In SMAC-Group/simts: Time Series Analysis Tools

Glue 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 it's 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(), stringr::str_interp() - Provides a similar interface as glue, but using ${} to delimit blocks to interpolate.
pystr::pystr_format()[^1], rprintf::rprintf() - Provide a interfaces similar to python string formatters with variable replacement, but not arbitrary interpolation.

knitr::opts_chunk$set(
  collapse = TRUE, comment = "#>",
  eval = as.logical(Sys.getenv("EVAL_VIGNETTES", "FALSE")),
  cache = FALSE)
library(glue)

plot_comparison <- function(x, ...) {
  library(ggplot2)
  library(microbenchmark)
  x$expr <- forcats::fct_reorder(x$expr, x$time)
  colors <- ifelse(levels(x$expr) == "glue", "orange", "grey")
  autoplot(x, ...) +
    theme(axis.text.y = element_text(color = colors)) +
      aes(fill = expr) + scale_fill_manual(values = colors, guide = FALSE)
}

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)

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().

Although 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.

Vectorized performance

Taking advantage of glue's vectorization is the best way to avoid performance. For instance the vectorized form of the previous benchmark is able to generate 100,000 strings in only 22ms with performance much closer to that of paste0() and sprintf(). NB: str_interp() does not support vectorization, and so was removed.

bar <- rep("bar", 1e5)

vectorized <-
  microbenchmark::microbenchmark(
  glue = 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)
)

print(unit = "ms", order = "median", signif = 4, vectorized)

plot_comparison(vectorized, log = FALSE)