library(glue)
library(ggplot2)
library(bench)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionGlue is advertised as
Fast, dependency free string literals
So what do we mean when we say that glue is fast? This does not mean glue is the fastest thing to use in all cases, however for the features it provides we can confidently say it is fast.
A good way to determine this is to compare its speed of execution to some alternatives.
-
base::paste0(),base::sprintf(): Functions in base R implemented in C that provide variable insertion (but not interpolation). -
R.utils::gstring(): Provides a similar interface as glue, but uses${}to delimit blocks to interpolate. -
pystr::pystr_format()1,rprintf::rprintf(): Provide an interface similar to python string formatters with variable replacement, but not arbitrary interpolation.
Note: stringr::str_interp() was previously included in
this benchmark, but is now formally marked as “superseded”, in favor of
stringr::str_glue(), which just calls
glue::glue().
Simple concatenation
bar <- "baz"
simple <- bench::mark(
glue = as.character(glue::glue("foo{bar}")),
gstring = R.utils::gstring("foo${bar}"),
paste0 = paste0("foo", bar),
sprintf = sprintf("foo%s", bar),
rprintf = rprintf::rprintf("foo$bar", bar = bar)
)
simple %>%
select(expression:total_time) %>%
arrange(median)
#> # A tibble: 5 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sprintf 473ns 581.96ns 1494554. 0B 149.
#> 2 paste0 929.8ns 1.03µs 844925. 0B 0
#> 3 glue 48.6µs 56.8µs 16310. 141.56KB 41.3
#> 4 gstring 130.1µs 147.67µs 6356. 2.45MB 23.3
#> 5 rprintf 167.2µs 186.26µs 5061. 510.91KB 10.3
# plotting function defined in a hidden chunk
plot_comparison(simple)
#> Warning: The `trans` argument of `continuous_scale()` is deprecated as of ggplot2 3.5.0.
#> ℹ Please use the `transform` argument instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
While glue() is slower than paste0 and
sprintf(), it is twice as fast as gstring(),
and rprintf().
Although paste0() and sprintf() don’t do
string interpolation and will likely always be significantly faster than
glue, glue was never meant to be a direct replacement for them.
rprintf::rprintf() does only variable interpolation, not
arbitrary expressions, which was one of the explicit goals of writing
glue.
So glue is ~2x as fast as the function (gstring()),
which has roughly equivalent functionality.
It also is still quite fast, with over 8000 evaluations per second on this machine.
Vectorized performance
Taking advantage of glue’s vectorization is the best way to improve
performance. In a vectorized form of the previous benchmark, glue’s
performance is much closer to that of paste0() and
sprintf().
bar <- rep("bar", 1e5)
vectorized <- bench::mark(
glue = as.character(glue::glue("foo{bar}")),
gstring = R.utils::gstring("foo${bar}"),
paste0 = paste0("foo", bar),
sprintf = sprintf("foo%s", bar),
rprintf = rprintf::rprintf("foo$bar", bar = bar)
)
vectorized %>%
select(expression:total_time) %>%
arrange(median)
#> # A tibble: 5 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 paste0 8.01ms 8.25ms 120. 781.3KB 4.12
#> 2 sprintf 8.52ms 8.74ms 113. 781.3KB 4.20
#> 3 gstring 10.19ms 10.48ms 95.0 1.53MB 6.48
#> 4 glue 11.51ms 11.77ms 84.2 2.29MB 12.0
#> 5 rprintf 22.19ms 22.56ms 43.3 3.05MB 6.83
# plotting function defined in a hidden chunk
plot_comparison(vectorized)
pystr is no longer available from CRAN due to failure to correct installation errors and was therefore removed from further testing.↩︎