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Okay so Rstudio had a bug and R doesn't support realky big numbers that you will never need except with a add on package. Horrible. Seriously though, R is ligh

by zwaps 8y ago

Okay so Rstudio had a bug and R doesn't support realky big numbers that you will never need except with a add on package. Horrible. Seriously though, R is lightyears ahead in things that actually matter. There are hundreds of advanced statistical models that you can only run in R. Coded by the people who invented those models, most likely. Python can do basic linear models via statsmodels, that's pretty much it. Not enough to cover even the baseline of common data processes or inferential tasks of a researcher or statistician. So either R, commercial packages or you code everything yourself, which makes so sense for hundreds of reasona, among them that you have to study the R codes of the authors anyway. So please, be real

zaarn 8y ago
> Rstudio had a bug Multiple bugs even, some of which were rather popular on the bug tracker. > R doesn't support realky big numbers that you will never need except with a add on package. Horrible. I needed them, so I don't particularly care that you think it only matters in add on packages. I do recall my dad once claiming nobody should need more than 2GB of memory ever. That was around 2005 or so. >There are hundreds of advanced statistical models that you can only run in R. Coded by the people who invented those models, most likely. Which is the most concerning fact of them all. > So please, be real I'm real. This was a real application of R as a software and it sucked. How is this not real? I mean I'm not saying you can't write anything in the language, you can certainly write code in it and make it work in a reasonable timeframe, atleast one advantage over Malbolge. But it doesn't change the fact that when I try to write something it feels wrong and painful and the software ecosystem seems to be written by non-engineers doing non-engineers leading to unmaintainable code everywhere with no standards whatsoever. I would not consider R to be a language fit for anything but statistics and there only by virtue of, as you mention, having some packages that might be useful, which is a horrible reason to keep using a language.
- zwaps 8y ago
  But R... is a language for statistics. It's not a language for anything else. RStudio is pretty good now, I have never had issues with it. But you could, you know, use your extensive programming skills to use something else? It's not that packages are useful. It's frankly that they are necessary. R is a complete statistical package that allows you do to your job or do research. It's the only statistical package that does this for free. Given that there are no alternatives, it's hard to even see your point here. You wanna switch languages because you don't like R? Great, but now you can not do your job. What's the point? In research, where R is used, you have to run a lot of models per project in a short amount of time. You could theoretically go back to the paper. Often, understanding the model on paper is quite different from understanding the implementation. So after some study, you dig up the code (in R), and programm the model into another language (where I think Python will be the only option bc. of NumPy). And then you have spent a couple of days doing basically nothing. Now you have wasted everyone's time, and you probably have bugs or don't implement a minor details correctly so that your results differ from R, so that you are giving wrong results or at least invite issues with replication... Look, I don't like using R either. For my research, I use prefer to use Matlab as I anyway do have to program most of the things myself. But when it comes to running quick stats analysis, I sure as hell fire up R, just like everyone else. I'd be a total idiot if I go and write up every standard and non-standard method in my language of choice. Also, I'd be fired. No, R is not for you, since you care about 64bit numbers and typecasting elegance and like to write everything yourself. But R is really popular with many other people, people who can fire it up, for free, do some rather advanced stats analyses without spending weeks of prep, and get their results out fast and in a reliable, replicatable manner.
  - zaarn 8y ago
    My point is that R is horrible, even when used for statistics but it's the only solution in statistics so people use it (probably with some self-reinforcing stockholm syndrome)
    - zwaps 8y ago
      Do you have any actual critiques that may apply to a day-to-day workflow of someone doing statistics? Assume packages and RStudio work as advertised and he/she does not need 64bits.
      - adwn 8y ago
        > Assume packages and RStudio work as advertised and he/she does not need 64bits. I.e., "Disregarding the weak points, what weak points are there?"
        bllguo 8y ago
        was this comment supposed to contribute anything? We've already went over those. Most reasonable people would probably not prefer to sit here and reiterate the same points multiple times. edit: And as was explained - they hardly have an effect on most R workflows. I write R. I know people who write R (and Python) for production models. We could care less about 64 bit, and I for one think RStudio is rock-solid and one of the best IDEs I've ever used. And the tens of comments in this very thread about the package system show that no, it is not a foregone conclusion that packages are poorly handled.
  - a008t 8y ago
    You can call R functions from Python. You can then write a Python wrapper around R packages that you need.
improbable22 8y ago
> There are hundreds of advanced statistical models that you can only run in R. Coded by the people who invented those models, most likely. Does this not seem worrying? If there's exactly one implementation in use by everyone, and nobody has read the original paper closely enough to build it independently & check?

by zwaps 8y ago

zaarn 8y ago

> Rstudio had a bug Multiple bugs even, some of which were rather popular on the bug tracker. > R doesn't support realky big numbers that you will never need except with a add on package. Horrible. I needed them, so I don't particularly care that you think it only matters in add on packages. I do recall my dad once claiming nobody should need more than 2GB of memory ever. That was around 2005 or so. >There are hundreds of advanced statistical models that you can only run in R. Coded by the people who invented those models, most likely. Which is the most concerning fact of them all. > So please, be real I'm real. This was a real application of R as a software and it sucked. How is this not real? I mean I'm not saying you can't write anything in the language, you can certainly write code in it and make it work in a reasonable timeframe, atleast one advantage over Malbolge. But it doesn't change the fact that when I try to write something it feels wrong and painful and the software ecosystem seems to be written by non-engineers doing non-engineers leading to unmaintainable code everywhere with no standards whatsoever. I would not consider R to be a language fit for anything but statistics and there only by virtue of, as you mention, having some packages that might be useful, which is a horrible reason to keep using a language.

zwaps 8y ago
But R... is a language for statistics. It's not a language for anything else. RStudio is pretty good now, I have never had issues with it. But you could, you know, use your extensive programming skills to use something else? It's not that packages are useful. It's frankly that they are necessary. R is a complete statistical package that allows you do to your job or do research. It's the only statistical package that does this for free. Given that there are no alternatives, it's hard to even see your point here. You wanna switch languages because you don't like R? Great, but now you can not do your job. What's the point? In research, where R is used, you have to run a lot of models per project in a short amount of time. You could theoretically go back to the paper. Often, understanding the model on paper is quite different from understanding the implementation. So after some study, you dig up the code (in R), and programm the model into another language (where I think Python will be the only option bc. of NumPy). And then you have spent a couple of days doing basically nothing. Now you have wasted everyone's time, and you probably have bugs or don't implement a minor details correctly so that your results differ from R, so that you are giving wrong results or at least invite issues with replication... Look, I don't like using R either. For my research, I use prefer to use Matlab as I anyway do have to program most of the things myself. But when it comes to running quick stats analysis, I sure as hell fire up R, just like everyone else. I'd be a total idiot if I go and write up every standard and non-standard method in my language of choice. Also, I'd be fired. No, R is not for you, since you care about 64bit numbers and typecasting elegance and like to write everything yourself. But R is really popular with many other people, people who can fire it up, for free, do some rather advanced stats analyses without spending weeks of prep, and get their results out fast and in a reliable, replicatable manner.
- zaarn 8y ago
  My point is that R is horrible, even when used for statistics but it's the only solution in statistics so people use it (probably with some self-reinforcing stockholm syndrome)
  - zwaps 8y ago
    Do you have any actual critiques that may apply to a day-to-day workflow of someone doing statistics? Assume packages and RStudio work as advertised and he/she does not need 64bits.
    - adwn 8y ago
      > Assume packages and RStudio work as advertised and he/she does not need 64bits. I.e., "Disregarding the weak points, what weak points are there?"
      - bllguo 8y ago
        was this comment supposed to contribute anything? We've already went over those. Most reasonable people would probably not prefer to sit here and reiterate the same points multiple times. edit: And as was explained - they hardly have an effect on most R workflows. I write R. I know people who write R (and Python) for production models. We could care less about 64 bit, and I for one think RStudio is rock-solid and one of the best IDEs I've ever used. And the tens of comments in this very thread about the package system show that no, it is not a foregone conclusion that packages are poorly handled.
- a008t 8y ago
  You can call R functions from Python. You can then write a Python wrapper around R packages that you need.

improbable22 8y ago

> There are hundreds of advanced statistical models that you can only run in R. Coded by the people who invented those models, most likely. Does this not seem worrying? If there's exactly one implementation in use by everyone, and nobody has read the original paper closely enough to build it independently & check?