Thanks Gabor, I probably should have done an example with fewer columns.
i will rework the example and post it up so the next guys who has this issue
can have a
clear example with a solution.
On Sat, Aug 7, 2010 at 5:04 PM, Gabor Grothendieck
<ggrothendi...@gmail.com>wrote:
> On Sat, Aug 7, 2010 at 4:49 PM, steven mosher <mosherste...@gmail.com>
> wrote:
> > Given a data frame, or it could be a matrix if I choose to.
> > The data consists of an ID, a year, and data for all 12 months.
> > Missing values are a factor AND missing years.
> >
> > 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)
> > Data
> > Index Year Jan Feb Mar Apr Jun July Aug Sep Oct Nov Dec
> > 1 67543 1989 12 6.0 12 12 12 4.000000 12 12 12 12 12
> > 2 67543 1990 14 7.0 NA NA 14 4.666667 NA 14 14 14 NA
> > 3 67543 1991 34 17.0 34 34 34 11.333333 34 34 34 34 34
> > 4 67543 1992 21 10.5 21 21 21 7.000000 21 21 21 21 21
> > 5 12345 1991 54 27.0 NA NA 54 18.000000 NA 54 54 54 NA
> > 6 12345 1993 65 32.5 65 65 65 21.666667 65 65 65 65 65
> > 7 12345 1994 23 11.5 23 23 23 7.666667 23 23 23 23 23
> > 8 89765 1991 12 6.0 NA NA 12 4.000000 NA 12 12 12 NA
> > 9 89765 1992 13 6.5 13 13 13 4.333333 13 13 13 13 13
> > 10 89765 1993 13 6.5 NA NA 13 4.333333 NA 13 13 13 NA
> > 11 89765 1994 13 6.5 13 13 13 4.333333 13 13 13 13 13
> > 12 89765 1995 14 7.0 14 14 14 4.666667 14 14 14 14 14
> >
> >
> > The Goal is to return a Time series object for each ID. Alternatively one
> > could return a matrix that I can turn into a Time series.
> > The final structure would be something like this ( done in matrix form
> for
> > illustration)
> > 1989.0 1989.083
> > 1991 ......1992....1993..... 1994 .... 1995
> > 67543 12 6.0 12 12 12 4.000000 12 12 12 12 12...
> > .34...........21.. NA.........NA........NA
> > 12345 NA, NA,
> > NA,.............................................................54 27
> >
> > Basically the time series will have patches at the front, middle and end
> > where you may have years of NA
> > The must be column ordered by time and aligned so that averages for all
> > series can be computed per month.
> >
> > Now I have looping code to do this, where I loop through all the IDs and
> map
> > the row of data into the correct
> > column. and create column names based on the data and row names based on
> the
> > ID, but it's painfully
> > slow. Any wizardry would help.
>
> Your email came out a bit garbled so its not clear what you want to
> get out but this code will produce a multivariate ts series, i.e. an
> mts series, with one column for each series:
>
> f <- function(x) ts(c(t(x[-(1:2)])), freq = 12, start = x$Year[1])
> do.call(cbind, by(Data, Data$Index, f))
>
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