I do most of my stats work in Matlab, why should I use R?
So, some reasons a Matlab user might want to consider learning R:
-- R is currently the lingua franca for academic statisticians. New methods papers, textbooks, and toolkits are much more likely to ship with R libraries and implementations then Matlab or anything else.
-- Speaking of statistics, the MathWorks' most recent revamping of the Statistics Toolbox is an obvious imitation and pale shadow of base R, giving you a more verbose way to do half of what base R does for statistics, and then R has everthing available on CRAN to add to that.
-- R is a smaller, yet more expressive language. To be fair, it has about the same density of WTF and non-orthogonality as Matlab (which see, Patrick Burns' "The R Inferno") but makes up for it by being much better at functional programming, and by having more of the Lisp nature in general (R is homoiconic; you can write R code that manipulates R code). If you want object-oriented programming you're about equally screwed in both languages though. R has syntactic support for named/optional arguments to functions, as opposed to Matlab's horrible InputParser/nargin hacks.
-- and in general the idea of giving names to things (rows and columns of a matrix, individual elements, function arguments) is pervasively supported in R. Matlab doesn't even have a decent approximation of a hashtable.
-- R is not quite as insistent on being its own universe. For example, you can write R scripts invokable directly from the shell, without jumping through awful "expect" style hoops and waiting 30 seconds for system startup / license server failure every invocation. For reproducible analysis you really want a build tool ( http://archive.nodalpoint.org/2007/03/18/a_pipeline_is_a_mak... ); so having your analysis scripts being callable by Make or SCons is a no-brainer.
-- Speaking of reproducibility, much of the "reproducible research" movement (which basically says, "hey, maybe scientific data analyses and papers should be done with version control, build automation, and maybe even testing, like software people have been doing since forever") is centered around R. I'm currently doing a project with "knitr," an R library that helps writing reproducible reports; if I want to talk about a particular graph or cite a p-value, I don't manually copypasta the data into a word processor; I write in my document the command to compute the value or plot the graph, and it gets updated whenever I render to a PDF. That ensures that results keep track of any changes in dataset or analysis.
-- The R community in general is more frank about its shortcomings and limitations, which might only be possible in a free software project. For both systems, you can say, word for word, that "there are a lot of awful decisions that (R/MATLAB) inherited due to (S/MATLAB)'s 1970s origins in (John Chambers/Cleve Moler)'s attempt to build a useful sort of interactive shell over Fortran numerics libraries, which it turns out should not be what you build a real programming language on top of." The difference is that the R folks will talk openly about the ways in which R sucks, but you won't get any such acknowledgement from the Mathworks.
-- R-help is both more active and contains smarter inhabitants than comp.soft-sys.matlab. Similarly for R/Matlab questions on StackOverflow.
-- R has a much better packaging system. Actually I should change the emphasis: R even has a packaging system. Libraries install from CRAN/Bioconductor/Rforce with one command, and installing them doesn't tromp all over the global namespace. The code is much higher quality than you find on the Mathworks File Exchange; most of the time I look on the File Exchange anything that looks like it solves my immediate problem hasn't been updated for 5 years and no longer works. CRAN on the other hand has maintainers who will remove packages that stop working. Consequently, people take more ownership of their packages.
-- ggplot2 is the best library in any language for taking your data and making a useful 2d plot out of it. I've written hundreds of lines of Matlab to build graphics that are a couple of phrases in ggplot. On the other hand, Matlab is better at 3d graphing (which I hardly use) and interactive graphics (but there's a lot of people attacking that on the R side.)
-- Simlarly I've written hundreds of lines of Matlab to do data manipulation operations that are like breathing air with Hadley's other great library, plyr.
-- Downsides? R is somewhat slower (if you want to compare two laughably slow languages; we're talking roughly CPython vs Ruby). Matlab has a better IDE with better debugging facilities. Depending on your field you might have more colleagues that are familiar with Matlab (true for engineering, definitely false for statistics.) R's online help is harder to navigate, which lends it a somewhat more difficult learning curve. Actually creating a package to distribute your code is pretty hard to figure out.
TL;DR don't use R if you work with large datasets
For R there is the bigmem package for mmapped arrays. And the "compiler" JIT packace is included since R 2.13.
I've seen that link before. See above re: one group's willingness to talk about the shortcomings versus another organization's preference to paper over it with marketing.
Is this the same in industry? I've heard a professor say that SAS is more widely used.
I use R almost exclusively and I absolutely love it. But people can get a little carried away with picking and recommending the "best" language for particular task.
If you feel very comfortable and skilled in Matlab, and you aren't finding that there are regular situations where Matlab can't do what you want, I wouldn't really advocate switching. Same goes for someone working primarily in Python.
The biggest reason I would recommend switching away from a language you're already very comfortable in would be the availability of statistical methods that aren't present in your language of choice. Statisticians tend to work in R, so much of the cutting edge work ends up in packages on CRAN.
So I wouldn't dump Matlab if you're happy with it, unless you're just looking to learn something new, out of intellectual curiosity, which is always fun.
But I also agree that if you're already proficient with Matlab and happy, then maybe you don't need to learn R (though often you can be blissfully ignorant of your possibilities if you are unaware of the vast libraries that another language/environment offers).
I've used both extensively (though not for statistics). R's syntax is a little more C-like and consistent than MATLAB's - however the biggest difference is documentation.
R's, like most open source documentation, is rather terse and often very unsatisfactory. This gets especially apparent once you get into 3rd party libraries and use things like Bioconductor. You’ll have no idea how things are designed to be used, and without a guru at hand to walk you through you’ll be in a world of pain. Googling for solutions is also very difficult (even using something like RSeek). There are some archaic boards that sometimes have what you need, but often you'll get stuck and not know what to do. What’s nice about MATLAB is that all the libraries are made by a competent team of engineers and they put in the money/resources to have good documentation. Even the more abstract rarely used libraries have decent documentation. In R, if you try using non standard libraries, you’re gunna get screwed.
The IDEs for R are also worse. RStudio is quite nice, but it's really bare-bones compared to MATLAB's IDE. The one really neat thing about it is that you can host it on a server and then remotely work on your work by just going to a URL.
Also I think there are legacy issues in R (though MATLAB has those too). So there are for instance matrices, dataframes and lists (which are list vectors, but not at all). Why there are these three formats that fundamentally do the same thing is beyond comprehension. (Maybe someone can give some insight) Functions will randomly return one type or another. I always find myself fighting to keep the types consistent and R keeps trying to mess with me. In MATLAB everything is a matrix, so that makes things a lot easier
Fundamentally the issue is that MATLAB has a much larger user-base than R, so you'll just have a much easier working with it.
If R's documentation and community was on the same level as MATLAB's then I would maybe consider recommending it. If you work in genetics and you need to use something like Bioconductor, then R is a must I guess. Most other libraries are Fundamentally it's just some syntax differences.
The expression "You get what you pay for" is really pertinent here.
Note: I personally still use R for plotting, because I’m personally more familiar with it. Otherwise I try not to touch it. Code organization for me always gets messy, but I guess that’s cus I’m used to writing in OO languages.
Many of your points are quite subjective. I could do the same thing with Matlab. For instance, I find it mind boggling that anyone could get anything done when you have to devote a separate file to every single functions. That seems incomprehensible to me. And yet, I realize that that's probably a mostly subjective thing that you get used to.
Personally, I find R's documentation excellent. When people complain about it, it's usually because they have mistaken it for a tutorial. It's not. It's documentation.
Without any data, I seriously doubt your claim that Matlab has a much larger user base. (There is considerably more activity in R on StackOverflow than in Matlab.)
Your complaint about matrices, lists and data frames is similar. Data frames exist for the same reason that there's a mean() function: a columnar data structure that holds differently data types in each column comes up so often and is considered so useful that it is built in.
pandas in Python was developed in a way that went out of its way to specifically _mimic_ these data structures because data frames are considered such a vital aspect of R.
And keep in mind that these criticisms are all coming from someone who _also_ recommended against switching...!
> When people complain about it, it's usually because they have mistaken it for a tutorial. It's not. It's documentation.
I don't really see the distinction. Documentation is supposed to explain to you how to use the code. You can call it whatever you want. If it's through a tutorial, then why not. R - and especially the non-standard packages you download through CRAN - have very terse documentation that barely explain how each function works on it's own, and much less how it works in the context of the rest of the package. You can't just tell the user what goes into the black box and what comes out and expect people to be able to use your software.
Sure they're are vignettes (I think that's the term), but they're really inadequate b/c they only scratch the surface of how the package is meant to be used.
Anyways, that's my 2 cents. I've spent soooo many hours fighting with R documentation trying to figure out how to get what I needed done. Sometimes months later I would find out there is a much better way to do something that simply was not explained anywhere. I'm OK at R now, but I went through a lot of pain to get to where I am now. I'd never wish it on anyone else.
My experience with MATLAB on the other hand has always been very pleasant. I spent like 3 hours going over the tutorial on how to use it (much better then R's "Introduction to R") and I hit the ground running. When I needed something a quick search through the help or online always turned up results.
I don't know whether it's an explicit or implicit design choice or just a happy accident, but I'm grateful that the R documentation doesn't try to hold anyone's hand and guide them through data analysis beyond their training.
R's documentation is terse and unorganized. Anyone who says different is obviously someone with more experience with the language (which is not how it should be). When people complain about the documentation it's because they're trying to learn, and if the documentation isn't together learning is painful. Learning R is painful.
Next- Learning is painful, and the documentation is horrible because: the actual built in functions or extensions are a nightmare of arguments that [sometimes do/sometimes don't work]. If you plot x and you don't want the key to show up then set auto.key=FALSE, if you plot class(x) = [something else] and don't want the key to show up set colorkey=FALSE, if you want to layer plots: load this library and format your data to a new S3/S4 (object) type, add them together, then plot them (without the library you can't add these objects)... In case you didn't catch that: I wanted to layer the plots so I had to load a function that made the objects I wanted to plot add, not load a library that would change the plotting driver.
The community around R is disenchanted. If you get into the right place on the web and ask about a feature that doesn't exist: more often than not I've seen the typical guru response of the type "It doesn't work that way - that's a feature, not a problem." Change is bad, and in my experience generally discouraged.
Finally (and perhaps this really doesn't need to be said) the more I've been working with it - the more I feel like the reason it is so popular is because you can load it up read your data set, google some of the more central functions, and poof - get that set of characteristic statistical answers everyone needs to have in their [presentation/HW assignment]. If you look at the answers in this thread you get 2 types A) it's impossible to use, B) it does standard statistical analysis really easily. In my narrow world view it appeals to people who don't work with software a lot - professionals who need some automated numbers on the side without hiring someone to do it right.
That all said, I still do use R. You can do some nifty statistical analysis pretty easily and push it to a vector plot, pop it open with your favorite editor and post edit it super pretty (in a few days).
(I'm not particularly an R fan. It's great for stats, numerical calculations and plotting, but it's not a good programming language.)
m <- lm(y ~ wage + degreeAttainment + jobTenure, data = mydataset) summary(m) plot(m) predict(m, myotherdataset)
Things like heteroskedasticity-robust hypothesis testing, GLM, time-series models are supported without purchasing extra packages or coding it yourself. The graphics are much better and lattice/trellis plots are supported natively (I don't think there's any good way to them in matlab).
Basically, the stats and graphics are easy enough to type that you can do all of the exploratory data analysis that you (should) feel guilty for skipping when you use matlab. :)
http://stackoverflow.com/questions/3534867/why-when-should-i...
But really I just feel like (certainly in my field) the open source community around R is huge and thriving and it's a better bet than Octave in terms of that.