When working with datasets too large to fit in memory it is usually best to use an actual database, read the data into the database, then pull the records that you want into R. There are several packages for working with databases, but 2 of the simplest are the RSQLite and sqldf packages (installing them will install the database backend for you). The read.csv.sql function in the sqldf package will read in a csv file by first reading it into the database, then pulling the desired subset (you need to know some basic sql) into R, all the database stuff is handled in the background for you.
On Thu, Sep 18, 2014 at 5:48 PM, Stephen HK Wong <hon...@stanford.edu> wrote: > Dear All, > > I have a table of 4 columns and many millions rows separated by > tab-delimited. I don't have enough memory to read.table in that 1 Gb file. > And actually I have 12 text files like that. Is there a way that I can just > randomly read.table() in 10% of rows ? I was able to do that using colbycol > package, but it is not not available. Many thanks!! > > > > Stephen HK Wong > Stanford, California 94305-5324 > > ______________________________________________ > R-help@r-project.org 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. -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com ______________________________________________ R-help@r-project.org 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.
