R 初学者问题数据帧转换为时间序列

Question

I have a flat-csv file containing data in long format, that needs to be converted to a time series object. The format of the file looks like this:

DATE       ID  REGION VALUE
2016-03-10 10  DE001  2332,23
2016-03-10 10  DE001  2332,23
2016-03-10 10  DE002  2332,23
2016-03-10 11  DE001  2332,23
2016-03-10 11  DE002  2332,23
2016-03-10 12  DE001  2332,23
2016-03-11 10  DE001  2332,23
2016-03-11 10  DE001  2332,23
2016-03-11 10  DE002  2332,23
2016-03-11 11  DE001  2332,23
2016-03-11 11  DE002  2332,23
2016-03-11 12  DE001  2332,23

I want to group by ID and then by region, so that i have a different time-series for each ID-group containing several region observations for the complete available time-span.

Answer 1

I misunderstood the OP's question.

You can use tapply to break up the original data frame (call it D). This is a bit tricky. You can't easily change D in the tapply

D$relTime <- NA 

L=tapply(1:nrow(D),D$ID, function(x) {
    # x contains the row numbers for each ID
    RT <- data.frame(row=x)
    T0 <- D$DATE[x][1]
    RT$val <- D$DATE[x]-T0 # if time series means offset from a base time
    RT
})
DL <- do.call('rbind',L)
# assuming you want it in  D
D$relTime[DL$row] <- DL$val

This will create a new column which contains the offset from the base time for each ID.

Edit: I use '=' for assingment which isn't considered best practice. I've changed them in the above.

Answer 2

You can use the as.Date function. Load the table using read.table("filename.csv"). The dates will be loaded as factors unless you specify stringsAsFactors=FALSE in the read.table call. However, doing so will apply to all character columns.

so,

D <- read.table("file.csv")
D$DATE <- as.Date(as.character(D$DATE), "%Y-%m-%d")

should do the trick. The as.character will ensure that the dates are passed as strings to as.Date even if they have been loaded as factors

More info:

https://stat.ethz.ch/R-manual/R-devel/library/base/html/as.Date.html

https://stat.ethz.ch/R-manual/R-devel/library/utils/html/read.table.html

Technically, it's not a csv file because the "c" in "csv" means "comma". Your separators are spaces. But you can still use the read.csv call if you specify sep=' ' in the call.