How well does R scale to text classification tasks? I am trying to get upto speed with R. I eventually want to use R libraries for doing text classification. I was just wondering what people's experiences are with regard to R's scalability when it comes to doing text classification. 
I am likely to run into high dimensional data (~300k dimensions). I am looking at using SVM and Random Forest in particular as classification algorithms. 
Would R libraries scale to my problem size?
Thanks.
EDIT 1: Just to clarify, my data set is likely to have 1000-3000 rows (perhaps a bit more) and 10 classes.
EDIT 2: Since I am very new to R, I will request the posters to be more specific where possible. For example, if you are suggesting a workflow/pipeline, please be sure to mention the R libraries involved in each step if possible. Some additional pointers (to examples, sample code etc.) would be icing on the cake.
EDIT 3: First off, thanks everyone for your comments. And secondly, I apologize, perhaps I should have given more context for the problem. I am new to R but not so much to text classification. I have already done pre-processing (stemming, stopword removal, tf-idf conversion etc.) on my some part of my data using tm package, just to get a feel for things. tm was so slow even on about 200docs that I got concerned about scalability. Then I started playing with FSelector and even that was really slow. And that's the point at which I made my OP.
EDIT 4: It just occurred to me that I have 10 classes and about ~300 training documents per class, and I am in fact building the termXdoc matrix out of the entire training set resulting in very high dimensionality. But how about reducing every 1-out-of-k classification problem to a series of binary classification problems? That would drastically reduce the number of training documents (and hence dimensionality) at each of the k-1 steps considerably, wouldn't it? So is this approach a good one? How does it compare in terms of accuracy to the usual multi-class implementation?    
 A: First, welcome!  Text processing is lots of fun, and doing it in R is getting easier all the time.
The short answer: yes - the tools in R are now quite good for dealing with this kind of data.  In fact, there's nothing special about R, C++, Groovy, Scala, or any other language when it comes to data storage in RAM: every language stores an 8 byte double float in...wait for it...wait for it... 8 bytes!
The algorithms and their implementation do matter, especially if implemented very poorly with regard to data structures and computational complexity.  If you are implementing your own algorithms, just take care.  If using other code, caveat emptor applies, as it does in any environment.
For R, you will need to consider:


*

*Your data representation (look at sparse matrices, especially in the Matrix package)

*Data storage (perhaps memory mapped, using bigmemory or ff; or distributed, using Hadoop)

*Your partitioning of data (how much can you fit in RAM is dependent on how much RAM you have)


The last point is really under your control.
When it comes to this dimensionality, it's not particularly big anymore.  The # of observations will be more of an impact, but you can partition your data to adjust for RAM usage, so there's not really much to get too worried about.
A: I agree with crayola that the number of rows is crucial here. For RF you will need at least 3x more RAM than your dataset weights and probably a lot of time (such number of attributes usually requires a lot of trees in the forest -- and note that there is no parallel implementation of RF in R).
About SVM, I doubt it is a good idea to fight with 300k dimensions while you probably can develop a kernel function that will be equivalent to your descriptors of text.
EDIT:
3k x 30k (real) matrix would occupy something like 7Gb, so all you need to do RF (using randomForest) on this data is a computer with 16GB RAM, some luck and quite a bit of time or just a computer with 24GB RAM and quite a bit of time. 
A: As requested in a comment, here are some pointers for processing steps.  A number of tools may be found at the CRAN Task View for Natural Language Processing.  You may also want to look at this paper on the tm (text mining) package for R.


*

*Prior to processing, consider normalization of the word tokens.  openNLP (for which there is an R package) is one route.

*For text processing, a common pre-processing step is to normalize the data via tf.idf -- term frequency * inverse document frequency - see the Wikipedia entry for more details.  There are other more recent normalizations, but this is a bread and butter method, so it's important to know it.  You can easily implement it in R: just store (docID, wordID, freq1, freq2) where freq1 is the count of times the word indexed by wordID has appeared in the given document and freq2 is the # of documents in which it appears.  No need to store this vector for words that don't appear in a given document.  Then, just take freq1 / freq2 and you have your tf.idf value.

*After calculating the tf.idf values, you can work with the full dimensionality of your data or filter out those words that are essentially uninformative.  For instance, any word that appears in only 1 document is not going to give much insight.  This may reduce your dimensionality substantially.  Given the small # of documents being examined, you may find that reducing to just 1K dimensions is appropriate.

*I wouldn't both recentering the data (e.g. for PCA), but you can store the data now in a term matrix (where entries are now tf.idf values) with ease, using the sparse matrices, as supported by the Matrix package.


At this point, you have a nicely pre-processed dataset.  I would recommend proceeding with the tools cited in the CRAN task view or the text mining package.  Clustering the data, for instance by projecting onto the first 4 or 6 principal components, could be very interesting to your group when the data is plotted.
One other thing: you may find that dimensionality reduction along the lines of PCA (*) can be helpful when using various classification methods, as you are essentially aggregating the related words.  The first 10-50 principal components may be all that you need for document classification, given your sample size.
(*) Note: PCA is just a first step.  It can be very interesting for someone just starting out with text mining and PCA, but you may eventually find that it is a bit of a nuisance for sparse data sets.  As a first step, though, take a look at it, especially via the prcomp and princomp functions.
Update: I didn't state a preference in this answer - I recommend prcomp rather than princomp.
