如何创建“NA"对于时间序列中的缺失数据 [英] How to create "NA" for missing data in a time series

问题描述

我有几个如下所示的数据文件:

I have several files of data that look like this:

X code year month day pp  
1 4515 1953     6   1  0  
2 4515 1953     6   2  0  
3 4515 1953     6   3  0  
4 4515 1953     6   4  0  
5 4515 1953     6   5  3.5

有时会丢失数据，但我没有 NA，这些行根本不存在.当数据丢失时，我需要创建 NA.虽然我可以通过将其转换为 zoo 对象并检查严格的规律性(我以前从未使用过 zoo)来识别何时发生，但我使用了以下代码:

Sometimes there is data missing, but I don't have NAs, the rows simply don't exist. I need to create NAs when the data is missing. I though I could start by identifying when that occurs by converting it to a zoo object and check for strict regularity (I never used zoo before), I used the following code:

z.date<-paste(CET$year, CET$month, CET$day, sep="/")
z <- read.zoo(CET,  order.by= z.date )
reg<-is.regular(z, strict = TRUE)

但答案总是正确的！

谁能告诉我为什么不工作?或者更好的是，告诉我一种在数据丢失时创建 NA 的方法(有或没有 zoo 包)?

Can anyone tell me why is not working? Or even better, tell me a way to create NAs when the data is missing (with or without zoo package)?

谢谢

推荐答案

seq 函数有一些有趣的功能，您可以使用这些功能轻松生成完整的日期序列.例如，以下代码可用于生成从 4 月 25 日开始的日期序列:

The seq function has some interesting features that you can use to easily generate a complete sequence of dates. For example, the following code can be used to generate a sequence of dates starting on April 25:

此功能记录在 ?seq.Date

start = as.Date("2011/04/25")
full <- seq(start, by='1 day', length=15)
full

 [1] "2011-04-25" "2011-04-26" "2011-04-27" "2011-04-28" "2011-04-29"
 [6] "2011-04-30" "2011-05-01" "2011-05-02" "2011-05-03" "2011-05-04"
[11] "2011-05-05" "2011-05-06" "2011-05-07" "2011-05-08" "2011-05-09"

现在使用相同的原理来生成一些缺失"行的数据，方法是每隔 2 天生成一次序列:

Now use the same principle to generate some data with "missing" rows, by generating the sequence for every 2nd day:

partial <- data.frame(
    date=seq(start, by='2 day', length=6),
    value=1:6
)
partial

        date value
1 2011-04-25     1
2 2011-04-27     2
3 2011-04-29     3
4 2011-05-01     4
5 2011-05-03     5
6 2011-05-05     6

要回答您的问题，可以使用矢量下标或 match 函数来创建具有 NA 的数据集:

To answer your question, one can use vector subscripting or the match function to create a dataset with NAs:

with(partial, value[match(full, date)])
 [1]  1 NA  2 NA  3 NA  4 NA  5 NA  6 NA NA NA NA

将此结果与原始完整数据相结合:

To combine this result with the original full data:

data.frame(Date=full, value=with(partial, value[match(full, date)]))
         Date value
1  2011-04-25     1
2  2011-04-26    NA
3  2011-04-27     2
4  2011-04-28    NA
5  2011-04-29     3
6  2011-04-30    NA
7  2011-05-01     4
8  2011-05-02    NA
9  2011-05-03     5
10 2011-05-04    NA
11 2011-05-05     6
12 2011-05-06    NA
13 2011-05-07    NA
14 2011-05-08    NA
15 2011-05-09    NA

这篇关于如何创建“NA"对于时间序列中的缺失数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！