On Tue, Mar 22, 2011 at 11:05 AM, Tonja Krueger <tonja.krue...@web.de> wrote: > > Dear List, > I have a data frame with approximately 500000 rows that looks like this: > > Date time value > … > 19.07.1956 12:00:00 4.84 > 19.07.1956 13:00:00 4.85 > 19.07.1956 14:00:00 4.89 > 19.07.1956 15:00:00 4.94 > 19.07.1956 16:00:00 4.99 > 19.07.1956 17:00:00 5.01 > 19.07.1956 18:00:00 5.04 > 19.07.1956 19:00:00 5.04 > 19.07.1956 20:00:00 5.04 > 19.07.1956 21:00:00 5.02 > 19.07.1956 22:00:00 5.01 > 19.07.1956 23:00:00 5.00 > 20.07.1956 00:00:00 4.99 > 20.07.1956 01:00:00 4.99 > 20.07.1956 02:00:00 5.00 > 20.07.1956 03:00:00 5.03 > 20.07.1956 04:00:00 5.07 > 20.07.1956 05:00:00 5.10 > 20.07.1956 06:00:00 5.14 > 20.07.1956 07:00:00 5.14 > 20.07.1956 08:00:00 5.11 > 20.07.1956 09:00:00 5.08 > 20.07.1956 10:00:00 5.03 > 20.07.1956 11:00:00 4.98 > 20.07.1956 12:00:00 4.94 > 20.07.1956 13:00:00 4.93 > … > > I want to calculate > the moving average of the right column. > I tried: > > dat$index<-1:length(dat$Zeit) > qs<- 43800 > erg<-c() > for (y in min(dat$index):max(dat$index)){ > m<- mean(dat[(dat$index>=y)&(dat$index<=y+qs+1),3]) > erg<-c(erg,m) > } > > It does works, but it takes ages. Is there a faster way to compute the moving > average? > > Thank you, > Tonja Krueger
There are rolling mean or sum functions written in C in the caTools, xts and TTR packages (and possibly other packages as well). There are also faster ways to do it even in pure R such as the rollmean function in zoo (although that would not be expected to be as fast as the C implementations). -- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at 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.