12 ms·

The R language, for programmers

110 points by tacon 12y ago

Alupis 12y ago
I actually quite like the R language. A buddy of mine is in his University's PoliSci program and one of the requirements is to learn R for statistical and trend analysis. He could not stop complaining until I offered to help him learn it by learning it with him. After doing his first assignment, we were both impressed with what could be easily done in R to visualize data. I think he now realizes how useful of a tool R can be in his future career.
- epistasis 12y ago
  I actually really like it too. The programming language features of it are quite different from what's going on in a Java/C++/C# world, but they are super convenient. Argument matching is really amazing and useful for prototyping. No doubt there's a penalty, but it's exactly the type of power that's needed to build expressive and useful reusable components with rapidly changing designs. And pattern matching like that really helps with the REPL because it allows far faster exploration with fewer keystrokes. Best programming practice in library code would be to have things more fully fleshed out however.
  - jghn 12y ago
    I've said for several years now that most complaints about R's syntax and idioms really boil down to "this isn't doing things the way I'm used to doing them, i.e. the C/C++ branch of the language family tree"
  - Alupis 12y ago
    Should note that R wasn't really designed for "programmer" programmers. It's largely used by statisticians, analysts, etc, so a lot of the syntax will vary from a C based language.
- viewer5 12y ago
  If I just want to learn Statistical Programming, is R good for that? I can't afford/not willing to buy SAS or Matlab.
  - Alupis 12y ago
    Yes, it is. The language itself is free and the runtime environment can be downloaded for free. There's even a good IDE for it called R Studio, which has a good free version. R is based on S, which itself was purely designed as a statistical programming language.
    - viewer5 12y ago
      How tough would it be to learn R, then get hired at someplace that uses SAS or Matlab or something, and try to learn that? It sounds like R isn't used quite as much as other options
capnrefsmmat 12y ago
I'd like to see a detailed explanation of R's scoping. It's not just lexical scoping; callees can deliberately manipulate the scope their arguments are evaluated in, for example. So you can call a function and pass arguments that are available in local scope, but the arguments are lazily evaluated, and the callee might evaluate them in an entirely different scope. Typically this is done for manipulating datasets. You might have a data frame with columns Width and Height, and so you want to be able to call do.stuff(Width, Height, data=foo), and have Width and Height automatically taken from within foo. But sometimes it crops up in unexpected places.
- claytonjy 12y ago
  Are you familiar with Hadley's Advanced-R book? You can buy a hardcopy, but it's free online: http://adv-r.had.co.nz http://adv-r.had.co.nz There's a section on lexical scoping, and lots of other non-basic stuff that is hard to find covered elsewhere at all, much less well. From what I've seen, this is absolutely the best reference for deep R stuff that exists.
  - capnrefsmmat 12y ago
    I've seen it but haven't read in depth. Now that I see the scoping section I'll have to read through it.
    - claytonjy 12y ago
      Now that it's in print I suspect updates are less frequent, but until a few months ago sections were being added and rewritten pretty frequently, so it might have things now that it didn't when you looked last
- rcthompson 12y ago
  You can think of the lazily evaluated arguments feature a lot like lisp macros. Basically, any function in R can either get the value of its arguments (i.e. acting like a regular function) or it can get the unevaluated expression passed for that argument (i.e. acting like a lisp macro). So in your "do.stuff" example, it's not a matter of special scoping, per se. The do.stuff function is actually getting the symbols "Width" and "Height" and then choosing to evaluate them with the value of the data argument as the innermost scope.
canjobear 12y ago
Why no discussion of dataframes? I find these to be the most useful aspect of R which I miss the most in other languages.
- eric_bullington 12y ago
  If you use Python, have you checked out Panda's dataframes? It's not quite the R experience, but pretty close plus you get all the benefits of the Python ecosystem.
minimaxir 12y ago
The biggest "gotcha" for learning R as a programmer is that R interprets character vectors of data frames as factor vectors by default, which will usually break something in your code. If you're learning R, learn to use dplyr for data manipulation and ggplot2 for plotting. Both will save you a lot of time.
- claytonjy 12y ago
  Agreed, this was a huge pain point for me early on; I started setting the necessary option to force stringsAsCharacters rather than specifying that arg in read.csv everytime. Edit: I had mentioned data.table as being better than dplyr for performance reasons, but it has a unique learning curve and isn't really good for beginners
- ggrothendieck 12y ago
  To be clear this applies to data frames, not the language as a whole. If you are creating a data frame by reading in data then `read.table(...whatever..., as.is = TRUE)` will read in character columns as character strings and not as factors. `data.frame()` has a `stringsAsFactors` argument which can be set to `FALSE` and one can change the global default with: `options(stringsAsFactors = FALSE)` .
- craigching 12y ago
  > If you're learning R, learn to use dplyr for data manipulation I had been learning data.table, but I really like dplyr's % operator and the compositional functions better. I think I'm going to make the move to dplyr.
  - hadley 12y ago
    FWIW The performance difference is insignificant unless your working with >>10 of millions of rows.
    - craigching 12y ago
      I thought dplyr (note, not plyr, dplyr is the next iteration of plyr implemented mostly in native code) was pretty much as fast as data.table.
mjt0229 12y ago
R is one of those languages that looks like it was designed in a vacuum by a very smart person. It has many common, modern PL constructs, but they're expressed syntactically in a way that in no way resembles any other language I've seen. The entire syntactic legacy of Algol, Pascal, C, etc, all are thrown by the wayside. Familiarity with any of those syntaxes felt to me like more of a liability than a help. That's not to say that the concepts don't apply, just the syntax.
- claytonjy 12y ago
  I agree, and I think that's exactly why an article like this exists. The R learning curve seems to be much gentler on people without too much serious programming experience in another language. Have you looked at Julia at all? I'm only mildly familiar, but it looks super promising and I'm curious if the syntax there seems more normal or predictable for an experienced dev.
  - mjt0229 12y ago
    Yeah, I should have mentioned that - R for Programmers is exactly the kind of thing I'd need, even if it's not useful for my friends and family (largely scientists rather than programmers for whom the legacy of programming language syntax is completely unknown). Julia looks cool; I think the syntax is meant to look familiar to people who've used Matlab or Octave extensively. I don't do tons of scientific computing, but Julia is on my list of tools to learn.
- chappi42 12y ago
  R is a 'rip-off' / open source re-implementation of the commercial S language by John Chambers. (There are some differences in scoping iirc). Haven't many of its 'modern' PL constructs already been used in Lisp (dialects) before?
  - jghn 12y ago
    Indeed, it's not the hodgepodge that many people believe it to be, it just comes from a heritage that's much further away in the PL family tree than they're used to. There are something things which were grafted on to S/R later on. For instance the S4 OOP system was added much later (in S4) and was based on Dylan's OOP system. So there's a case where the GP is correct that it was taking structure from another PL but it's also one which is fairly foreign to a lot of OOP devs
jmount 12y ago
To my mind some of R's plusses are data frames and the ability to indicate missing values in vectors of any type. Some of the weird stuff is the lazy evaluation of arguments, the ability to know the names of variables bound to function arguments, and the ability to snoop up the environment stack. Some distinct minuses are the changing of types (dropping of dimensions on select), semi-reserved terms, and c()'s squashing of complex types. One of my articles on the topic: Survive R http://www.win-vector.com/blog/2009/09/survive-r/ http://www.win-vector.com/blog/2009/09/survive-r/ .
- craigching 12y ago
  And plotting, especially ggplot2!
daveloyall 12y ago
TL;DR: Tragically, R has a lot in common with old-skool PHP and MS Excel, at the same time.
- deleted 12y ago
  [deleted]
- kyberias 12y ago
  I don't know why exactly you're getting downvoted. I've tried learning R many times, but the resemblance to PHP problems is just too much.
  - daveloyall 12y ago
    Probably there aren't legitimate semantic or syntactic similarities between the three. But for proposes of a TL;DR, I believe that a qualitative description of the situation should suffice. I'll try again: TL;DR: The language R lacks the quality without a name. Or: Jeeze, now I know why none of my previous attempts to learn a little R were fruitful. Or: The language R seems to have been developed in isolation and thus it fails to adhere to any particular convention--it is its own beast. Further, it sometimes lacks self-referential integrity and coherence. Or: haha cf. PHP or Excel. :)
    - nkurz 12y ago
      I voted you up here because you were below zero and I was also about to post a comment comparing R to PHP, but I'll mention that I frequently downvote one line posts that start with "TL;DR". The concept of "too long didn't read" implies (to me, and probably others) that that the article isn't worth reading. If I think the article is worth reading, and the short comment isn't incredibly insightful, I'd usually prefer such comments to be at the bottom of the page and grayed out.
      - daveloyall 12y ago
        Oh, I hadn't considered "too long, DON'T read". Hm. This article is worth reading if you're interested in R but not yet well acquainted with it.
chuckcode 12y ago
I find R to be a great language for exploring a data set and doing some prototyping. There are a lot of wonderful statistical tools available through the core packages and even more through the various community extensions. It does have some significant issues that I've found limit the usefulness outside of prototyping - pass by value only means code tends to end up as monolithic functions - very slow in loops so lot contorting to move things to matrix operations - they just last year got a version out that starts support for vectors and matrices with > 2^31 -1 elements which limits larger data applications. I find the plotting with ggplot and statistical functionality to be second to none though.
- rm999 12y ago
  > pass by value only means code tends to end up as monolithic functions I've actually found R works very well as a functional language with very lean functions. It's perhaps worth noting that R doesn't copy a dataframe in a function call if you don't modify it, which is a very common use-case for me. (I'm not sure if this extends to other datatypes) > very slow in loops so lot contorting to move things to matrix operations This is a fair criticism, I think more modern languages like Julia will win out here. That said, R has huge library support, I've often found there are compiled versions for a lot of what I want to do. > they just last year got a version out that starts support for vectors and matrices with > 2^31 -1 elements which limits larger data applications Again, a fair criticism. I've never considered R a "big data" tool, my workflow is usually a funnel where each step involves reducing data size by 1-3 orders of magnitude. For example, I may have 1 PB of transactional data, aggregate it in Hadoop to 20 TB of daily aggregated data, run a query that filters and aggregates it further, and then run my analysis in R on final data. In the end I may end up with 20 GB of data, which R can very easily handle.
  - chuckcode 12y ago
    Python has better and better support for R with Rpy2 and R like data frames with Pandas, which is helping me take advantage of the incredibly useful analysis libraries in R. Also note that loops are slow enough that it is really worth learning the *apply() functions in R to avoid iterating over collections. For a relatively in depth explanation check out Hadley Wickham's book http://adv-r.had.co.nz/Functionals.html http://adv-r.had.co.nz/Functionals.html
elliott34 12y ago
I love pandas much more than R but GOD I love Rstudio. Such a great IDE. Rstudio server, actually. My equivalent is running ipython notebook on an ec2 instance, which....is fine, but is a lot of scrolling.
- claytonjy 12y ago
  As an every-day R user but only-occasional python user, everytime I do a python project I spend some time looking for a comparable IDE. Closest i found was Spyder, but random lock ups made it unusable. Back to terminal+ipython and sublime. Sublime REPL + ipython doesn't cut it either. What do you love about pandas, is it performance, syntax, access to other python modules? If performance, take a look at R's data.table package: almost any manipulation can be done by reference.
  - Toenex 12y ago
    PyCharm is worth a look but I agree with the terminal+ipython resort, a place I often find myself too. RStudio was great and then I tried the server version and it suddenly became bloody amazing as I was able to keep R close to the data and have a completely remote IDE.
  - elliott34 12y ago
    Now if we can only get sublime text 2 within an ipython notebook on the cloud..... For me, R was my first language, and then I learned Python, and beautiful things like list comprehensions, and it just clicks with my brain a bit more. In pandas, a group by operation is beautiful dataset2=dataset1.groupby([stuff], as_index=False).mean() Same with pivot table... When I did this with dplyr2 my work flow would be a few more steps, creating the "summarise" object and so forth and so on, which seems like more steps.
- psychometry 12y ago
  RStudio is great for R files, but has awful memory leaks if you're using it for RSweave/knitr documents. I weaned myself off RSTudio onto Sublime+Various Packages+R Console and couldn't be happier.
aagha 12y ago
I'm curious for others thoughts on what to use for complex statistics if you needed high performance/speed. Running an R script on a server to process data isn't efficient, but does that mean you have to roll your own stats package if you want to have a Java (for example) back-end?
- scroy 12y ago
  Not sure how complex your use case is, but I've found Pandas (on Python) to be just as powerful and much more performant than R for working with scientific data. It's built on Numpy so you can use Scipy's statistical functions with it seamlessly.
  - hadley 12y ago
    Recent benchmarks show the performance of pandas, data.table and dplyr to be pretty similar, with data.table usually being the fastest.
- arun_sriniv 12y ago
  Here's a benchmark Matt recently did comparing data.table, dplyr and pandas on 50GB and 100GB: https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A...
plinkplonk 12y ago
An "R for programmers" style book is Hadley Wickham's glorious "Advanced R" (available at http://adv-r.had.co.nz/ http://adv-r.had.co.nz/). R never made too much sense to me till I read through this. Reccomended.
fallat 12y ago
Haskell is better than R, for both programmers and data analysts.
- redacted 12y ago
  Could you expand on why? I'll be starting a data-heavy side project and have meant to make an effort with Haskell for a while I always thought that R is fairly unmatched in both breadth and depth for statistical work and general data analysis, with the Python stack in second (e.g., numpy+scipy+pandas+...).
- michaeltoth 12y ago
  It's going to be harder to do just about anything related to data analysis in Haskell than in R. In R I can load a dataset, do some formatting, and produce a well designed plot in fewer than 5 lines of code. Haskell might run faster, and it's certainly more versatile, but for data analysis it isn't even comparable.
- ww2 12y ago
  People use R as a convenient tool.i.e., R users are tool/library users, not tool builders in general. Haskell cannot compete with R for this user base.
lottin 12y ago
For me the main problem with R is that I find it hard to break lines nicely. I tend to end up with massive one-liners, and no matter where I break it never looks quite right.
chappi42 12y ago
The language might experience the 'peak R' point right now. 'Badass' statistic packages but R always felt a bit 'hacked together'. With Julia on the other hand, I get the impression that there are developers in charge which have a deep understanding about programming languages and computer science. It's (too) early times for Julia but I wouldn't be surprised if in two years many users will (partly) switch.
kyberias 12y ago
When reading this article, I started to wonder whether it would be plausible to create a REPL or a compiler from some "real" programming language (like, I don't know, C#, C++ or Python) to R to utilize R's statistical libraries without going insane. This might be a fun exercise as an LLVM backend. :)
xname 12y ago
One thing I don't like about R is the OOP part of R. I don't see any reason of R including OOP into its design and sometimes it just creates confusions.
- jghn 12y ago
  R didn't include OOP into its design, it was all grafted on later, are there are multiple systems. There's the S3 OOP system, the S4 OOP system, reference classes and at least one add on package on CRAN which does something different. So which one are you complaining about? :)
nkurz 12y ago
I've been looking at a variety of R packages, mostly for the purposes of rewriting them in C++ for greater speed, and my assessment is that most of them are of very low code quality. I don't mean that they don't work (they usually do), or that they are too slow (they usually are, but this is explained by selection bias given the reasons I'm looking at them), but that there is little standardization even with a given package, and the 'foundations' seem weak. Variable names are a hodgepodge of unhelpful single letter abbreviations theSecondArgumentToTheFunction; functions alternate between camelCase, dots, and underscores; and any form of architecture seems at best an afterthought. It seems like the base language encourages this, or at least does nothing to prevent it. It's commonplace to pick on Perl, but the overall quality of popular packages seems considerably lower on CRAN than CPAN. Perhaps this is because Perl is so conscious of its reputation at this point that the remaining programmers take great pain to write clear code? I feel like R is currently in the stage where Perl and PHP were as the internet was just when the internet started to explode. The first-to-market CGI scripts and libraries, often written by domain expert non-programmers, became the default choices which the rest of the infrastructure was built on. At some point, the weight became too great for the shoddy[1] construction, and most people moved on to languages with better attention to maintainability and foundational detail (Python, Ruby). Those who remained with the language evolved it in similar directions, by replacing the earlier libraries with better designed ones and by setting a higher standard for community norms. I'm not sure about PHP, but contrary to reputation, modern Perl is often a really clean and consistent language. Julia seems to be playing a parallel role for R, although the new-found strength of Python in the data analysis space complicates the analogy. But I wonder: is R undergoing (or about to undergo) a similar renaissance? Are there already examples of "Modern R" out there to serve as templates for the future direction of the language? Or is R happy where it is? [1] Did you know that 'shoddy' was originally a legitimate but low grade of wool, and wasn't necessarily pejorative?
- jghn 12y ago
  I'll admit that as someone who has a package on CRAN, has been using R since ~2001 and who is a normal software developer in their day to day job that the lack of standardization is something I'm guilty of. For me what happened was that my thoughts on appropriate naming, structure, etc has evolved over the 6 (I think?) years of the package's existence but I simply haven't had the time to make the wholesale changes necessary. It's on my todo list, but frankly things like "fix actual bugs" have been sitting on that list for a very long time as well. In general though, I've always found that most packages are pretty crappy and not just for code quality. With a relatively small amount of exceptions what I found over the years was that if you needed to do something it was almost always better to write something yourself than shoehorn someone else's junk into your system. There's an exception w/ Bioconductor, particularly the packages created and maintained by the core devs. And on your point about the renaissance, yes I believe that has been happening, largely driven by Hadley Wickham.
sytelus 12y ago
To me writing tutorials for teaching R these days is same as writing tutorials for Fortran (and I'm sure Fortran still has some nice goodies not available elsewhere). It misdirecting people eager to learn something to a wrong thing. As you can see in this article, every 3rd section of R book or tutorial is often dedicated in gotachas to deal with. We have iPython, Notebook, sci-kitlearn, numpy etc and massive number of R-packages already migrated. I hope there is little need to take trouble of learning R for most people new to it.
- minopret 12y ago
  In the Python portfolio I'd mention matplotlib. Then Sage (sagemath.org) just dazzles me. It's a grand integrated environment using Python with lots of math/stat software built in (including NumPy and R) and lots more optional (including Matlab). You can just go see it and try it at cloud.sagemath.com. If you like it you can continue to use it there or you can download it - it's free open-source software.

110 points by tacon 12y ago

I actually quite like the R language. A buddy of mine is in his University's PoliSci program and one of the requirements is to learn R for statistical and trend analysis. He could not stop complaining until I offered to help him learn it by learning it with him. After doing his first assignment, we were both impressed with what could be easily done in R to visualize data. I think he now realizes how useful of a tool R can be in his future career.

epistasis 12y ago
I actually really like it too. The programming language features of it are quite different from what's going on in a Java/C++/C# world, but they are super convenient. Argument matching is really amazing and useful for prototyping. No doubt there's a penalty, but it's exactly the type of power that's needed to build expressive and useful reusable components with rapidly changing designs. And pattern matching like that really helps with the REPL because it allows far faster exploration with fewer keystrokes. Best programming practice in library code would be to have things more fully fleshed out however.
- jghn 12y ago
  I've said for several years now that most complaints about R's syntax and idioms really boil down to "this isn't doing things the way I'm used to doing them, i.e. the C/C++ branch of the language family tree"
- Alupis 12y ago
  Should note that R wasn't really designed for "programmer" programmers. It's largely used by statisticians, analysts, etc, so a lot of the syntax will vary from a C based language.
viewer5 12y ago
If I just want to learn Statistical Programming, is R good for that? I can't afford/not willing to buy SAS or Matlab.
- Alupis 12y ago
  Yes, it is. The language itself is free and the runtime environment can be downloaded for free. There's even a good IDE for it called R Studio, which has a good free version. R is based on S, which itself was purely designed as a statistical programming language.
  - viewer5 12y ago
    How tough would it be to learn R, then get hired at someplace that uses SAS or Matlab or something, and try to learn that? It sounds like R isn't used quite as much as other options

capnrefsmmat 12y ago

I'd like to see a detailed explanation of R's scoping. It's not just lexical scoping; callees can deliberately manipulate the scope their arguments are evaluated in, for example. So you can call a function and pass arguments that are available in local scope, but the arguments are lazily evaluated, and the callee might evaluate them in an entirely different scope. Typically this is done for manipulating datasets. You might have a data frame with columns Width and Height, and so you want to be able to call do.stuff(Width, Height, data=foo), and have Width and Height automatically taken from within foo. But sometimes it crops up in unexpected places.

claytonjy 12y ago
Are you familiar with Hadley's Advanced-R book? You can buy a hardcopy, but it's free online: http://adv-r.had.co.nz http://adv-r.had.co.nz There's a section on lexical scoping, and lots of other non-basic stuff that is hard to find covered elsewhere at all, much less well. From what I've seen, this is absolutely the best reference for deep R stuff that exists.
- capnrefsmmat 12y ago
  I've seen it but haven't read in depth. Now that I see the scoping section I'll have to read through it.
  - claytonjy 12y ago
    Now that it's in print I suspect updates are less frequent, but until a few months ago sections were being added and rewritten pretty frequently, so it might have things now that it didn't when you looked last
rcthompson 12y ago
You can think of the lazily evaluated arguments feature a lot like lisp macros. Basically, any function in R can either get the value of its arguments (i.e. acting like a regular function) or it can get the unevaluated expression passed for that argument (i.e. acting like a lisp macro). So in your "do.stuff" example, it's not a matter of special scoping, per se. The do.stuff function is actually getting the symbols "Width" and "Height" and then choosing to evaluate them with the value of the data argument as the innermost scope.

canjobear 12y ago

Why no discussion of dataframes? I find these to be the most useful aspect of R which I miss the most in other languages.

eric_bullington 12y ago
If you use Python, have you checked out Panda's dataframes? It's not quite the R experience, but pretty close plus you get all the benefits of the Python ecosystem.

minimaxir 12y ago

The biggest "gotcha" for learning R as a programmer is that R interprets character vectors of data frames as factor vectors by default, which will usually break something in your code. If you're learning R, learn to use dplyr for data manipulation and ggplot2 for plotting. Both will save you a lot of time.

claytonjy 12y ago
Agreed, this was a huge pain point for me early on; I started setting the necessary option to force stringsAsCharacters rather than specifying that arg in read.csv everytime. Edit: I had mentioned data.table as being better than dplyr for performance reasons, but it has a unique learning curve and isn't really good for beginners
ggrothendieck 12y ago
To be clear this applies to data frames, not the language as a whole. If you are creating a data frame by reading in data then `read.table(...whatever..., as.is = TRUE)` will read in character columns as character strings and not as factors. `data.frame()` has a `stringsAsFactors` argument which can be set to `FALSE` and one can change the global default with: `options(stringsAsFactors = FALSE)` .
craigching 12y ago
> If you're learning R, learn to use dplyr for data manipulation I had been learning data.table, but I really like dplyr's % operator and the compositional functions better. I think I'm going to make the move to dplyr.
- hadley 12y ago
  FWIW The performance difference is insignificant unless your working with >>10 of millions of rows.
  - craigching 12y ago
    I thought dplyr (note, not plyr, dplyr is the next iteration of plyr implemented mostly in native code) was pretty much as fast as data.table.

mjt0229 12y ago

R is one of those languages that looks like it was designed in a vacuum by a very smart person. It has many common, modern PL constructs, but they're expressed syntactically in a way that in no way resembles any other language I've seen. The entire syntactic legacy of Algol, Pascal, C, etc, all are thrown by the wayside. Familiarity with any of those syntaxes felt to me like more of a liability than a help. That's not to say that the concepts don't apply, just the syntax.

claytonjy 12y ago
I agree, and I think that's exactly why an article like this exists. The R learning curve seems to be much gentler on people without too much serious programming experience in another language. Have you looked at Julia at all? I'm only mildly familiar, but it looks super promising and I'm curious if the syntax there seems more normal or predictable for an experienced dev.
- mjt0229 12y ago
  Yeah, I should have mentioned that - R for Programmers is exactly the kind of thing I'd need, even if it's not useful for my friends and family (largely scientists rather than programmers for whom the legacy of programming language syntax is completely unknown). Julia looks cool; I think the syntax is meant to look familiar to people who've used Matlab or Octave extensively. I don't do tons of scientific computing, but Julia is on my list of tools to learn.
chappi42 12y ago
R is a 'rip-off' / open source re-implementation of the commercial S language by John Chambers. (There are some differences in scoping iirc). Haven't many of its 'modern' PL constructs already been used in Lisp (dialects) before?
- jghn 12y ago
  Indeed, it's not the hodgepodge that many people believe it to be, it just comes from a heritage that's much further away in the PL family tree than they're used to. There are something things which were grafted on to S/R later on. For instance the S4 OOP system was added much later (in S4) and was based on Dylan's OOP system. So there's a case where the GP is correct that it was taking structure from another PL but it's also one which is fairly foreign to a lot of OOP devs

jmount 12y ago

To my mind some of R's plusses are data frames and the ability to indicate missing values in vectors of any type. Some of the weird stuff is the lazy evaluation of arguments, the ability to know the names of variables bound to function arguments, and the ability to snoop up the environment stack. Some distinct minuses are the changing of types (dropping of dimensions on select), semi-reserved terms, and c()'s squashing of complex types. One of my articles on the topic: Survive R http://www.win-vector.com/blog/2009/09/survive-r/ http://www.win-vector.com/blog/2009/09/survive-r/ .

craigching 12y ago
And plotting, especially ggplot2!

daveloyall 12y ago

TL;DR: Tragically, R has a lot in common with old-skool PHP and MS Excel, at the same time.

deleted 12y ago
[deleted]
kyberias 12y ago
I don't know why exactly you're getting downvoted. I've tried learning R many times, but the resemblance to PHP problems is just too much.
- daveloyall 12y ago
  Probably there aren't legitimate semantic or syntactic similarities between the three. But for proposes of a TL;DR, I believe that a qualitative description of the situation should suffice. I'll try again: TL;DR: The language R lacks the quality without a name. Or: Jeeze, now I know why none of my previous attempts to learn a little R were fruitful. Or: The language R seems to have been developed in isolation and thus it fails to adhere to any particular convention--it is its own beast. Further, it sometimes lacks self-referential integrity and coherence. Or: haha cf. PHP or Excel. :)
  - nkurz 12y ago
    I voted you up here because you were below zero and I was also about to post a comment comparing R to PHP, but I'll mention that I frequently downvote one line posts that start with "TL;DR". The concept of "too long didn't read" implies (to me, and probably others) that that the article isn't worth reading. If I think the article is worth reading, and the short comment isn't incredibly insightful, I'd usually prefer such comments to be at the bottom of the page and grayed out.
    - daveloyall 12y ago
      Oh, I hadn't considered "too long, DON'T read". Hm. This article is worth reading if you're interested in R but not yet well acquainted with it.

chuckcode 12y ago

I find R to be a great language for exploring a data set and doing some prototyping. There are a lot of wonderful statistical tools available through the core packages and even more through the various community extensions. It does have some significant issues that I've found limit the usefulness outside of prototyping - pass by value only means code tends to end up as monolithic functions - very slow in loops so lot contorting to move things to matrix operations - they just last year got a version out that starts support for vectors and matrices with > 2^31 -1 elements which limits larger data applications. I find the plotting with ggplot and statistical functionality to be second to none though.

rm999 12y ago
> pass by value only means code tends to end up as monolithic functions I've actually found R works very well as a functional language with very lean functions. It's perhaps worth noting that R doesn't copy a dataframe in a function call if you don't modify it, which is a very common use-case for me. (I'm not sure if this extends to other datatypes) > very slow in loops so lot contorting to move things to matrix operations This is a fair criticism, I think more modern languages like Julia will win out here. That said, R has huge library support, I've often found there are compiled versions for a lot of what I want to do. > they just last year got a version out that starts support for vectors and matrices with > 2^31 -1 elements which limits larger data applications Again, a fair criticism. I've never considered R a "big data" tool, my workflow is usually a funnel where each step involves reducing data size by 1-3 orders of magnitude. For example, I may have 1 PB of transactional data, aggregate it in Hadoop to 20 TB of daily aggregated data, run a query that filters and aggregates it further, and then run my analysis in R on final data. In the end I may end up with 20 GB of data, which R can very easily handle.
- chuckcode 12y ago
  Python has better and better support for R with Rpy2 and R like data frames with Pandas, which is helping me take advantage of the incredibly useful analysis libraries in R. Also note that loops are slow enough that it is really worth learning the *apply() functions in R to avoid iterating over collections. For a relatively in depth explanation check out Hadley Wickham's book http://adv-r.had.co.nz/Functionals.html http://adv-r.had.co.nz/Functionals.html

elliott34 12y ago

I love pandas much more than R but GOD I love Rstudio. Such a great IDE. Rstudio server, actually. My equivalent is running ipython notebook on an ec2 instance, which....is fine, but is a lot of scrolling.

claytonjy 12y ago
As an every-day R user but only-occasional python user, everytime I do a python project I spend some time looking for a comparable IDE. Closest i found was Spyder, but random lock ups made it unusable. Back to terminal+ipython and sublime. Sublime REPL + ipython doesn't cut it either. What do you love about pandas, is it performance, syntax, access to other python modules? If performance, take a look at R's data.table package: almost any manipulation can be done by reference.
- Toenex 12y ago
  PyCharm is worth a look but I agree with the terminal+ipython resort, a place I often find myself too. RStudio was great and then I tried the server version and it suddenly became bloody amazing as I was able to keep R close to the data and have a completely remote IDE.
- elliott34 12y ago
  Now if we can only get sublime text 2 within an ipython notebook on the cloud..... For me, R was my first language, and then I learned Python, and beautiful things like list comprehensions, and it just clicks with my brain a bit more. In pandas, a group by operation is beautiful dataset2=dataset1.groupby([stuff], as_index=False).mean() Same with pivot table... When I did this with dplyr2 my work flow would be a few more steps, creating the "summarise" object and so forth and so on, which seems like more steps.
psychometry 12y ago
RStudio is great for R files, but has awful memory leaks if you're using it for RSweave/knitr documents. I weaned myself off RSTudio onto Sublime+Various Packages+R Console and couldn't be happier.

aagha 12y ago

I'm curious for others thoughts on what to use for complex statistics if you needed high performance/speed. Running an R script on a server to process data isn't efficient, but does that mean you have to roll your own stats package if you want to have a Java (for example) back-end?

scroy 12y ago
Not sure how complex your use case is, but I've found Pandas (on Python) to be just as powerful and much more performant than R for working with scientific data. It's built on Numpy so you can use Scipy's statistical functions with it seamlessly.
- hadley 12y ago
  Recent benchmarks show the performance of pandas, data.table and dplyr to be pretty similar, with data.table usually being the fastest.
arun_sriniv 12y ago
Here's a benchmark Matt recently did comparing data.table, dplyr and pandas on 50GB and 100GB: https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A...

plinkplonk 12y ago

An "R for programmers" style book is Hadley Wickham's glorious "Advanced R" (available at http://adv-r.had.co.nz/ http://adv-r.had.co.nz/). R never made too much sense to me till I read through this. Reccomended.

fallat 12y ago

Haskell is better than R, for both programmers and data analysts.

redacted 12y ago
Could you expand on why? I'll be starting a data-heavy side project and have meant to make an effort with Haskell for a while I always thought that R is fairly unmatched in both breadth and depth for statistical work and general data analysis, with the Python stack in second (e.g., numpy+scipy+pandas+...).
michaeltoth 12y ago
It's going to be harder to do just about anything related to data analysis in Haskell than in R. In R I can load a dataset, do some formatting, and produce a well designed plot in fewer than 5 lines of code. Haskell might run faster, and it's certainly more versatile, but for data analysis it isn't even comparable.
ww2 12y ago
People use R as a convenient tool.i.e., R users are tool/library users, not tool builders in general. Haskell cannot compete with R for this user base.

lottin 12y ago

For me the main problem with R is that I find it hard to break lines nicely. I tend to end up with massive one-liners, and no matter where I break it never looks quite right.

chappi42 12y ago

The language might experience the 'peak R' point right now. 'Badass' statistic packages but R always felt a bit 'hacked together'. With Julia on the other hand, I get the impression that there are developers in charge which have a deep understanding about programming languages and computer science. It's (too) early times for Julia but I wouldn't be surprised if in two years many users will (partly) switch.

kyberias 12y ago

When reading this article, I started to wonder whether it would be plausible to create a REPL or a compiler from some "real" programming language (like, I don't know, C#, C++ or Python) to R to utilize R's statistical libraries without going insane. This might be a fun exercise as an LLVM backend. :)

xname 12y ago

One thing I don't like about R is the OOP part of R. I don't see any reason of R including OOP into its design and sometimes it just creates confusions.

jghn 12y ago
R didn't include OOP into its design, it was all grafted on later, are there are multiple systems. There's the S3 OOP system, the S4 OOP system, reference classes and at least one add on package on CRAN which does something different. So which one are you complaining about? :)

nkurz 12y ago

I've been looking at a variety of R packages, mostly for the purposes of rewriting them in C++ for greater speed, and my assessment is that most of them are of very low code quality. I don't mean that they don't work (they usually do), or that they are too slow (they usually are, but this is explained by selection bias given the reasons I'm looking at them), but that there is little standardization even with a given package, and the 'foundations' seem weak. Variable names are a hodgepodge of unhelpful single letter abbreviations theSecondArgumentToTheFunction; functions alternate between camelCase, dots, and underscores; and any form of architecture seems at best an afterthought. It seems like the base language encourages this, or at least does nothing to prevent it. It's commonplace to pick on Perl, but the overall quality of popular packages seems considerably lower on CRAN than CPAN. Perhaps this is because Perl is so conscious of its reputation at this point that the remaining programmers take great pain to write clear code? I feel like R is currently in the stage where Perl and PHP were as the internet was just when the internet started to explode. The first-to-market CGI scripts and libraries, often written by domain expert non-programmers, became the default choices which the rest of the infrastructure was built on. At some point, the weight became too great for the shoddy[1] construction, and most people moved on to languages with better attention to maintainability and foundational detail (Python, Ruby). Those who remained with the language evolved it in similar directions, by replacing the earlier libraries with better designed ones and by setting a higher standard for community norms. I'm not sure about PHP, but contrary to reputation, modern Perl is often a really clean and consistent language. Julia seems to be playing a parallel role for R, although the new-found strength of Python in the data analysis space complicates the analogy. But I wonder: is R undergoing (or about to undergo) a similar renaissance? Are there already examples of "Modern R" out there to serve as templates for the future direction of the language? Or is R happy where it is? [1] Did you know that 'shoddy' was originally a legitimate but low grade of wool, and wasn't necessarily pejorative?

jghn 12y ago
I'll admit that as someone who has a package on CRAN, has been using R since ~2001 and who is a normal software developer in their day to day job that the lack of standardization is something I'm guilty of. For me what happened was that my thoughts on appropriate naming, structure, etc has evolved over the 6 (I think?) years of the package's existence but I simply haven't had the time to make the wholesale changes necessary. It's on my todo list, but frankly things like "fix actual bugs" have been sitting on that list for a very long time as well. In general though, I've always found that most packages are pretty crappy and not just for code quality. With a relatively small amount of exceptions what I found over the years was that if you needed to do something it was almost always better to write something yourself than shoehorn someone else's junk into your system. There's an exception w/ Bioconductor, particularly the packages created and maintained by the core devs. And on your point about the renaissance, yes I believe that has been happening, largely driven by Hadley Wickham.

sytelus 12y ago

To me writing tutorials for teaching R these days is same as writing tutorials for Fortran (and I'm sure Fortran still has some nice goodies not available elsewhere). It misdirecting people eager to learn something to a wrong thing. As you can see in this article, every 3rd section of R book or tutorial is often dedicated in gotachas to deal with. We have iPython, Notebook, sci-kitlearn, numpy etc and massive number of R-packages already migrated. I hope there is little need to take trouble of learning R for most people new to it.

minopret 12y ago
In the Python portfolio I'd mention matplotlib. Then Sage (sagemath.org) just dazzles me. It's a grand integrated environment using Python with lots of math/stat software built in (including NumPy and R) and lots more optional (including Matlab). You can just go see it and try it at cloud.sagemath.com. If you like it you can continue to use it there or you can download it - it's free open-source software.