Fast EXISTS in data.table

Question

What is the fastest way to check if a value exists in a data.table?. Suppose that

• dt is a data.table of n columns with k columns being the key
• keys is a l

Answer 1

Short answer: In addition to nomatch=0, I think mult="first" would help speed it even more.

Long answer: Assuming that you want to check if a value (or more than 1 value) is present in the key column of a data.table or not, this seems to be much faster. The only assumption here is that the data.table has only 1 key column (as this is quite ambiguous to me).

my.values = c(1:100, 1000)
require(data.table)
set.seed(45)
DT <- as.data.table(matrix(sample(2e4, 1e6*100, replace=TRUE), ncol=100))
setkey(DT, "V1")
# the data.table way
system.time(all(my.values %in% .subset2(DT[J(my.values), mult="first", nomatch=0], "V1")))
   user  system elapsed 
  0.006   0.000   0.006 

# vector (scan) approach
system.time(all(my.values %in% .subset2(DT, "V1")))
   user  system elapsed 
  0.037   0.000   0.038 

You can change all to any if you want to check if at least 1 value is present in the subset or not. The only difference between the two is that you first subset using data.table's approach (taking advantage of key and mult argument). As you can see the it's extremely faster (and also scales well). And then to retrieve the key columns from the subset (call it the_subset),

.subset2(the_subset, "V1") (or) the_subset$V1 (or) the_subset[["V1"]]

But, the_subset[, V1] will be slower.

Of course the same idea could be extended to many columns as well, but I'll have to know exactly what you want to do after.