减少部分排序

您可以从排序的向量中获取尾部:

set.seed(42)
x <- sample(1:100, 50)
# sort(x, partial = 1:5)[1:5] ## head

p <- length(x)+1 - (1:5) ## tail
sort(x, partial = p)[p]

如果需要,可以使用 rev()

您仍然可以从速度提升中获益,比如(假设为数字数据):

-sort(-x, partial = 1:5)[1:5]

标杆管理:

set.seed(3)
x <- sample(1:100000, 500000, replace = TRUE)

bench::mark(
  snoram = -sort(-x, partial = 1:5)[1:5],
  OP = sort(x, decreasing = TRUE)[1:5],
  sotos_check = x[order(x, decreasing = TRUE)][1:5],
  jogo = {p <- length(x) - 0:4; sort(x, partial = p)[p]}
)
# A tibble: 4 x 14
  expression       min     mean   median      max `itr/sec` mem_alloc  n_gc n_itr total_time result    memory             time     gc               
  <chr>       <bch:tm> <bch:tm> <bch:tm> <bch:tm>     <dbl> <bch:byt> <dbl> <int>   <bch:tm> <list>    <list>             <list>   <list>           
1 snoram        6.87ms   7.77ms   7.43ms  15.04ms     129.     5.72MB     9    34      264ms <int [5]> <Rprofmem [3 x 3]> <bch:tm> <tibble [43 x 3]>
2 OP            17.4ms  18.96ms  18.56ms  24.37ms      52.7    3.81MB     3    21      398ms <int [5]> <Rprofmem [2 x 3]> <bch:tm> <tibble [24 x 3]>
3 sotos_check  14.65ms  17.07ms  16.48ms  25.58ms      58.6    3.81MB     4    23      393ms <int [5]> <Rprofmem [2 x 3]> <bch:tm> <tibble [27 x 3]>
4 jogo          4.98ms   5.45ms   5.35ms   8.91ms     184.     3.81MB     6    37      201ms <int [5]> <Rprofmem [2 x 3]> <bch:tm> <tibble [43 x 3]>