Function composition in R: compose versus %>% - r

Question

I am working with a code that make an extensive use of the function composition. Usually I use the compose function from the purrr package but it has some drawbacks, for instance if you print the composed function you don't get useful information.
log_sqrt_compose <- purrr::compose(log, sqrt)
print(log_sqrt_compose)
function (...)
{
out <- last(...)
for (f in rev(rest)) {
out <- f(out)
}
out
}
Instead with the pipe you can get a nicer results:
library(magrittr)
log_sqrt_pipe <- . %>% sqrt %>% log
print(log_sqrt_pipe)
Functional sequence with the following components:
1. sqrt(.)
2. log(.)
Use 'functions' to extract the individual functions.
Performing a benchmark it seems that the pipe solution is even faster
microbenchmark::microbenchmark(log_sqrt_compose(10), log_sqrt_pipe(10), times = 10000)
Unit: microseconds
expr min lq mean median uq max neval
log_sqrt_compose(10) 2.531 2.872 3.201496 3.058 3.296 54.786 10000
log_sqrt_pipe(10) 2.007 2.411 2.767140 2.643 2.949 42.422 10000
Given the previous results, is there some other reason to prefer one of the two method in you experience?
EDIT:
Following #mt1022's comment I add also the base version
log_sqrt_f <- function(x) log(sqrt(x))
print(log_sqrt_f)
function(x) log(sqrt(x))
microbenchmark(log_sqrt_compose(10), log_sqrt_pipe(10), log_sqrt_f(10), times = 10000)
Unit: nanoseconds
expr min lq mean median uq max neval
log_sqrt_compose(10) 2529 2874 3151.7432 3052 3285 234039 10000
log_sqrt_pipe(10) 2051 2474 2770.7148 2708 2990 16194 10000
log_sqrt_f(10) 251 288 634.0649 341 420 2659789 10000