Consider this comparison:
require(Rcpp)
require(microbenchmark)
cppFunction('int cppFun (int x) {return x;}')
RFun = function(x) x
x=as.integer(2)
microbenchmark(RFun=RFun(x),cppFun=cppFun(x),times=1e5)
Unit: nanoseconds
expr min lq mean median uq max neval cld
RFun 302 357 470.2047 449 513 110152 1e+06 a
cppFun 2330 2598 4045.0059 2729 2879 68752603 1e+06 b
cppFun seems slower than RFun. Why is it so? Do the times for calling the functions differ? Or is it the function itself that differ in running time? Is it the time for passing and returning arguments? Is there some data conversion or data copying I am unaware of when the data are passed to (or returned from) cppFun?
This simply is not a well-posed or thought-out question as the comments above indicate.
The supposed baseline of an empty function simply is not one. Every function created via cppFunction() et al will call one R function interfacing to some C++ function. So this simply cannot be equal.
Here is a slightly more meaningful comparison. For starters, let's make the R function complete with curlies. Second, let's call another compiler (internal) function:
require(Rcpp)
require(microbenchmark)
cppFunction('int cppFun (int x) {return x;}')
RFun1 <- function(x) { x }
RFun2 <- function(x) { .Primitive("abs")(x) }
print(microbenchmark(RFun1(2L), RFun2(2L), cppFun(2L), times=1e5))
On my box, I see a) a closer gap between versions 1 and 2 (or the C++ function) and b) little penalty over the internal function. But calling ANY compiled function from R has cost.
Unit: nanoseconds
expr min lq mean median uq max neval
RFun1(2L) 171 338 434.136 355 408 2659984 1e+05
RFun2(2L) 683 937 1334.046 1257 1341 7605628 1e+05
cppFun(2L) 721 1131 1416.091 1239 1385 8544656 1e+05
As we say in the real world: there ain't no free lunch.
