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R 3.0.0 Released
DaBombDotCom writes "R, a popular software environment for statistical computing and graphics, version 3.0.0 codename "Masked Marvel" was released. From the announcement: 'Major R releases have not previously marked great landslides in terms of new features. Rather, they represent that the codebase has developed to a new level of maturity. This is not going to be an exception to the rule. Version 3.0.0, as of this writing, contains only [one] really major new feature: The inclusion of long vectors (containing more than 2^31-1 elements!). More changes are likely to make it into the final release, but the main reason for having it as a new major release is that R over the last 8.5 years has reached a new level: we now have 64 bit support on all platforms, support for parallel processing, the Matrix package, and much more.'"
Someone who can't afford license fee of SAS or Matlab, this is the best alternative out there. And in some cases a better alternative.
Not well known but R's accessibility support is far better. Here is an example from a paper accepted in R Journal
Statistical Software from a Blind Person's Perspective
A. Jonathan R. Godfrey
http://journal.r-project.org/accepted/2012-14/Godfrey.pdf
As in: when they release it, you can trust it to work.
Hence they didn't mess around with major reconstruction of R's guts until they could release something that's finished (and well-tested !) and bumped the version number to 3.0.0 when they did in order to properly differentiate it from previous versions.
This is one of the differences between amateur OSS offerings (like for example KDE with its miriad half-baked Kxxx packages, sundry horrible OSS games, etc.) and genuine production-quality OSS (like R, Lapack, Octave, Libre Office, PostgressQL, MySQL, GRASS GIS, QGIS, Maria DB, GNU CC, the Linux kernel etc.)
This is very gratifying as R happens to see widespread use in academia, government and business when it comes to data analysis and statistics.
If R has a weakness, it is that uses an in-memory approach to data-processing, unlike e.g. SPSS, which keeps almost nothing in memory and simply makes passes through datafiles whenever it needs something. R is also a bit memory-hungry, so the need for genuine 64-bit implementations should be clear.
Apart from sporting about 4000 useful and ready-to-run statistical applications packages, R has convenient and efficient integration with C code and has what's probably a contender for the best support for data-graphics anywhere.
For those who didn't know, even packages like SPSS and SAS have incorporated R interfaces to tap into the wealth of application packages that R offers. Can't think of a more significant compliment right now.
I recently switched my scientific programming from R to Python with NumPy and Matplotlib, as I couldn't bear programming in such a misdesigned and underdocumented language any more. R is fine as a statistical analysis system, i.e. as a command line interface to the many ready-made packages available in CRAN, but for programming it's a perfect example of how not to design and implement a programming language. It's also unusably slow unless you vectorise your code or have a tiny amount of data. Unfortunately, vectorisation is not always possible (i.e. the algorithm may be inherently serial), and even when it is, it tends to yield utterly unreadable code. Then there is the disfunctional memory management system which leads you to run out of memory long before you should, and documentation even of the core library that leaves you no choice but to program by coincidence.
As an example of a fundamental problem, here's an R add-on package that has as its goal to be "[..] a set of simple wrappers that make R's string functions more consistent, simpler and easier to use. It does this by ensuring that: function and argument names (and positions) are consistent, all functions deal with NA's and zero length character appropriately, and the output data structures from each function matches the input data structures of other functions.". Needless to say that there is absolutely no excuse for having such problems in the first place; if you can't write consistent interfaces, you have no business designing the core API of any programming language, period.
Python has its issues as well, but it's overall much nicer to work with. It has sane containers including dictionaries (R's lists are interface-wise equivalent to Python's dictionaries, but the complexity of the various operations is...mysterious.) and with NumPy all the array computation features I need. Furthermore it has at least a rudimentary OOP system (speaking of Python 2 here, I understand they've overhauled it in 3, but I haven't looked into that) and much better performance than R. On the other hand, for statistics you'd probably be much better off with R than with Python. I haven't looked at available libraries much, but I don't think the Python world is anywhere near R in that respect.
Anyway, for doing statistics I don't really think there's anything more extensive out there than R, proprietary or not, although some proprietary packages have easier to learn GUIs. In that field, R is not going to go anywhere in the foreseeable future. For programming, almost anything is better than R, and I agree that those improvements you mention are not doing much to improve Rs competitiveness in that area.
The best-known one is called 'R commander' (package name = Rcmdr ). It gives you a point-and-click interface and (like SPSS) drops the R code to repeat what you did using the menu (so that your work is reproducable).
Functionality includes: data summaries, contingency tables, means tests, proportions tests, variance tests, ANOVA, cluster analysis, model fitting (linear, generalised linear, logit), various graphs, tests for comparison between fitted models, plus draws and lookup tables for lots of continuous and discrete distributions. Rcmdr allows for plugins, and a number of them are also downloadable as R packages (e.g. experimental design).
The second one I know about is called 'Deducer' (package name Deducer), which provides a GUI loosely resembling that of SPSS.
Both GUIs are workable and allow you to do simple things simply.
There's also a rather nice IDE, called RStudio (which is a separate download).
Despite R's weaknesses as a programming language, R has such a large number of well-documented, well-tested, statistical functions with a wide array of arguments to vary that it is very difficult for another language to match. For example, maybe you want to build an arima time series model. OK, not too tough to find a library in Python or C++ that does that. Now what if you want to add an exogenous variable to the arima model? Maybe a seasonal component? Next maybe you want to automatically pick the best model according to AIC? Oops, make that BIC. Looking at it again maybe a Vector Autoregressive model is best. Or a VECM?
While I'm sure there are excellent implementations of all of these wrinkles in other languages, with R, I have great confidence that the functions that I want and need now and in the future are going to be there and are going to be implemented correctly, and kudos to the R team for giving us that kind of confidence.
R does have a lot of problems, among the worst is loop performance. It really forces you to vectorize everything, which leads to less maintainable code, and is generally a coding technique that new hires coming from other languages will face a steep learning curve with. What I have found useful is to use R as a data exploration and model parameterization tool, but once the model is ready to be put into production, you can use the parameters calculated by R in an implementation in the language of your choice, e.g., C++.
I guess this is a long winded way of saying that as with so many questions of "which language is best," the real question is "which question is best for you and your application?" R is usually the best language only for people who are regularly using a such a wide variety of statistical analyses that you won't find a large part of what you need in the libraries of other languages. For me, I couldn't imagine working without it.