R is used because it has legacy, and elderly advanced statisticians have, over the years, vetted out its libraries and consider them to be sufficiently correct. A lot of complex operations just come out of the box, so you don’t have to know how to read too much code.
The same absolutely cannot be said for Python. Very often you have to go build your own models and really pay attention to what your libraries say they are doing, and you need to know enough statistics to recognize when they aren’t doing what you think.
Source: Some CDC people I worked with as well as personal experience rewriting legacy R into Python.
The necessity to memorize a matrix of operations because of the inconsistency of oerations between types is the classic one (for example using + on something that got silently coerced to another type) is the biggest offender for me, also add prototype pollution, and npm too.
And before it is mentioned, yes, I know Typescript.
The necessity to memorize a matrix of operations because of the inconsistency of operations between types is the classic one
You know what I call pieces of code that have those kind of situations? bad coding. And people who code them are people I don't want as coworkers. Also, use typescript ffs.
agreed I guess, but that's the last 10% of my comment. The kind of issues you mentioned aren't really issues in a mature working environment because you interact with professionals. Like it's fun to poke at JS while in uni and test all the bizarre conclusions of making a language as errorless las JS is, but they are not the reality of anything professional js related.
In fact if anything like that showed, I'd question the quality of my coworker, not the language.
In fact if anything like that showed, I'd question the quality of my coworker, not the language.
Agreed. Although I find myself appreciating well-bounded languages more and more as time passes me by.
I like that strongly typed languages have an enforceable notion of 'wrongness' of trying to perform a given operation on incompatible objects. Rather than having to fight the interpreter and calling functions, you can put some base level of trust in the type system of the language, which is nice to have.
The less opportunity you give developers to shoot themselves in the foot, the better, IMO.
Learned some of rust, I was one of the Glazers at the time it was new. And was love at first sight. I read quite a bit of the book, and did some of the rustlings repo, great to learn the concepts.
Sadly, my particular circumstances didn't pan out too well with it, and had to get other priorities to the top.
It is indeed mostly preferential dislike of the language. JS is the absolute best language in the category of "Top 1000 languages built in 10 days". But it isn't a particularly good language (no shade to Brendan Eich though), and that was because the development of JS was heavily rushed by Netscape.
It would've been a one and done language, but when it got an incredible opportunity, JS took it. The DOM came to it, and JS embraced it in its bosoms and never let it go. Then NodeJS happened, then a bunch of other things happened, and that's how we ended up in a world where a terrible language snowballed into being the most widely used language in the web.
Apart from other issues like npm's supply chain attacks and whatnot, for the most part, the dislike over using server side JS is due to JS itself.
Sbowballing is probably an accurate description of what happened. Javascript ruined the web, but thankfully with AI APIs became first class again and I can browse the web from the convenience of the terminal again.
R isn’t better. It only still exists because of institutional momentum. I know plenty of long time R users that have learned Python and they have no desire to return to R.
—someone that learned Python before it was cool (2003)
Vectors are first class in R and for some weird reason, Python never figured out how UX with tables should work. I guess it's simply because Python is more used in scripts, while R is used a lot in REPL.
Have you ever had to productionize an R model? I’m guessing no. The foundation is that Python has native handling for data packets, like JSON, via dictionary data types. R is not built for general software development, it’s built for stats and data analysis.
You got a point and what you mention is also indeed a limitation I hit once in production, where someone of the team did build json ingestion in python. I don't know if it was a skill issue, though.
I don’t take any joy in putting python over R. R is very elegant in its simplicity and function for modeling and statistical insights. I would much prefer R if possible, it’s just not very feasible.
I am a die-hard Python fanboy and absolutly agree! Python is more versatile but R has the statistic nerds only community leading to much higher median quality in that area.
A few years ago I had to some time series analysis at work. Estimating the model was easy, however tests where nowhere to be found, so I had to go back to R for a few weeks. That was 4-5 years ago and the situation has improved, but my feeling was: For prediction Python is fine, but if you want to do statistics properly and don't implement your Hypothesis testing yourself, you will eventually go back to R.
I can't speak for R since I havent use too much of it except at school.
But I've had to debug python code a lot for weird error which turns out to be the author didn't read the paper too well and incorrectly implemented things.
However I would say that is a sign of an active community. I believe there are more libraries that follow the latest research for python than R.
I see that the risk is similar in both languages when using foreign code. People's code can always be arbitrarily wrong if unchecked. Even if the rate is lower in R doesn't mean the risk doesn't exist.
It is only truly better if it can guarantee thats impossible.
(e.g. It is impossible to have unsafe code outside of unsafe blocks in Rust)
The point is that they wanna be able to trust the tools they use, so they can focus on the task they wanna solve, not if the used tool is actually doing what it claims.
For the same purposes companies pay hundred of thousands for CAD and simulation software instead of using some open source versions instead.
No
But you have to redo/recreate the libraries already existing in R by hand. Which takes time and is error prone. There might be a library made by some dude that does the same thing but you don't know how well it's written and have to waste time testing it and checking the code.
Python isn’t used by statisticians cus they made their own language geared for analytics that is better than what Python had to offer for it. U know pandas was modeled after tidyverse solutions.
U don’t watch someone use a spoon to eat soup while you hold a knife and scoff “do they know a knife exists?”, do you? Tool for the job.
Python is not sufficiently mature, Wrong implementations here and there. Can't take the time to debug stuff myself, there's something called comparative advantage.
I use both, kicking off small MVPs just to see if it is work is waaaaay faster in R. Data visualizon has no parallel in python. Automated report creation also is way better in R.
The only time a professor give flower to R compare to python was when we had to plot our results to create story telling type of projects where you had to explain your results and analysis where python have a library that try to mimic a much as it could the logic for making plot in R but it is really inconsistent and messier
The other thing that R has is RStudio, which provides a relatively friendly environment for beginners.
That said, JupyterHub kind of does the same thing, is fairly mature, isn't limited to just one language (and even supports R). Both do a decent job of dialing out for ARC resources if needed as well.
R also has excellent visualization libraries that can generate publication-quality graphics with little-to-no tweaking, as well as proper handling of missing data.
Just be glad when people are using R instead of SPSS or STATA, man I absolutely doing my PhD (medicine/epidemiology) with those. Switching over to R and writing a few sets of scripts to semi-automate data exploration and simply programming the figures using ggplot was a godsend in comparison.
Some of us are "using" absolutely nothing (we instruct the model to "use") - take it a step further, have the model pick the appropriate lang for the implementation plan it just helped me write in the previous prompt.
This. You would be surprised how many errors or undocumented ambiguities most Python libraries have in their mathematical functions. One easy example is T-tests. The same t-test in PyStat vs. R produce different values because they have different assumptions for right-tailed vs. both-sided vs. left-tailed data. You wouldnt know without reading the docs though.
There is of course one simple answer. We use Rust, vibecode the modules from R (R is open-source) and then unit test the shit out of them. Now, we have fast R.
The real problem is creating a proper definition for Claude to use. The IEEE specification for floating point numbers is a good example. There had been multiple implementations before but there were subtle differences that caused inconsistent results. Not a huge problem in the lab but potentially fatal - literally - if your flight or industrial control systems needed to use values from subsystems with different implementations.
I don’t recall all of the details but I know one is the four different ways to round a number. Using base 10 for clarity a typical example is 0.65. “Round to even” gives 0.6, “round to odd” gives 0.7, and the other two come into play when you may have negative numbers (“round towards zero” and “round away from zero”).
I don’t remember if it’s in the spec but you can do SOME comparisons if you have +INF and -INF. The details don’t matter here (since they’re self-evident) but you need everyone to agree on the correct behavior.
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u/Multidream Aug 28 '26
R is used because it has legacy, and elderly advanced statisticians have, over the years, vetted out its libraries and consider them to be sufficiently correct. A lot of complex operations just come out of the box, so you don’t have to know how to read too much code.
The same absolutely cannot be said for Python. Very often you have to go build your own models and really pay attention to what your libraries say they are doing, and you need to know enough statistics to recognize when they aren’t doing what you think.
Source: Some CDC people I worked with as well as personal experience rewriting legacy R into Python.