匹配不同长度的时间向量:一个棘手的问题 [英] matching time vectors of different length: a tricky one

问题描述

我有两组来自不同机器的测量值.它们随着时间的推移以略有不同的间隔进行测量 - 例如一个每 5 分钟测量一次，而另一个每 3 分钟测量一次.优点是每 5 分钟一次计算为整个间隔的平均值，因此这些值应该大致对应.我想用每 5 分钟测量一次(光)来扩展向量，使其值与每 5 分钟进行一次测量的向量中的值大致同步.然后应该用前面的值填充间隙

I have two sets of measurements from different machines. They are measured over time, at slightly different intervals - e.g. one makes a measurement every 5 mins, but the other, every 3 mins. The advantage is that the one every 5 mins is computed as an average over the whole interval so the values should correspond roughly to one another. I would like to expand the vector with measurements every 5 minutes (Light) so that its values are roughly synchronous with the values in vector of measurements made every 5 minutes. The gap should then be filled with the preceding value

这是一个每 5 分钟的数据示例

Here is an example of the data every 5 minutes

Date             Light 
26/05/2011 16:00 -529.98            
26/05/2011 16:05 -276.68            
26/05/2011 16:10 -179.63            
26/05/2011 16:15 -385.57            
26/05/2011 16:20 -1273.6            
26/05/2011 16:25 -1109.7 

和每 3 分钟的数据

    Date             Flux 
26/05/2011 16:01     0.64
26/05/2011 16:04    -1.96
26/05/2011 16:07    -0.51
26/05/2011 16:10    -1.34
26/05/2011 16:13    -1.28
26/05/2011 16:15    -0.22

我也不应该认为光测量的矢量(每 5 分钟)比每 3 分钟的矢量短.因此，目标是使 5 分钟测量的向量与 3 分钟向量的长度相同.

I should also not that the vector of light measurement (every 5 mins) is shorter than the vector every 3 minutes. The goal is thus to make the vector of 5 min measurements the same length as the 3 minute vector.

我意识到这是一个相当棘手的问题，但任何建议都会非常受欢迎.

I realise that this is quite a tricky problem, but any suggestions would be greatfuly received.

推荐答案

如果我理解正确，这可以通过 zoo 或 xts 轻松完成.首先，这是您的示例数据:

If I understand correctly, this is easily accomplished with either zoo or xts. First, here's your sample data:

Lines1 <- "Date,Light
26/05/2011 16:00,-529.98
26/05/2011 16:05,-276.68
26/05/2011 16:10,-179.63
26/05/2011 16:15,-385.57
26/05/2011 16:20,-1273.6
26/05/2011 16:25,-1109.7"

Lines2 <- "Date,Flux
26/05/2011 16:01,0.64
26/05/2011 16:04,-1.96
26/05/2011 16:07,-0.51
26/05/2011 16:10,-1.34
26/05/2011 16:13,-1.28
26/05/2011 16:15,-0.22"

con <- textConnection(Lines1)
Light <- read.csv(con, stringsAsFactors=FALSE, header=TRUE)
close(con)
con <- textConnection(Lines2)
Flux <- read.csv(con, stringsAsFactors=FALSE, header=TRUE)
close(con)

现在我们加载 xts 包，它也加载了 zoo.然后我们将 Light 和 Flux data.frame 对象转换为 xts 对象.

Now we load the xts package, which also loads zoo. Then we convert the Light and Flux data.frame objects to xts objects.

library(xts)
light <- xts(Light$Light, as.POSIXct(Light$Date, format="%d/%m/%Y %H:%M"))
flux <- xts(Flux$Flux, as.POSIXct(Flux$Date, format="%d/%m/%Y %H:%M"))

这是很棒的部分.merge.xts 和 merge.zoo 将按索引对齐每个系列.na.locf 用之前的值填充每个 NA.

Here's the awesome part. merge.xts and merge.zoo will align each series by index. na.locf fills in each NA with the previous value.

Data <- merge(light,flux)
#                        light  flux
# 2011-05-26 16:00:00  -529.98    NA
# 2011-05-26 16:01:00       NA  0.64
# 2011-05-26 16:04:00       NA -1.96
# 2011-05-26 16:05:00  -276.68    NA
# 2011-05-26 16:07:00       NA -0.51
# 2011-05-26 16:10:00  -179.63 -1.34
# 2011-05-26 16:13:00       NA -1.28
# 2011-05-26 16:15:00  -385.57 -0.22
# 2011-05-26 16:20:00 -1273.60    NA
# 2011-05-26 16:25:00 -1109.70    NA
Data <- na.locf(Data)

最后，我们可以从合并后的 Data 对象中提取 3 分钟索引.

Finally, we can extract the 3 minute index from the merged Data object.

Data[index(flux),]
#                       light  flux
# 2011-05-26 16:01:00 -529.98  0.64
# 2011-05-26 16:04:00 -529.98 -1.96
# 2011-05-26 16:07:00 -276.68 -0.51
# 2011-05-26 16:10:00 -179.63 -1.34
# 2011-05-26 16:13:00 -179.63 -1.28
# 2011-05-26 16:15:00 -385.57 -0.22

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