Creating regular 15-minute time-series from irregular time-series
I have an irregular time-series (with DateTime and RainfallValue) in a csv file C:\SampleData.csv:
DateTime,RainInches
1/6/2000 11:59,0
1/6/2000 23:59,0.01
1/7/2000 11:59,0
1/13/2000 23:59,0
1/14/2000 0:00,0
1/14/2000 23:59,0
4/14/2000 3:07,0.01
4/14/2000 3:12,0.03
4/14/2000 3:19,0.01
12/31/2001 22:44,0
12/31/2001 22:59,0.07
12/31/2001 23:14,0
12/31/2001 23:29,0
12/31/2001 23:44,0.01
12/31/2001 23:59,0.01
Note: The irregular time-steps could be 1 min, 15 min, 1 hour, etc. Also, there could be multiple observations in a desired 15-min interval.
I am trying to create a regular 15-minute time-series from 2000-01-01 to 2001-12-31 that should look like:
2000-01-01 00:15:00 0.00
2000-01-01 00:30:00 0.00
2000-01-01 00:45:00 0.00
...
2001-12-31 23:30:00 0.01
2001-12-31 23:45:00 0.01
Note: The time-series is regular with 15-minute intervals, filling the missing data with 0. If there are more than one data point in a 15 minute intervals, they are summed.
Here's is my code:
library(zoo)
library(xts)
filename = "C:\\SampleData.csv"
ReadData <- read.zoo(filename, format = "%m/%d/%Y %H:%M", sep=",", tz="UTC", header=TRUE) # read .csv as a ZOO object
RawData <- aggregate(ReadData, index(ReadData), sum) # Merge duplicate time stamps and SUM the corresponding data (CAUTION)
RawDataSeries <- as.xts(RawData,order.by =index(RawData)) #convert to an XTS object
RegularTimes <- seq(as.POSIXct("2000-01-01 00:00:00", tz = "UTC"), as.POSIXct("2001-12-31 23:45:00", tz = "UTC"), by = 60*15)
BlankTimeSeries <- xts((rep(0,length(RegularTimes))),order.by = RegularTimes)
MergedTimeSeries <- merge(RawDataSeries,BlankTimeSeries)
TS_sum15min <- period.apply(MergedTimeSeries,endpoints(MergedTimeSeries, "minutes", 15), sum, na.rm = TRUE )
TS_align15min <- align.time( TS_sum15min [endpoints(TS_sum15min , "minutes", 15)], n=60*15)
Problem: The output time series TS_align15min:
(a) has repeating blocks of time-stamps
(b) starts (mysteriously) from 1999, as:
1999-12-31 19:15:00 0
1999-12-31 19:30:00 0
1999-12-31 19:45:00 0
1999-12-31 20:00:00 0
1999-12-31 20:15:00 0
1999-12-31 20:30:00 0
What am I doing wrong?
Thank you for any direction!
xts extends zoo, and zoo has extensive examples for this in its vignettes and documentation.
Here is a worked example. I think I have done that more elegantly in the past, but this is all I am coming up with now:
R> twohours <- ISOdatetime(2012,05,02,9,0,0) + seq(0:7)*15*60
R> twohours
[1] "2012-05-02 09:15:00 GMT" "2012-05-02 09:30:00 GMT"
[3] "2012-05-02 09:45:00 GMT" "2012-05-02 10:00:00 GMT"
[5] "2012-05-02 10:15:00 GMT" "2012-05-02 10:30:00 GMT"
[7] "2012-05-02 10:45:00 GMT" "2012-05-02 11:00:00 GMT"
R> set.seed(42)
R> observation <- xts(1:10, order.by=twohours[1]+cumsum(runif(10)*60*10))
R> observation
[,1]
2012-05-02 09:24:08.883625 1
2012-05-02 09:33:31.128874 2
2012-05-02 09:36:22.812594 3
2012-05-02 09:44:41.081170 4
2012-05-02 09:51:06.128481 5
2012-05-02 09:56:17.586051 6
2012-05-02 10:03:39.539040 7
2012-05-02 10:05:00.338998 8
2012-05-02 10:11:34.534372 9
2012-05-02 10:18:37.573243 10
A two hour time grid, and some random observations leaving some cells empty and some filled.
R> to.minutes15(observation)[,4]
observation.Close
2012-05-02 09:24:08.883625 1
2012-05-02 09:44:41.081170 4
2012-05-02 09:56:17.586051 6
2012-05-02 10:11:34.534372 9
2012-05-02 10:18:37.573243 10
That is a 15 minutes grid aggregation but not on our time grid.
R> twoh <- xts(rep(NA,8), order.by=twohours)
R> twoh
[,1]
2012-05-02 09:15:00 NA
2012-05-02 09:30:00 NA
2012-05-02 09:45:00 NA
2012-05-02 10:00:00 NA
2012-05-02 10:15:00 NA
2012-05-02 10:30:00 NA
2012-05-02 10:45:00 NA
2012-05-02 11:00:00 NA
R> merge(twoh, observation)
twoh observation
2012-05-02 09:15:00.000000 NA NA
2012-05-02 09:24:08.883625 NA 1
2012-05-02 09:30:00.000000 NA NA
2012-05-02 09:33:31.128874 NA 2
2012-05-02 09:36:22.812594 NA 3
2012-05-02 09:44:41.081170 NA 4
2012-05-02 09:45:00.000000 NA NA
2012-05-02 09:51:06.128481 NA 5
2012-05-02 09:56:17.586051 NA 6
2012-05-02 10:00:00.000000 NA NA
2012-05-02 10:03:39.539040 NA 7
2012-05-02 10:05:00.338998 NA 8
2012-05-02 10:11:34.534372 NA 9
2012-05-02 10:15:00.000000 NA NA
2012-05-02 10:18:37.573243 NA 10
2012-05-02 10:30:00.000000 NA NA
2012-05-02 10:45:00.000000 NA NA
2012-05-02 11:00:00.000000 NA NA
New xts object, and merged object. Now use na.locf() to carry the observations
forward:
R> na.locf(merge(twoh, observation)[,2])
observation
2012-05-02 09:15:00.000000 NA
2012-05-02 09:24:08.883625 1
2012-05-02 09:30:00.000000 1
2012-05-02 09:33:31.128874 2
2012-05-02 09:36:22.812594 3
2012-05-02 09:44:41.081170 4
2012-05-02 09:45:00.000000 4
2012-05-02 09:51:06.128481 5
2012-05-02 09:56:17.586051 6
2012-05-02 10:00:00.000000 6
2012-05-02 10:03:39.539040 7
2012-05-02 10:05:00.338998 8
2012-05-02 10:11:34.534372 9
2012-05-02 10:15:00.000000 9
2012-05-02 10:18:37.573243 10
2012-05-02 10:30:00.000000 10
2012-05-02 10:45:00.000000 10
2012-05-02 11:00:00.000000 10
And then we can merge again as an inner join on the time-grid xts twoh:
R> merge(twoh, na.locf(merge(twoh, observation)[,2]), join="inner")[,2]
observation
2012-05-02 09:15:00 NA
2012-05-02 09:30:00 1
2012-05-02 09:45:00 4
2012-05-02 10:00:00 6
2012-05-02 10:15:00 9
2012-05-02 10:30:00 10
2012-05-02 10:45:00 10
2012-05-02 11:00:00 10
R>
Here is a data.table solution, this can be neatly done using a rolling join:
library(data.table)
library(xts)
lu <- data.table(index=as.POSIXct("2012-05-02") + (0:7)*15*60)
observation <- xts(1:10,
order.by=lu[1,index +cumsum(runif(10)*60*10)])
observation.dt <- as.data.table(observation)
observation.dt[lu,on="index",roll=T]