README.md

In vgherard/gsample: Efficient Weighted Sampling Without Replacement

gsample

gsample offers a drop-in replacement for the R base::sample() functions for random sampling, with considerably better performance for the case of weighted sampling without replacement (both from the speed and memory point of view).

Installation

You can install gsample from GitHub with:

# install.packages("devtools")
devtools::install_github("vgherard/gsample")

Example

The gsample API is identical to the one of base::sample():

library(gsample)
n <- 1e3
size <- 1e2
prob <- exp(rnorm(n, sd = 3))
gsample.int(n, size, prob = prob)
#>   [1] 898 905 225 504 298 893 166 430 990 464 683 993  13 404 229 862 379 494
#>  [19] 135 226 232  22 370 236 985 562 186 395 487 873 527 561 810 420 480 572
#>  [37] 891 120 919  40 556  42  43 799 161 479 878  48 258 400 577 896 398 511
#>  [55] 270 593 314 776 840 928 771 478 448 446 441   6 998 286 293  10 757 877
#>  [73] 941 545 939 434 662 115 583 602  63 148 542 674 986 126 317 975  33 794
#>  [91] 965 741 752 994  56 940 530  52 785 745

x <- letters
size <- 10
prob <- exp(rnorm(length(letters), sd = 3))
gsample(x, size, prob = prob)
#>  [1] "r" "z" "c" "y" "e" "f" "o" "u" "i" "l"

Here are some simple benchmark comparisons with base::sample():

library(dplyr)
library(ggplot2)
set.seed(840)
n <- 1e6
prob <- rexp(n)

bm <- lapply(10 ^ (1:5), function(size) {
    bench::mark(
        gsample.int(n, size, prob = prob),
        sample.int(n, size, prob = prob),
        check = FALSE
        ) %>%
        select(expression, median) %>%
        mutate(expression = as.character(expression)) %>%
            mutate(size = size)
    }) 

bind_rows(bm) %>%
    ggplot(aes(x = size, y = 1e3 * median, colour = expression)) +
        geom_line() +
        scale_x_continuous(trans = "log10") +
        scale_y_continuous("median (ms)", trans = "log10")

vgherard/gsample documentation built on Jan. 26, 2021, 1:41 a.m.