super

Overview

super is a fork / reimplementation of the glue package with a focus on efficiency and simplicity at a cost of flexibility.

Examples

library(super)

Simple concatenation

bar <- "baz"
glue("foo{bar}")
#> [1] "foobaz"

List-like input

dat <- head(cbind(car = rownames(mtcars), mtcars))
glue("{car} does {mpg} mpg.", dat)
#> [1] "Mazda RX4 does 21 mpg."           "Mazda RX4 Wag does 21 mpg."      
#> [3] "Datsun 710 does 22.8 mpg."        "Hornet 4 Drive does 21.4 mpg."   
#> [5] "Hornet Sportabout does 18.7 mpg." "Valiant does 18.1 mpg."          

Trimmed output

name <- "Fred"
age <- 50
anniversary <- as.Date("1991-10-12")
out <- glut("
    My name is {name},
    my age next year is {age},
    my anniversary is {anniversary}.
")
cat(out)
#> My name is Fred,
#> my age next year is 50,
#> my anniversary is 1991-10-12.

Partially vectorised

Over embraced arguments

head(glue("Item {LETTERS}"))
#> [1] "Item A" "Item B" "Item C" "Item D" "Item E" "Item F"

But not over input strings (yet)

glue(letters)
#> `x` must be a character vector of length <= 1.

Relative timing benchmarks

library(microbenchmark)

Simple concatenation

bar <- "baz"
bob <- 20

microbenchmark(
    sprintf    = sprintf("foo%s %d", bar, bob),
    paste0     = paste0("foo", bar, " ", bob),
    super   = super::glue("foo{bar} {bob}"),
    glue    = as.character(glue::glue_safe("foo{bar} {bob}", .trim = FALSE)),
    unit    = "relative",
    check   = "identical"
)
#> Unit: relative
#>     expr       min        lq      mean    median        uq       max neval
#>  sprintf  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   100
#>   paste0  2.836661  2.590583  2.120857  2.294360  2.121540  1.550206   100
#>    super  8.854809  8.203512  6.404605  7.296424  6.673285  2.245947   100
#>     glue 73.586207 66.836393 50.447605 58.493122 51.859807 11.218727   100

Data frame input

dat <- head(cbind(car = rownames(mtcars), mtcars))

microbenchmark(
    sprintf = with(dat, sprintf("%s does %.3g mpg.", car, mpg)),
    paste0  = with(dat, paste(car, "does", mpg, "mpg.")),
    super   = super::glue("{car} does {mpg} mpg.", dat),
    glue    = as.character(glue::glue_data(dat, "{car} does {mpg} mpg.")),
    unit    = "relative",
    check   = "identical"
)
#> Unit: relative
#>     expr       min        lq      mean    median        uq       max neval
#>  sprintf  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   100
#>   paste0  1.659651  1.593393  1.520196  1.524206  1.466060  1.216451   100
#>    super  2.726097  2.639392  2.607647  2.589537  2.507347  2.255755   100
#>     glue 17.672880 16.931698 16.167487 16.004685 15.223437 20.757720   100

Trimmed output

microbenchmark(
    super   = super::glut("
                  My name is {name},
                  my age next year is {age},
                  my anniversary is {anniversary}.
              "),
    glue    = as.character(glue::glue("
                  My name is {name},
                  my age next year is {age},
                  my anniversary is {anniversary}.
              ")),
    unit    = "relative",
    check   = "identical"
)
#> Unit: relative
#>   expr      min       lq     mean   median       uq      max neval
#>  super 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000   100
#>   glue 4.142515 3.911418 3.763997 3.817862 3.835196 1.625525   100

Vectorized performance

For larger input with both glue::glue() and super::glue(), the performance becomes dominated by the internally constructed call to paste0(), hence the convergence observed below.

bar <- rep("baz", 1e5)
microbenchmark(
    sprintf    = sprintf("foo%s %d", bar, bob),
    paste0     = paste0("foo", bar, " ", bob),
    super   = super::glue("foo{bar} {bob}"),
    glue    = as.character(glue::glue_safe("foo{bar} {bob}", .trim = FALSE)),
    unit    = "relative",
    check   = "identical"
)
#> Unit: relative
#>     expr      min       lq     mean   median       uq      max neval
#>  sprintf 1.315949 1.315905 1.280219 1.286472 1.253286 1.029561   100
#>   paste0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000   100
#>    super 1.020899 1.021252 1.012460 1.012929 1.000928 1.014163   100
#>     glue 1.126174 1.155097 1.173894 1.164373 1.206051 1.121786   100