R is a programming language and environment developed for
statistical analysis.
R is free and open source software allowing anyone to use and
modify it.
R is cross platform (GNU/Linux, Macintosh, MW/Windows) and runs
on both 32bit and 64bit processors.
The R command line is much more powerful than a graphical user
interface.
R has over 1400 packages available specialising in topics like
from Econometrics, Data Mining, Spatial Analysis, Bio-Informatics.
R well integrates packages in different languages, including
Java (hence the RWeka package), Fortran (hence
randomForest), C (hence arules), C++, and
Python.
R has active user groups where questions can be asked and are
often quickly responded to, and often responded to by the very
people who have developed the environment--this support is second
to none. Have you ever tried getting support from people who really
know SAS or are core developers of SAS?
New books for R (the Springer Use R! series) are emerging and
there will soon be a very good library of books for using R.
No license restrictions (other than ensuring our freedom to use
it at our own discretion) and so you can run R anywhere and at any
time.
Cons:
R has a steep learning curve--it does take a while to get used
to the power of R--but no steeper than, for example, SAS.
There is no graphical user interface that compares with the
SAS/Enterprise Guide or SAS/JMP interfaces which are more
comfortable for the new and infrequent users.
Many R commands give little thought to memory management
and so R can very quickly consume all available memory.
