SPSS and StataR remains a tough language to master. This is largely because R uses misleading function and parameter names. If SAS, SPSS and Stata use the sort command to sort data sets, R has the same command but it does not sort data sets; instead R uses the command to sort individual variables. In R, one must use the order function to sort data sets and that too happens in a rather convoluted manner. In addition, R suffers from sparse non-standard output, and it has too many commands to master. R also provides a sloppy control over variables and naming or remaining variables is an overly complex exercise, at least for the novice.
Python, on the other hand, is much easier to mastereven though it may still be harder than other programming languages used to develop web applications. The fact that Python is used to develop web applications is what makes it an attractive choice for data science. If you are struggling to find qualified data scientists, why not train your existing Python developers to work in your data science teams? Furthermore, given the wide applicability of the language, we are witnessing what Tal Yarkoni of UT Austin calls the Pythonification of tools that are appropriate for data science.
The increasing homogenization (Pythonification?) of the tools I use on a regular basis primarily reflects the spectacular recent growth of the Python ecosystem. A few years ago, you couldnt really do statistics in Python unless you wanted to spend most of your time pulling your hair out and wishing Python were more like R (which, is a pretty remarkable confession considering what R is like). Neuroimaging data could be analyzed in SPM (MATLAB-based), FSL, or a variety of other packages, but there was no viable full-featured, free, open-source Python alternative. Packages for machine learning, natural language processing, web application development, were only just starting to emerge.
These days, tools for almost every aspect of scientific computing are readily available in Python. And in a growing number of cases, theyre eating the competitions lunch.
While there is little doubt that Python is going to become a dominant language for data scientists, how is it faring against other languages of the web? The chart below provides some insights.
The growing popularity of Python is not surprising given its versatility. To be sure, R still is far more powerful when it comes to data analytics. However, Python is catching up, but does this really mean that its large number of followers are going to supplant R? The chart above needs to be nuanced because it compares apples and oranges. Charts like these are often used to make misguided arguments about Rs impending demise. So, how does demand for R compare with other statistical tools such as SAS?
This helps us nuance our understanding and see that while Python has significant traction, given its use in domains other than data science, the demand for R is also on the rise and the latter is not going to become obsolete anytime soon. R continues to enjoy popularity among academics.
We would love to hear how you are staffing your current teams and what role R and Python play in your environment.
See a follow-up post on this topic: Can Python Replace R for Developing Predictive Models?
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