使用相同索引向量对两个向量进行子集化的推荐方法答案

【问题标题】：Recommended way to subset two vectors with the same index vector使用相同索引向量对两个向量进行子集化的推荐方法
【发布时间】：2019-11-07 04:57:22
【问题描述】：

我正在寻找一种有效的方法来对具有相同索引向量的两个向量进行子集化。有效的类别可以是：

错字保存
内存消耗
可读性
计算时间

假设我有两个向量 x 和 y

x <- 1:10
y <- 10:1

当x 小于y 时，我想用y 的值覆盖x 的值。我可以用 (1) 做到这一点：

x[x < y] <- y[x < y]

但是在这里我必须写两次x < y，错字有什么缺点，并且在进行更新时我可能会忘记在双方都这样做。所以我可以创建一个索引向量（2）：

idx <- x < y
x[idx] <- y[idx]
rm(idx)

但是在这里我创建了一个额外的向量，它可能需要内存和时间。我也可以使用for-loop (3)：

for(i in seq_along(x)) {
   if(x[i] < y[i]) x[i] <- y[i]
}

这可能很慢，我不知道seq_along(x) 是否分配内存。我可以在像 (4) 这样的环境中使用delayedAssign：

(function() {
  delayedAssign("idx", x < y)
  x[idx] <<- y[idx]
})()

或（5）：

evalq({
  delayedAssign("idx", x < y)
  x[idx] <<- y[idx]}, envir = new.env(), enclos = parent.frame())

我希望delayedAssign 不会在内存中创建idxvector。 base 中已经有其他几种可能性，例如：

x <- ifelse(x < y, y, x) #(6)
x <- sapply(seq_along(x), function(i) {if(x[i] < y[i]) y[i] else x[i]}) #(7)
with(data.frame(idx = x < y), x[idx] <<- y[idx]) #(8)

x < y 可以替换为which(x < y) 可以减小向量大小并提高execution time。

最后我对所有这些方法都不满意。

有没有推荐的方法用一个索引向量对两个向量进行子集化？对我来说 Typo-save 和 内存消耗 比 执行时间 更重要。

有没有办法在执行过程中查看不同方法的内存消耗，例如使用microbenchmark 查看执行时间，或者只能通过创建巨大的向量并查看系统进程？

【问题讨论】：

也许你需要pmax(x, y)（虽然这不是我想的最快的方法）
你是不是想多了？还是一些特殊的应用？以上一些绝对被认为是不好的做法，但有些似乎很好。你有什么不满意的地方？
@markus x < y 只是获取索引向量的占位符，可以与生成索引向量的任何条件进行交换，该索引向量用于子集到其他向量。
这对我来说听起来像是过早的优化，因为你不知道如果你对上述方法有问题，而是你在推测它们可能有问题。一种可以减少拼写错误的方法是定义一个函数。例如：'%s%' <- function(a, b){a[a < b] <- b[a < b]; a} 现在是 x %s% y。（注意：' 应该是 `。）
@sindri_baldur 也许你的想法是对的。就我个人而言，我喜欢先创建一个索引，然后在两个向量上使用它，但在这里我不满意这个索引向量使用的内存在巨大的向量上会出现问题。

标签： r performance memory indexing subset

【解决方案1】：

[…] 但在这里我创建了一个额外的向量

不，你不是。实际上，您创建的向量比之前的代码少一个，因为您只计算了一次 x < y 而不是两次。

顺便说一句，我会看到在代码中明确使用 rm 是一种代码异味。相反，请限制计算范围，以便 idx 变量是短暂的。要明确地做到这一点，您可以使用local¹:

x = local({
    idx = x < y
    x[idx] = y[idx]
})

(但如图所示，这需要重新分配给x，这会导致R不太可能优化掉的另一个副本；替代方法是通过<<-或assign在@987654330内使用全局重新分配@调用。）

[…] 我希望delayedAssign 不会在内存中创建idxvector

再一次，是什么让你这么想？当然它会在内存中创建一个向量——毕竟，您随后会使用它。您可能会认为计算是延迟执行的，但尽管 R 最近通过 ALTREP 获得了此功能，但很少有情况会自动创建此类表达式，它们与此处无关。

¹ 您对evalq 的使用类似，只是更复杂。 local 是 eval.parent(quote(…)) 的便捷包装。

【讨论】：

这意味着使用idx 的方法（2）接近我正在寻找的解决方案？因为它是 Typo-save 并且使用更少的内存并且还具有可读性？
@user10488504 在列出的替代方案中，考虑到您列出的标准，它肯定是最好的，是的。如果您的条件是稀疏的，并且您更关心性能而不是可读性，您可以将条件包装在 which 中，但我宁愿不这样做。

【解决方案2】：

我想介绍这个基准，至少在速度部分。我希望一切都好，并希望它贡献一些有用的东西。

基准测试

# Unit: milliseconds
# expr       min          lq       mean      median          uq        max neval cld
# M1    669.5364    772.9185   2360.234   2285.9725   2691.0597   6748.697    10  a 
# M2    460.5103    540.4875   1477.873    710.7905   2112.8210   4538.728    10  a 
# M2a   776.5886   1879.7114   3596.316   2482.3449   3514.3662  10049.601    10  a 
# M3   7530.8926   7556.8909   7589.172   7587.4924   7619.6962   7668.825    10  a 
# M4    442.4082    545.2067   1671.283    641.0817   2232.8275   6821.164    10  a 
# M5    572.0651    603.7959   1536.910    783.5842   1681.0030   6199.584    10  a 
# M6   2045.0549   2222.2072   5613.928   3949.3604   7877.2988  14514.625    10  a 
# M7 143646.6301 156567.2780 165822.856 165018.5859 166944.0531 221897.671    10   b
# M8    446.6539    552.2921   1044.842    827.3231   1766.1650   2168.388    10  a 
# M9    388.7266    406.7127    684.946    529.0503    554.9486   2093.648    10  a

代码

set.seed(42)
n <- 1e8
x <- sample(1:9, n, replace=TRUE)
y <- sample(1:9, n, replace=TRUE)

library(microbenchmark)
microbenchmark(M1=x[x < y] <- y[x < y],
               M2={
                 idx <- x < y
                 x[idx] <- y[idx]
               },
               M2a=local({
                 idx=x < y
                 x[idx]=y[idx]
               }),
               M3=for(i in seq_along(x)) {
                 if(x[i] < y[i]) x[i] <- y[i]
               },
               M4={
                 (function() {
                   delayedAssign("idx", x < y)
                   x[idx] <<- y[idx]
                 })()
               },
               M5=evalq({
                 delayedAssign("idx", x < y)
                 x[idx] <<- y[idx]}, envir=new.env(), enclos=parent.frame()),
               M6=ifelse(x < y, y, x),
               M7=sapply(seq_along(x), function(i) {
                 if(x[i] < y[i]) y[i] else x[i]
                 }),
               M8=with(data.frame(idx=x < y), x[idx] <<- y[idx]),
               M9=pmax(x, y),  # off topic
               times=5L)

【讨论】：

谢谢！是否也可以在执行期间显示不同方法的内存消耗，例如microbenchmark 显示执行时间？
@user10488504 欢迎您。不，microbenchmark 不可能，但我看到 bench::mark 可能有这个选项。

【解决方案3】：

如果你定义了如下的赋值函数（从this question改进，很抱歉我没有打包的版本建议）

`<<-` <- function(e1, e2, value){
  if(missing(value)) 
    eval.parent(substitute(.Primitive("<<-")(e1, e2)))
  else {
    cond <- e1 < e2
    if(any(cond)) 
      replace(e1, cond, value)
    else e1
  }
}

你可以用x < y <- y[x <y]代替x[x < y] <- y[x <y]

现在，如果在它之上使用dotdot，它将右轴中的.. 替换为左轴，您可以执行以下操作：

library(dotdot)
x <- 1:10
y <- 10:1
x < y := y[..]
x
# [1] 10  9  8  7  6  6  7  8  9 10

不确定它的可读性如何，我通常将修改后的<<- 用于x < 0 <- NA 和带有简单lhs 的点点，但它可能是你能得到的最紧凑的！

请注意，x < y 仍将在此处计算两次，因为 dotdot 的编码并未考虑到这种奇怪的情况:)。

【讨论】：

【解决方案4】：

目前看起来像这样使用

idx <- which(x < y)
x[idx] <- y[idx]
rm(idx)

可以推荐使用相同索引向量对两个向量进行子集化。

如果您在一侧使用位置，而在另一侧使用值，例如在 更新连接中，

idx <- match(x, y)
idxn <- which(!is.na(idx))
x[idxn] <- -y[idx[idxn]]
rm(idx, idxn)

可以推荐。

详情：

library(microbenchmark)

fun <- alist(m1 = x[x < y] <- y[x < y]
           , m1w = x[which(x < y)] <- y[which(x < y)]
           , m2 = {idx <- x < y; x[idx] <- y[idx]; rm(idx)}
           , m2w = {idx <- which(x < y); x[idx] <- y[idx]; rm(idx)}
           , m2wl = {local({idx <- which(x < y); x[idx] <<- y[idx]})}
           , m3 = {for(i in seq_along(x)) {if(x[i] < y[i]) x[i] <- y[i]}}
           , m3w = {for(idx in which(x < y)) {x[idx] <- y[idx]}}
           , m3wl = {local(for(idx in which(x < y)) {x[idx] <<- y[idx]})}
           , m4 = {(function() {delayedAssign("idx", x < y); x[idx] <<- y[idx]})()}
           , m4w = {(function() {delayedAssign("idx", which(x < y)); x[idx]<<- y[idx]})()}
           , m5 = {evalq({delayedAssign("idx", x < y); x[idx] <<- y[idx]}, envir = new.env(), enclos = parent.frame())}
           , m5w = {evalq({delayedAssign("idx", which(x < y)); x[idx] <<- y[idx]}, envir = new.env(), enclos = parent.frame())}
           , m6 = x <- ifelse(x < y, y, x)
           , m7 = x <- sapply(seq_along(x), function(i) {if(x[i] < y[i]) y[i] else x[i]})
           , m8 = with(data.frame(idx = x < y), x[idx] <<- y[idx])
           , m8w = with(data.frame(idx = which(x < y)), x[idx] <<- y[idx])
           , m9 = x <- pmax(x, y)
          )

memUse <- function(list, setup = "", gctort = FALSE) {
  as.data.frame(lapply(list, function(z) {
    eval(setup)
    tt <- sum(.Internal(gc(FALSE, TRUE, TRUE))[13:14])
    gctorture(on = gctort)
    eval(z)
    gctorture(on = FALSE)
    sum(.Internal(gc(FALSE, FALSE, TRUE))[13:14]) - tt
  }))
}

n <- 1e6L #On my pc m7 takes very long with longer vectors

#50% repacement
xC <- rep_len(0:9, n)
x <- xC
y <- rep_len(9:0, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr        min          lq        mean      median          uq        max neval
#   m1   9.362981    9.941294   30.185996   12.315266   23.531700  141.28603    20
#  m1w  10.029621   10.971671   17.328038   12.256211   17.934435   81.65052    20
#   m2   7.177270    7.368008   11.875662    9.524422   12.633254   37.91534    20
#  m2w   7.083238    7.844280   11.196914    8.550595   14.602815   22.86438    20
# m2wl   7.050052    7.258657   10.318978    7.923862    9.745804   25.74206    20
#   m3 109.944807  110.882601  114.673555  111.885780  117.075936  129.19246    20
#  m3w  35.628572   36.300217   46.996217   37.574246   40.024844  116.80551    20
# m3wl 363.532255  373.069300  431.522826  395.528996  456.413863  597.30239    20
#   m4   7.116324    8.001434   11.823148    9.022419   13.660199   27.59086    20
#  m4w   6.963074    7.048622   13.242077    8.084297   11.645519   78.60026    20
#   m5   7.067611    7.327816   16.069991    9.284865   15.416912  115.43513    20
#  m5w   7.034584    8.382096   16.781882    9.726437   16.823329  108.70100    20
#   m6  17.651948   18.796132   26.044715   19.585389   27.150359   95.99133    20
#   m7 933.336461 1028.588463 1082.014782 1077.447541 1142.779436 1203.75604    20
#   m8   7.304279    7.613715   12.683940    9.259327   16.367387   46.73193    20
#  m8w   7.306591    7.446765   13.722586    8.317214   13.281609   83.86340    20
#   m9   3.404972    3.541755    3.796737    3.685642    4.012589    4.54105    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#    m1  m1w m2  m2w m2wl   m3 m3w m3wl m4  m4w m5  m5w   m6    m7   m8  m8w  m9
#1 24.8 24.8 21 15.2 15.2 59.2  68   68 21 15.2 21 15.2 34.3 106.1 21.1 15.3 3.8

#100% repacement
xC <- rep_len(0, n)
x <- xC
y <- rep_len(1, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr        min         lq        mean      median          uq         max neval
#   m1  13.648232   14.82093   28.621411   15.936936   26.199469   99.553175    20
#  m1w  15.920691   16.54614   23.413209   17.117911   19.579158  113.066464    20
#   m2  11.551213   11.89491   20.940656   13.153880   22.355784   94.305937    20
#  m2w  12.313997   12.47287   19.400817   13.687655   20.209957   89.129126    20
# m2wl  12.276096   12.42314   14.322066   13.107012   14.680684   21.261685    20
#   m3 137.168517  139.28248  143.018082  140.939792  144.204528  165.616204    20
#  m3w  66.463903   66.66168   69.924619   67.901559   71.515786   79.278128    20
# m3wl 690.311636  703.53159  748.621164  718.447933  748.375469  970.204829    20
#   m4  11.441186   11.74227   17.736055   12.714788   16.740723   80.710164    20
#  m4w  12.286395   12.40667   14.305248   12.501939   13.737790   31.441008    20
#   m5  11.473874   11.53943   17.929906   11.960568   13.638548   83.077290    20
#  m5w  12.278804   12.38792   19.689197   13.446629   18.157106  100.649735    20
#   m6  18.118748   19.03396   29.584077   19.753099   26.499255   99.333902    20
#   m7 847.875768 1032.68963 1071.521727 1072.907583 1151.036749 1249.079078    20
#   m8  12.020599   12.77211   14.821545   13.188046   14.576952   24.397931    20
#  m8w  12.551370   12.63402   19.263936   12.859222   17.794158   86.014839    20
#   m9   3.393315    3.61704    4.039384    3.711276    4.456398    5.489078    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#    m1  m1w   m2  m2w m2wl   m3  m3w m3wl   m4  m4w   m5  m5w m6    m7   m8  m8w  m9
#1 38.2 38.2 34.3 26.7 26.7 61.6 72.6 72.6 34.3 26.7 34.3 26.7 42 108.2 34.5 26.8 7.6

#0% repacement
xC <- rep_len(1, n)
x <- xC
y <- rep_len(0, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr        min          lq        mean      median          uq         max neval
#   m1   6.589096    6.664210    9.885122    6.684262   13.775726   21.878444    20
#  m1w   6.842137    6.904734   14.664578    6.947293   13.320292  109.027443    20
#   m2   3.401598    3.413235    4.953130    3.438164    3.963643   14.268839    20
#  m2w   3.005197    3.017770    3.094672    3.042470    3.074060    4.049775    20
# m2wl   3.981523    4.005924    9.785378    4.021979    4.156620   92.515420    20
#   m3  80.912504   81.154859   81.901925   81.631556   82.670752   84.190109    20
#  m3w   5.377001    5.416671    7.138954    5.483028    6.511429   16.243317    20
# m3wl   2.919146    2.947492    4.005262    2.964488    2.981436   12.981625    20
#   m4   4.453434    4.485750    5.874224    4.536671    5.562121   13.933019    20
#  m4w   3.991118    4.008097    6.597674    4.028864    4.635915   22.966413    20
#   m5   4.461602    4.494989    7.666011    4.511920    9.758226   24.383190    20
#  m5w   3.959898    4.002523    7.709664    4.050941    4.173436   53.543674    20
#   m6  17.661630   17.791470   23.846625   19.567245   28.225758   38.622220    20
#   m7 933.661883 1033.959171 1077.598475 1096.121598 1136.886472 1175.809700    20
#   m8   4.719020    4.745232    9.919018    4.781921    6.507246   75.527422    20
#  m8w   4.239784    4.271047    9.102203    4.299095    7.165185   57.379780    20
#   m9   3.366839    3.452929    4.988191    3.481065    3.564584   19.815845    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#    m1  m1w   m2 m2w m2wl   m3 m3w m3wl   m4  m4w   m5  m5w m6    m7   m8  m8w  m9
#1 22.9 22.9 11.5 7.6 15.3 44.9 7.6  7.6 19.1 15.3 19.1 15.3 42 128.4 19.2 15.4 7.6


#With a more complex condition than x < y
fun <- alist(m1 = x[tan(x) < tan(y)] <- y[tan(x) < tan(y)]
     , m1w = x[which(tan(x) < tan(y))] <- y[which(tan(x) < tan(y))]
     , m2 = {idx <- tan(x) < tan(y); x[idx] <- y[idx]; rm(idx)}
     , m2w = {idx <- which(tan(x) < tan(y)); x[idx] <- y[idx]; rm(idx)}
     , m2wl = {local({idx <- which(tan(x) < tan(y)); x[idx] <<- y[idx]})}
     , m3 = {for(i in seq_along(x)) {if(tan(x[i]) < tan(y[i])) x[i] <- y[i]}}
     , m3w = {for(idx in which(tan(x) < tan(y))) {x[idx] <- y[idx]}}
     , m3wl = {local(for(idx in which(tan(x) < tan(y))) {x[idx] <<- y[idx]})}
     , m4 = {(function() {delayedAssign("idx", tan(x) < tan(y)); x[idx] <<- y[idx]})()}
     , m4w = {(function() {delayedAssign("idx", which(tan(x) < tan(y))); x[idx] <<- y[idx]})()}
     , m5 = {evalq({delayedAssign("idx", tan(x) < tan(y)); x[idx] <<- y[idx]}, envir = new.env(), enclos = parent.frame())}
     , m5w = {evalq({delayedAssign("idx", which(tan(x) < tan(y))); x[idx] <<- y[idx]}, envir = new.env(), enclos = parent.frame())}
     , m6 = x <- ifelse(tan(x) < tan(y), y, x)
     , m7 = x <- sapply(seq_along(x), function(i) {if(tan(x[i]) < tan(y[i])) y[i] else x[i]})
     , m8 = with(data.frame(idx = tan(x) < tan(y)), x[idx] <<- y[idx])
     , m8w = with(data.frame(idx = which(tan(x) < tan(y))), x[idx] <<- y[idx])
)

#50% repacement
xC <- rep_len(0:9, n)
x <- xC
y <- rep_len(9:0, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr        min         lq       mean     median         uq        max neval
#   m1  150.53809  151.25090  164.24548  152.94124  170.73427  223.97944    20
#  m1w  150.56266  151.84806  161.74585  153.18321  160.00592  223.86287    20
#   m2   76.88262   78.13652   85.72190   82.40141   87.19680  150.77076    20
#  m2w   76.53983   77.40472   87.74890   77.94618   82.32798  156.58522    20
# m2wl   76.93366   77.58086   82.68244   78.15460   82.18667  107.52469    20
#   m3  232.22004  232.99509  237.67218  234.10627  235.14893  304.04201    20
#  m3w  105.59221  105.86459  110.04552  106.89158  111.67208  133.75261    20
# m3wl  435.25394  446.68204  487.41579  453.01685  521.65402  705.62717    20
#   m4   77.32980   77.89973   84.78399   79.00021   79.94984  160.29572    20
#  m4w   76.99555   77.94441   81.50439   78.60553   85.55179   96.93830    20
#   m5   76.66992   77.33471   89.60525   79.20687   86.08836  172.16646    20
#  m5w   76.37683   77.05510   82.85917   77.53271   82.16778  144.24078    20
#   m6   87.37572   88.45537   98.99907   89.02368   96.81267  165.25716    20
#   m7 1027.99068 1150.86229 1199.66760 1201.45385 1239.21803 1315.94626    20
#   m8   76.86455   77.93917   79.76095   78.96807   79.60475   89.65809    20
#  m8w   78.69934   79.13237   83.26944   80.81390   87.51346  101.49319    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#    m1  m1w   m2  m2w m2wl   m3  m3w m3wl   m4  m4w   m5  m5w   m6    m7   m8  m8w
#1 55.3 55.3 36.2 30.5 30.5 55.9 79.5 79.5 36.2 30.5 36.2 30.5 49.6 133.9 36.3 30.6

#100% repacement
xC <- rep_len(0, n)
x <- xC
y <- rep_len(1, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr       min         lq       mean     median         uq        max neval
#   m1  72.50779   73.85809   81.62600   75.17883   83.09049  143.10837    20
#  m1w  74.33753   75.62555   81.39149   77.06395   85.34476  105.24806    20
#   m2  40.73727   41.48906   52.22463   42.93811   50.68852  119.13366    20
#  m2w  41.43466   41.72756   50.53189   44.94449   52.37125  121.17726    20
# m2wl  41.60436   42.25114   49.84177   43.86556   50.83141  127.52145    20
#   m3 214.33331  215.56117  223.15401  216.54753  221.73404  304.96538    20
#  m3w  96.23799   96.71452   98.80659   97.73016   99.23308  106.56888    20
# m3wl 716.11847  736.26573  839.67672  775.01016  932.43366 1054.35836    20
#   m4  40.75406   41.78469   45.13181   42.54718   49.61718   56.45373    20
#  m4w  41.51614   41.79747   50.39626   43.04746   53.30470  112.03821    20
#   m5  40.69414   41.41407   46.72030   42.44355   50.06923   72.67644    20
#  m5w  41.53582   41.62817   48.23472   42.68742   50.58984  110.01377    20
#   m6  47.13042   48.38094   58.69262   53.26532   59.43038  143.99039    20
#   m7 992.82221 1102.50031 1139.33831 1137.81362 1161.28651 1277.99506    20
#   m8  40.92674   42.25611   47.05092   46.78702   50.60716   58.58826    20
#  m8w  42.59176   43.02294   53.50535   47.65410   53.70309  120.47093    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#    m1  m1w   m2 m2w m2wl   m3  m3w m3wl   m4 m4w   m5 m5w   m6    m7   m8  m8w
#1 53.4 53.4 49.6  42   42 64.6 90.9 90.9 49.6  42 49.6  42 49.6 111.8 49.7 42.1

#0% repacement
xC <- rep_len(1, n)
x <- xC
y <- rep_len(0, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr        min         lq       mean     median         uq        max neval
#   m1   64.13278   65.17428   76.80236   72.93639   74.11106  139.21633    20
#  m1w   64.20926   64.36072   68.26227   65.65780   73.27032   81.91496    20
#   m2   32.55641   32.73825   40.57034   33.73059   42.21539  106.96577    20
#  m2w   32.08899   32.17692   37.02755   32.42990   33.41896  104.51287    20
# m2wl   33.21636   33.30782   40.70415   34.41295   42.56047   90.67323    20
#   m3  143.45435  144.07389  145.11642  144.96065  145.85825  147.79313    20
#  m3w   35.12864   35.40720   37.85706   35.58504   40.38525   44.57139    20
# m3wl   32.11977   32.22333   37.60191   32.42593   37.28842   97.82210    20
#   m4   33.59010   33.79237   42.43258   34.75375   42.61118  140.07139    20
#  m4w   33.18575   33.38007   40.68604   33.50068   42.04799  111.38275    20
#   m5   33.69306   34.00844   36.43207   34.82090   36.07427   46.25004    20
#  m5w   33.18643   33.28609   39.02025   34.24648   42.53507   71.25813    20
#   m6   46.95918   47.91826   57.68163   55.85067   56.73664  139.46774    20
#   m7 1012.54640 1101.79435 1136.96233 1143.16804 1169.20692 1315.41621    20
#   m8   33.92067   34.15995   39.46636   35.18619   43.15650   57.13766    20
#  m8w   34.33384   34.49190   38.55053   35.13948   43.33685   57.35593    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#    m1  m1w   m2  m2w m2wl   m3  m3w m3wl   m4  m4w   m5  m5w   m6    m7   m8  m8w
#1 45.8 45.8 26.7 22.9 30.5 56.2 22.9 22.9 34.3 30.5 34.3 30.5 49.6 110.3 34.5 30.6

#update join
n <- 1e5L

fun <- alist(m1 = x[!is.na(match(x, y))] <- -y[match(x, y)[!is.na(match(x, y))]]
             , m1b = x[!is.na(match(x, y))] <- -y[na.omit(match(x, y))]
             , m2 = {idx <- match(x, y); x[!is.na(idx)] <- -y[idx[!is.na(idx)]]; rm(idx)}
             , m2b = {idx <- match(x, y); x[!is.na(idx)] <- -y[na.omit(idx)]; rm(idx)}
             , m2c = {idx <- match(x, y); idxn <- !is.na(idx); x[idxn] <- -y[idx[idxn]]; rm(idx, idxn)}
             , m2d = {idx <- match(x, y); idxn <- which(!is.na(idx)); x[idxn] <- -y[idx[idxn]]; rm(idx, idxn)}

             , m3 = {for(i in seq_along(x)) {idx <- match(x[i], y); if(!is.na(idx)) {x[i] <- -y[idx]}}}
             )

#50% repacement
xC <- rep_len(0:9, n)
x <- xC
y <- rep_len(4:0, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr         min          lq        mean      median          uq         max neval
#   m1    5.099619    5.171017    5.312834    5.216994    5.283100    6.283539    20
#  m1b    4.385434    4.445713    4.605092    4.483453    4.551095    5.875877    20
#   m2    2.417861    2.452343    2.492067    2.480543    2.530377    2.660484    20
#  m2b    2.926915    2.961653    3.084997    3.021751    3.077324    4.220686    20
#  m2c    2.123245    2.195435    2.533441    2.278206    2.856874    3.800974    20
#  m2d    2.103713    2.238050    2.674693    2.355113    3.362142    3.637493    20
#   m3 9286.882351 9307.611081 9544.051457 9437.133414 9814.396820 9982.323049    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#   m1 m1b  m2 m2b m2c m2d   m3
#1 9.9 8.9 5.6 6.7 4.8 4.3 57.5

#100% repacement
xC <- rep_len(0:9, n)
x <- xC
y <- rep_len(9:0, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr         min          lq        mean      median          uq         max neval
#   m1    5.883064    5.915986    6.128063    5.995118    6.092747    7.247831    20
#  m1b    4.194141    4.218877    4.386549    4.266843    4.332457    5.502114    20
#  m2c    2.600136    2.645534    2.758381    2.662184    2.773307    3.999435    20
#  m2d    2.692732    2.733519    2.875407    2.753938    2.768791    3.969404    20
#   m2    2.904083    2.926344    3.008820    2.954213    3.036742    3.571559    20
#  m2b    2.568076    2.587834    2.636594    2.627963    2.678503    2.758791    20
#   m3 6618.263068 6635.364325 6907.177674 6779.890271 6923.410849 8135.443601    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#    m1 m1b  m2 m2b m2c m2d   m3
#1 10.7 7.4 6.4 5.2 5.6 4.8 57.6

#0% repacement
xC <- rep_len(5:9, n)
x <- xC
y <- rep_len(4:0, n)

microbenchmark(list = fun, setup = x <- xC, times = 20)
#Unit: milliseconds
# expr          min           lq         mean       median           uq          max neval
#   m1     4.175989     4.213025     4.319972     4.241362     4.379613     4.853646    20
#  m1b     4.068414     4.111130     4.184327     4.162872     4.214517     4.393815    20
#   m2     1.736049     1.775315     1.873898     1.849657     1.894493     2.286939    20
#  m2b     2.766647     2.807789     2.871928     2.824930     2.902665     3.312260    20
#  m2c     1.579129     1.637973     1.724936     1.704182     1.780902      2.028083    20
#  m2d     1.507742     1.619949     1.683850     1.681409     1.747972      1.852088    20
#   m3 10724.757111 12385.576878 12515.708573 12446.907482 12835.884597 13177.535579    20

memUse(list=fun, setup = quote(x <- xC), gctort = FALSE)
#   m1 m1b  m2 m2b m2c m2d   m3
#1 8.7 8.5 4.5 6.4 3.7 3.3 57.4


version
#platform       x86_64-pc-linux-gnu         
#arch           x86_64                      
#os             linux-gnu                   
#system         x86_64, linux-gnu           
#status                                     
#major          3                           
#minor          6.0                         
#year           2019                        
#month          04                          
#day            26                          
#svn rev        76424                       
#language       R                           
#version.string R version 3.6.0 (2019-04-26)
#nickname       Planting of a Tree

【讨论】：