On Sat, Aug 7, 2010 at 9:18 PM, steven mosher <mosherste...@gmail.com> wrote:
> Very Slick.
> Gabor this is a Huge speed up for me. Thanks. ha, Now I want to rewrite a
> bunch of working code.
>
>
>
> Id<-c(rep(67543,4),rep(12345,3),rep(89765,5))
> Years<-c(seq(1989,1992,by =1),1991,1993,1994,seq(1991,1995,by=1))
> Values2<-c(12,NA,34,21,NA,65,23,NA,13,NA,13,14)
> Values<-c(12,14,34,21,54,65,23,12,13,13,13,14)
> Data<-data.frame(Index=Id,Year=Years,Jan=Values,Feb=Values/2,Mar=Values2,Apr=Values2,Jun=Values)
> Data
> Index Year Jan Feb Mar Apr Jun
> 1 67543 1989 12 6.0 12 12 12
> 2 67543 1990 14 7.0 NA NA 14
> 3 67543 1991 34 17.0 34 34 34
> 4 67543 1992 21 10.5 21 21 21
> 5 12345 1991 54 27.0 NA NA 54
> 6 12345 1993 65 32.5 65 65 65
> 7 12345 1994 23 11.5 23 23 23
> 8 89765 1991 12 6.0 NA NA 12
> 9 89765 1992 13 6.5 13 13 13
> 10 89765 1993 13 6.5 NA NA 13
> 11 89765 1994 13 6.5 13 13 13
> 12 89765 1995 14 7.0 14 14 14
> # Gabor's solution
> f <- function(x) ts(c(t(x[-(1:2)])), freq = 12, start = x$Year[1])
> do.call(cbind, by(Data, Data$Index, f))
> 12345 67543 89765
The original data had consecutive months in each series (actually
there was a missing 1992 in one case but I assumed that was an
inadvertent omission and the actual data was complete); however, here
we have missing 6 month chunks in addition. That makes the series
non-consecutive so to solve that we could either apply this to the
data (after putting the missing 1992 year back in):
Data <- cbind(Data, NA, NA, NA, NA, NA, NA)
or we could use a time series class that can handle irregularly spaced data:
library(zoo)
f <- function(x) {
dat <- x[-(1:2)]
tim <- as.yearmon(outer(x$Year, seq(0, length = ncol(dat))/12, "+"))
zoo(c(as.matrix(dat)), tim)
}
do.call(cbind, by(Data, Data$Index, f))
The last line is unchanged from before. This code will also handle
the original situation correctly even if the missing 1992 is truly
missing.
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