This produces the x-axis is the index, and the y-axis is time. It has all of the time information on the same axis, allowing me to plot cumulative occurrences by time (my original plan) if the times are sorted, which they should be.
I think I'll end up using some variant of plot(tt,seq_along(tt)), putting the time axis along the bottom. Thanks Tom On Sat, Jun 20, 2009 at 11:15 AM, Gabor Grothendieck < ggrothendi...@gmail.com> wrote: > Try this: > > plot(seq_along(tt), tt) > > > On Sat, Jun 20, 2009 at 10:55 AM, Thomas Levine<thomas.lev...@gmail.com> > wrote: > > Here's what I get > >> head(tt) > > [1] "2008-02-20 03:09:51 EST" "2008-02-20 12:12:57 EST" > > [3] "2008-03-05 09:11:28 EST" "2008-03-05 17:59:40 EST" > > [5] "2008-03-09 09:00:09 EDT" "2008-03-29 15:57:16 EDT" > > > > But I can't figure out how to plot this now. plot(tt) does not appear to > be > > univariate. I get the same plot with plot(as.Date(tt)), which would make > > sense if time is used because of the range of the dates and the > > insignificance of the times of day. > >> head(as.Date(tt)) > > [1] "2008-02-20" "2008-02-20" "2008-03-05" "2008-03-05" "2008-03-09" > > [6] "2008-03-29" > > > > plot(tt) and plot(as.Date(tt)) give something like year as a function of > the > > rest of the date. Here they are > > > > > > Here are the addresses > > http://thomaslevine.org/time/tt.png > > http://thomaslevine.org/time/as.Date.tt.png > > > > Tom > > > > On Fri, Jun 19, 2009 at 6:21 PM, Gabor Grothendieck > > <ggrothendi...@gmail.com> wrote: > >> > >> Try this: > >> > >> > >> Lines <- "Sun, 14 Jun 2009 07:33:00 -0700 > >> Sun, 14 Jun 2009 08:35:10 -0700 > >> Sun, 14 Jun 2009 21:26:34 -0700 > >> Mon, 15 Jun 2009 19:47:47 -0700 > >> Wed, 17 Jun 2009 21:50:41 -0700" > >> > >> # L <- readLines("myfile.txt") > >> L <- readLines(textConnection(Lines)) > >> tt <- as.POSIXct(L, format = "%a, %d %b %Y %H:%M:%S") > >> > >> > >> > >> On Fri, Jun 19, 2009 at 6:06 PM, Thomas Levine<thomas.lev...@gmail.com> > >> wrote: > >> > I am analysing occurrences of a phenomenon by time, and each of these > >> > timestamps taken from email headers represents one occurrence. (The > last > >> > number is the time zone.) I can easily change the format. > >> > > >> > Sun, 14 Jun 2009 07:33:00 -0700 > >> > Sun, 14 Jun 2009 08:35:10 -0700 > >> > Sun, 14 Jun 2009 21:26:34 -0700 > >> > Mon, 15 Jun 2009 19:47:47 -0700 > >> > Wed, 17 Jun 2009 21:50:41 -0700 > >> > > >> > I've found documentation for a plethora of ways of importing time > data, > >> > but > >> > I can't decide how to approach it. Any ideas on what may be the > cleanest > >> > way? The only special concern is that I'll want to plot these data by > >> > date > >> > and time, meaning that I would rather not bin all of the occurrences > >> > from > >> > one day. > >> > > >> > The time zone isn't important as these are all local times; the time > >> > zone > >> > only changes as a function of daylight savings time, so I probably > >> > shouldn't > >> > use it at all. > >> > > >> > Tom > >> > > >> > [[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. > >> > > > > > > [[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.
