(Apologies if this is better suited for R-help.)
On my system (macOS Sierra, late 2014 MacBook Pro; R 3.4.1, Homebrew build), I
found that it is faster to construct a function using eval(call("function",
...)) than using as.function(list(...)). Example:
make_fn_1 <- function(a, b) eval(call("function", a, b), env =
parent.frame())
make_fn_2 <- function(a, b) as.function(c(a, list(b)), env = parent.frame())
a <- as.pairlist(alist(x = , y = ))
b <- quote(x + y)
library("microbenchmark")
microbenchmark(make_fn_1(a, b), make_fn_2(a, b))
# Unit: microseconds
# expr min lq mean median uq max neval cld
# make_fn_1(a, b) 1.671 1.8855 2.13297 2.039 2.1950 9.852 100 a
# make_fn_2(a, b) 3.541 3.7230 4.13400 3.906 4.1055 23.153 100 b
At first I thought the gap was due to the overhead of calling c(a, list(b)).
But this turns out not to be the case:
make_fn_weird <- function(a, b) as.function(c(a, b), env = parent.frame())
b_wrapped <- list(b)
make_fn_weirder <- function(a_b) as.function(a_b, env = parent.frame())
a_b <- c(a, b_wrapped)
microbenchmark(make_fn_1(a, b), make_fn_2(a, b),
make_fn_weird(a, b_wrapped), make_fn_weirder(a_b))
# Unit: microseconds
# expr min lq mean median uq max
neval cld
# make_fn_1(a, b) 1.718 1.8990 2.12119 1.9860 2.1605 8.057
100 a
# make_fn_2(a, b) 3.393 3.5865 4.03029 3.6655 3.9615 27.499
100 c
# make_fn_weird(a, b_wrapped) 3.354 3.5005 3.77190 3.6405 3.9425 6.839
100 c
# make_fn_weirder(a_b) 2.488 2.6290 2.83352 2.7215 2.8800 7.007
100 b
One IRC user pointed out that as.function() takes its own path through the
code, namely do_asfunction() (in src/main/coerce.c). What is it about this code
path that's 50% slower than whatever happens during eval(call("function", a,
b))?
Obviously this is a trivial micro-optimization and it doesn't matter to 99% of
users. Mostly asking out of curiosity, but also wondering if there's a more
general lesson to be learned here.
Thanks!
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