cbind vs rbind with data.table

Question

I've noticed that cbind takes considerably longer than rbind for data.tables. What is the reason for this?

> dt <- as.data.table(mtcars)                             
> new.dt <- copy(dt)                                      
> timeit({for (i in 1:100) dt.new <- rbind(dt.new, dt)})  
   user  system elapsed                                   
  0.237   0.012   0.253                                   
> new.dt <- copy(dt)                                      
> timeit({for (i in 1:100) dt.new <- cbind(dt.new, dt)})  
   user  system elapsed                                   
 14.795   0.090  14.912    

Where

timeit <- function(expr)
{
    ptm <- proc.time()
    expr
    proc.time() - ptm
}

Answer 1

Ultimately I think this comes down to alloc.col being slow due to a loop where it removes various attributes from the columns. I'm not entirely sure why that's done, perhaps Arun or Matt can explain.

As you can see below, the basic operations for cbind are much faster than rbind:

cbind.dt.simple = function(...) {
  x = c(...)
  setattr(x, "class", c("data.table", "data.frame"))
  ans = .Call(data.table:::Calloccolwrapper, x, max(100L, ncol(x) + 64L), FALSE)
  .Call(data.table:::Csetnamed, ans, 0L)
}

library(microbenchmark)

microbenchmark(rbind(dt, dt), cbind(dt, dt), cbind.dt.simple(dt, dt))
#Unit: microseconds
#                    expr      min        lq      mean    median        uq       max neval
#           rbind(dt, dt)  785.318  996.5045 1665.1762 1234.4045 1520.3830 21327.426   100
#           cbind(dt, dt) 2350.275 3022.5685 3885.0014 3533.7595 4093.1975 21606.895   100
# cbind.dt.simple(dt, dt)   74.125  116.5290  168.5101  141.9055  180.3035  1903.526   100