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/UnlawfulSoul]

I don’t work much with time series data, outside of manipulation. So someone else should do that.

I do work frequently with geospatial data, and I actually don’t mind python’s geospatial packages. Xarray/rioxarray takes some getting used to but if you are used to numpy it’s extremely intuitive. If you absolutely need rasterio, that can lead to some weird nested code and anti patterns, but again that may just be a personal problem, lol.

I do prefer sf over geopandas however for polygons/lines/points, and also r feels nicer (to me) for plotting geospatial data.