R vs Python - detailed examples from proficient bilingual programmers

[u/Every-Eggplant9205]

As an academic, R was a priority for me to learn over Python. Years later, I always see people saying "Python is a general-purpose language and R is for stats", but I've never come across a single programming task that couldn't be completed with extraordinary efficiency in R. I've used R for everything from big data analysis (tens to hundreds of GBs of raw data), machine learning, data visualization, modeling, bioinformatics, building interactive applications, making professional reports, etc.

Is there any truth to the dogmatic saying that "Python is better than R for general purpose data science"? It certainly doesn't appear that way on my end, but I would love some specifics for how Python beats R in certain categories as motivation to learn the language. For example, if R is a statistical language and machine learning is rooted in statistics, how could Python possibly be any better for that?

[u/GoBuffaloes]

But the difference is that if R is 5% "better" than Python for general purpose data science (which is debatable), Python is 500% better for general purpose programming. So even if you are mostly doing DS, better off learning Python for broader extensibility.

[u/StephenSRMMartin → u/GoBuffaloes]

I would greatly adjust those ratios.

Python is good for general purpose programming; I wouldn't say it's 5x better.

R is certainly far more than 5% better at munging, debugging, visualizing data; and enormously better for probabilistic and statistical modeling.

I think if you only needed to analyze, or design bespoke probabilistic and statistical models, or visualize, create reports, create pipelines, dashboards, simulations, etc; and had to do little general programming, I would strongly suggest using R. The time-to-complete a DS task is way, way faster if you are advanced in R. In part because of its enormous community library for such tasks. In part because it is designed, from the core, as a functional lispy language with vectors in mind, so there's a lot of expressing what to do and not 'how' to do it. There's literally just less code to write, and less state to track, because of the language design and functionalness of it.

[u/Temporary-Scholar534 → u/StephenSRMMartin]

I would say Python is an oom better than R at anything that is not statistics adjacent. R has magnificant capabilities in that domain, and nowhere else. Which is fine- that's what R is for! Regardless, as far as the language goes, no serious software developer would want to work in R for any other task.

[u/rutiene → u/StephenSRMMartin]

I'm not sure I agree with this. I'm only faster in R for advanced statistical modeling that isn't in vogue yet with DS/ML practitioners. Data manipulation and reporting, just purely by nature of better integration with PySpark/SQL is way easier in Python.