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I have a question on the data.table idiom for "non-joins", inspired from Iterator's question . Here is an example: library(data.table) dt1 <- data.table(A1=letters[1:10], B1=sample(1:5,10, replace=TRUE)) dt2 <- data.table(A2=letters[c(1:5, 11:15)], B2=sample(1:5,10, replace=TRUE)) setkey(dt1, A1) setkey(dt2, A2) The data.table s look like this > dt1 > dt2 A1 B1 A2 B2 [1,] a 1 [1,] a 2 [2,] b 4 [2,] b 5 [3,] c 2 [3,] c 2 [4,] d 5 [4,] d 1 [5,] e 1 [5,] e 1 [6,] f 2 [6,] k 5 [7,] g 3 [7,] l 2 [8,] h 3 [8,] m 4 [9,] i 2 [9,] n 1 [10,] j 4 [10,] o 1 To find which rows in dt2 have the same key in dt1 , set the which option to TRUE : > dt1[dt2, which=TRUE] [1] 1 2 3 4 5 NA NA NA NA NA Matthew suggested in this answer , that a "non join" idiom dt1[-dt1[dt2, which=TRUE]] to subset dt1 to those rows that have indexes that don't appear in dt2 . On my machine with data.table v1.7.1 I get an error: Error in `[.default`(x[[s]], irows): only 0's may be mixed with negative subscripts Instead, with the option nomatch=0 , the "non join" works > dt1[-dt1[dt2, which=TRUE, nomatch=0]] A1 B1 [1,] f 2 [2,] g 3 [3,] h 3 [4,] i 2 [5,] j 4 Is this intended behavior?

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