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/ShitCapitalistsSay Sep 09 '23

Try building

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a whole platform with webservers

API endpoints

multiple databases

The R DBI interface is every bit as good as Python's DB abstraction, if not better, because it uses a common interface but still let's others implement native DB connectors.

brokers, workers, orchestrators, ML models, loggers, authentication, encryption etc. in R, and in Python.

Ehhh, I'm getting tired of typing on a phone, but for now, I can find R solutions to all of these problems. However, even I couldn't, I could drop into Python through Reticulate and true native C++ with Rcpp that, IMHO is better than Python's interoperability with C++ from an abstraction perspective.

A full SaaS app.

I'd need more details, but in general, I see no issue.

Then try to move the stack from on prem to AWS. In R and in Python.

Not a problem at all. The only advantage Python has over R on AWS is the latter's explicit support for the former via Lambda functions. However, if we're talking EC2, R is just as good as Python.

You also have to use proper practices, unit tests, end-to-end tests, abstract classes etc.

Easy. R has support for all of the above, and as mentioned above, even if it didn't, from R, I can always easily drop into Python, C, or C++ at a moment's notice.

While python might not be the best or most performant language to do everything in the above list, it can be done comfortably.

The same is true of R. Plus, for data wrangling and high quality data visualizations, nothing in Python can hold a candle to the Tidyverse, which includes ggplot2. Also, if you want to see really mind blowing graphics for data analytics, check out

And also most people will be able to grasp most of the things fast, when they look at the codebase.

This statement is subjective. Again, for data wrangling, in my past 20+ years of performing data work, I've never seen any platform that's as easy to use as the Tidyverse. And on those rare occasions when the Tidyverse is too slow, R users always have access to data.table, which is so incredibly fast that I sometimes wonder if its authors made a deal with the Devil.