hi patrick. thanks. I think you are right.. combined <- merge( main, aggregate.data, by="day", all.x=TRUE, all.y=FALSE ) lm( stockreturn ~ marketreturn, data=combined )
becomes something like main <- as.data.table(main) setkey(main, "yyyymmdd") aggregate.data <- as.data.table(aggregate.data) setkey(aggregate.data, "yyyymmdd") main <- main[ aggregate.data ] this is fast and memory efficient. for me, on the plus side, data.table is a data.frame, so it can be used easily elsewhere. on the plus side, data.table is super-efficient. on the minus side, data.table often has very strange syntax. for example, main[aggregate.data] is counterintuitive. passing of functions in a slot that should be a tensor index is also strange. I would much prefer it if all non-tensor functionality was in functions, and not in arguments following [ ]. I have written this before: Given that applied end users of statistics typically use data.frame as their main container for data sets, data.frame should be as efficient and tuned as possible. cell assignments should be fast. indexing and copying should be fast. it would give R a whole lot more appeal. the functionality in data.table should be core functionality, not requiring an add-on with strange syntax. just my 5 cents...of course, the R developers are saints, putting in a lot of effort with no compense, so complaining is unfair. and thanks to Matthew Doyle for writing data.table, without which I couldn't do this AT ALL. regards, /iaw ---- Ivo Welch (ivo.we...@gmail.com) On Sun, Oct 9, 2011 at 10:42 AM, Patrick Burns <pbu...@pburns.seanet.com> wrote: > I think you are looking for the 'data.table' > package. > > On 09/10/2011 17:31, ivo welch wrote: >> >> Dear R experts---I am struggling with memory and speed issues. Advice >> would be appreciated. >> >> I have a long data set (of financial stock returns, with stock name >> and trading day). All three variables, stock return, id and day, are >> irregular. About 1.3GB in object.size (200MB on disk). now, I need >> to merge the main data set with some aggregate data (e.g., the S&P500 >> market rate of return, with a day index) from the same day. this >> "market data set" is not a big data set (object.size=300K, 5 columns, >> 12000 rows). >> >> let's say my (dumb statistical) plan is to run one grand regression, >> where the individual rate of return is y and the market rate of return >> is x. the following should work without a problem: >> >> combined<- merge( main, aggregate.data, by="day", all.x=TRUE, all.y=FALSE >> ) >> lm( stockreturn ~ marketreturn, data=combined ) >> >> alas, the merge is neither space-efficient nor fast. in fact, I run >> out of memory on my 16GB linux machine. my guess is that by whittling >> it down, I could work it (perhaps doing it in chunks, and then >> rbinding it), but this is painful. >> >> in perl, I would define a hash with the day as key and the market >> return as value, and then loop over the main data set to supplement >> it. >> >> is there a recommended way of doing such tasks in R, either super-fast >> (so that I merge many many times) or space efficient (so that I merge >> once and store the results)? >> >> sincerely, >> >> /iaw >> >> ---- >> Ivo Welch (ivo.we...@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. >> > > -- > Patrick Burns > pbu...@pburns.seanet.com > twitter: @portfolioprobe > http://www.portfolioprobe.com/blog > http://www.burns-stat.com > (home of 'Some hints for the R beginner' > and 'The R Inferno') > ______________________________________________ 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.
