r - 按时间仓提取向量名称

Question

我编写了这个循环来提取在时间间隔(bin)内出现的向量的每个元素的名称。我想知道我是否缺少一种更快的方法来执行此操作...我想对长度为 1000 的向量实现随机化方面，因此不想依赖循环。

mydata <- structure(c(1199.91666666667, 1200.5, 1204.63333333333, 1205.5, 
                      1206.3, 1208.73333333333, 1209.06666666667, 1209.93333333333, 
                      1210.98333333333, 1214.56666666667, 1216.06666666667, 1216.63333333333, 
                      1216.91666666667, 1219.13333333333, 1221.35, 1221.51666666667, 
                      1225.35, 1225.53333333333, 1225.96666666667, 1227.61666666667, 
                      1228.91666666667, 1230.31666666667, 1233.53333333333, 1235.8, 
                      1237.51666666667, 1239.41666666667, 1241.6, 1247.08333333333, 
                      1247.45, 1252.7, 1253.26666666667), .Names = c("B", "A", "B", 
                                                                     "E", "A", "A", "B", "G", "G", "C", "A", "D", "E", "B", "B", "E", 
                                                                     "E", "G", "F", "A", "C", "A", "F", "B", "A", "F", "F", "G", "F", 
                                                                     "G", "F"))


mydata

      B        A        B        E        A        A        B        G        G        C        A        D        E        B        B        E        E 
1199.917 1200.500 1204.633 1205.500 1206.300 1208.733 1209.067 1209.933 1210.983 1214.567 1216.067 1216.633 1216.917 1219.133 1221.350 1221.517 1225.350 
       G        F        A        C        A        F        B        A        F        F        G        F        G        F 
1225.533 1225.967 1227.617 1228.917 1230.317 1233.533 1235.800 1237.517 1239.417 1241.600 1247.083 1247.450 1252.700 1253.267 

这些代表事件的连续时间(以秒为单位)。假设我们想让间隔为 5 秒。我的方法是创建每个间隔开始的向量，然后使用循环查找该间隔内出现的元素的名称:

N=5
ints <- seq(mydata[1], mydata[length(mydata)], N)

out<-list()
for(i in 1:length(ints)){
  out[[i]] <- names(mydata[mydata>=ints[i] & mydata<ints[i]+N])
}

out


[[1]]
[1] "B" "A" "B"

[[2]]
[1] "E" "A" "A" "B"

[[3]]
[1] "G" "G" "C"

[[4]]
[1] "A" "D" "E" "B"

[[5]]
[1] "B" "E"

[[6]]
[1] "E" "G" "F" "A" "C"

[[7]]
[1] "A" "F"

[[8]]
[1] "B" "A" "F"

[[9]]
[1] "F"

[[10]]
[1] "G" "F"

[[11]]
[1] "G" "F"

这对于小样本来说很好 - 但我可以看到，在处理排列了 1000 次的非常大的样本时，这会变得很慢。

Answer 1

我的建议是使用 findInterval (基于 this earlier question of mine 的答案):

mydata2 = c(-Inf, mydata)
ints <- seq(mydata[1], mydata[length(mydata)]+5, N)
idx = findInterval(ints-1e-10, mydata2)

out<-list()
for(i in 1:(length(ints)-1)){
  out[[i]] <- names(mydata2[(idx[i]+1):(idx[i+1])])
}

正如您所看到的，我必须对开头进行一些修改(添加小于第一个断点的第一个值，添加一个 epsilon)。这是结果，与您的结果相同:

> out
[[1]]
[1] "B" "A" "B"

[[2]]
[1] "E" "A" "A" "B"

[[3]]
[1] "G" "G" "C"

[[4]]
[1] "A" "D" "E" "B"

[[5]]
[1] "B" "E"

[[6]]
[1] "E" "G" "F" "A" "C"

[[7]]
[1] "A" "F"

[[8]]
[1] "B" "A" "F"

[[9]]
[1] "F"

[[10]]
[1] "G" "F"

[[11]]
[1] "G" "F"

就示例的速度而言，有一些改进:

> microbenchmark( jalapic = {out<-list(); for(i in 1:length(ints)){out[[i]] <- names(mydata[mydata>=ints[i] & mydata<ints[i]+N])}},
+   mts = {idx = findInterval(ints2-1e-10, mydata2); out<-list(); for(i in 1:(length(ints)-1)){out[[i]] <- names(mydata2[(idx[i]+1):(idx[i+1])])}}, 
+   alexis = {split(names(mydata), findInterval(mydata, ints))},
+   R_Yoda = {dt[, groups := cut2(data,ints)]; result <- dt[, paste0(names, collapse=", "), by=groups]})
Unit: microseconds
    expr      min        lq       mean    median       uq      max neval
 jalapic   67.177   76.9725   85.73347   82.8035   95.866  119.890   100
     mts   43.851   52.7150   62.72116   58.3130   73.007   96.099   100
  alexis   75.573   86.5360   95.72593   91.4340  100.531  234.649   100
  R_Yoda 2032.066 2158.4870 2303.68887 2191.3750 2281.409 8719.314   100

对于较大的向量(我选择长度 2000)，这更清楚:

set.seed(123)
mydata = sort(runif(n = 2000, min = 0, max = 100))
names(mydata) = sample(LETTERS[1:7], size = 2000, replace = T)
mydata2 = c(-Inf, mydata)
ints2 <- seq(mydata[1], mydata[length(mydata)]+5, N)
dt <- data.table(data=mydata, names=names(mydata) )
> microbenchmark( jalapic = {out<-list(); for(i in 1:length(ints)){out[[i]] <- names(mydata[mydata>=ints[i] & mydata<ints[i]+N])}},
+                 mts = {idx = findInterval(ints2-1e-10, mydata2); out<-list(); for(i in 1:(length(ints)-1)){out[[i]] <- names(mydata2[(idx[i]+1):(idx[i+1])])}}, 
+                 alexis = {split(names(mydata), findInterval(mydata, ints))},
+                 R_Yoda = {dt[, groups := cut2(data,ints)]; result <- dt[, paste0(names, collapse=", "), by=groups]})
Unit: microseconds
    expr      min        lq      mean    median        uq       max neval
 jalapic  804.243  846.9275  993.9957  862.0890  883.3140  7140.218   100
     mts   77.439   88.8685  100.6148  100.0640  106.5955   188.466   100
  alexis  187.066  204.7930  220.1689  215.5225  225.3190   299.026   100
  R_Yoda 3831.348 4066.4640 4366.5382 4140.1700 4248.8635 11829.923   100