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?
See Question&Answers more detail:
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