[R] Questions about doing analysis based on time
John Kane
jrkrideau at inbox.com
Mon Jun 25 15:23:47 CEST 2012
Arrgh yes I did mean dput(head(mydata, 100)). Thanks for catching it.
John Kane
Kingston ON Canada
> -----Original Message-----
> From: michael.weylandt at gmail.com
> Sent: Fri, 22 Jun 2012 14:25:30 -0500
> To: jrkrideau at inbox.com
> Subject: Re: [R] Questions about doing analysis based on time
>
> On Fri, Jun 22, 2012 at 2:18 PM, John Kane <jrkrideau at 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: mikeedinger16 at gmail.com
>>> Sent: Fri, 22 Jun 2012 09:21:40 -0700 (PDT)
>>> To: r-help at 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.
>>>
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