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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