I would say it is quite tedious to process timestamps in such a mixed basis unless it includes the daylight/standard time indicators in the data itself. Up until you work with time in the repeated hour in the autumn you can skate by, but those times require the extra indicator, and if it isn't in the data then you have to create it from context (e.g. assuming the data are in sequential order). If there are no gaps in some incompletely-specified local-time data, it may actually be easier to ignore the timestamps in the file and recreate them within R.

POSIXct times are stored internally as UTC, so once they are read merging the data should be easy.
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Bill Harris <bill_harris@facilitatedsystems.com> wrote:

Jeff Newmiller <jdnewmil@dcn.davis.ca.us> writes: > Use Sys.setenv(TZ="Etc/GMT+8") before you read the files. Thanks, Jeff and all for the suggestions (assuming this works, as it seems it should, I may go with this one). I'll try them tomorrow. After posting my query, I discovered a related problem which I have to research more, but perhaps someone here knows what is standardly done. As I said, I have one dataset that stays in standard time to avoid the DST issue. I have to merge() it with another that uses local time. In the spring, there are thus no datapoints from 1:59 until 3. I'm not yet sure what happens in the fall. Is there a best practice for dealing with periodic (hourly, quarter-hourly, etc.) data that spans the DST change such that graphs of time series, etc., are easily understood? Thanks, Bill -- Bill Harris http://makingsense.facilitatedsystems.com/ Facilitated Systems Everett, WA 98208 USA http://www.facilitatedsystems.com/ phone: +1 425 374-1845 


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