This code performs the same operation in about 1/10th the time on my
machine.
Give it a try.
look <- function(i) {
# look for subsets
dif <- m[, i] - m
apply(dif, 2, min) > -0.5
}
nosubsets <- function(df) {
# eliminate events that are subsets of other events in terms of
attendance
m <- table(df$B, df$A)
nevents <- dim(m)[2]
found <- sapply(seq(nevents), look)
diag(found) <- FALSE
df[df$A %in% dimnames(m)[[2]][rowSums(found)<0.5], ]
}
nosubsets(DF)
Jean
mdvaan wrote on 03/13/2012 10:56:33 PM:
> Hi,
>
> I have data on individuals (B) who participated in events (A). If ALL
> participants in an event are a subset of the participants in another
event I
> would like to remove the smaller event and if the participants in one
event
> are exactly similar to the participants in another event I would like to
> remove one of the events (I don't care which one). The following example
> does that however it is extremely slow (and the true dataset is very
large).
> What would be a more efficient way to solve the problem? I really
appreciate
> your help. Thanks!
>
> DF <- data.frame(read.table(textConnection(" A B
> 12095 69832
> 12095 51750
> 12095 6734
> 18774 51750
> 18774 51733
> 18774 6734
> 18774 69833
> 19268 51750
> 19268 6734
> 19268 51733
> 19268 65251
> 5169 54441
> 5169 15480
> 5169 3228
> 5966 51733
> 5966 65251
> 5966 68197
> 5966 6734
> 5966 51750
> 5966 69833
> 7189 135523
> 7189 65251
> 7189 51733
> 7189 69833
> 7189 135522
> 7189 68197
> 7189 6734
> 7797 51750
> 7797 6734
> 7797 69833
> 7866 6734
> 7866 69833
> 7866 51733
> 8596 51733
> 8596 51750
> 8596 65251
> 8677 6734
> 8677 51750
> 8677 51733
> 8936 68197
> 8936 6734
> 8936 65251
> 8936 51733
> 9204 51750
> 9204 69833
> 9204 6734
> 9204 51733"),head=TRUE,stringsAsFactors=FALSE))
>
> data <- unique(DF$A)
> for (m in 1:length(data))
> {
> for (m in 1:length(data))
> {
> tdata <- data[-m]
> q <- 0
> for (n in 1:length(tdata))
> {
> if (length(which(DF[DF$A == data[m], 2] %in% DF[DF$A ==
> tdata[n], 2] ==
> TRUE)) == length(DF[DF$A == data[m], 2]))
> {
> q <- q + 1
> }
> }
> if (q > 0)
> {
> data <- data[-m]
> m <- m - 1
> }
> }
> }
> DF <- DF[DF$A %in% data,]
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