Please read the rw-FAQ Q2.9. There are ways to raise the limit, and you have not told us that you used them (nor the version of R you used, which matters as the limits are version-specific).
Beyond that, there are ways to use read.table more efficiently: see its help page and the 'R Data Import/Export' manual. In particular, did you set nrows and colClasses? But for the size of problem you have I would use a 64-bit build of R. On Thu, 4 Jan 2007, domenico pestalozzi wrote: > I think the question is discussed in other thread, but I don't exactly find > what I want . > I'm working in Windows XP with 2GB of memory and a Pentium 4 - 3.00Ghx. > I have the necessity of working with large dataset, generally from 300,000 > records to 800,000 (according to the project), and about 300 variables > (...but a dataset with 800,000 records could not be "large" in your > opinion...). Because of we are deciding if R will be the official software > in our company, I'd like to say if the possibility of using R with these > datasets depends only by the characteristics of the "engine" (memory and > processor). > In this case we can improve the machine (for example, what memory you > reccomend?). > > For example, I have a dataset of 200,000 records and 211 variables but I > can't load the dataset because R doesn't work : I control the loading > procedure (read.table in R) by using the windows task-manager and R is > blocked when the file paging is 1.10 GB. > After this I try with a sample of 100,000 records and I can correctly load > tha dataset, but I'd like to use the package tree, but after some seconds ( > I use this tree(variable1~., myDataset) ) I obtain the message "Reached > total allocation of 1014Mb". > > I'd like your opinion and suggestion, considering that I could improve (in > memory) my computer. > > pestalozzi > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.