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Comment by skydaddy | Hacker News Reader

Light years ahead? It doesn't even support 64-bit integers. The R language cannot count past 2.1 billion. In 2018. Don't get me started on the package system.

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I'm always hearing about these and other problems that programmers have with the R language. In practice, none of them seem to be a big deal to statisticians.

64 bit integers? Never had a need for this, but maybe there is a package that can add them in?

What's wrong with the package system? I maintain an R package on CRAN and I'm quite smitten with the package system.

I think bit64 gives integer64, but like you I never need large integers - I don't do general purpose programming in R.

What is the overflow behavior? If it fails and warns you that some operation overflowed, I don't see an issue.

It converts really large integers to floats, which can lose precision. You need to handle really large numbers as characters, or use bit64.

I only really started hitting this in the private sector though, never a problem when I was in academia.

Oh. That's kindof... gross. Better than wrapping silently I guess. Can you at least tell when a number has been 'promoted' to a float so you know you need a bigger type?

Also fairly critically; a mathematician can go from assuming that one exists as a concept to multiple ordinals of infinity with about 20 minutes of scribbling.

The fact that 64 bit is a modern standard doesn't exclude the fact that R cannot under any circumstance handle the vast and overwhelming majority of known numbers.

I feel sorry for the poor souls who turn out to need it, though. R sucks like that.

> 64 bit integers? Never had a need for this, but maybe there is a package that can add them in?

https://cran.r-project.org/web/packages/bit64/index.html

> I'm always hearing about these and other problems that > programmers have with the R language. In practice, none > of them seem to be a big deal to statisticians. > 64 bit integers? Never had a need for this

This kind of thinking is pervasive in a wide corner of statistics and this is pretty much why machine learning stole its crown, that's why I get to hear quips like "Statistics ? Is that even relevant ?"

Crown of what? They are complementary fields. Try doing ML without any statistics and lets see how far you get.

They are closer than complementary and that is exactly what I am trying to point out.

How much funding, support, mind share, conferences, venture capital yadda yadda does stats get compared to ML -- thats what I mean by losing its crown. All that ML has now could easily have been stat's were it not for the attitude I draw attention to.

Isnt that sad, given that ML in many cases is just stats without the baggage "R can solve all of my problems and whatever R cant solve isnt a stats problem"

Yes I agree wholeheartedly, sorry I originally missed your point. I do think it's more of a DL vs. the world problem than stats vs. ML but everything you said rings true. I was kind of odd in that I started with DL and then became fascinated with statistics and lost focus with DL. Statistics is just so much more useful outside of the niche domains where DL is extremely effective.

> Statistics is just so much more useful outside of the niche domains where DL is extremely effective.

This. Sorry had to.

A language is made popular by its ecosystem. R has not only the latest statistical tools implemented through packages but also domain-specific ones. I use both R and Python, but for data analysis, modeling, and plotting - R is much ahead of Python. Find an obscure or cutting edge method, and chances are someone has implemented it in R.

And the package system is absolutely smooth. I have never till today had any issue and I have used it on a weekly basis for the past 6 years.

I think I once had a problem with R's package system that I couldn't resolve without pulling from a project repository. With python, it seems every other pip install I do causes some kind of error that regurgitates a metre of error messages and ends up asking me to compile a couple of packages myself, and whatnot.

R has a lot of antiquated, clunky syntax and libraries and mightily unhelpful package documentation, but one thing that isn't borked is the package system - and that's especially compared to python.

32 bit integers or not, that's a bit tangential to R's dominance as a statistical language.

I work in population-level genomics and haven't found this to be a problem (yet?).

Furthermore, what's wrong with the package system? That's one of R's strengths.

I honestly find it far superior to python's 10 different solutions, none of which really work in a foolproof manner (and I use both languages daily).

That's not true. The bit64 package works just fine for 64 bit integers. Also, for most use cases in R's domain, it doesn't even matter.

https://cran.r-project.org/web/packages/bit64/index.html

I agree that its pretty annoying though.

Whats your issue with the package system? Genuinely curious

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