If you are already joining a data science team just focus on whatever tools they use. On the other hand, if you are just starting and want to make a choice based on what’s more popular on the job market then Python wins hands down. As of 2020, Python is the 3rd most popular programming language according to GitHub (R doesn’t even make the top 20) As for job outlook, Python wins by a landslide.
Times more likely to appear
On a job’s posting. Python users are more phone number list loyal while at least 10% of surveyed data scientists are reporting a transition to Python. Once we take a look at the trends, it seems like R is quickly becoming a dying language. Except that over half of all data scientists are using both on their daily routine.
But why? R’s ecosystem
Powerful I’ve never been much of a we compete with the largest software outsourcing statistics buff. I know enough to analyze data just fine, but once I take a look at R and its packages I feel like an undergrad seeing statistics 101 for the first time. R has a long tradition in academia and statistics experts. The sheer amount of available projects is staggering. As of right now CRAN, R’s biggest repository, has over 12000 packages that are being updated.
Need a Lavaan test?
You got it! Factor analysis? It’s right email data there in the Psych package. Structural equations? Please, at least try to make it a challenge. Keep in mind that Python has a lot of these things implemented as well, but for the really opaque stuff, R still reigns as king. Since most R developers are academics, most of these packages are specifically designed to solve academic problems.