Use matrix operations where appropriate

[Bisaloo]

For better performance

x <- matrix(
  runif(1e6),
  1000,
  1000
)

bench::mark(
  rowMeans(x),
  apply(x, 1, mean),
  iterations = 100
)
#> # A tibble: 2 × 6
#>   expression             min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>        <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 rowMeans(x)         2.92ms   2.98ms     334.     28.7KB       0 
#> 2 apply(x, 1, mean)   9.74ms  10.25ms      90.3    19.2MB     168.

bench::mark(
  tcrossprod(x),
  x %*% t(x),
  iterations = 100
)
#> # A tibble: 2 × 6
#>   expression         min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>    <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 tcrossprod(x)   9.39ms   12.5ms      74.2    7.63MB     24.7
#> 2 x %*% t(x)     22.42ms   26.9ms      34.9   15.26MB     28.6

df <- as.data.frame(x)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

bench::mark(
  df / 100,
  mutate(df, across(everything(), ~ .x / 100)),
  iterations = 100
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression                             min median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                          <bch:> <bch:>     <dbl> <bch:byt>    <dbl>
#> 1 df/100                                25ms 28.2ms      33.3    8.13MB     33.3
#> 2 mutate(df, across(everything(), ~.… 42.7ms 62.4ms      15.7    14.8MB     22.6

^{Created on 2026-07-31 with reprex v2.1.1}

Some of these changes are not completely trivial and I hope I didn't mess anything up. Please double check.

Related to openjournals/joss-reviews#10860.

[EricMarcon]

Thanks.
In commit 730026d, the division returns a data.frame when a tibble is needed. I'll add as_tibble() after merging.