On Tue, Mar 13, 2012 at 08:56:33PM -0700, mdvaan wrote:
> Hi,
>
> I have data on individuals (B) who participated in events (A). If ALL
> participants in an event are a subset of the participants in another event I
> would like to remove the smaller event and if the participants in one event
> are exactly similar to the participants in another event I would like to
> remove one of the events (I don't care which one). The following example
> does that however it is extremely slow (and the true dataset is very large).
> What would be a more efficient way to solve the problem? I really appreciate
> your help. Thanks!
>
> DF <- data.frame(read.table(textConnection(" A B
> 12095 69832
> 12095 51750
...
Hi.
Try the following.
data <- unique(DF$A)
gr <- split(DF$B, f=factor(DF$A, levels=data))
gr <- lapply(gr, FUN=sort)
gr <- lapply(gr, FUN=unique)
elim <- rep(FALSE, times=length(gr))
for (i in seq.int(along=gr)) {
gr.i <- gr[[i]]
for (j in seq.int(along=gr)) {
gr.j <- gr[[j]]
if (j < i && identical(gr.i, gr.j)) {
elim[i] <- TRUE
} else if (i != j) {
both <- unique(sort(c(gr.i, gr.j)))
if (identical(gr.j, both) && !identical(gr.i, both)) {
elim[i] <- TRUE
}
}
}
}
DF1 <- DF[DF$A %in% data[!elim], ]
How frequent it is that an event is eliminated in the real data?
Petr Savicky.
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