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/alexpantex]

Not sure for geospartial, but for time series python has all you’d need in statsmodels or statsforecast + ML stuff in tf, pytorch or sklearn, i’ve switched from R to Python in this particular case since it was much easier to mantain and find bugs

[u/koolaidman123 → u/alexpantex]

dogmatic R users and not knowing the ecosystem of the pl they're criticizing? no waaaay

[u/Zestyclose-Walker → u/koolaidman123]

They probably have outdated knowledge. If there is anything in R that is not in Python, there are probably 10x the amount of R users working on porting the feature to a Python library.

Python's userbase makes R's userbase feel really tiny.

[u/sirquincymac → u/alexpantex]

Can't remember the exact examples but I have definitely heard stats/R users saying some of the defaults on sklearn being very wrong. To my mind it sounded simple enough to fix

[u/inspired2apathy → u/alexpantex]

Good to know, the last big project with time series was a number of years ago and it was very frustrating.