Arrgh yes I did mean dput(head(mydata, 100)). Thanks for catching it. John Kane Kingston ON Canada
> -----Original Message----- > From: michael.weyla...@gmail.com > Sent: Fri, 22 Jun 2012 14:25:30 -0500 > To: jrkrid...@inbox.com > Subject: Re: [R] Questions about doing analysis based on time > > On Fri, Jun 22, 2012 at 2:18 PM, John Kane <jrkrid...@inbox.com> wrote: >> Hi and welcome to the R-help list. >> >> It would be much better for readers to get your data in a more easily >> used format. >> >> There is a function called dput() that will output your data in a way >> that R can read easily. >> >> We don't need to see all the data but perhaps hundred lines of it would >> be nice. >> >> Try this where your file is called "mydata" >> # just copy the line below and paste into R >> head(mydata, 100) > > I think you mean dput(head(mydata, 100)) > > OP: Once you put this up I'll give more reply, but for now I'd suggest > you try to put your data in a proper time series class (zoo/xts if I > might give a personal-ish plug) which will make all these calculations > much easier. > > Best, > Michael > >> >> # Now copy the output and paste it into your wordprocess as a reply to >> the list and we will have decent data to work with. >> >> John Kane >> Kingston ON Canada >> >> >>> -----Original Message----- >>> From: mikeedinge...@gmail.com >>> Sent: Fri, 22 Jun 2012 09:21:40 -0700 (PDT) >>> To: r-help@r-project.org >>> Subject: [R] Questions about doing analysis based on time >>> >>> >>> I have a spreadsheet that I've read into R using read.csv. I've also >>> attached it. It looks like this (except there are 1600+ entries): >>> >>>> Sunday >>> SunDate SunTime SunScore >>> 1 5/9/2010 0:00 0:00 127 >>> 2 6/12/2011 0:00 0:00 125 >>> 3 6/15/2008 0:04 0:04 98 >>> 4 8/3/2008 0:07 0:07 118 >>> 5 7/24/2011 0:07 0:07 122 >>> 6 5/25/2008 0:09 0:09 104 >>> 7 5/20/2012 0:11 0:11 124 >>> 8 10/18/2009 0:12 0:12 121 >>> 9 3/14/2010 0:12 0:12 117 >>> 10 1/2/2011 0:12 0:12 131 >>> >>> SunDate and SunTime are both factors. In order to change the class to >>> something I can work with, I use the following: >>> >>> Sunday$SunTime<-as.POSIXlt(SunTime,tz=””,”%H:%M”) >>> Sunday$SunDate<-as.POSIXlt(SunDate,tz=””,”%m/%d/%Y %H:%M”) >>> >>> Now, the str(Sunday) command yields: >>> >>> 'data.frame': 1644 obs. of 3 variables: >>> $ SunDate : POSIXlt, format: "2010-05-09 00:00:00" "2011-06-12 >>> 00:00:00" >>> ... >>> $ SunTime : POSIXlt, format: "2012-06-18 00:00:00" "2012-06-18 >>> 00:00:00" >>> ... >>> $ SunScore: int 127 125 98 118 122 104 124 121 117 131 ... >>> >>> I think all the elements in Sunday are correct for me to do what I want >>> to >>> do, but I don't know how to do them. >>> >>> 1. How can I get the mean score by hour? For example, I want the mean >>> score >> >> >>> of all the entries between 0:00 and 0:59, then 1:00 and 1:59, etc. >>> 2. Is it possible for me to create a histogram by hour for each score >>> over a >>> certain point? For example, I want to make a histogram of all scores >>> above >>> 140 by the hour they occurred in. Is that possible? >>> >>> These last few might not be possibe (at least with R), but I'll ask >>> anyway. >>> I've got another data set similar to the one above, except it's got >>> 12,000 >>> entries over four years. If I do the same commands as above to turn >>> Date >>> and Time into POSIXlt, is it possible for me to do the following: >>> >>> 1. The data was recorded at irregular intervals, and the difference >>> between >>> recorded points can range from anywhere between 1 hour and up to 7. Is >>> it >>> possible, when data isn't recorded between two points, to insert the >>> hours >>> that are unrecorded along with the average of what that hour is. This >>> is >>> sort of a pre-requisite for the next two. >>> 2. If one of the entries has a Score above a certain point, is it >>> possible >>> to determine how long it was above that point and determine the mean >>> for >>> all >>> the instances this occurred. For example: >>> 01/01/11 01:00 AM >>> 101 >>> 01/01/11 02:21 AM >>> 142 >>> 01/01/11 03:36 AM >>> 156 >>> 01/01/11 04:19 AM >>> 130 >>> 01/01/11 05:12 AM >>> 146 >>> 01/01/11 06:49 AM >>> 116 >>> 01/01/11 07:09 AM >>> 111 >>> There are two spans where it's above 140. The two and three >>> o'clock >>> hours, >>> and the 5 o'clock hour. So the mean time would be 1.5 hours. Is it >>> possible for R to do this over a much larger time period? >>> >>> 3. If a score reaches a certain point, is it possible for R to >>> determine >>> the average time between that and when the score reaches another point. >>> For >>> example: >>> 01/01/11 01:01 AM >>> 101 >>> 01/01/11 02:21 AM >>> 121 >>> 01/01/11 03:14 AM >>> 134 >>> 01/01/11 04:11 AM >>> 149 >>> 01/01/11 05:05 AM >>> 119 >>> 01/01/11 06:14 AM >>> 121 >>> 01/01/11 07:19 AM >>> 127 >>> 01/01/11 08:45 AM >>> 134 >>> 01/01/11 09:11 AM >>> 142 >>> 01/01/11 10:10 AM >>> 131 >>> The score goes above 120 during the 2 AM hour and doesn't go above 140 >>> until >>> the 4 AM hour. Then it goes above 120 again in the 6 AM hour, but >>> doesn't >>> go above 140 until the 9 AM hour. So the average time to go from 120 >>> to >>> 140 >>> is 2.5 hours. Can R does this over a much larger time frame? >>> >>> If anyone knows how to easily do any of these (particularly the first >>> part), >>> I'd greatly appreciate it. >>> >>> If some of these are possible, but aren't simple commands and require >>> more >>> in depth programming knowledge and time commitment, can someone at >>> least >>> tell me what sort of thing to look up? >>> >>> -- >>> View this message in context: >>> http://r.789695.n4.nabble.com/Questions-about-doing-analysis-based-on-time-tp4634230.html >>> Sent from the R help mailing list archive at Nabble.com. >>> >>> ______________________________________________ >> . >> >> ____________________________________________________________ >> FREE 3D EARTH SCREENSAVER - Watch the Earth right on your desktop! >> >> ______________________________________________ >> 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. ____________________________________________________________ FREE ONLINE PHOTOSHARING - Share your photos online with your friends and family! 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