如何根据R中的时间间隔对数据进行分组

Question

我的数据看起来像这样:

library(plyr)
dates<-data.frame(datecol=as.POSIXct(c(
  "2010-04-03 03:02:38 UTC",
  "2010-04-03 03:03:14 UTC",
  "2010-04-20 03:05:52 UTC",
  "2010-04-20 03:07:42 UTC",
  "2010-04-21 03:09:38 UTC",
  "2010-04-21 03:10:14 UTC",
  "2010-04-21 03:12:52 UTC",
  "2010-04-23 03:13:42 UTC",
  "2010-04-23 03:15:42 UTC",
  "2010-04-23 03:16:38 UTC",
  "2010-04-23 03:18:14 UTC",
  "2010-04-24 03:21:52 UTC",
  "2010-04-24 03:22:42 UTC",
  "2010-04-24 03:24:19 UTC",
  "2010-04-24 03:25:19 UTC"
)), x = cumsum(runif(15)*10),y=cumsum(runif(15)*20))

我想将我的数据分组为5天,因此所有5天或更短时间的点都放在一个组中.我尝试了这里建议的内容:

gr<-ddply(dates,.(cut(datecol,"5 day",include.lowest = TRUE)),"[")

但由于某些原因,我最终得到3组而不是2组,而04/21和04/23的分数分成不同的组,即使它们相隔不到5天.

这是我想得到的:

         group             datecol         x          y
1            1 2010-04-03 03:02:38  8.112423   4.790036
2            1 2010-04-03 03:03:14 11.184709  22.903475
3            2 2010-04-20 03:05:52 17.306835  32.286891
4            2 2010-04-20 03:07:42 24.071488  38.941709
5            2 2010-04-21 03:09:38 26.451493  48.378477
6            2 2010-04-21 03:10:14 33.090645  53.148149
7            2 2010-04-21 03:12:52 38.536416  64.346574
8            2 2010-04-23 03:13:42 40.911074  79.419002
9            2 2010-04-23 03:15:42 41.977579  89.760210
10           2 2010-04-23 03:16:38 46.838773  95.266709
11           2 2010-04-23 03:18:14 48.367159 112.619969
12           2 2010-04-24 03:01:52 57.470412 113.594423
13           2 2010-04-24 03:02:42 63.202005 123.653370
14           2 2010-04-24 03:04:19 65.615348 137.184153
15           2 2010-04-24 03:25:19 75.177633 137.559003

Answer 1

怎么样cumsum,如果必要的检查滞后值和更新？我们使用库中的shift()函数data.table来实现滞后.

library(data.table)
dates$group <- cumsum(ifelse(difftime(dates$datecol,
                                  shift(dates$datecol, fill = dates$datecol[1]), 
                                  units = "days") >= 5 
                         ,1, 0)) + 1

head(dates)
#              datecol         x         y group
#1 2010-04-03 03:02:38  4.776196  5.160336     1
#2 2010-04-03 03:03:14 13.388291 14.731241     1
#3 2010-04-20 03:05:52 17.769262 30.057454     2
#4 2010-04-20 03:07:42 20.217235 31.742392     2
#5 2010-04-21 03:09:38 20.924025 49.248819     2
#6 2010-04-21 03:10:14 21.918687 56.030278     2

这假设您的数据按时间从最小到最大排序