Thanks again,
They worked for me as well. I did a simpler example with fewer years just to
show that it worked...( shorted here for display)
f <- function(x) {
+ dat <- x[-(1:2)]
+ tim <- as.yearmon(outer(x$Year, seq(0, length = ncol(dat))/12,
"+"))
+ zoo(c(as.matrix(dat)), tim)
+ }
> g<-do.call(cbind, by(Data, Data$Index, f))
> g
X12345 X34567 X56789
Jan 1989 NA 3 6
Feb 1989 NA 3 6
Mar 1989 NA 3 6
Apr 1989 NA 3 6
May 1989 NA 3 6
Jun 1989 NA 3 6
Jul 1989 NA 3 6
Aug 1989 NA 3 6
Sep 1989 NA 3 6
Oct 1989 NA 3 6
Nov 1989 NA 3 6
Dec 1989 NA 3 6
Jan 1990 2 4 6
Feb 1990 2 4 6
Mar 1990 2 4 6
Apr 1990 2 4 6
May 1990 2 4 6
Jun 1990 2 4 6
Jul 1990 2 4 6
Aug 1990 2 4 6
Sep 1990 2 4 6
Oct 1990 2 4 6
Nov 1990 2 4 6
Dec 1990 2 4 6
Jan 1991 NA 5 NA
.............
z<-as.zooreg(as.ts(g))
> z
X12345 X34567 X56789
1989(1) NA 3 6
1989(2) NA 3 6
1989(3) NA 3 6
1989(4) NA 3 6
1989(5) NA 3 6
1989(6) NA 3 6
1989(7) NA 3 6
1989(8) NA 3 6
1989(9) NA 3 6
1989(10) NA 3 6
1989(11) NA 3 6
1989(12) NA 3 6
1990(1) 2 4 6
1990(2) 2 4 6
1990(3) 2 4 6
1990(4) 2 4 6
1990(5) 2 4 6
1990(6) 2 4 6
1990(7) 2 4 6
1990(8) 2 4 6
1990(9) 2 4 6
1990(10) 2 4 6
1990(11) 2 4 6
1990(12) 2 4 6
1991(1) NA 5 NA
1991(2) NA 5 NA
1991(3) NA 5 NA
1991(4) NA 5 NA
1991(5) NA 5 NA
1991(6) NA 5 NA
1991(7) NA 5 NA
1991(8) NA 5 NA
1991(9) NA 5 NA
1991(10) NA 5 NA
1991(11) NA 5 NA
1991(12) NA 5 NA
1992(1) 2 NA NA
1992(2) 2 NA NA
*******************************
The interesting this is the change from months to the (1)...
On Sun, Aug 8, 2010 at 8:55 AM, Gabor Grothendieck
<ggrothendi...@gmail.com>wrote:
> On Sun, Aug 8, 2010 at 11:21 AM, steven mosher <mosherste...@gmail.com>
> wrote:
> > Ok,
> > I'm a bit confused by what you mean by "regularly spaced"
> > After I do the do.call I do get a data structure with all the times
> present
> > and every time has a NA or a data value.
> > Steve
> >
>
> regularly spaced means that every observation is one month later than
> the prior. If there are missing 6 month chunks or missing entire
> years then the observations are not regularly spaced since there are
> some months not present.
>
> It works for me:
>
> > 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,July=Values/3,Aug=Values2,Sep=Values,
> + Oct=Values,Nov=Values,Dec=Values2)
> >
> > 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))
> X12345 X67543 X89765
> Jan 1989 NA 12.000000 NA
> Feb 1989 NA 6.000000 NA
> Mar 1989 NA 12.000000 NA
> Apr 1989 NA 12.000000 NA
> May 1989 NA 12.000000 NA
> Jun 1989 NA 4.000000 NA
> Jul 1989 NA 12.000000 NA
> Aug 1989 NA 12.000000 NA
> Sep 1989 NA 12.000000 NA
> Oct 1989 NA 12.000000 NA
> Nov 1989 NA 12.000000 NA
> Jan 1990 NA 14.000000 NA
> Feb 1990 NA 7.000000 NA
> Mar 1990 NA NA NA
> Apr 1990 NA NA NA
> May 1990 NA 14.000000 NA
> Jun 1990 NA 4.666667 NA
> Jul 1990 NA NA NA
> Aug 1990 NA 14.000000 NA
> Sep 1990 NA 14.000000 NA
> Oct 1990 NA 14.000000 NA
> Nov 1990 NA NA NA
> Jan 1991 54.000000 34.000000 12.000000
> Feb 1991 27.000000 17.000000 6.000000
> Mar 1991 NA 34.000000 NA
> Apr 1991 NA 34.000000 NA
> May 1991 54.000000 34.000000 12.000000
> Jun 1991 18.000000 11.333333 4.000000
> Jul 1991 NA 34.000000 NA
> Aug 1991 54.000000 34.000000 12.000000
> Sep 1991 54.000000 34.000000 12.000000
> Oct 1991 54.000000 34.000000 12.000000
> Nov 1991 NA 34.000000 NA
> Jan 1992 NA 21.000000 13.000000
> Feb 1992 NA 10.500000 6.500000
> Mar 1992 NA 21.000000 13.000000
> Apr 1992 NA 21.000000 13.000000
> May 1992 NA 21.000000 13.000000
> Jun 1992 NA 7.000000 4.333333
> Jul 1992 NA 21.000000 13.000000
> Aug 1992 NA 21.000000 13.000000
> Sep 1992 NA 21.000000 13.000000
> Oct 1992 NA 21.000000 13.000000
> Nov 1992 NA 21.000000 13.000000
> Jan 1993 65.000000 NA 13.000000
> Feb 1993 32.500000 NA 6.500000
> Mar 1993 65.000000 NA NA
> Apr 1993 65.000000 NA NA
> May 1993 65.000000 NA 13.000000
> Jun 1993 21.666667 NA 4.333333
> Jul 1993 65.000000 NA NA
> Aug 1993 65.000000 NA 13.000000
> Sep 1993 65.000000 NA 13.000000
> Oct 1993 65.000000 NA 13.000000
> Nov 1993 65.000000 NA NA
> Jan 1994 23.000000 NA 13.000000
> Feb 1994 11.500000 NA 6.500000
> Mar 1994 23.000000 NA 13.000000
> Apr 1994 23.000000 NA 13.000000
> May 1994 23.000000 NA 13.000000
> Jun 1994 7.666667 NA 4.333333
> Jul 1994 23.000000 NA 13.000000
> Aug 1994 23.000000 NA 13.000000
> Sep 1994 23.000000 NA 13.000000
> Oct 1994 23.000000 NA 13.000000
> Nov 1994 23.000000 NA 13.000000
> Jan 1995 NA NA 14.000000
> Feb 1995 NA NA 7.000000
> Mar 1995 NA NA 14.000000
> Apr 1995 NA NA 14.000000
> May 1995 NA NA 14.000000
> Jun 1995 NA NA 4.666667
> Jul 1995 NA NA 14.000000
> Aug 1995 NA NA 14.000000
> Sep 1995 NA NA 14.000000
> Oct 1995 NA NA 14.000000
> Nov 1995 NA NA 14.000000
>
**********************************************************************************
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