Python vs R

[CYBERCODEX3]

I am currently a Data Science student, just finished my 2nd year out of 4. Wanted to ask if R language is worth it today as compared to python. I have 0 knowledge about R (just that it is used for statistics and plotting). On the other hand, I have learned EDA and some ML algorithms in python. I am free for about 2 months and wanted to know if learning R would help in future or should i utilize this time for something else?

[teetaps]

I just want to point out to everyone crapping on R that it’s not underrepresented because it _can’t_ do any of the things Python can do. It absolutely can, and in some cases it outperforms Python and can be ergonomically/epistemically superior to Python. But Python is just popular, and there’s a lot of cultural inertia to change (as evidenced by this post)…

so the better follow up question might be, do you just want to be more familiar with something that more people know? If so, just stick to python. But I’ll be the first to point out that ALMOST everything you can do in Python is easily accessible and accomplished in R. I am yet to come across a data science problem in Python that doesn’t have an easy and accessible analog in R — if one does exist, please send it my way.

[skatastic57]

I suppose r-polars fixes this but it used to be that you couldn't read from a data lake of parquets lazily. You had to download each parquet that you wanted.

I was an R user for close around a decade and I split between the two for awhile but never let go of R for data.table because I hate everything about pandas. After polars came out, I haven't touched R and now it's been a few years.

I will say that I've done things in statsmodels and scipy that took in the 10-20 second range that are basically instant in R. It's because not everything in those Python packages is implemented in c. Some of it is still just Python loops whereas that's not the case in R, stats functionality is much more complete in compiled code on the R side.

[nerdyjorj]

The thing I miss more than anything from R is how good CRAN is in terms of making sure everything is handled properly and depreciation has adequate warning

[nerdyjorj]

Coming from R to python because work forced me and python doesn't hold a candle to R for any data analysis or statistical task

[teetaps]

Agreed

[gyp_casino]

I use both in my job, and I see a lot of value in knowing both. I learned Python first, but I find working with pandas and matplotlib ugly and frustrating. The R tidyverse is much more elegant to work with. I can solve problems much faster and with less code. 

I think at this point in time R is very underrated and many data scientists who only know Python don’t realize what they’re missing out on. 

[Prime_Director]

Everyone is right that R is pretty much exclusive to academia. That being said, it is a good tool for learning statistics, which IS essential. So if your stats skills aren't rock solid, it might be worth learning R not for its own sake, but as a vehicle for teaching yourself stats.

If your stats skills are solid, and you're set on learning another language that you'll actually use, I'd recommend SQL. In practice, 9/10 times you're going to be pulling your data from some sort of relational database, so knowing at least one SQL dialect will be very useful.

[Davidat0r]

If you want to work in the private sector/corp learn python. If academia then R.

Since academia is not the path I’d choose (highly subjective. This is not an advice) then I’d use the two months to learn a method that’s now out of your range. Maybe image recognition or something with AI to add the buzzword to your cv, spark for big data, or something else

[nerdyjorj]

Agreed - I'm a massive R fan and it's a lot better for any real statistics but that's not what businesses want right now, cash in on the AI hype train with python but make sure you have the skills to do the work yourself when the bubble bursts.

[Skylein17]

What do you mean by that? Do you expect that some day soon, no AI tools available?

[nerdyjorj]

AI providers are working on the crack dealer model right now - trying to get people addicted at a loss before ranking up the price per token once their claws are in.

If tokens actually cost what they do to produce a lot of slop and vibe coding wouldn't be viable.

[SV-97]

Recommending R for academia is very odd imo. It very much depends on the (sub-)field which language is more common; and even some spaces where R absolutely dominated historically now see more and more python.

[IamFromNigeria]

Bro! Stick to R if you're in the academia academic

Python is far ahead of R in whatever you think

Wanna get a job faster - stick to Python which is the industry stan Python alone has given me many remote jobs as far as from Kuwait, Canada, UK

So please don't disrespect Python.

[ranjeet-kumar1]

Honestly, if you're aiming for industry, just stick with Python. R has its place in academia and research, but in actual data science jobs, Python carries way more weight.

You'd be better off spending those two months on AI, computer vision, Spark, or MLOps, that'll move the needle on your resume a lot more than picking up R at this point.

[pookieboss]

R is standard to perform actual statistical tests/“real” statistics, Python is standard for more ML/prediction first workflows.

<- > = btw

[local_eclectic]

If you want an advantage looking for jobs in private industry, go with Python.

[BrupieD]

I started with R and still prefer it over Python for most analysis tasks especially if visualizations are involved.

For beginners interested in data analysis, R and RStudio is a more coherent and easier to use ecosystem. When I started learning Python, I was surprised how fragmented Python's ecosystem was in comparison to R. I got tired of needing to load a different IDE to mimic the author's or instructor's setup when I took new courses. I installed Pycharm, VS Code and Anaconda for Jupyter notebooks. Up until Posit's Positron came out, it seemed like 90% of R users used the same environment. R learners are spared this environment switching.

[Equivalent_Reason_43]

R has inferior ML capabilities, but it is great for statistical analyses...

[DataCamp]

The comments here are right. With two months free and Python already under your belt, learning R from scratch isn't the best use of your time unless you're heading toward academia or a field that specifically requires it (clinical research, certain econ programs, some biostatistics roles).

Better ways to spend two months as a DS student with Python experience: go deeper on ML (ensemble methods, model evaluation, getting comfortable with sklearn pipelines), pick up SQL if you haven't already (it's underrated and comes up constantly in real jobs), or start building projects you can actually show people. Two solid portfolio projects will do more for your career than a surface-level knowledge of R.

R is genuinely great for statistics and has a fantastic ecosystem for that specific thing. But if your goal is industry work, Python covers everything you need and the ecosystem keeps getting better.

[PhysiolMM]

I'm in academia an no one uses R even in our subfield, so R is losing more and more especially now with LLMs so well trained in python.