不规则的时间序列数据 - 我可以定期吗？在r

Question

我的数据被编程为每5小时获取一次信息，这意味着每天有多个数据点。问题是有时数据记录器失败或电池耗尽或其他任何错误的数据或失败的尝试。

我所做的所有分析都是基于这些日期。我需要根据此日期/时间戳排序和过滤并选择所有内容。截至目前，我正在通过“冗长的”脚本来选择和分配我的数据。另外我有几个数据记录器（个人），所以我也是批处理。例如，我想要每天只使用一个数据点，我就

    #Subset data by minimum time interval
    SubsetData = T
    MinInterval = 47 #minimum time interval (in hours) between consecutive locations
    ###########
    ##Setting the date and time to be recognized as date time
    loc$DateTime = as.POSIXct(strptime(loc$DT,"%m/%d/%Y %H:%M",tz="UTC"),tz="UTC")
    ### Code to subset data based on minimum time interval between locations
    if (SubsetData ==T){
             row = 0
             EndLoc = 2
             SubData1 = loc[1,]
             IDNames = levels(loc$ID)
    for (n in 1:length(IDNames)){
         IndivData = loc[loc$ID==IDNames[n],]
         row = row+1
         SubData1[row,]=IndivData[1,]
     while (EndLoc<nrow(IndivData) ){
           timediff = difftime(IndivData$DateTime[EndLoc],SubData1$DateTime[row],units = "hours")
   if (timediff>MinInterval){
       row = row+1
       SubData1=rbind(SubData1,IndivData[EndLoc,])
       EndLoc = EndLoc+1
         } else{
               EndLoc = EndLoc+1
              }
         } #end while loop
       } #end loop through individuals
     loc = SubData1
   } #end conditional to subset data
    }
   ### End Code to subset data'

那么，有没有办法让我的数据成为时间序列数据，使操作更容易？我看过Zoo的讨论包xts - 但这些似乎要求数据是在常规时间序列中。我甚至不得不使用某些脚本，因为它们要求数据采用ts格式，而我无法弄清楚如何将数据转换为良好的格式。有没有一个很好的资源来教我自己？我应该使用ts吗？

以下是我的数据的示例：     structure(list(ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("c_002", "c_402"), class = "factor"), Long = c(-147.635233, -147.635232, -147.63632, -147.636136, -147.63628, -147.635743, -147.631785, -147.619598, -147.605088, -147.602119, -147.179737, -147.133275, -147.106584, -147.033649, -147.016078, -147.031872, -146.940969, -146.862538, -146.751214, -146.696238, -146.710799), Latitude = c(67.911827, 67.911193, 67.911048, 67.911169, 67.911625, 67.911767, 67.919419, 67.926327, 67.925352, 67.926318, 70.017026, 70.041163, 70.057988, 70.087812, 70.117134, 70.147184, 70.145115, 70.136103, 70.1276, 70.103395, 70.079301), DT = structure(c(2L, 3L, 5L, 6L, 8L, 9L, 10L, 11L, 12L, 14L, 393L, 395L, 396L, 398L, 399L, 401L, 402L, 404L, 405L, 407L, 409L), .Label = c("05/01/2006 00:00", "05/01/2006 00:01", "05/01/2006 05:00", "05/01/2006 05:01", "05/01/2006 10:00", "05/01/2006 15:01", "05/01/2006 20:00", "05/01/2006 20:01", "05/02/2006 01:00", "05/02/2006 06:00", "05/02/2006 11:00", "05/02/2006 16:01", "05/02/2006 21:00", "05/02/2006 21:01", "05/03/2006 02:00", "05/03/2006 02:01", "05/03/2006 07:00", "05/03/2006 07:01", "05/03/2006 12:00", "05/03/2006 17:00", "05/03/2006 17:01", "05/03/2006 22:00", "05/03/2006 22:01", "05/04/2006 03:00", "05/04/2006 03:01", "05/04/2006 08:00", "05/04/2006 08:01", "05/04/2006 13:00", "05/04/2006 18:00", "05/04/2006 23:00", "05/04/2006 23:01", "05/05/2006 04:00", "05/05/2006 09:00", "05/05/2006 09:01", "05/05/2006 14:00", "05/05/2006 19:00", "05/06/2006 00:00", "05/06/2006 00:01", "05/06/2006 05:00", "05/06/2006 05:01", "05/06/2006 10:00", "05/06/2006 10:01", "05/06/2006 15:01", "05/06/2006 20:00", "05/06/2006 20:01", "05/07/2006 01:00", "05/07/2006 01:01", "05/07/2006 06:00", "05/07/2006 06:01", "05/07/2006 11:00", "05/07/2006 16:00", "05/07/2006 16:01", "05/07/2006 21:00", "05/08/2006 02:00", "05/08/2006 07:01", "05/08/2006 12:00", "05/08/2006 17:00", "05/08/2006 17:01", "05/08/2006 22:00", "05/08/2006 22:01", "05/09/2006 03:00", "05/09/2006 03:01", "05/09/2006 08:00", "05/09/2006 08:01", "05/09/2006 13:00", "05/09/2006 13:01", "05/09/2006 18:00", "05/09/2006 23:00", "05/09/2006 23:01", "05/10/2006 04:00", "05/10/2006 04:01", "05/10/2006 09:00", "05/10/2006 09:01", "05/10/2006 14:00", "05/10/2006 14:01", "05/10/2006 19:00", "05/10/2006 19:03", "05/11/2006 00:00", "05/11/2006 05:00", "05/11/2006 10:00", "05/11/2006 10:01", "05/11/2006 15:01", "05/11/2006 15:02", "05/11/2006 20:00", "05/11/2006 20:01", "05/12/2006 01:00", "05/12/2006 01:01", "05/12/2006 06:00", "05/12/2006 11:00", "05/12/2006 16:00", "05/12/2006 16:01", "05/12/2006 21:00", "05/12/2006 21:01", "05/13/2006 02:00", "05/13/2006 07:00", "05/13/2006 07:01", "05/13/2006 12:00", "05/13/2006 17:00", "05/13/2006 22:00", "05/13/2006 22:01", "05/14/2006 03:00", "05/14/2006 08:00", "05/14/2006 13:00", "05/14/2006 18:00", "05/14/2006 18:01", "05/14/2006 23:00", "05/15/2006 04:00", "05/15/2006 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21:00", "07/26/2006 21:01", "07/27/2006 02:00", "07/27/2006 02:01", "07/27/2006 07:00", "07/27/2006 07:01", "07/27/2006 12:00", "07/27/2006 17:00", "07/27/2006 22:01", "07/28/2006 03:00", "07/28/2006 03:01", "07/28/2006 08:00", "07/28/2006 08:01", "07/28/2006 13:00", "07/28/2006 18:00", "07/28/2006 23:01", "07/29/2006 04:00", "07/29/2006 04:03", "07/29/2006 09:00", "07/29/2006 14:00", "07/29/2006 19:00", "07/30/2006 00:00", "07/30/2006 00:01", "07/30/2006 05:00", "07/30/2006 05:01", "07/30/2006 10:00", "07/30/2006 10:01", "07/30/2006 15:00", "07/30/2006 20:01", "07/31/2006 01:01", "07/31/2006 01:03", "07/31/2006 06:00", "07/31/2006 06:01", "07/31/2006 11:00", "07/31/2006 11:01", "07/31/2006 16:00", "07/31/2006 21:01"), class = "factor")), .Names = c("ID", "Long", "Latitude", "DT"), class = "data.frame", row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 706L, 707L, 708L, 709L, 710L, 711L, 712L, 713L, 714L, 715L, 716L))

Answer 1

图书馆（动物园）可以处理不规则的时间序列，正如Gabor所说，Zoo FAQ有每天聚合多个观察的方法：

z <- zoo( x=data.frame(....), order.by=loc$DateTime ) # translate 'loc' into zoo somehow 

za <- aggregate(z, identity, mean)  # take the mean of identically timed objects 

...并使用本地适合的数据填写缺失的数据：

g  <- seq(start(z),end(z),by=1/frequency(z)) 
zi <- na.locf(z,xout=g)