[R] How to create 10 minute time series from hourly data

[Bert Gunter]

1. Always cc the list.

2. Assuming all you want to to do is produce a regular sequence 6
times as long, ?seq

Please go through an R tutorial or two to learn basic R functionality like this.
Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Wed, Feb 1, 2017 at 11:25 AM, Mary Ann Middleton <maberg at sfu.ca> wrote:
> Thank you.  I will try that for interpolating the values between hours.
>
> But before I can do that, do you have any suggestions how I can expand the
> time series from hourly to 10-minute intervals?
>
> I think I will need to create an time series and then merge it with the
> existing data frame, at which point I could apply 'approx' to fill the
> remaining values, but this is where I'm really stuck.
>
> ~Mary Ann
> ________________________________
> From: "Bert Gunter" <bgunter.4567 at gmail.com>
> To: "Mary Ann Middleton" <maberg at sfu.ca>
> Cc: "R-help" <r-help at r-project.org>
> Sent: Wednesday, February 1, 2017 10:56:19 AM
> Subject: Re: [R] How to create 10 minute time series from hourly data
>
> Perhaps:
>
> ?approx
>
> Cheers,
> Bert
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
>
> On Wed, Feb 1, 2017 at 8:41 AM, Mary Ann Middleton <maberg at sfu.ca> wrote:
>> Hello,
>>
>> Apologies if this is a duplicate. I think I sent it to the wrong list
>> yesterday.
>>
>> I would appreciate some direction/suggestions with a problem with a time
>> series.
>>
>> I have a regular time series dataframe with hourly data. I need to create
>> a time series with a 10 minute interval for $Level_m to compare to another
>> time series.
>>
>> I would like to apply an approximation and create a time series with 10
>> minute intervals from the data series I have. I do not want to smooth the
>> data and so I think a linear approximation would suffice.
>>
>> I have also searched xts for possible solutions but haven't had luck. Any
>> input is greatly appreciated.
>>
>> ~Mary Ann Middleton, PhD
>>
>>
>>
>>
>> Here is a sample of the data I have:
>>
>> Date Time date.time ms LEVEL TEMPERATURE Level_m
>> 1 2016-05-31 15:25:00 2016-05-31 15:25:00 0 92.1767 25.171 9.401814
>> 2 2016-05-31 16:25:00 2016-05-31 16:25:00 0 92.1498 18.023 9.399071
>> 3 2016-05-31 17:25:00 2016-05-31 17:25:00 0 92.0781 17.951 9.391757
>> 4 2016-05-31 18:25:00 2016-05-31 18:25:00 0 92.0664 16.312 9.390564
>> 5 2016-05-31 19:25:00 2016-05-31 19:25:00 0 92.0250 15.043 9.386341
>> 6 2016-05-31 20:25:00 2016-05-31 20:25:00 0 91.9732 14.015 9.381058
>>
>> Here is the str()
>>
>> 'data.frame': 164 obs. of 7 variables:
>> $ Date : chr " 2016-05-31 " " 2016-05-31 " " 2016-05-31 " " 2016-05-31 "
>> ...
>> $ Time : chr "15:25:00" "16:25:00" "17:25:00" "18:25:00" ...
>> $ date.time : POSIXct, format: " 2016-05-31 15:25:00" " 2016-05-31
>> 16:25:00" ...
>> $ ms : int 0 0 0 0 0 0 0 0 0 0 ...
>> $ LEVEL : num 92.2 92.1 92.1 92.1 92 ...
>> $ TEMPERATURE: num 25.2 18 18 16.3 15 ...
>> $ Level_m : num 9.4 9.4 9.39 9.39 9.39 ...
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.