Skip to contents

Benchmarks - sb_x

Source: vignettes/articles/a_benchmarks_x.Rmd
a_benchmarks_x.Rmd
library(squarebrackets)
#> Run `?squarebrackets::squarebrackets_help` to open the introduction help page of 'squarebrackets'.

 

Introduction

Due to the many checks and conversions performed by the squarebrackets:: functions, to make sub-setting more programmatically and beginner friendly, the functions are almost necessarily slower than base R’s [-like operators.

However, a considerable effort was made to keep the speed loss to a minimum. Generally, the speed loss is indeed negligible, and in some cases there is even speed improvement (thanks to the heavy lifting performed by the ‘collapse’ package).

Below are some benchmarks to give one an idea of the speed loss. These are just examples; speed is determined by a great number of factors.

 

Atomic objects

Matrix 


n <- 5e3
x.mat <- matrix(seq_len(n*n), ncol = n)
colnames(x.mat) <- sample(c(letters, NA), n, TRUE)
sel.rows <- 1:100
sel.cols <- rep(sample(letters[1:13]), 10)
bm.sb_x.matrix <- bench::mark(
  "squarebrackets" = sb_x(x.mat, n(sel.rows, sel.cols)),
  "base R" = x.mat[sel.rows, lapply(sel.cols, \(i) which(colnames(x.mat) == i)) |> unlist(), drop = FALSE],
  min_iterations = 500
)
bm.sb_x.matrix
summary(bm.sb_x.matrix)
#> # A tibble: 2 × 6
#>   expression          min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>     <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 squarebrackets   4.96ms   5.27ms      186.    9.71MB    1.12 
#> 2 base R           8.13ms   9.83ms      101.    14.6MB    0.818

 

Array (3D) 

x.dims <- c(5000, 2000, 4)
x.3d <- array(1:prod(x.dims), x.dims)
sel.rows <- 1:900
sel.lyrs <- c(TRUE, FALSE, TRUE, FALSE)
bm.sb_x.3d <- bench::mark(
  "squarebrackets" =  sb_x(x.3d, n(sel.rows, sel.lyrs), c(1,3)),
  "base R + abind" = abind::asub(x.3d, idx = list(sel.rows, sel.lyrs), dims = c(1,3)),
  min_iterations = 500
)
summary(bm.sb_x.3d)
#> # A tibble: 2 × 6
#>   expression          min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>     <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 squarebrackets   9.64ms   10.6ms      94.1    13.7MB     7.52
#> 2 base R + abind    9.7ms   10.7ms      92.8    13.7MB     6.56

 

Plot 

#> Loading required namespace: tidyr

 

Data.frame-like objects

data.frame 

n <- 1e5
chrmat <- matrix(
  sample(letters, n*400, replace = TRUE), ncol = 400
)
intmat <- matrix(
  seq.int(n*400), ncol = 400
)
x <- cbind(chrmat, intmat) |> as.data.frame()
rm(list = c("chrmat", "intmat"))
colnames(x) <- make.names(colnames(x), unique = TRUE)
sel.cols <- rep(sample(names(x), 10), 4)
sel.rows <- 1:1000
bm.sb_x.df <- bench::mark(
  "squarebrackets" = sb2_x(x, obs = sel.rows, vars = sel.cols),
  "base R" = x[sel.rows, sel.cols, drop = FALSE],
  min_iterations = 500
)
summary(bm.sb_x.df)
#> # A tibble: 2 × 6
#>   expression          min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>     <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 squarebrackets   99.8µs    112µs     7360.     318KB     2.34
#> 2 base R          282.5µs    306µs     3097.     377KB     0

 

data.table 

x <- as.data.table(x)
tempfun <- function(x, i, j) {
  x <- collapse::ss(x, i, j, check = TRUE)
  names(x) <- make.names(names(x), unique = TRUE)
  return(x)
}
bm.sb_x.dt <- bench::mark(
  "squarebrackets" = sb2_x(x, sel.rows, sel.cols),
  "data.table + collapse" = tempfun(x, sel.rows, sel.cols),
  min_iterations = 1e4
)
summary(bm.sb_x.dt)
#> # A tibble: 2 × 6
#>   expression                 min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>            <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 squarebrackets           134µs    147µs     6156.     342KB     1.23
#> 2 data.table + collapse    110µs    122µs     7255.     341KB     1.45

 

plot