Jeff08 wrote:
Sample Data.Frame formatName is Returns.nodup X id ticker date_ adjClose totret RankStk 427225 427225 00174410 AHS 2001-11-13 21.66 100 1235 "id" uniquely defines a row What I am trying to do is filter out id's that have less than 1500 data points (by date) First, I used total<-by(Returns.nodup, Returns.nodup$id,nrow) which subsetted by ID and calculated the number of data points for each ID Now I am trying to figure out a way to use this to filter out the original data.frame (Returns.nodup) I have tried using the following, but it is VERY slow: z<-unlist(lapply(1:length(y), function(i) which(a$id==y[i]) )) Returns.filtered<-Returns.nodup[z,] Is there a faster way to do this?
Most likely, yes. But without a reproducible example, it's difficult to think about the problem. Can you please give us one?
If not, you can probably cobble something together using ?table and ?%in% I'm guessing.
______________________________________________ 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.
