You could do it with zoo:
DF1 <- data.frame(Datetime = ISOdate(2000, 1:2, 1), Temp = 1:2)
DF2 <- data.frame(Datetime = ISOdate(2000, 2:3, 1), Temp = 3:4)
library(zoo)
# convert to zoo
z1 <- zoo(DF1[,2], DF1[,1])
z2 <- zoo(DF2[,2], DF2[,1])
# merge
z <- merge(z1, z2, all = TRUE)
# take rowmeans
zz <- zoo(rowMeans(z, na.rm = TRUE), time(z))
# or
DF3 <- data.frame(data = rowMeans(z, na.rm = TRUE), Time = time(z))
On Fri, Feb 22, 2008 at 1:36 PM, stephen sefick <[EMAIL PROTECTED]> wrote:
> I have two dataframes in R that were tab seperated .txt files
>
> y<-read.table("foo.txt", header=T)
> x<-read.table("foo.txt", header=T)
>
> these are set up like this:
>
> Datetime Temp
> 01/01/07 00:01 11.5
> 01/01/07 00:16 11.6
>
> etc etc to 66000 rows in y and 33000 rows in x
>
> The two files overlap with the same data for a period of time but
> contain different values outside of these. Is there a way to merge
> these two data sets based on the shared date time column into one
> large dataset of ~90,000 lines. I want to eventually make this into a
> time series with frequency= 1/15.
>
> Thanks
>
> Stephen
> --
> Let's not spend our time and resources thinking about things that are
> so little or so large that all they really do for us is puff us up and
> make us feel like gods. We are mammals, and have not exhausted the
> annoying little problems of being mammals.
>
> -K. Mullis
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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