【发布时间】:2016-06-14 22:02:03
【问题描述】:
我有一个单变量时间序列对象,我将其转换为 xts 对象,以便使用 aggregatets() 函数。这是它的样子:
> head(B, 20)
[,1]
2015-09-16 09:16:00 1
2015-09-16 09:16:26 1
2015-09-16 09:16:46 1
2015-09-16 09:17:28 -1
2015-09-16 09:19:17 1
2015-09-16 09:22:06 -1
时区是“IST”(印度标准时间),如上所示。当我在其上运行聚合函数时,它会将时区更改为“GMT”。我需要它留在“IST”中,因为我需要比较“IST”时区中的其他变量。它还会显示一条警告消息:
> C<- aggregatets(B, FUN = "sum")
> head(C, 20)
ts3
2015-09-16 03:47:00 3
2015-09-16 03:48:00 -1
2015-09-16 03:49:00 NA
2015-09-16 03:50:00 1
2015-09-16 03:51:00 NA
2015-09-16 03:52:00 NA
Warning message:
timezone of object (GMT) is different than current timezone ().
我也尝试通过 indexTZ() 函数手动更改时区,但这也无济于事。
> indexTZ(C)<- "IST"
> head(C)
ts3
2015-09-16 03:47:00 3
2015-09-16 03:48:00 -1
2015-09-16 03:49:00 NA
2015-09-16 03:50:00 1
2015-09-16 03:51:00 NA
2015-09-16 03:52:00 NA
Warning message:
timezone of object (IST) is different than current timezone ().
我还尝试更改聚合函数本身中的时区选择以创建一个新函数,但它仍然吐出同样的东西。
这真的很奇怪,因为我在尝试分析的其他一些变量中使用了相同的方法,但它并没有给我同样的问题。
如果我遗漏了一些东西,或者我应该如何解决我的问题并将该系列保持在“IST”中,有人可以说明一下吗? 任何帮助将不胜感激,如果您需要更多信息,请告诉我。
编辑:按要求添加
whole_data<- read.csv(file = file,header = FALSE,sep = "", col.names = c("DateTime","Seq","BP1","BQ1","BO1","AP1","AQ1","AO1","BP2","BQ2","BO2","AP2","AQ2","AO2","BP3","BQ3","BO3","AP3","AQ3","AO3","BP4","BQ4","BO4","AP4","AQ4","AO4","BP5","BQ5","BO5","AP5","AQ5","AO5","BP6","BQ6","BO6","AP6","AQ6","AO6","BP7","BQ7","BO7","AP7","AQ7","AO7","BP8","BQ8","BO8","AP8","AQ8","AO8","BP9","BQ9","BO9","AP9","AQ9","AO9","BP10","BQ10","BO10","AP10","AQ10","AO10"), colClasses = c(NA, rep("integer",31), rep("NULL", 30)))
whole_data<- whole_data[which(whole_data$DateTime != 0),]
whole_data$DateTime= as.POSIXct(whole_data$DateTime/(10^9), origin="1970-01-01")
trades<- whole_data[!complete.cases(whole_data),]
colnames(trades) <- c("DateTime", "Seq", "Price", "Qty", "TradeSide")
trades[,5][trades[,5]==2]<- -1
B<- as.xts(trades$TradeSide, order.by = trades$DateTime)
C<- aggregatets(B, FUN = "sum", on = on, k = k)
整个数据集很大。对于这个特定的变量,我只需要交易行,所以我可以通过 !complete.cases
访问我仍然无法解决这个问题。你能对此有所了解吗?
【问题讨论】:
标签: r datetime time aggregate xts