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dev/articles/FAQ.html

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dev/articles/FAQ.md

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@@ -338,10 +338,10 @@ void add_one(cpp11::sexp x_sexp) {
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``` r
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x <- c(1L, 2L, 3L, 4L)
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.Internal(inspect(x))
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#> @557ef31d5c58 13 INTSXP g0c2 [MARK,REF(2)] (len=4, tl=0) 1,2,3,4
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#> @55f96daedbb8 13 INTSXP g0c2 [MARK,REF(2)] (len=4, tl=0) 1,2,3,4
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add_one(x)
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.Internal(inspect(x))
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#> @557ef31d5c58 13 INTSXP g0c2 [MARK,REF(5)] (len=4, tl=0) 2,3,4,5
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#> @55f96daedbb8 13 INTSXP g0c2 [MARK,REF(5)] (len=4, tl=0) 2,3,4,5
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x
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#> [1] 2 3 4 5
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```
@@ -590,7 +590,7 @@ bench::mark(
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#> # A tibble: 2 × 6
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#> expression min median `itr/sec` mem_alloc `gc/sec`
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#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
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#> 1 test_extract_cpp11(x) 39.72ms 42.17ms 22.9 0B 42.0
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#> 1 test_extract_cpp11(x) 39.73ms 42.43ms 23.0 0B 42.2
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#> 2 test_extract_r_api(x) 1.86ms 1.87ms 532. 0B 0
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```
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dev/articles/cpp11.html

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dev/articles/cpp11.md

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@@ -102,7 +102,7 @@ cpp_function('int add(int x, int y, int z) {
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add
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#> function (x, y, z)
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#> {
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#> .Call("_code_1e816e075ad7_add", x, y, z, PACKAGE = "code_1e816e075ad7")
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#> .Call("_code_1e36502a7839_add", x, y, z, PACKAGE = "code_1e36502a7839")
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#> }
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add(1, 2, 3)
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#> [1] 6
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#> # A tibble: 3 × 6
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#> expression min median `itr/sec` mem_alloc `gc/sec`
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#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
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#> 1 sum(x) 2.03µs 2.05µs 472213. 0B 0
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#> 2 sum_cpp(x) 1.93µs 2µs 462616. 0B 46.3
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#> 3 sum_r(x) 25.95µs 26.23µs 37557. 31.7KB 0
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#> 1 sum(x) 2.02µs 2.05µs 475823. 0B 0
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#> 2 sum_cpp(x) 1.92µs 1.98µs 468563. 0B 46.9
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#> 3 sum_r(x) 25.95µs 26.23µs 37544. 31.7KB 0
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```
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### Vector input, vector output
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#> # A tibble: 2 × 6
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#> expression min median `itr/sec` mem_alloc `gc/sec`
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#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
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#> 1 pdist_r(0.5, y) 4.79ms 4.89ms 203. 7.63MB 103.
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#> 2 pdist_cpp(0.5, y) 3.81ms 3.89ms 256. 7.63MB 125.
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#> 1 pdist_r(0.5, y) 4.82ms 4.91ms 202. 7.63MB 103.
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#> 2 pdist_cpp(0.5, y) 3.83ms 3.9ms 255. 7.63MB 124.
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```
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On my computer, it takes around 5 ms with a 1 million element `y`
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#> # A tibble: 2 × 6
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#> expression min median `itr/sec` mem_alloc `gc/sec`
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#> <bch:expr> <dbl> <dbl> <dbl> <dbl> <dbl>
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#> 1 r 43.1 42.9 1 32.3 9.99
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#> 2 cpp 1 1 42.7 1 1
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#> 1 r 42.8 42.4 1 32.3 10.0
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#> 2 cpp 1 1 42.1 1 1
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```
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### R vectorisation versus C++ vectorisation
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#> # A tibble: 3 × 6
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#> expression min median `itr/sec` mem_alloc `gc/sec`
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#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
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#> 1 vacc1 1.55ms 1.62ms 591. 7.86KB 31.1
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#> 2 vacc2 40.91µs 43.79µs 20934. 146.68KB 37.6
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#> 3 vacc3 12.04µs 12.33µs 73802. 14.02KB 14.8
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#> 1 vacc1 1.54ms 1.6ms 611. 7.86KB 32.9
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#> 2 vacc2 41.69µs 43.4µs 21615. 146.68KB 39.8
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#> 3 vacc3 12.15µs 12.4µs 79688. 14.02KB 15.9
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```
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Not surprisingly, our original approach with loops is very slow.

dev/news/index.html

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dev/news/index.md

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## cpp11 (development version)
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- Removed non-API usage of `ATTRIB()`
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([\#481](https://github.com/r-lib/cpp11/issues/481)).
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- Improved hygiene around using C++ specific C compatibility headers
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(i.e. by using `<cstring>` rather than `<string.h>` and `<cstddef>`
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rather than `<stddef.h>`)

dev/pkgdown.yml

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@@ -7,7 +7,7 @@ articles:
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FAQ: FAQ.html
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internals: internals.html
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motivations: motivations.html
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last_built: 2026-01-19T13:41Z
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last_built: 2026-01-19T15:14Z
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urls:
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reference: https://cpp11.r-lib.org/reference
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article: https://cpp11.r-lib.org/articles

dev/search.json

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