Simple concatenation
bar <- "baz"
simple <-
microbenchmark::microbenchmark(
glue = glue::glue("foo{bar}"),
gstring = R.utils::gstring("foo${bar}"),
paste0 = paste0("foo", bar),
sprintf = sprintf("foo%s", bar),
str_interp = stringr::str_interp("foo${bar}"),
rprintf = rprintf::rprintf("foo$bar", bar = bar)
)
print(unit = "eps", order = "median", signif = 4, simple)
#> Unit: evaluations per second
#> expr min lq mean median uq max neval cld
#> rprintf 265.70 1848 1935 1957 2129 2331 100 a
#> gstring 19.56 2189 2312 2358 2522 2885 100 a
#> str_interp 203.40 2783 3060 3050 3549 3845 100 a
#> glue 476.60 5028 5392 5498 6154 7384 100 a
#> sprintf 53900.00 411900 579900 501300 599000 1534000 100 b
#> paste0 111000.00 312700 527000 535700 633300 1065000 100 b
plot_comparison(simple)
While glue() is slower than paste0,sprintf() it is twice as fast as str_interp() and gstring(), and on par with rprintf().
paste0(), sprintf() don’t do string interpolation and will likely always be significantly faster than glue, glue was never meant to be a direct replacement for them.
rprintf() does only variable interpolation, not arbitrary expressions, which was one of the explicit goals of writing glue.
So glue is ~2x as fast as the two functions (str_interp(), gstring()) which do have roughly equivalent functionality.
It also is still quite fast, with over 6000 evaluations per second on this machine.
