try this:
> x <- read.table(text = "2012-07-22 12:12:00, 21
+ 2012-07-22 12:15:00, 22
+ 2012-07-22 12:18:00, 24
+ 2012-07-22 12:39:00, 21
+ 2012-07-22 12:45:00, 25
+ 2012-07-22 12:49:00, 26
+ 2012-07-22 12:53:00, 20
+ 2012-07-22 13:00:00, 18
+ 2012-07-22 13:06:00, 22", colClasses = c("POSIXct", "integer"), sep = ',')
> # get minimum at an hour granularity
> tMin <- trunc(min(x$V1), units = 'hour')
> # back off 7.5 minute
> tMin <- tMin - (7.5 * 60)
> # create sequence for 'cut'
> cSeq <- seq(tMin, max(x$V1) + (7.5 * 60), by = '15 min')
> # now split and average
> cCut <- cut(x$V1, cSeq)
> # compute means
> tapply(x$V2, cCut, mean)
2012-07-22 11:52:30 2012-07-22 12:07:30 2012-07-22 12:22:30 2012-07-22 12:37:30
NA 22.33333 NA 24.00000
2012-07-22 12:52:30
20.00000
>
On Mon, Aug 27, 2012 at 9:53 AM, Jason Gilmore <w...@wjgilmore.com> wrote:
>
> Hi,
>
> I'm pretty new to R and have run into a task which although I'm certain is
> within R's capabilities, falls outside of mine. :-) Consider the following
> data set:
>
> 2012-07-22 12:12:00, 21
> 2012-07-22 12:15:00, 22
> 2012-07-22 12:18:00, 24
> 2012-07-22 12:39:00, 21
> 2012-07-22 12:45:00, 25
> 2012-07-22 12:49:00, 26
> 2012-07-22 12:53:00, 20
> 2012-07-22 13:00:00, 18
> 2012-07-22 13:06:00, 22
>
> My task involves creating a data set which *averages* these values at a
> resolution of 15 minutes, meaning that I need to average the values falling
> within 7.5 minutes of a 15 minute increment. Therefore given the above data
> set I need to treat it as three groups:
>
> 2012-07-22 12:12:00, 21
> 2012-07-22 12:15:00, 22
> 2012-07-22 12:18:00, 24
>
> 2012-07-22 12:39:00, 21
> 2012-07-22 12:45:00, 25
> 2012-07-22 12:49:00, 26
>
> 2012-07-22 12:53:00, 20
> 2012-07-22 13:00:00, 18
> 2012-07-22 13:06:00, 22
>
> The end result should look like this:
>
> 2012-07-22 12:15:00, 22.33
> 2012-07-22 12:30:00, NA <- Because this 15 minute slot did not previously
> exist
> 2012-07-22 12:45:00, 24
> 2012-07-22 1:00:00, 20
>
> Any help much appreciated. I've been working on this for several hours with
> little success. I'm able to identify the missing (NA) value using zoo/xts
> but can't seem to sort out the averaging matter.
>
> Thanks so much!
> Jason
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> 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.
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
______________________________________________
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.
