filtering

filter.R

# vector filtering --------------------------------------------------------

# Modeled from `base::Filter()` but less generalized (therefore more efficient).
# These functions also use a vector as the first argument rather than the
# filtering function.

base::Filter
#> function (f, x) 
#> {
#>     f <- match.fun(f)
#>     ind <- as.logical(unlist(lapply(x, f)))
#>     x[which(ind)]
#> }
#> <bytecode: 0x000002053df873b8>
#> <environment: namespace:base>

filter_vec <- function(x, FUN, ...) {
  FUN <- match.fun(FUN)
  x[which(FUN(x, ...))]
}

filter_apply <- function(x, FUN, ...) {
  FUN <- match.fun(FUN)
  x[which(vapply(x, FUN, NA, ...))]
}

is_even <- function(x) x %% 2 == 0

# Examples ----------------------------------------------------------------

x <- sample(1:10, 30, TRUE)
filter_vec(x, is_even)
#>  [1]  8 10  8  8  8 10  6 10  8  2  4  8  8  6  6  6
filter_vec(x, Negate(is_even))
#>  [1] 1 1 1 3 5 1 1 5 7 9 5 1 1 7

# Benchmarks --------------------------------------------------------------

x <- round(runif(1e5) * 100)
bench::mark(
  Filter       = Filter(is_even, x),
  filter_vec   = filter_vec(x, is_even),
  filter_apply = filter_apply(x, is_even)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
#> # A tibble: 3 × 6
#>   expression        min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>   <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 Filter        75.51ms  79.23ms      12.5     2.1MB     25.0
#> 2 filter_vec     2.33ms   2.82ms     311.      2.1MB     13.8
#> 3 filter_apply  73.41ms  74.39ms      13.2    1.34MB     26.4