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Try this:
time type loc call p cnt
1 1304945116000 a b 1fb646244dbd6c807005811838b1e5ec 53115 0
2 1304945116000 a b 6b3dafa1ff257c3891fc87abc78e137e 1 0
3 1304945116000 a b 6e6f8c68c86173aa7e98b6375e752f89 NA 0
4 1304945116000 a b d02922f7a545c15632c9ecb39e6da9a2 NA 0
5 1304945116000 a b 11ce203ca480e56f03a7d1a9591de0e9 NA 0
6 1304945116000 a b 7dabc9db21bbdc080780f493fe93f2af 53115 0
7 1304945116001 a b 6b3dafa1ff257c3891fc87abc78e137e 1 1
8 1304945116001 a b 6e6f8c68c86173aa7e98b6375e752f89 NA 1
9 1304945116001 a b d02922f7a545c15632c9ecb39e6da9a2 NA 1
10 1304945116001 a b 11ce203ca480e56f03a7d1a9591de0e9 NA 1
# now compare these two, they should be the same:
q.2 %>% group_by(time, type, loc, call) %>% summarize(n=n()) %>% filter(n>1)
q.2 %>% data.table() %>% group_by(time, type, loc, call) %>% summarize(n=n()) %>% filter(n>1)