I tought that aggregate was the way to go, but only for large dataframes it is faster.
> df <- read.table(stdin(),header=TRUE) 0: Location Time X Y 1: 1 0 1.6 9.3 2: 1 3 4.2 10.4 3: 1 6 2.7 16.3 4: 2 0 0.5 2.1 5: 2 3 NA 3.6 6: 2 3 5.0 0.06 7: 2 6 3.4 14.0 8: > aggregate(df[,3:4],df[,1:2],FUN=mean,na.rm=TRUE) Location Time X Y 1 1 0 1.6 9.30 2 2 0 0.5 2.10 3 1 3 4.2 10.40 4 2 3 5.0 1.83 5 1 6 2.7 16.30 6 2 6 3.4 14.00 > system.time( aggregate(df[,3:4],df[,1:2],FUN=mean,na.rm=TRUE) ) [1] 0.008 0.000 0.008 0.000 0.000 > > system.time( do.call(rbind, by(df, df[2:1], colMeans, na.rm = TRUE))) [1] 0.005 0.000 0.005 0.000 0.000 > > > df <- data.frame(Location=rep(1:50,50), + Time=sample(rep(1:50,each=10),2500,replace=TRUE), + X=runif(2500),Y=runif(2500)) > > system.time( aggregate(df[,3:4],df[,1:2],FUN=mean,na.rm=TRUE) ) [1] 0.162 0.000 0.163 0.000 0.000 > > system.time( do.call(rbind, by(df, df[2:1], colMeans, na.rm = TRUE))) [1] 2.179 0.006 2.216 0.000 0.000 Kees Gabor Grothendieck wrote: > Grouping the data frame by the first two columns, apply colMeans > and then rbind the resulting by-structure together: > > do.call(rbind, by(DF, DF[2:1], colMeans, na.rm = TRUE)) > > > On 10/5/06, Greg Tarpinian <[EMAIL PROTECTED]> wrote: >> R 2.3.1, WinXP: >> >> I have a puzzling problem that I suspect may be solved using >> grep or a regular expression but am at a loss how to actually do it... >> My data.frame looks like >> >> Location Time X Y >> -------- ---- --- --- >> 1 0 1.6 9.3 >> 1 3 4.2 10.4 >> 1 6 2.7 16.3 >> 2 0 0.5 2.1 >> 2 3 NA 3.6 >> 2 3 5.0 0.06 >> 2 6 3.4 14.0 >> >> and so forth. I would like to search for duplicate Time values >> within a Location and take the numerical average (where possible) >> of the elements in X and Y. These numerical averages should >> then be used to create a single row where multiple rows once >> existed. So, I would like to obtain >> >> 2 3 5.0 1.83 >> >> for the two Time = 3 rows for Location = 2 and use it to replace >> these two rows. Ideally, avoiding for(i in 1:blah) loops would be >> nice because the data.frame has about 10,000 rows that need to >> be searched and processed. My intent is to do some comparing of >> SAS to R -- the DATA step processing in SAS is quite fast and >> using the RETAIN statement along with the LAG( ) function allows >> this sort of thing to be done rapidly. >> >> >> Thanks in advance, >> >> Greg >> >> ______________________________________________ >> 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. >> > > ______________________________________________ > 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. > ______________________________________________ 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.