22 ms·

R generation

74 points by kgwgk 8y ago

remarkEon 8y ago
R was the first language I learned (I ... do not recommend this) and while I don’t use it Day to Day at work, I’m always using it for fun side projects about everything from baseball statistics to urban planning. I’m not a professional statistician, by any means, but fully appreciate the work the community does with this toolset.
sampo 8y ago
Thinking about the long run: Is there any version of the future where the best and largest library of statistical procedures could be somewhat language independent, and not organically part of a small language that itself is an unholy alliance of a matlab-like quick-and-dirty syntax and a hobbyist-level scheme interpreter runtime?
- disgruntledphd2 8y ago
  If you really want this to happen, you should probably start contributing to Julia.
  - msaharia 8y ago
    There is an xkcd comic for this one on JavaScript Frameworks.
clircle 8y ago
I'm a statistician that knows just enough about computer programming to be dangerous. It's hard to overstate how incredibly important R is to my discipline. No other language allows me to express my thoughts and prototype statistical ideas quickly and effortlessly. If statistical programming was still stuck in the SAS era, I think there is a non-zero probability I would have lost interest in the profession years ago.
- et2o 8y ago
  Glad you're still interested! I agree–R is light-years ahead of the competition (Python, essentially) in most statistical domains. It seems to fit the workflow better.
  - skydaddy 8y ago
    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.
    - clircle 8y ago
      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.
      - z2210558 8y ago
        I think bit64 gives integer64, but like you I never need large integers - I don't do general purpose programming in R.
        infogulch 8y ago
        What is the overflow behavior? If it fails and warns you that some operation overflowed, I don't see an issue.
        disgruntledphd2 8y ago
        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.
Cieplak 8y ago
If you want to sprinkle some type safety onto your R, check out this lovely piece of kit: https://tweag.github.io/HaskellR/ https://tweag.github.io/HaskellR/
amrrs 8y ago
R and Rstudio have been my primary bread and butter maker. You might argue that Rstudio has financial interest in growing R community but it's fair and it has helped overall R as a language entirely (not just as statistical one). Hadley Wickham's tidyverse is an amazing example of how one can simply get stuff done. Along with %>% pipe it is almost like idiomatic python. The ecosystem is amazing, I can write a web scraper in a couple of lines of code as simple as beautifulsoup. I think that's the strength of R. The intuitive UI and easy setup of RStudio just help someone get started with no hassle. That's a key I believe. I have also trained non-programmers and they managed to do data analysis in R using tidyverse in a matter of a month or two max.
_Wintermute 8y ago
R was the first programming language I learned and I still use it a couple a times a week for work, but it wasn't until about 2 years into using R when I started learning python that I realised programming isn't difficult and frustrating, R is just badly designed.
j7ake 8y ago
In my opinion, R is the best language for quickly exploring, iterating models, validating the models, and repeating the cycle. I would be curious to hear whether Python, which I use for preprocessing the data into flat tables, is as good as R in this domain. Also ggplot2 and tidyverse packages are powerful for accelerating your iterative cycles.
- curiousgal 8y ago
  Absolutely not! If you're given a dataset and you're trying to fit a model or even run exploratory analysis, Python doesn't even come close to R. Even with Jupyter, the experience is not as smooth as R+Rstudio.
  - kgwgk 8y ago
    Or R+ESS :-)
  - laichzeit0 8y ago
    Except if you like Vim keystrokes. The R Studio implementation (as I understand it's just the ACE library) is really crap. I find even the Juptyer-notebook Vim plugin is better than the R Studio implementation :(
    - j7ake 8y ago
      I use Rstudio with its vim implementation. I would be curious to know what are the things that are missing compared to vim?
      - laichzeit0 8y ago
        There's a bunch: https://support.rstudio.com/hc/en-us/community/posts/200879313-Better-vim-mode https://support.rstudio.com/hc/en-us/community/posts/2008793... The most annoying one for me is the way x behaves. Go to the end of a line. Press x (delete character). It's supposed to delete the character, then move the cursor to the left, so you can delete the next character. It just stands there. That's wrong (try Vim or Visual Studio with vim keybindings). It just annoys me a lot because that's how I've always gone about deleting chars instead of using backspace or using ctrl-h.
FranzFerdiNaN 8y ago
I've tried both R and Python for data analysis and data wrangling and the experience with R was a whole lot better for me (I work as a business intelligence consultant). Python is really steeped in programmer culture, which you notice in everything from its user base to its manuals. Which is great for people who want to learn programming or study CS or so. But I'm not a programmer and I don't want to be. My biggest program ever written is maybe 150 lines to covert a mess of excel files to something usable. Programming is a necessary means to an end for me to do my job, but every time I tried to learn actual programming it bored me to tears. R, while I fully admit has a syntax which is clunky at best (although the tidyverse really helps here), does exactly what I need. Its users are all active in roughly the same space (data analysis in all its forms) so finding help is easy. The community is incredibly friendly, easily found on Twitter with the #rstats tag. It has packages for everything I would ever need. Basically, R helps me get things done, which is all it needs to do for me.
- fritkot 8y ago
  I think your post illustrates well why both languages are important to have. I also work in the Business Intelligence - space, but my job is usually more focussed on setting up data pipelines and developing a small specialized web app here and there. So if I'm already doing that, and I do analysis on top of that, of course I'm going to pick Python, because I can stay in the same ecosystem and I already have a better feeling for how things are going to work. But this wouldn't make sense at all for someone who generally works more on strategy and decision making and wants to dig a little deeper from time to time. Although when people ask me for a recommendation on what to learn, and they're actually interested in programming and building things themselves, I always recommend them to at least give Python a shot and see if they like it, as it then also gives them the possibility to do a lot of other things with the language.
zestyping 8y ago
It's really interesting that there are such divergent opinions on R and Python. I'm curious why some people seem to find R incredibly frustrating and Python entirely sensible, and others find R miraculous and smooth and Python inaccessible. Is there some set of improvements to Python that would make it popular with R users, or vice versa? Or is it a fundamental difference in taste that will never be bridged?
- SJetKaran 8y ago
  I think one reason people may not readily like R, is because it is a functional language. Most programmers are comfortable with object oriented programming rather than the functional style. It takes a while to get used to it. I have seen people who started programming with R, and they get fluent in it pretty fast.
- krutulis 8y ago
  I've been programming for a long time and had to learn R a few years back for a series of projects. The documentation and community seemed to me, as a newcomer, to be very focused on how to solve particular statistical and modeling problems, and I was almost always able to do what needed to be done relatively quickly. (Python documentation and communities, in contrast, span all kinds of applications that can be difficult even for an experienced programmer learning Python to sort through and evaluate. I can also imagine the migration to 3.x has been a challenge for newcomers.) Although I enjoyed learning and using R, as a CS person I was bothered that I understood how to do X in R, but I had no clue about what was happening when I did X. I found this paper to be particularly useful in describing R from a CS perspective: http://www.lirmm.fr/~ducour/Doc-objets/ECOOP2012/ECOOP/ecoop/104.pdf http://www.lirmm.fr/~ducour/Doc-objets/ECOOP2012/ECOOP/ecoop...

74 points by kgwgk 8y ago

R was the first language I learned (I ... do not recommend this) and while I don’t use it Day to Day at work, I’m always using it for fun side projects about everything from baseball statistics to urban planning. I’m not a professional statistician, by any means, but fully appreciate the work the community does with this toolset.

sampo 8y ago

Thinking about the long run: Is there any version of the future where the best and largest library of statistical procedures could be somewhat language independent, and not organically part of a small language that itself is an unholy alliance of a matlab-like quick-and-dirty syntax and a hobbyist-level scheme interpreter runtime?

disgruntledphd2 8y ago
If you really want this to happen, you should probably start contributing to Julia.
- msaharia 8y ago
  There is an xkcd comic for this one on JavaScript Frameworks.

clircle 8y ago

I'm a statistician that knows just enough about computer programming to be dangerous. It's hard to overstate how incredibly important R is to my discipline. No other language allows me to express my thoughts and prototype statistical ideas quickly and effortlessly. If statistical programming was still stuck in the SAS era, I think there is a non-zero probability I would have lost interest in the profession years ago.

et2o 8y ago
Glad you're still interested! I agree–R is light-years ahead of the competition (Python, essentially) in most statistical domains. It seems to fit the workflow better.
- skydaddy 8y ago
  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.
  - clircle 8y ago
    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.
    - z2210558 8y ago
      I think bit64 gives integer64, but like you I never need large integers - I don't do general purpose programming in R.
      - infogulch 8y ago
        What is the overflow behavior? If it fails and warns you that some operation overflowed, I don't see an issue.
        disgruntledphd2 8y ago
        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.

Cieplak 8y ago

If you want to sprinkle some type safety onto your R, check out this lovely piece of kit: https://tweag.github.io/HaskellR/ https://tweag.github.io/HaskellR/

amrrs 8y ago

R and Rstudio have been my primary bread and butter maker. You might argue that Rstudio has financial interest in growing R community but it's fair and it has helped overall R as a language entirely (not just as statistical one). Hadley Wickham's tidyverse is an amazing example of how one can simply get stuff done. Along with %>% pipe it is almost like idiomatic python. The ecosystem is amazing, I can write a web scraper in a couple of lines of code as simple as beautifulsoup. I think that's the strength of R. The intuitive UI and easy setup of RStudio just help someone get started with no hassle. That's a key I believe. I have also trained non-programmers and they managed to do data analysis in R using tidyverse in a matter of a month or two max.

_Wintermute 8y ago

R was the first programming language I learned and I still use it a couple a times a week for work, but it wasn't until about 2 years into using R when I started learning python that I realised programming isn't difficult and frustrating, R is just badly designed.

j7ake 8y ago

In my opinion, R is the best language for quickly exploring, iterating models, validating the models, and repeating the cycle. I would be curious to hear whether Python, which I use for preprocessing the data into flat tables, is as good as R in this domain. Also ggplot2 and tidyverse packages are powerful for accelerating your iterative cycles.

curiousgal 8y ago
Absolutely not! If you're given a dataset and you're trying to fit a model or even run exploratory analysis, Python doesn't even come close to R. Even with Jupyter, the experience is not as smooth as R+Rstudio.
- kgwgk 8y ago
  Or R+ESS :-)
- laichzeit0 8y ago
  Except if you like Vim keystrokes. The R Studio implementation (as I understand it's just the ACE library) is really crap. I find even the Juptyer-notebook Vim plugin is better than the R Studio implementation :(
  - j7ake 8y ago
    I use Rstudio with its vim implementation. I would be curious to know what are the things that are missing compared to vim?
    - laichzeit0 8y ago
      There's a bunch: https://support.rstudio.com/hc/en-us/community/posts/200879313-Better-vim-mode https://support.rstudio.com/hc/en-us/community/posts/2008793... The most annoying one for me is the way x behaves. Go to the end of a line. Press x (delete character). It's supposed to delete the character, then move the cursor to the left, so you can delete the next character. It just stands there. That's wrong (try Vim or Visual Studio with vim keybindings). It just annoys me a lot because that's how I've always gone about deleting chars instead of using backspace or using ctrl-h.

FranzFerdiNaN 8y ago

I've tried both R and Python for data analysis and data wrangling and the experience with R was a whole lot better for me (I work as a business intelligence consultant). Python is really steeped in programmer culture, which you notice in everything from its user base to its manuals. Which is great for people who want to learn programming or study CS or so. But I'm not a programmer and I don't want to be. My biggest program ever written is maybe 150 lines to covert a mess of excel files to something usable. Programming is a necessary means to an end for me to do my job, but every time I tried to learn actual programming it bored me to tears. R, while I fully admit has a syntax which is clunky at best (although the tidyverse really helps here), does exactly what I need. Its users are all active in roughly the same space (data analysis in all its forms) so finding help is easy. The community is incredibly friendly, easily found on Twitter with the #rstats tag. It has packages for everything I would ever need. Basically, R helps me get things done, which is all it needs to do for me.

fritkot 8y ago
I think your post illustrates well why both languages are important to have. I also work in the Business Intelligence - space, but my job is usually more focussed on setting up data pipelines and developing a small specialized web app here and there. So if I'm already doing that, and I do analysis on top of that, of course I'm going to pick Python, because I can stay in the same ecosystem and I already have a better feeling for how things are going to work. But this wouldn't make sense at all for someone who generally works more on strategy and decision making and wants to dig a little deeper from time to time. Although when people ask me for a recommendation on what to learn, and they're actually interested in programming and building things themselves, I always recommend them to at least give Python a shot and see if they like it, as it then also gives them the possibility to do a lot of other things with the language.

zestyping 8y ago

It's really interesting that there are such divergent opinions on R and Python. I'm curious why some people seem to find R incredibly frustrating and Python entirely sensible, and others find R miraculous and smooth and Python inaccessible. Is there some set of improvements to Python that would make it popular with R users, or vice versa? Or is it a fundamental difference in taste that will never be bridged?

SJetKaran 8y ago
I think one reason people may not readily like R, is because it is a functional language. Most programmers are comfortable with object oriented programming rather than the functional style. It takes a while to get used to it. I have seen people who started programming with R, and they get fluent in it pretty fast.
krutulis 8y ago
I've been programming for a long time and had to learn R a few years back for a series of projects. The documentation and community seemed to me, as a newcomer, to be very focused on how to solve particular statistical and modeling problems, and I was almost always able to do what needed to be done relatively quickly. (Python documentation and communities, in contrast, span all kinds of applications that can be difficult even for an experienced programmer learning Python to sort through and evaluate. I can also imagine the migration to 3.x has been a challenge for newcomers.) Although I enjoyed learning and using R, as a CS person I was bothered that I understood how to do X in R, but I had no clue about what was happening when I did X. I found this paper to be particularly useful in describing R from a CS perspective: http://www.lirmm.fr/~ducour/Doc-objets/ECOOP2012/ECOOP/ecoop/104.pdf http://www.lirmm.fr/~ducour/Doc-objets/ECOOP2012/ECOOP/ecoop...