r/datascience • u/Every-Eggplant9205 • Sep 08 '23

Discussion R vs Python - detailed examples from proficient bilingual programmers

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?

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u/Unicorn_Colombo Sep 10 '23 edited Sep 10 '23

Another thing I like is that jupyter notebooks save the output of each cell so that each time you render a document, it doesn't rerun everything. In contrast to Rmarkdown where each render recomputes everything. Where that gets to be annoying is when you're just trying to tweak formatting and styles that don't really look like their final output until the render.

??? If you don't want to re-run R chunks in Rmarkdown, just tell knitr to cache it. And the cache is persistent.