将多个不规则时间序列转换为规则时间序列

Question

我有一个包含多个不规则时间序列的data.frame (data.frame)，它看起来像这样

station   Time     WaterTemp
1       01-01-1974  5.0000000
1       01-02-1974  5.0000000
1       01-03-1974  8.6000004
1       01-05-1974  8.1333332
1       01-07-1974  12.7999999
2       01-01-1974  5.0000000
2       01-02-1974  5.0000000
2       01-04-1974  8.6000004
2       01-06-1974  8.1333332
2       01-08-1974  12.7999999

我想把它转换成常规的时间序列(ts)对象，它应该是这样的

Time        Staion1     Station2
 Jan1974    5.0000000  5.0000000
 Feb1974    5.0000000  5.0000000
 Mar1974    8.6000004  NA
 Apr1974    NA         8.6000004
 May1974    8.1333332  NA
 June1974   NA         8.1333332
 July1974   12.7999999 NA
  Aug1974  NA         12.7999999
  Sep1974  NA         NA
  Oct1974  7.9         NA
  Nov1974  NA         NA
  Dec1974  NA         7.4

我该怎么做?虽然对于单个时间序列有很多解决方案，但我还没有遇到一个处理多个时间序列的解决方案。

谢谢,

Answer 1

如果DF是你的数据帧，那么试试这个。在最后一行转换为ts使其成为常规，然后我们转换回zoo:

library(zoo)
z <- read.zoo(DF, split = 1, index = 2, format = "%d-%m-%Y")
z.ym <- aggregate(z, as.yearmon, identity) # convert to yearmon
zm <- aggregate(as.zoo(as.ts(z.ym)), as.yearmon, identity)

最后一行的替代方法是这两行:

g <- zoo(, seq(start(z.ym), end(z.ym), deltat(z.ym))) # grid
zm <- merge(z.ym, g)

在任何一种情况下，此时coredata(zm)是数据部分，time(zm)是索引，尽管您可能希望将其保留为动物园对象，以便您可以使用其其他时间序列功能和许多其他接受该形式的时间序列的包。

注意:这是一个完整的自包含可复制的示例:

DF <- structure(list(station = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L), Time = structure(c(1L, 2L, 3L, 5L, 7L, 1L, 2L, 4L, 6L, 8L
), .Label = c("01-01-1974", "01-02-1974", "01-03-1974", "01-04-1974", 
"01-05-1974", "01-06-1974", "01-07-1974", "01-08-1974"), class = "factor"), 
    WaterTemp = c(5, 5, 8.6000004, 8.1333332, 12.7999999, 5, 
    5, 8.6000004, 8.1333332, 12.7999999)), .Names = c("station", 
"Time", "WaterTemp"), class = "data.frame", row.names = c(NA, 
-10L))
library(zoo)
z <- read.zoo(DF, split = 1, index = 2, format = "%d-%m-%Y")
z.ym <- aggregate(z, as.yearmon, identity) # convert to yearmon
zm <- aggregate(as.zoo(as.ts(z.ym)), as.yearmon, identity)

给:

> zm
                 1         2
Jan 1974  5.000000  5.000000
Feb 1974  5.000000  5.000000
Mar 1974  8.600000        NA
Apr 1974        NA  8.600000
May 1974  8.133333        NA
Jun 1974        NA  8.133333
Jul 1974 12.800000        NA
Aug 1974        NA 12.800000

更新一些更正和改进。