Gustaf Rydevik wrote: > On Dec 10, 2007 2:28 PM, Johannes Graumann <[EMAIL PROTECTED]> > wrote: >> Hello, >> >> I have a large data frame (1006222 rows), which I subject to a crude >> clustering attempt that results in a vector stating whether the datapoint >> represented by a row belongs to a cluster or not. Conceptually this looks >> something like this: >> Value Cluster? >> 0.01 FALSE >> 0.03 TRUE >> 0.04 TRUE >> 0.05 TRUE >> 0.07 FALSE >> ... >> What I'm looking for is an efficient strategy to extract all consecutive >> rows associated with "TRUE" as a single cluster (data.frame >> representation?) without cluttering memory with thousends of data.frames. >> I was thinking of an independent data.frame that would contain a column >> of lists that reference all indexes from the big one which are contained >> in one cluster ... >> Can anyone kindly nudge me and let me know how to deal with this >> efficiently? >> >> Joh >> > > How about : > orig.data<-sample(c(TRUE,FALSE),100,replace=T) > Cluster<-data.frame(c.ndx=cumsum(rle(orig.data)$lengths),c.size=rle(orig.data)$lengths,c.type=rle(orig.data)$values) > Cluster<-Cluster[Cluster$c.type==TRUE,] > > ##Then, to get all original data belonging to cluster three: > orig.data[rev(Cluster[3,"c.ndx"]-seq(length.out=Cluster[3,"c.size"])+1)] > > > Not the neatest solution, but I'm sure someone here can improve on it. > /Gustaf
Thank you for this example! "rle" was indeed what safed my day! Joh ______________________________________________ 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.