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I am attempting to understand the logic in the data.table from the documentation and a bit unclear. I know I can just try this and see what happens but I would like to make sure that there is no pathological case and therefore would like to know how the logic was actually coded. When two data.table objects have a different number of key columns, for example a has 2 and b has 3, and you run c <- a[b] , will a and b be merged simply on the first two key columns or will the third column in a be automatically merged to the 3rd key column in b ? An example: require(data.table) a <- data.table(id=1:10, t=1:20, v=1:40, key=c("id", "t")) b <- data.table(id=1:10, v2=1:20, key="id") c <- a[b] This should select rows of a that match the id key column in b . For example, for id==1 in b , there are 2 rows in b and 4 rows in a that should generate 8 rows in c . This is indeed what seems to happen: > head(c,10) id t v v2 1: 1 1 1 1 2: 1 1 21 1 3: 1 11 11 1 4: 1 11 31 1 5: 1 1 1 11 6: 1 1 21 11 7: 1 11 11 11 8: 1 11 31 11 9: 2 2 2 2 10: 2 2 22 2 The other way to try it is to do: d <-b[a] This should do the same thing: for every row in a it should select the matching row in b : since a has an extra key column, t , that column should not be used for matching and a join based only on the first key column, id should be done. It seems like this is the case: > head(d,10) id v2 t v 1: 1 1 1 1 2: 1 11 1 1 3: 1 1 1 21 4: 1 11 1 21 5: 1 1 11 11 6: 1 11 11 11 7: 1 1 11 31 8: 1 11 11 31 9: 2 2 2 2 10: 2 12 2 2 <

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