﻿ 在R中,使用向量化在列表中查找向量的元素 - 代码日志

在R中,使用向量化在列表中查找向量的元素

``````v1 = c(1, 200, 4000)
``````

``````> L1
[[1]]
[1] 1 2 3 4

[[2]]
[1] 100 200 300 400

[[3]]
[1] 1000 2000 3000 4000
``````

``````v1 <- c(1, 200, 4000)
L1 <- list(1:4, 1:4*100, 1:4*1000)

sequence(lengths(L1))[match(v1, unlist(L1))]
# [1] 1 2 4
sequence(lengths(L1))[which(unlist(L1) %in% v1)]
# [1] 1 2 4
``````
``````library(microbenchmark)
library(tidyverse)

microbenchmark(
akrun_sapply = {sapply(L1, function(x) which(x %in% v1))},
akrun_Vectorize = {Vectorize(function(x) which(x %in% v1))(L1)},
akrun_mapply = {mapply(function(x, y) which(x %in% y), L1, v1)},
akrun_mapply_match = {mapply(match, v1, L1)},
akrun_map2 = {purrr::map2_int(L1, v1, ~ .x %in% .y %>% which)},
CPak = {setNames(rep(1:length(L1), times=lengths(L1)), unlist(L1))[as.character(v1)]},
zacdav = {sequence(lengths(L1))[match(v1, unlist(L1))]},
zacdav_which = {sequence(lengths(L1))[which(unlist(L1) %in% v1)]},
times = 10000
)

Unit: microseconds
expr     min       lq      mean   median       uq        max neval
akrun_sapply  18.187  22.7555  27.17026  24.6140  27.8845   2428.194 10000
akrun_Vectorize  60.119  76.1510  88.82623  83.4445  89.9680   2717.420 10000
akrun_mapply  19.006  24.2100  29.78381  26.2120  29.9255   2911.252 10000
akrun_mapply_match  14.136  18.4380  35.45528  20.0275  23.6560 127960.324 10000
akrun_map2 217.209 264.7350 303.64609 277.5545 298.0455   9204.243 10000
CPak  15.741  19.7525  27.31918  24.7150  29.0340    235.245 10000
zacdav   6.649   9.3210  11.30229  10.4240  11.5540   2399.686 10000
zacdav_which   7.364  10.2395  12.22632  11.2985  12.4515   2492.789 10000
``````