Question

我最初有15分钟的流量数据，看起来像这样

structure(list(t = structure(c(1136062800, 1136063700, 1136064600, 
1136065500, 1136066400, 1136067300, 1136068200, 1136069100, 1136070000, 
1136070900, 1136071800, 1136072700, 1136073600, 1136074500, 1136075400, 
1136076300, 1136077200, 1136078100, 1136079000, 1136079900, 1136080800, 
1136081700, 1136082600, 1136083500, 1136084400, 1136085300, 1136086200, 
1136087100, 1136088000, 1136088900, 1136089800, 1136090700), class = c("POSIXct", 
"POSIXt"), tzone = "EST"), flow = c(23, 31, 42, 59, 59, 59, 50, 
48, 37, 33, 31, 31, 30, 30, 27, 27, 30, 31, 33, 37, 38, 42, 42, 
48, 48, 46, 42, 38, 37, 35, 33, 35)), .Names = c("t", "flow"), row.names = 35003:35034, class = "data.frame")

我使用此代码将此数据剪切为2小时平均数据时间序列

data <- data.frame(t=streamflowDateTime,flow=streamflow) data2hr <- data data2hr$time <- cut(data2hr$t,breaks="2 hours") smoothedData <- aggregate(flow~time,data2hr,mean)

现在我想扩展＆＃39; smoothedData＆＃39;通过使它成为每小时时间序列的时间序列，但我希望新的时间序列以2小时的间隔从smoothedData时间序列中保留其值，并且我希望将原始数据中的平均每小时数据插入到现有时间序列之间。救命啊！

Answer 1

您可以使用函数approxfun来实现此结果。

首先，让我们将time中的smoothedData列转换回POSIX时间格式：

smoothedData$time <- as.POSIXct(smoothedData$time)
#                  time   flow
# 1 2005-12-31 16:00:00 46.375
# 2 2005-12-31 18:00:00 30.750
# 3 2005-12-31 20:00:00 37.625
# 4 2005-12-31 22:00:00 39.250

然后，创建一个小时分隔的时间序列：

data1hr <- data.frame(time=seq(min(smoothedData$time),max(smoothedData$time),by='1 hour'))

...制作插值函数，用于计算smoothedData数据集的两小时间隔内的平均值：

interp.time <- approxfun(x=smoothedData$time,y=smoothedData$flow)

最后，将此函数应用于小时分隔的时间序列：

data1hr$flow <- interp.time(data1hr$time)
#                  time    flow
# 1 2005-12-31 16:00:00 46.3750
# 2 2005-12-31 17:00:00 38.5625
# 3 2005-12-31 18:00:00 30.7500
# 4 2005-12-31 19:00:00 34.1875
# 5 2005-12-31 20:00:00 37.6250
# 6 2005-12-31 21:00:00 38.4375
# 7 2005-12-31 22:00:00 39.2500

修改

根据OP的评论，

我觉得我以一种比它更复杂的方式解释了我的问题成为。基本上，在偶数小时时间戳，即00:00:00， 02:00:00我想要2小时平均值，但是在奇数小时时间戳 01：00：00,03：00：00我想要每小时平均值

我想提出另一个问题的解决方案：

data <- structure(list(t = structure(c(1136062800, 1136063700, 1136064600, 1136065500, 1136066400, 1136067300, 1136068200, 1136069100, 1136070000, 1136070900, 1136071800, 1136072700, 1136073600, 1136074500, 1136075400, 1136076300, 1136077200, 1136078100, 1136079000, 1136079900, 1136080800, 1136081700, 1136082600, 1136083500, 1136084400, 1136085300, 1136086200, 1136087100, 1136088000, 1136088900, 1136089800, 1136090700), class = c("POSIXct", "POSIXt"), tzone = "EST"), flow = c(23, 31, 42, 59, 59, 59, 50, 48, 37, 33, 31, 31, 30, 30, 27, 27, 30, 31, 33, 37, 38, 42, 42, 48, 48, 46, 42, 38, 37, 35, 33, 35)), .Names = c("t", "flow"), row.names = 35003:35034, class = "data.frame") data2hr <- data data2hr$time <- cut(data2hr$t,breaks="2 hours") smoothedData2hr <- aggregate(flow~time,data2hr,mean) # time flow # 1 2005-12-31 16:00:00 46.375 # 2 2005-12-31 18:00:00 30.750 # 3 2005-12-31 20:00:00 37.625 # 4 2005-12-31 22:00:00 39.250 data1hr <- data data1hr$time <- cut(data1hr$t,breaks="1 hour") smoothedData1hr <- aggregate(flow~time,data1hr,mean) # time flow # 1 2005-12-31 16:00:00 38.75 # 2 2005-12-31 17:00:00 54.00 # 3 2005-12-31 18:00:00 33.00 # 4 2005-12-31 19:00:00 28.50 # 5 2005-12-31 20:00:00 32.75 # 6 2005-12-31 21:00:00 42.50 # 7 2005-12-31 22:00:00 43.50 # 8 2005-12-31 23:00:00 35.00 result <- smoothedData1hr result$flow[match(smoothedData2hr$time,result$time)] <- smoothedData2hr$flow # time flow # 1 2005-12-31 16:00:00 46.375 # 2 2005-12-31 17:00:00 54.000 # 3 2005-12-31 18:00:00 30.750 # 4 2005-12-31 19:00:00 28.500 # 5 2005-12-31 20:00:00 37.625 # 6 2005-12-31 21:00:00 42.500 # 7 2005-12-31 22:00:00 39.250 # 8 2005-12-31 23:00:00 35.000

延长时间序列

1 个答案: