Hi All, First let me state that I did search for a while on r-help, google, and using the "sos" package inside of 'R', without much luck. I want to know how to create a univariate time series from a set of data that will have huge time gaps in it. For instance, here is a snapshot of a piece of data that I would like to analyze:
*Row queued_time processTime 50 2010-06-15 21:50:42.443 6.399989e-02 secs 63 2010-06-15 21:51:57.347 6.300020e-02 secs 156 2010-06-29 14:53:26.073 3.011863e+06 secs 175 2010-07-22 10:14:57.503 4.334879e+06 secs 278 2010-08-05 11:29:56.713 6.155674e+06 secs 509 2010-08-05 11:29:57.443 3.120779e+06 secs 531 2010-08-05 11:29:57.543 3.120779e+06 secs 555 2010-08-05 11:29:57.647 3.120779e+06 secs 190 2010-08-05 11:29:57.943 3.120778e+06 secs 230 2010-08-05 11:29:58.047 3.120778e+06 secs 211 2010-08-05 11:29:58.917 3.120777e+06 secs 251 2010-08-05 11:29:59.077 3.120777e+06 secs 298 2010-08-05 11:29:59.297 3.120777e+06 secs 320 2010-08-05 11:29:59.397 3.120777e+06 secs 366 2010-08-05 11:29:59.707 3.120777e+06 secs 342 2010-08-05 11:30:00.987 3.120775e+06 secs 380 2010-08-05 11:30:01.200 3.120775e+06 secs 120 2010-08-19 09:31:47.207 2.358866e+06 secs 141 2010-08-19 09:31:47.500 2.358866e+06 secs 842 2010-09-03 13:58:21.463 3.641194e+06 secs * I would like to be able to take the second column, the "processTime", and put it into a time series using the first column as the key to say when it occurred. But everything I could find, such as ts(), went on the assumption that I had fully univariate data to start with, and all I needed to do was set the frequency & start date (in the case of ts() ). I can adjust the "queued time" arbitrarily as needed, so that if, for instance, the data set would end up far too sparse & empty by keeping the current precision, I could cut the "queued_time" precision down to just the year, month, day, hour. But in that case, how would the time series handle the fact that there are several (varying) entries with the same value stored. The reason I want to do this is because I next want to be able to use all the very nice modeling capabilities that a univariate time series allows, such as arima, etc. Thanks in advance! Mike "Telescopes and bathyscaphes and sonar probes of Scottish lakes, Tacoma Narrows bridge collapse explained with abstract phase-space maps, Some x-ray slides, a music score, Minard's Napoleanic war: The most exciting frontier is charting what's already here." -- xkcd -- Help protect Wikipedia. Donate now: http://wikimediafoundation.org/wiki/Support_Wikipedia/en [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list 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.