不规则的时间序列数据 - 我可以定期吗？在r

Question

我有数据被编程为每5小时获取一次信息，这意味着每天有多个数据点。问题在于数据记录器有时会出现故障或电池不能工作，并且缺少数据或尝试失败。

我所做的所有分析都基于这些日期。我需要根据此日期/时间戳进行排序和筛选。截至目前，我正在经历“长篇大论”的脚本，以选择和分类我的数据。另外我有几个数据记录器（个人），所以我也是批处理。举例来说，我想子集，并且每天只使用一个数据点和我

#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，有没有一种方法，使我的数据转换成时间序列数据，使操作更容易吗？我看到了关于Zoo包的讨论 - 但这些似乎要求数据按常规时间序列。我甚至不得不使用某些脚本，因为他们需要的数据是ts格式，我无法弄清楚如何让我的数据变成漂亮的格式。有没有一个很好的资源来教我自己呢？我应该甚至使用ts？

这里是什么我的数据看起来像一个示例： 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 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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

库（动物园）可以处理不规则的时间序列，并作为的Gabor说，动物园常见问题有聚集每天多次的观察方法：

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 

...并填补缺失数据下局部拟合数据：

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