r - 计算某个时间戳正在进行的事件数

Question

我有一系列时间戳标记某些事件的开始和结束。

library(chron)
start <- structure(c(14246.3805439815, 14246.3902662037, 14246.3909606481, 
14246.3992939815, 14246.4013773148, 14246.4034606481, 14246.4062384259, 
14246.4069328704, 14246.4069328704, 14246.4097106481, 14246.4097106481, 
14246.4104050926, 14246.4117939815, 14246.4117939815, 14246.4117939815, 
14246.4145717593, 14246.4152546296, 14246.4152662037, 14246.4152662037, 
14246.4159606481), format = structure(c("m/d/y", "h:m:s"), .Names = c("dates", 
"times")), origin = structure(c(1, 1, 1970), .Names = c("month", 
"day", "year")), class = c("chron", "dates", "times"))

finish <- structure(c(14246.436099537, 14246.4666550926, 14246.4083217593, 
14246.4374884259, 14246.4847106481, 14246.4867939815, 14246.4305439815, 
14246.4659606481, 14246.4520717593, 14246.9097106481, 14246.4930439815, 
14246.4763773148, 14246.4326273148, 14246.4291550926, 14246.4187384259, 
14246.9145717593, 14246.4395601852, 14246.4395717593, 14246.4395717593, 
14246.4367939815), format = structure(c("m/d/y", "h:m:s"), .Names = c("dates", 
"times")), origin = structure(c(1, 1, 1970), .Names = c("month", 
"day", "year")), class = c("chron", "dates", "times"))

events <- data.frame(start, finish)
head(event, 5)

                start              finish
1 (01/02/09 09:07:59) (01/02/09 10:27:59)
2 (01/02/09 09:21:59) (01/02/09 11:11:59)
3 (01/02/09 09:22:59) (01/02/09 09:47:59)
4 (01/02/09 09:34:59) (01/02/09 10:29:59)
5 (01/02/09 09:37:59) (01/02/09 11:37:59)

我现在想计算在特定时间戳有多少事件正在进行。

intervals <- structure(c(14246.3958333333, 14246.40625, 14246.4166666667, 
14246.4270833333, 14246.4375), format = structure(c("m/d/y", 
"h:m:s"), .Names = c("dates", "times")), origin = structure(c(1, 
1, 1970), .Names = c("month", "day", "year")), class = c("chron", 
"dates", "times"))

intervals

[1] (01/02/09 09:30:00) (01/02/09 09:45:00) (01/02/09 10:00:00) (01/02/09 10:15:00) (01/02/09 10:30:00)

所以我想要的输出如下:

            intervals count
1 (01/01/09 09:30:00)     3
2 (01/01/09 09:45:00)     7
3 (01/01/09 10:00:00)    19
4 (01/01/09 10:15:00)    18
5 (01/01/09 10:30:00)    12

虽然以编程方式解决这个问题很简单，但我希望在 210,000 个间隔和超过 120 万个事件中完成这个。我目前的方法涉及利用 data.table包和 &运算符检查每个事件的开始时间和结束时间之间是否存在间隔。

library(data.table)
events <- data.table(events)
data.frame(intervals, count = sapply(1:5, function(i) sum(events[, start <= intervals[i] & intervals[i] <= finish])))

但是考虑到我的数据的大小，这种方法需要很长时间才能运行。关于在 R 中完成此任务的更好替代方法的任何建议？

干杯。

Answer 1

R 中快速执行代码的秘诀是将所有内容保存在向量或数组中，它们实际上只是伪装的数组。

这是一个专门使用基本 R 数组的解决方案。你的数据样本很小，所以我使用 replicate和 system.time结合起来衡量性能。

我的解决方案比 sapply 的解决方案快大约 6 倍和 data.table . (我的解决方案需要 0.6 秒才能解决您的小样本数据集 1,000 次。)

为您的解决方案计时

system.time(replicate(1000, 
    XX <- data.frame(
      intervals, 
      count = sapply(1:5, function(i) sum(events[, start <= intervals[i] & intervals[i] <= finish])))
))

   user  system elapsed 
   4.04    0.05    4.11 

我的解决方案。首先创建两个辅助函数来创建大小相等的数组，其中事件在列中运行，间隔在行中运行。然后做一个简单的向量比较，然后是 colSums :

event.array <- function(x, interval){
  len <- length(interval)
  matrix(rep(unclass(x), len), ncol=len)
}

intervals.array <- function(x, intervals){
  len <- length(x)
  matrix(rep(unclass(intervals), len), nrow=len, byrow=TRUE)
} 


a.start <- event.array(start, intervals)
a.finish <- event.array(finish, intervals)
a.intervals <- intervals.array(start, intervals)

data.frame(intervals, 
           count=colSums(a.start <= a.intervals & a.finish >= a.intervals))

            intervals count
1 (01/02/09 09:30:00)     3
2 (01/02/09 09:45:00)     7
3 (01/02/09 10:00:00)    19
4 (01/02/09 10:15:00)    18
5 (01/02/09 10:30:00)    12

计时我的解决方案

system.time(replicate(1000, 
  YY <- data.frame(
          intervals, 
          count=colSums(a.start <= a.intervals & a.finish >= a.intervals))
))

   user  system elapsed 
   0.67    0.02    0.69 

all.equal(XX, YY)
[1] TRUE