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：
假设我有两个向量x和y
x <- 1:10
y <- 10:1
，并且当x小于y时，我想用x覆盖y的值。我可以用（1）做到这一点：
x[x < y] <- y[x < y]
但是在这里我不得不写两次x < y，这是打字时有缺点的地方，当进行更新时，我会忘记在两侧都这样做。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)是否分配了内存。
我可以在（4）之类的环境中使用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idx向量。 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)，这样可以减小向量大小并改善execution time。
最后，我对所有这些方法都不满意。
是否有推荐的方法将一个索引向量的两个向量子集化？对我来说， Typo-save 和 Memory Consumption 比 Execution time 更重要。
是否可以查看执行过程中不同方法的内存消耗，例如使用microbenchmark查看执行时间，还是只能通过创建巨大的向量并具有一个看系统进程吗？
答案 0 :(得分：6)
[…]但是在这里我创建了一个附加向量
不，你不是。实际上，您创建的向量比以前的代码少，因为您只计算一次x < y而不是两次。
顺便说一句，我会在代码中看到rm的任何显式使用，这是一种代码味道。而是限制计算范围，以使idx变量短暂存在。要明确实现此目的，您可以使用local 1 ：
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不太可能优化掉另一个副本；替代方法是通过<<-或{{1 }}在assign调用中。）
[…]我希望
local不在内存中创建delayedAssign向量
再次，您是怎么想的？ 当然它会在内存中创建向量-毕竟，您随后会使用它。您可能会认为计算是延迟执行的，但是R最近通过ALTREP获得了此功能，但是在极少数情况下会自动创建此类表达式，因此在这里与它们无关。
1 您对idx的使用与此类似，只是更加复杂。 evalq是local周围的便利包装。
答案 1 :(得分：1)
我想提出这个基准，至少在速度方面。我希望一切都很好，并且它可以为您带来一些有益的帮助。
# 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
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)
答案 2 :(得分：1)
如果您定义了以下功能（例如，this question进行了改进，对不起，我没有打包版本可以提出）
`<<-` <- 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
}
}
您可以执行x < y <- y[x <y]而不是x[x < y] <- y[x <y]
现在，如果您在其顶部使用dotdot（用lhs代替rhs中的..），则可以执行以下操作：
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，我通常会用简单的lhs将经过修改的<<-用于x < 0 <- NA和dotdot之类的东西，但这可能是您可以获得的最紧凑的东西！
请注意，x < y仍将在此处进行两次评估，因为在考虑到这种奇怪的情况时，点点代码并未被编码：）。
答案 3 :(得分：0)
当前看起来像使用
idx <- which(x < y)
x[idx] <- y[idx]
rm(idx)
建议将具有相同索引向量的两个向量子集化。
如果您在一侧使用位置，而在另一侧使用值，例如 update join
idx <- match(x, y)
idxn <- which(!is.na(idx))
x[idxn] <- -y[idx[idxn]]
rm(idx, idxn)
可以推荐。
详细信息：
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