Why is running “unique” faster on a data frame than a matrix in R?

Question

I\'ve begun to believe that data frames hold no advantages over matrices, except for notational convenience. However, I noticed this oddity when running unique

Answer 1

1. In this implementation, unique.matrix is the same as unique.array

   > identical(unique.array, unique.matrix)

   [1] TRUE

2. unique.array has to handle multi-dimensional arrays which requires additional processing to ‘collapse’ the extra dimensions (those extra calls to paste()) which are not needed in the 2-dimensional case. The key section of code is:

   collapse <- (ndim > 1L) && (prod(dx[-MARGIN]) > 1L)

   temp <- if (collapse) apply(x, MARGIN, function(x) paste(x, collapse = "\r"))

3. unique.data.frame is optimised for the 2D case, unique.matrix is not. It could be, as you suggest, it just isn't in the current implementation.

Note that in all cases (unique.{array,matrix,data.table}) where there is more than one dimension it is the string representation that is compared for uniqueness. For floating point numbers this means 15 decimal digits so

NROW(unique(a <- matrix(rep(c(1, 1+4e-15), 2), nrow = 2)))

is 1 while

NROW(unique(a <- matrix(rep(c(1, 1+5e-15), 2), nrow = 2)))

and

NROW(unique(a <- matrix(rep(c(1, 1+4e-15), 1), nrow = 2)))

are both 2. Are you sure unique is what you want?