Efficiently computing a linear combination of data.table columns

Question

I have nc columns in a data.table, and nc scalars in a vector. I want to take a linear combination of the columns, but I don\'t know ahead of time

Answer 1

This is almost 2x faster for me than your manual version:

Reduce("+", lapply(names(DT), function(x) DT[[x]] * cf[x]))

benchmark(manual = DT[, list(cf['A']*A+cf['B']*B+cf['C']*C+cf['D']*D)],
          reduce = Reduce('+', lapply(names(DT), function(x) DT[[x]] * cf[x])))
#    test replications elapsed relative user.self sys.self user.child sys.child
#1 manual          100    1.43    1.744      1.08     0.36         NA        NA
#2 reduce          100    0.82    1.000      0.58     0.24         NA        NA

And to iterate over just mycols, replace names(DT) with mycols in lapply.

Answer 2

Add this option to your benchmark call:

ops = as.matrix(DT) %*% cf

On my device it was 30% faster than the matrix multiplication you tried.