R 3.0.0 Released 75
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.'"
Congratulations R Team (Score:5, Interesting)
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
Re:Congratulations R Team (Score:2, Interesting)
Tell that to all the "scientists" and "researchers" paying money for _and_ investing lifetimes worth of effort into writing libraries for Matlab, Maple, Mathematica, LabView and other proprietary environments, instead of contributing to make the existing free environments better.
Matlab --> GNU Octave, Scilab, NumPy/SciPy
Maple, Mathematica --> Maxima, SymPy
Re:Congratulations R Team (Score:5, Interesting)
I have a license for SAS through my university. I gave up trying to convince the stupid thing to install. If the installer wasn't crashing, the license manager was.
MatLab has similar, though less severe problems.
R had a nice double click installer that worked the first time. Later I compiled it, which worked without any headaches. There's a nice bridge from R to Python and you can extend either one, or embed either or both in other applications.
You meant R has better accessibility options for the disabled but it's just plain more accessible.
Re:How modern! (Score:3, Interesting)
I can somewhat relate to the documentation issue although I believe that it is more a question of organizing the documentation.
When you mention "a fundamental problem" you mention function implementations, thus library rather than language issues. R itself is an extremely expressive, functional (or rather multi-paradigm) language that can be programmed to run efficient code. Yet it is syntactically minimalistic without unneeded syntax (as opposed to all of the scripting languages perl/python/ruby). This makes it a truly postmodern language IMO. Efficiency can sometimes be a problem but the break-even point for implementing parts in say C/C++ is only slightly different than for other languages (say perl/python) and is enabled by an excellent interface (Rcpp package).
For myself the biggest change to make was to start thinking in functional concepts coming from a procedural background. Much of R criticism IMO stems on a failure to realize conceptual differences between functional and procedural programming. Another problem that might spoil the impression of R sometimes is the plethora of packages of highly varying quality.
Re:How modern! (Score:4, Interesting)
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.