Try this:
foo <- function(x) {
data.frame(Id = unique(x$Id), rbind(range(x$time)), dose = unique(x$dose))
}
t(sapply(split(as.data.frame(orig.data),
with(rle(orig.data[,'dose']), rep(seq_along(lengths),
lengths))),
foo))
On Wed, Mar 24, 2010 at 12:27 PM, Marie-Pierre Sylvestre
<mp.sylvestre at gmail.com> wrote:
> Hi,
>
> I have a data set in which the variable 'dose' is time-varying. Currently,
> the data set is in a long format, with 1 row for each time unit of follow-up
> for each individual "Id". It looks like this:
>
>
> orig.data <- cbind(Id = c(rep(1,4), rep(2,5)), time = c(1:4, 1:5), dose =
> c(1,1,1,0,1,0,1,1,0))
>
> orig.data
> Id time dose
> [1,] 1 1 1
> [2,] 1 2 1
> [3,] 1 3 1
> [4,] 1 4 0
> [5,] 2 1 1
> [6,] 2 2 0
> [7,] 2 3 1
> [8,] 2 4 1
> [9,] 2 5 0
>
> What I would like to do is to convert the data set into an interval format.
> By that I mean a data set in which each row has a 'Start' and a 'Stop' value
> that indicates the time units in which the 'dose' is constant. For example,
> my orig.data example would now be:
>
> int.data <- cbind(Id = c(rep(1,2), rep(2,4)), Start = c(1,4,1,2,3,5), Stop
> = c(3,4,1,2,4,5), dose = c(1,0,1,0,1,0))
>
> int.data
> Id Start Stop dose
> [1,] 1 1 3 1
> [2,] 1 4 4 0
> [3,] 2 1 1 1
> [4,] 2 2 2 0
> [5,] 2 3 4 1
> [6,] 2 5 5 0
>
> Basically, this implies collapsing rows that have the same "Id" and "dose"
> and creating "Start" and "Stop" to index the time.
>
> While I can write a clumsy routine with multiple loops to do it, it will be
> inefficient and will not work for large data set.
>
> I wonder if people know of a function that would reshape my data set from
> 'long' to 'interval'?
>
> Best,
>
> MP
>
> [[alternative HTML version deleted]]
>
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>
--
Henrique Dallazuanna
Curitiba-Paraná-Brasil
25° 25' 40" S 49° 16' 22" O