How to sum and group data by DATE in data frame

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How to sum and group data by DATE in data frame

clair.crossupton@googlemail.com
Dear all,

Lets say I have a data frame as follows:


> Date <- as.Date(c('2006-08-23', '2006-08-30', '2006-09-06', '2006-09-13', '2006-09-20'))
> Income <- c(73.79, 72.46, 76.32, 72.43, 72.62)
> data.frame(Date, Income)
        Date Income
1 2006-08-23  73.79
2 2006-08-30  72.46
3 2006-09-06  76.32
4 2006-09-13  72.43
5 2006-09-20  72.62
>

is there a way to group the data by month (summing the values in each
month), i.e.

Date       Income
2006-08  146.25
2006-09  221.37

Thanks in advance,
C.C.

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Re: How to sum and group data by DATE in data frame

Mohamed Lajnef
Hi Clair,
try to use this code

aggregate(toto$Income,by=list((substr(toto$Date,1,7))),sum)

Regards
mohamed

[hidden email] a écrit :

> Dear all,
>
> Lets say I have a data frame as follows:
>
>
>  
>> Date <- as.Date(c('2006-08-23', '2006-08-30', '2006-09-06', '2006-09-13', '2006-09-20'))
>> Income <- c(73.79, 72.46, 76.32, 72.43, 72.62)
>> data.frame(Date, Income)
>>    
>         Date Income
> 1 2006-08-23  73.79
> 2 2006-08-30  72.46
> 3 2006-09-06  76.32
> 4 2006-09-13  72.43
> 5 2006-09-20  72.62
>  
>
> is there a way to group the data by month (summing the values in each
> month), i.e.
>
> Date       Income
> 2006-08  146.25
> 2006-09  221.37
>
> Thanks in advance,
> C.C.
>
> ______________________________________________
> [hidden email] 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.
>
>  


--
Mohamed Lajnef
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40 rue de Mesly. 94000 Créteil.
Courriel : [hidden email]
tel. : 01 49 81 31 31 (poste 18470)
Sec : 01 49 81 32 90
fax : 01 49 81 30 99

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Re: How to sum and group data by DATE in data frame

Henrique Dallazuanna
In reply to this post by clair.crossupton@googlemail.com
Try this:

rowsum(Income, format(Date, '%m-%Y'))

or

tapply(Income, format(Date, '%m-%Y'), sum)

On Wed, Sep 9, 2009 at 7:51 AM, [hidden email] <
[hidden email]> wrote:

> Dear all,
>
> Lets say I have a data frame as follows:
>
>
> > Date <- as.Date(c('2006-08-23', '2006-08-30', '2006-09-06', '2006-09-13',
> '2006-09-20'))
> > Income <- c(73.79, 72.46, 76.32, 72.43, 72.62)
> > data.frame(Date, Income)
>        Date Income
> 1 2006-08-23  73.79
> 2 2006-08-30  72.46
> 3 2006-09-06  76.32
> 4 2006-09-13  72.43
> 5 2006-09-20  72.62
> >
>
> is there a way to group the data by month (summing the values in each
> month), i.e.
>
> Date       Income
> 2006-08  146.25
> 2006-09  221.37
>
> Thanks in advance,
> C.C.
>
> ______________________________________________
> [hidden email] 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.
>


--
Henrique Dallazuanna
Curitiba-Paraná-Brasil
25° 25' 40" S 49° 16' 22" O

        [[alternative HTML version deleted]]


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Re: How to sum and group data by DATE in data frame

Cren
Hello,

I have a time series with intraday datas, sampled every 30'; I would need to aggregate them in this way: summing up all datas within a day.

I tried to use aggregate(...) function to get my goal, but it aggregates in wrong way (I did not understand how so far); what I need is like sum(...) function applied to datas within the same day.

Thank you :)
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Re: How to sum and group data by DATE in data frame

Michael Weylandt
How are you using aggregate()? It seems to sum for me...

z <- zoo(1:50, seq.POSIXt(from = Sys.time(), by = "30 min", length.out = 50))

aggregate(z, as.Date(time(z)), sum)

Best,
Michael

On Tue, May 15, 2012 at 11:52 AM, Cren <[hidden email]> wrote:

> Hello,
>
> I have a time series with intraday datas, sampled every 30'; I would need to
> aggregate them in this way: summing up all datas within a day.
>
> I tried to use *aggregate(...)* function to get my goal, but it aggregates
> in wrong way (I did not understand how so far); what I need is like
> *sum(...)* function applied to datas within the same day.
>
> Thank you :)
>
> --
> View this message in context: http://r.789695.n4.nabble.com/How-to-sum-and-group-data-by-DATE-in-data-frame-tp903708p4630119.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [hidden email] 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.

______________________________________________
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https://stat.ethz.ch/mailman/listinfo/r-help
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Re: How to sum and group data by DATE in data frame

Cren
This post was updated on .
Thank you for your help, Michael.

I used z <- aggregate(x, by = timeSequence(by = "day"), FUN = sum) but the results of z[1] is very different from sum(x[1:13]), where 13 is the number of daily observations I've sampled during the first day.
Michael Weylandt wrote
How are you using aggregate()? It seems to sum for me...

z <- zoo(1:50, seq.POSIXt(from = Sys.time(), by = "30 min", length.out = 50))

aggregate(z, as.Date(time(z)), sum)

Best,
Michael

On Tue, May 15, 2012 at 11:52 AM, Cren <[hidden email]> wrote:
> Hello,
>
> I have a time series with intraday datas, sampled every 30'; I would need to
> aggregate them in this way: summing up all datas within a day.
>
> I tried to use *aggregate(...)* function to get my goal, but it aggregates
> in wrong way (I did not understand how so far); what I need is like
> *sum(...)* function applied to datas within the same day.
>
> Thank you :)
>
> --
> View this message in context: http://r.789695.n4.nabble.com/How-to-sum-and-group-data-by-DATE-in-data-frame-tp903708p4630119.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [hidden email] 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.

______________________________________________
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https://stat.ethz.ch/mailman/listinfo/r-help
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Re: How to sum and group data by DATE in data frame

Michael Weylandt
Can you provide a reproducible example? use dput() to send your data
in a reproducible and user-friendly format. (It might not look user
friendly, but trust me, it is) If x is really long, perhaps just
dput(head(x, 25)) will suffice.

Just a guess as to your problem: I'm not familiar with the
timeSequence function -- what package is it from? My guess is that the
days it's producing are not properly aligned with the days of your
time series object. Can you try it with something closer to my
example?

Best,
Michael

On Tue, May 15, 2012 at 12:42 PM, Cren <[hidden email]> wrote:

> Thank you for your help, Michael.
>
> I used *aggregate(x, by = timeSequence(by = "day"), FUN = sum)* but the
> results is very different from *sum(x[1:13])*, where 13 is the number of
> daily observations I've sampled.
>
> Michael Weylandt wrote
>>
>> How are you using aggregate()? It seems to sum for me...
>>
>> z <- zoo(1:50, seq.POSIXt(from = Sys.time(), by = "30 min", length.out =
>> 50))
>>
>> aggregate(z, as.Date(time(z)), sum)
>>
>> Best,
>> Michael
>>
>> On Tue, May 15, 2012 at 11:52 AM, Cren &lt;oscar.soppelsa@&gt; wrote:
>>> Hello,
>>>
>>> I have a time series with intraday datas, sampled every 30'; I would need
>>> to
>>> aggregate them in this way: summing up all datas within a day.
>>>
>>> I tried to use *aggregate(...)* function to get my goal, but it
>>> aggregates
>>> in wrong way (I did not understand how so far); what I need is like
>>> *sum(...)* function applied to datas within the same day.
>>>
>>> Thank you :)
>>>
>>> --
>>> View this message in context:
>>> http://r.789695.n4.nabble.com/How-to-sum-and-group-data-by-DATE-in-data-frame-tp903708p4630119.html
>>> Sent from the R help mailing list archive at Nabble.com.
>>>
>>> ______________________________________________
>>> R-help@ 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.
>>
>> ______________________________________________
>> R-help@ 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.
>>
>
>
> --
> View this message in context: http://r.789695.n4.nabble.com/How-to-sum-and-group-data-by-DATE-in-data-frame-tp903708p4630139.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [hidden email] 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.

______________________________________________
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https://stat.ethz.ch/mailman/listinfo/r-help
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Re: How to sum and group data by DATE in data frame

Cren
Michael Weylandt wrote
Can you provide a reproducible example?
Of course, Michael.

Consider the following time series:


11/2/2011 14:30 123.53
11/2/2011 15:00 123.78
11/2/2011 15:30 124.24
11/2/2011 16:00 124.2
11/2/2011 16:30 124.07
11/2/2011 17:00 123.91
11/2/2011 17:30 123.44
11/2/2011 18:00 123.0616
11/2/2011 18:30 123.06
11/2/2011 19:00 123.13
11/2/2011 19:30 123.745
11/2/2011 20:00 123.96
11/2/2011 20:30 123.99
11/2/2011 21:00 123.99
11/3/2011 14:30 124.3
11/3/2011 15:00 124.38
11/3/2011 15:30 124.67
11/3/2011 16:00 125.19
11/3/2011 16:30 124.9
11/3/2011 17:00 125.27
11/3/2011 17:30 125.5
11/3/2011 18:00 125.58
11/3/2011 18:30 125.91
11/3/2011 19:00 125.8
11/3/2011 19:30 125.83
11/3/2011 20:00 126.215
11/3/2011 20:30 126.25
11/3/2011 21:00 126.25
11/4/2011 14:30 124.901
11/4/2011 15:00 124.43
11/4/2011 15:30 124.4654
11/4/2011 16:00 124.46
11/4/2011 16:30 124.68
11/4/2011 17:00 124.86
11/4/2011 17:30 124.73
11/4/2011 18:00 125.22
11/4/2011 18:30 125.48
11/4/2011 19:00 125.5601
11/4/2011 19:30 125.4091
11/4/2011 20:00 125.15
11/4/2011 20:30 125.43
11/4/2011 21:00 125.481
11/7/2011 15:30 125.91
11/7/2011 16:00 125.29
11/7/2011 16:30 124.79
11/7/2011 17:00 124.77
11/7/2011 17:30 124.7
11/7/2011 18:00 124.37
11/7/2011 18:30 124.56
11/7/2011 19:00 124.86
11/7/2011 19:30 125.3
11/7/2011 20:00 125.59
11/7/2011 20:30 125.95
11/7/2011 21:00 125.73
11/7/2011 21:30 126.27
11/7/2011 22:00 126.26
11/8/2011 15:30 127.33
11/8/2011 16:00 126.37
11/8/2011 16:30 126.46
11/8/2011 17:00 126
11/8/2011 17:30 126.06
11/8/2011 18:00 126.2662
11/8/2011 18:30 126.23
11/8/2011 19:00 126.4499
11/8/2011 19:30 127.12
11/8/2011 20:00 127.48
11/8/2011 20:30 127.49
11/8/2011 21:00 127.69
11/8/2011 21:30 127.88
11/8/2011 22:00 127.88
11/9/2011 15:30 124.51
11/9/2011 16:00 124.42
11/9/2011 16:30 124.92
11/9/2011 17:00 125.18
11/9/2011 17:30 125.23
11/9/2011 18:00 124.81
11/9/2011 18:30 125.07
11/9/2011 19:00 124.61
11/9/2011 19:30 123.8869
11/9/2011 20:00 123.24
11/9/2011 20:30 123.3329
11/9/2011 21:00 123.6
11/9/2011 21:30 123.19
11/9/2011 22:00 123.161

The rownames are datas plus hour, the data column is the time series' value.
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Re: How to sum and group data by DATE in data frame

Michael Weylandt
Fascinating... dput() has never given me anything that looks like
that.... I would have expected something much more like

z <- structure(c(123.53, 123.78, 124.24, 124.2, 124.07, 123.91, 123.44,
123.0616, 123.06, 123.13, 123.745, 123.96, 123.99, 123.99, 124.3,
124.38, 124.67, 125.19, 124.9, 125.27, 125.5, 125.58, 125.91,
125.8, 125.83, 126.215, 126.25, 126.25, 124.901, 124.43, 124.4654,
124.46, 124.68, 124.86, 124.73, 125.22, 125.48, 125.5601, 125.4091,
125.15, 125.43, 125.481, 125.91, 125.29, 124.79, 124.77, 124.7,
124.37, 124.56, 124.86, 125.3, 125.59, 125.95, 125.73, 126.27,
126.26, 127.33, 126.37, 126.46, 126, 126.06, 126.2662, 126.23,
126.4499, 127.12, 127.48, 127.49, 127.69, 127.88, 127.88, 124.51,
124.42, 124.92, 125.18, 125.23, 124.81, 125.07, 124.61, 123.8869,
123.24, 123.3329, 123.6, 123.19, 123.161), index = structure(c(1320258600,
1320260400, 1320262200, 1320264000, 1320265800, 1320267600, 1320269400,
1320271200, 1320273000, 1320274800, 1320276600, 1320278400, 1320280200,
1320282000, 1320345000, 1320346800, 1320348600, 1320350400, 1320352200,
1320354000, 1320355800, 1320357600, 1320359400, 1320361200, 1320363000,
1320364800, 1320366600, 1320368400, 1320431400, 1320433200, 1320435000,
1320436800, 1320438600, 1320440400, 1320442200, 1320444000, 1320445800,
1320447600, 1320449400, 1320451200, 1320453000, 1320454800, 1320697800,
1320699600, 1320701400, 1320703200, 1320705000, 1320706800, 1320708600,
1320710400, 1320712200, 1320714000, 1320715800, 1320717600, 1320719400,
1320721200, 1320784200, 1320786000, 1320787800, 1320789600, 1320791400,
1320793200, 1320795000, 1320796800, 1320798600, 1320800400, 1320802200,
1320804000, 1320805800, 1320807600, 1320870600, 1320872400, 1320874200,
1320876000, 1320877800, 1320879600, 1320881400, 1320883200, 1320885000,
1320886800, 1320888600, 1320890400, 1320892200, 1320894000), class =
c("POSIXct",
"POSIXt"), tzone = ""), class = "zoo")

which is about 100x more convenient....

With that,

aggregate(z, as.Date(time(z)), sum)

and

aggregate(z, format(time(z), "%m %d"), sum)

give different results (at least in my time zone) so try the latter
(it seems to be what you were probably looking for)

If that doesn't nail it down, I'll need you to answer the questions I
asked in my previous email.

Best,
Michael

On Wed, May 16, 2012 at 6:14 AM, Cren <[hidden email]> wrote:

>
> Michael Weylandt wrote
>>
>> Can you provide a reproducible example?
>>
> Of course, Michael.
>
> Consider the following time series:
>
>
> 11/2/2011 14:30 123.53
> 11/2/2011 15:00 123.78
> 11/2/2011 15:30 124.24
> 11/2/2011 16:00 124.2
> 11/2/2011 16:30 124.07
> 11/2/2011 17:00 123.91
> 11/2/2011 17:30 123.44
> 11/2/2011 18:00 123.0616
> 11/2/2011 18:30 123.06
> 11/2/2011 19:00 123.13
> 11/2/2011 19:30 123.745
> 11/2/2011 20:00 123.96
> 11/2/2011 20:30 123.99
> 11/2/2011 21:00 123.99
> 11/3/2011 14:30 124.3
> 11/3/2011 15:00 124.38
> 11/3/2011 15:30 124.67
> 11/3/2011 16:00 125.19
> 11/3/2011 16:30 124.9
> 11/3/2011 17:00 125.27
> 11/3/2011 17:30 125.5
> 11/3/2011 18:00 125.58
> 11/3/2011 18:30 125.91
> 11/3/2011 19:00 125.8
> 11/3/2011 19:30 125.83
> 11/3/2011 20:00 126.215
> 11/3/2011 20:30 126.25
> 11/3/2011 21:00 126.25
> 11/4/2011 14:30 124.901
> 11/4/2011 15:00 124.43
> 11/4/2011 15:30 124.4654
> 11/4/2011 16:00 124.46
> 11/4/2011 16:30 124.68
> 11/4/2011 17:00 124.86
> 11/4/2011 17:30 124.73
> 11/4/2011 18:00 125.22
> 11/4/2011 18:30 125.48
> 11/4/2011 19:00 125.5601
> 11/4/2011 19:30 125.4091
> 11/4/2011 20:00 125.15
> 11/4/2011 20:30 125.43
> 11/4/2011 21:00 125.481
> 11/7/2011 15:30 125.91
> 11/7/2011 16:00 125.29
> 11/7/2011 16:30 124.79
> 11/7/2011 17:00 124.77
> 11/7/2011 17:30 124.7
> 11/7/2011 18:00 124.37
> 11/7/2011 18:30 124.56
> 11/7/2011 19:00 124.86
> 11/7/2011 19:30 125.3
> 11/7/2011 20:00 125.59
> 11/7/2011 20:30 125.95
> 11/7/2011 21:00 125.73
> 11/7/2011 21:30 126.27
> 11/7/2011 22:00 126.26
> 11/8/2011 15:30 127.33
> 11/8/2011 16:00 126.37
> 11/8/2011 16:30 126.46
> 11/8/2011 17:00 126
> 11/8/2011 17:30 126.06
> 11/8/2011 18:00 126.2662
> 11/8/2011 18:30 126.23
> 11/8/2011 19:00 126.4499
> 11/8/2011 19:30 127.12
> 11/8/2011 20:00 127.48
> 11/8/2011 20:30 127.49
> 11/8/2011 21:00 127.69
> 11/8/2011 21:30 127.88
> 11/8/2011 22:00 127.88
> 11/9/2011 15:30 124.51
> 11/9/2011 16:00 124.42
> 11/9/2011 16:30 124.92
> 11/9/2011 17:00 125.18
> 11/9/2011 17:30 125.23
> 11/9/2011 18:00 124.81
> 11/9/2011 18:30 125.07
> 11/9/2011 19:00 124.61
> 11/9/2011 19:30 123.8869
> 11/9/2011 20:00 123.24
> 11/9/2011 20:30 123.3329
> 11/9/2011 21:00 123.6
> 11/9/2011 21:30 123.19
> 11/9/2011 22:00 123.161
>
> The rownames are datas plus hour, the data column is the time series' value.
>
> --
> View this message in context: http://r.789695.n4.nabble.com/How-to-sum-and-group-data-by-DATE-in-data-frame-tp903708p4630228.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [hidden email] 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.

______________________________________________
[hidden email] mailing list
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Re: How to sum and group data by DATE in data frame

Cren
Thank you, Michael :)
Michael Weylandt wrote
If that doesn't nail it down, I'll need you to answer the questions I
asked in my previous email.
Previously I made a mistake with dput(), this is the correct output:

> dput(X)
new("timeSeries"
    , .Data = structure(c(124.3, 124.38, 124.67, 125.19, 124.9, 125.27, 125.5,
125.58, 125.91, 125.8, 125.83, 126.215, 126.25, 126.25, 124.901,
124.43, 124.4654, 124.46, 124.68, 124.86, 124.73, 125.22, 125.48,
125.5601, 125.4091, 125.15, 125.43, 125.481, 125.91, 125.29,
124.79, 124.77, 124.7, 124.37, 124.56, 124.86, 125.3, 125.59,
125.95, 125.73, 126.27, 126.26, 127.33, 126.37, 126.46, 126,
126.06, 126.2662, 126.23, 126.4499, 127.12, 127.48, 127.49, 127.69,
127.88, 127.88, 124.51, 124.42, 124.92, 125.18, 125.23, 124.81,
125.07, 124.61, 123.8869, 123.24, 123.3329, 123.6, 123.19, 123.161,
123.96, 123.58, 123.42, 123.68, 124.19, 123.985, 124.24, 124.61,
124.6566, 124.18, 123.795, 124.36, 124.32, 124.32, 126.1101,
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1332523800, 1332525600, 1332527400, 1332529200, 1332768600, 1332770400,
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1333031400, 1333033200, 1333035000, 1333036800, 1333038600, 1333040400,
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1334064600, 1334066400, 1334068200, 1334070000, 1334071800, 1334073600,
1334075400, 1334077200, 1334079000, 1334080800, 1334082600, 1334084400,
1334086200, 1334088000, 1334151000, 1334152800, 1334154600, 1334156400,
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1334241000, 1334242800, 1334244600, 1334246400, 1334248200, 1334250000,
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1334590200, 1334592000, 1334593800, 1334595600, 1334597400, 1334599200,
1334601000, 1334602800, 1334604600, 1334606400, 1334669400, 1334671200,
1334673000, 1334674800, 1334676600, 1334678400, 1334680200, 1334682000,
1334683800, 1334685600, 1334687400, 1334689200, 1334691000, 1334692800,
1334755800, 1334757600, 1334759400, 1334761200, 1334763000, 1334764800,
1334766600, 1334768400, 1334770200, 1334772000, 1334773800, 1334775600,
1334777400, 1334779200, 1334842200, 1334844000, 1334845800, 1334847600,
1334849400, 1334851200, 1334853000, 1334854800, 1334856600, 1334858400,
1334860200, 1334862000, 1334863800, 1334865600, 1334928600, 1334930400,
1334932200, 1334934000, 1334935800, 1334937600, 1334939400, 1334941200,
1334943000, 1334944800, 1334946600, 1334948400, 1334950200, 1334952000,
1335187800, 1335189600, 1335191400, 1335193200, 1335195000, 1335196800,
1335198600, 1335200400, 1335202200, 1335204000, 1335205800, 1335207600,
1335209400, 1335211200, 1335274200, 1335276000, 1335277800, 1335279600,
1335281400, 1335283200, 1335285000, 1335286800, 1335288600, 1335290400,
1335292200, 1335294000, 1335295800, 1335297600, 1335360600, 1335362400,
1335364200, 1335366000, 1335367800, 1335369600, 1335371400, 1335373200,
1335375000, 1335376800, 1335378600, 1335380400, 1335382200, 1335384000,
1335447000, 1335448800, 1335450600, 1335452400, 1335454200, 1335456000,
1335457800, 1335459600, 1335461400, 1335463200, 1335465000, 1335466800,
1335468600, 1335470400, 1335533400, 1335535200, 1335537000, 1335538800,
1335540600, 1335542400, 1335544200, 1335546000, 1335547800, 1335549600,
1335551400, 1335553200, 1335555000, 1335556800, 1335792600, 1335794400,
1335796200, 1335798000, 1335799800, 1335801600, 1335803400, 1335805200,
1335807000, 1335808800, 1335810600, 1335812400, 1335814200, 1335816000,
1335879000, 1335880800, 1335882600, 1335884400, 1335886200, 1335888000,
1335889800, 1335891600, 1335893400, 1335895200, 1335897000, 1335898800,
1335900600, 1335902400, 1335965400, 1335967200, 1335969000, 1335970800,
1335972600, 1335974400, 1335976200, 1335978000, 1335979800, 1335981600,
1335983400, 1335985200, 1335987000, 1335988800, 1336051800, 1336053600,
1336055400, 1336057200, 1336059000, 1336060800, 1336062600, 1336064400,
1336066200, 1336068000, 1336069800, 1336071600, 1336073400, 1336075200,
1336138200, 1336140000, 1336141800, 1336143600, 1336145400, 1336147200,
1336149000, 1336150800, 1336152600, 1336154400, 1336156200, 1336158000,
1336159800, 1336161600, 1336397400, 1336399200, 1336401000, 1336402800,
1336404600, 1336406400, 1336408200, 1336410000, 1336411800, 1336413600,
1336415400, 1336417200, 1336419000, 1336420800, 1336483800, 1336485600,
1336487400, 1336489200, 1336491000, 1336492800, 1336494600, 1336496400,
1336498200, 1336500000, 1336501800, 1336503600, 1336505400, 1336507200,
1336570200, 1336572000, 1336573800, 1336575600, 1336577400, 1336579200,
1336581000, 1336582800, 1336584600, 1336586400, 1336588200, 1336590000,
1336591800, 1336593600, 1336656600, 1336658400, 1336660200, 1336662000,
1336663800, 1336665600, 1336667400, 1336669200, 1336671000, 1336672800,
1336674600, 1336676400, 1336678200, 1336680000, 1336743000, 1336744800,
1336746600, 1336748400, 1336750200, 1336752000, 1336753800, 1336755600,
1336757400, 1336759200, 1336761000, 1336762800, 1336764600, 1336766400,
1337002200, 1337004000, 1337005800, 1337007600, 1337009400, 1337011200,
1337013000, 1337014800, 1337016600, 1337018400, 1337020200, 1337022000,
1337023800, 1337025600, 1337088600, 1337090400, 1337092200, 1337094000,
1337095800, 1337097600, 1337099400, 1337101200, 1337103000, 1337104800,
1337106600, 1337108400, 1337110200, 1337112000, 1337175000, 1337176800,
1337178600, 1337180400, 1337182200, 1337184000, 1337185800, 1337187600,
1337189400, 1337191200, 1337193000, 1337194800, 1337196600, 1337198400
)
    , format = "%Y-%m-%d %H:%M:%S"
    , FinCenter = "GMT"
    , recordIDs = structure(list(), .Names = character(0), row.names = integer(0), class = "data.frame")
    , title = "Time Series Object"
    , documentation = "Thu May 17 08:44:10 2012"
)

Those ten-digits numbers are read by R from Excel spreadsheet and their original source they're dates in format "%m-%d-%y %hour:%min"
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Re: How to sum and group data by DATE in data frame

Michael Weylandt
Well, it's not quite the dput(head(X, 25)) I asked for, but it
works....thank you :-)

Do take a look at the examples for aggregate -- then the right syntax
is pretty clear

z <- aggregate(x, timeSequence(from = start(x),  to = end(x), by = "day"), sum)

but there seems to be some little issue that makes you miss the
endpoints of each day -- I think that's because your first and last
points aren't quite on the midnight boundary. This might work better

aggregate(x, timeSequence(from = as.Date(start(x)), to =
as.Date(end(x)), by = "day"), FUN = sum)

Personally, I find the timeSeries framework inordinately painful to
work with and I'd suggest you use zoo/xts objects instead

Best,
Michael

On Thu, May 17, 2012 at 2:48 AM, Cren <[hidden email]> wrote:

> Thank you, Michael :)
>
> Michael Weylandt wrote
>>
>> If that doesn't nail it down, I'll need you to answer the questions I
>> asked in my previous email.
> Previously I made a mistake with *dput()*, this is the correct output:
>
>> dput(X)
> new("timeSeries"
>    , .Data = structure(c(124.3, 124.38, 124.67, 125.19, 124.9, 125.27,
> 125.5,
> 125.58, 125.91, 125.8, 125.83, 126.215, 126.25, 126.25, 124.901,
> 124.43, 124.4654, 124.46, 124.68, 124.86, 124.73, 125.22, 125.48,
> 125.5601, 125.4091, 125.15, 125.43, 125.481, 125.91, 125.29,
> 124.79, 124.77, 124.7, 124.37, 124.56, 124.86, 125.3, 125.59,
> 125.95, 125.73, 126.27, 126.26, 127.33, 126.37, 126.46, 126,
> 126.06, 126.2662, 126.23, 126.4499, 127.12, 127.48, 127.49, 127.69,
> 127.88, 127.88, 124.51, 124.42, 124.92, 125.18, 125.23, 124.81,
> 125.07, 124.61, 123.8869, 123.24, 123.3329, 123.6, 123.19, 123.161,
> 123.96, 123.58, 123.42, 123.68, 124.19, 123.985, 124.24, 124.61,
> 124.6566, 124.18, 123.795, 124.36, 124.32, 124.32, 126.1101,
> 126.42, 126.8181, 126.79, 126.675, 126.68, 126.685, 126.725,
> 126.65, 126.77, 126.55, 126.63, 126.67, 126.66, 125.8829, 126.05,
> 125.97, 125.99, 125.59, 125.21, 125.31, 125.46, 125.39, 125.23,
> 124.93, 125.32, 125.46, 125.46, 125.62, 125.34, 125.163, 125.01,
> 125.115, 125.67, 126.08, 126.15, 126.33, 126.19, 126.3955, 126.71,
> 126.083, 126.083, 125.23, 124.96, 125.595, 125.77, 125.4, 125.38,
> 125.54, 126.01, 126.05, 126.01, 125.72, 124.79, 124.05, 124.08,
> 123.35, 123.85, 123.75, 123.5, 123.58, 122.9285, 122.14, 122.02,
> 121.86, 121.58, 121.79, 121.68, 122.18, 122.105, 122.37, 122.02,
> 122.4765, 122.31, 121.99, 121.8401, 121.85, 122.17, 122.27, 122.26,
> 122.08, 122.02, 121.979, 121.979, 119.74, 119.55, 118.95, 119.12,
> 119.14, 118.91, 119.0201, 119.235, 118.93, 119.51, 119.51, 120.04,
> 119.63, 119.661, 119.77, 119.78, 118.89, 118.85, 118.9, 119.2671,
> 119.73, 119.8, 119.52, 119.43, 119.16, 119.28, 119.19, 119.19,
> 117.43, 117.18, 117.3, 116.87, 117.07, 117.0565, 117.4497, 117.47,
> 117.07, 116.856, 116.91, 117.075, 116.6, 116.56, 117.09, 117.361,
> 117.35, 117.2787, 116.72, 116.76, 116.34, 116.34, 116.34, 119.88,
> 119.98, 119.8095, 119.995, 119.88, 119.685, 119.48, 119.845,
> 119.505, 119.65, 119.36, 119.02, 119.73, 119.71, 120.44, 120.65,
> 120.45, 120.53, 120.41, 120.67, 120.399, 120.39, 119.91, 120.069,
> 120.18, 120.37, 120.05, 120.05, 123.9711, 124.01, 124.47, 124.14,
> 124.14, 124.12, 124.14, 123.94, 123.86, 124, 124.07, 124.33,
> 125.04, 124.99, 125.47, 125.39, 124.91, 124.5802, 124.88, 124.89,
> 124.48, 124.88, 125.335, 125.12, 125.07, 125.25, 124.96, 124.97,
> 126.02, 126.41, 126.19, 125.93, 125.5199, 125.575, 125.6702,
> 125.471, 125.506, 125.41, 125.14, 125.18, 124.86, 124.86, 126.63,
> 127.01, 126.881, 126.86, 126.81, 126.86, 127.05, 126.945, 126.2946,
> 126.1511, 126.28, 125.54, 126.24, 126.22, 125.99, 126.41, 126.12,
> 126.05, 126.17, 126.13, 126.14, 126.43, 126.26, 126.35, 126.89,
> 126.645, 126.26, 126.26, 125.5, 125.12, 125.49, 125.86, 125.96,
> 126.28, 126.37, 126.35, 126.115, 126.1, 125.83, 126.42, 126.73,
> 126.73, 125.79, 125.32, 125.4, 124.98, 125.0601, 125, 124.84,
> 124.5, 124.6246, 124.56, 124.29, 124.78, 123.98, 123.95, 125.13,
> 125.24, 125.48, 125.84, 125.73, 125.76, 125.87, 126.02, 125.9,
> 126.09, 126.19, 126.21, 126.05, 126.05, 124.208, 123.96, 124.04,
> 123.9, 123.4, 123.48, 123.5046, 123.55, 123.5911, 123.59, 123.71,
> 123.825, 124.19, 124.21, 125.38, 125.0199, 124.64, 124.64, 124.85,
> 124.675, 124.79, 124.67, 124.79, 124.53, 123.86, 123.1, 123.05,
> 123.05, 122.42, 122.25, 121.81, 121.77, 121.66, 121.94, 122.1484,
> 122.5, 122.0654, 122.07, 121.67, 121.96, 121.71, 121.74, 123.06,
> 122.57, 122.21, 122.55, 122.31, 122.71, 122.47, 122.4773, 122.405,
> 122.31, 122.18, 122.37, 122.18, 122.185, 122.44, 122.68, 122.69,
> 122.51, 121.9, 121.74, 121.6, 121.78, 121.5999, 121.46, 121.67,
> 121.57, 121.59, 121.59, 122.05, 121.892, 121.47, 121.28, 120.85,
> 121.09, 121.15, 121.02, 121.24, 121.02, 121.07, 120.21, 120.32,
> 120.29, 122.99, 123.3, 123.35, 123.47, 123.4254, 123.469, 123.54,
> 123.72, 123.7, 123.72, 123.9, 123.97, 123.92, 123.93, 123.26,
> 123.49, 123.31, 123.13, 123.2, 122.85, 123.34, 123.34, 123.59,
> 123.57, 123.73, 124.03, 124.17, 124.17, 124.98, 124.97, 124.78,
> 124.8, 124.88, 125.25, 125.22, 125.3, 125.201, 125.3289, 125.1,
> 125.345, 125.4, 125.27, 125.66, 125.68, 125.73, 125.946, 126.04,
> 126.11, 126.2, 126.08, 126.11, 126.02, 126.05, 126.005, 126.41,
> 126.39, 126.77, 126.78, 126.39, 126.57, 126.55, 126.635, 126.58,
> 126.55, 126.72, 126.62, 126.7, 126.65, 126.49, 126.49, 125.74,
> 125.4, 125.39, 125.1, 125.05, 125.08, 124.94, 125.14, 125.29,
> 125.13, 124.99, 124.78, 124.88, 124.83, 125.42, 125.6991, 125.83,
> 125.64, 125.9505, 125.8001, 125.83, 125.8077, 125.88, 125.89,
> 125.86, 126, 126.12, 126.12, 126.1685, 126.08, 126.11, 126.22,
> 126.03, 126, 126.04, 126, 126.04, 125.78, 125.839, 125.94, 125.5,
> 125.5, 127.95, 128.19, 128.24, 128.18, 127.955, 127.937, 127.52,
> 127.56, 127.73, 127.85, 128.04, 127.78, 127.49, 127.495, 127.36,
> 126.9246, 126.9991, 127.15, 127.3, 127.58, 127.61, 127.44, 127.68,
> 127.6, 127.71, 127.62, 127.63, 127.7, 126.68, 127.04, 126.83,
> 127.34, 127.37, 127.54, 127.89, 128.01, 128.07, 127.86, 127.86,
> 127.93, 128.04, 128.04, 127.43, 127.59, 127.7054, 127.93, 128.04,
> 128.02, 127.82, 127.74, 128.01, 127.99, 127.855, 127.83, 127.82,
> 127.71, 127.8, 128.1372, 127.72, 127.6, 127.711, 127.89, 127.87,
> 127.95, 127.99, 128.031, 127.97, 128, 127.99, 128.02, 129.34,
> 129.41, 129.4101, 129.4, 129.33, 128.989, 129.13, 129.069, 129.16,
> 129.185, 129.28, 129.18, 129.15, 129.13, 128.63, 128.86, 128.79,
> 128.7, 128.86, 128.9195, 128.96, 129.1072, 129.02, 128.95, 129.02,
> 129.35, 129.25, 129.2, 129.3, 128.86, 128.74, 128.94, 129.025,
> 129.16, 129.275, 129.29, 129.2859, 129.23, 129.49, 129.495, 129.51,
> 129.51, 128.48, 128.09, 128.29, 128.53, 128.52, 128.3, 128.4,
> 128.17, 128.44, 128.549, 128.62, 128.8, 129.03, 128.84, 130.05,
> 129.95, 129.86, 130.02, 130, 130.01, 129.85, 129.86, 129.86,
> 129.76, 129.38, 129.26, 129.31, 129.34, 129.8199, 130.11, 130.1,
> 130, 129.98, 130.37, 130.45, 130.395, 130.28, 130.63, 130.549,
> 130.73, 130.77, 130.77, 131.18, 131.1301, 131.2489, 131.46, 131.5274,
> 131.38, 131.36, 131.54, 131.37, 131.43, 131.22, 131.42, 131.46,
> 131.46, 131.26, 131.3, 131.12, 131.0617, 131.08, 131.148, 131.21,
> 131.36, 131.22, 131.12, 131.19, 131.305, 131.95, 131.54, 132.07,
> 132.015, 131.949, 131.7, 131.33, 131.19, 131.16, 131.35, 131.485,
> 131.46, 131.55, 131.6899, 131.58, 131.61, 130.79, 130.98, 131.2,
> 131.38, 131.22, 131.2101, 131.36, 131.17, 131.27, 131.37, 131.32,
> 131.47, 131.46, 131.46, 130.8301, 131.11, 131.2, 131.34, 131.2846,
> 131.369, 131.49, 132.12, 132.464, 131.99, 132.28, 132.52, 132.56,
> 132.56, 133.29, 132.68, 132.2699, 132.48, 132.32, 132.18, 132.36,
> 132.2766, 132.03, 131.815, 131.75, 131.41, 131.88, 131.88, 131.76,
> 131.66, 131.56, 131.64, 131.45, 131.38, 131.24, 131.455, 131.63,
> 131.52, 131.72, 132, 131.79, 131.82, 130.4, 130.21, 130.53, 130.72,
> 130.7179, 130.84, 130.79, 130.87, 131.13, 131.29, 131.24, 131.34,
> 131.37, 131.37, 131.58, 131.49, 131.009, 130.87, 130.88, 131,
> 131.011, 131.24, 130.97, 131.28, 131.29, 131.38, 131.21, 131.32,
> 132.28, 132.66, 132.74, 132.68, 132.691, 132.911, 132.8985, 132.999,
> 132.85, 132.91, 132.85, 132.67, 132.47, 132.47, 132.62, 132.71,
> 132.88, 132.76, 132.59, 132.47, 132.5031, 132.421, 132.6389,
> 132.64, 132.635, 132.66, 132.71, 132.68, 134.2, 134.21, 134.26,
> 134.38, 134.27, 134.4, 134.45, 134.42, 134.35, 134.465, 134.5,
> 134.42, 134.568, 134.54, 134.129, 134.08, 134.14, 134.2685, 134.31,
> 134.22, 134.37, 134.285, 134.18, 134.27, 134.28, 134.34, 134.46,
> 134.45, 133.931, 134.04, 134.56, 134.81, 134.66, 134.69, 134.78,
> 134.73, 134.83, 134.68, 134.6, 134.98, 134.79, 134.79, 135.13,
> 135.13, 134.91, 134.46, 134.56, 134.69, 134.82, 134.87, 134.78,
> 134.92, 135.07, 135.079, 135.19, 135.19, 135.29, 134.71, 135,
> 134.98, 135.27, 135.26, 135.41, 135.521, 135.4603, 135.52, 135.41,
> 135.42, 135.36, 135.36, 134.02, 134.11, 134.21, 134.18, 134.28,
> 134.23, 134.45, 134.23, 133.99, 134.16, 134.12, 134.13, 134.361,
> 134.361, 135.17, 134.98, 135.0385, 135.04, 135.14, 135.06, 135.17,
> 135.27, 135.32, 135.39, 135.43, 135.395, 135.35, 135.36, 134.93,
> 134.86, 134.71, 134.91, 134.92, 134.86, 134.69, 134.61, 134.75,
> 134.41, 134.51, 134.32, 135.22, 135.19, 135.53, 135.43, 135.4,
> 135.65, 135.77, 135.615, 134.9899, 135.06, 135.15, 134.96, 134.46,
> 134.4603, 134.54, 134.56, 134.67, 135.09, 134.92, 135.155, 135.59,
> 135.68, 135.74, 135.9, 135.96, 135.9786, 136.0172, 135.99, 136.04,
> 136.049, 136.4101, 136.28, 136.1761, 136.0854, 136.145, 136.1246,
> 136.15, 136.25, 136.29, 136.34, 136.36, 136.52, 136.41, 136.41,
> 136.43, 136.6, 136.94, 136.92, 136.82, 136.88, 136.82, 136.897,
> 136.69, 136.49, 136.18, 136.19, 136.48, 136.47, 136.27, 136.31,
> 136.3, 136.2, 135.95, 135.98, 136, 136.26, 136.29, 136.29, 136.05,
> 136.11, 136.011, 136.032, 135.67, 135.93, 136.36, 136.44, 136.3901,
> 136.46, 136.39, 136.31, 136.39, 136.4199, 136.41, 136.64, 136.631,
> 136.631, 136.95, 136.85, 136.82, 137.06, 137.15, 137.1399, 137.115,
> 137.04, 136.98, 136.815, 136.76, 136.79, 136.93, 136.93, 136.09,
> 136.43, 136.7, 137.125, 137.1, 137.07, 137.28, 137.16, 137.12,
> 137.18, 137.27, 137.25, 137.16, 137.16, 137.53, 137.29, 137.48,
> 137.57, 137.515, 137.461, 137.56, 137.49, 137.18, 137.11, 137.28,
> 137.51, 137.56, 137.559, 138.1, 137.75, 137.27, 136.85, 137.28,
> 137.495, 137.4, 137.33, 137.54, 137.51, 137.29, 137.09, 136.54,
> 137.02, 137.5, 137.59, 137.46, 137.56, 137.68, 137.755, 137.77,
> 137.95, 137.92, 137.775, 137.77, 137.56, 137.71, 137.73, 137.47,
> 137.75, 137.58, 137.54, 137.325, 137.25, 137.28, 137.11, 137.27,
> 137.28, 137.385, 137.47, 137.32, 137.312, 137.05, 136.5, 136.6,
> 136.529, 136.47, 136.49, 136.3716, 136.59, 136.45, 136.68, 136.82,
> 136.925, 136.75, 136.75, 135.07, 134.942, 134.86, 135.145, 134.83,
> 134.6179, 134.72, 134.61, 134.65, 134.505, 134.425, 134.58, 134.69,
> 134.75, 135.15, 135.08, 135.1572, 135.5854, 135.64, 135.62, 135.69,
> 135.63, 135.581, 135.72, 135.73, 135.7595, 135.712, 135.69, 136.51,
> 136.33, 136.575, 136.72, 136.79, 136.9099, 136.98, 136.915, 136.86,
> 136.8513, 137.195, 137.205, 137.012, 137.04, 137.2301, 137.71,
> 137.86, 137.7507, 137.85, 137.79, 137.78, 137.765, 137.87, 137.76,
> 137.46, 137.54, 137.57, 137.57, 137.52, 137.35, 137.3476, 137.28,
> 137.2, 137.36, 137.37, 137.43, 137.535, 137.58, 137.62, 137.615,
> 137.58, 137.58, 138.16, 138.26, 138.49, 138.62, 138.76, 138.79,
> 138.9, 138.91, 138.81, 138.76, 138.77, 139.6, 140.06, 140.062,
> 140.3, 140.31, 140, 139.88, 139.93, 139.85, 140.07, 140.28, 139.7,
> 139.7, 139.765, 139.8, 139.9, 139.91, 140.03, 140.23, 140.15,
> 140.26, 140.29, 140.63, 140.63, 140.51, 140.46, 140.57, 140.63,
> 140.68, 140.681, 140.72, 140.13, 140.14, 140.37, 140.25, 140.35,
> 140.36, 140.31, 140.32, 140.36, 140.37, 140.37, 140.275, 140.3,
> 140.3, 140.57, 140.44, 140.5798, 140.511, 140.84, 140.9, 141.04,
> 141.21, 141.2017, 141.21, 141.0691, 140.981, 140.87, 140.85,
> 139.95, 139.97, 140.125, 140.21, 140.09, 140.14, 140.21, 140.31,
> 140.4, 140.475, 140.47, 140.53, 140.41, 140.44, 140.36, 140.31,
> 140.02, 140.15, 140.205, 140.49, 140.51, 140.395, 140.36, 140.43,
> 140.46, 140.555, 140.17, 140.21, 139.34, 139.25, 138.95, 139.07,
> 139.12, 139.23, 139.38, 139.29, 139.235, 139.24, 139.075, 139,
> 139.16, 139.2, 139.06, 138.7899, 139.16, 139.4, 139.55, 139.4201,
> 139.59, 139.67, 139.53, 139.72, 139.69, 139.6575, 139.66, 139.65,
> 140.76, 141.01, 141.0281, 140.955, 140.92, 140.85, 140.84, 140.94,
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> 1329939000, 1329940800, 1330003800, 1330005600, 1330007400, 1330009200,
> 1330011000, 1330012800, 1330014600, 1330016400, 1330018200, 1330020000,
> 1330021800, 1330023600, 1330025400, 1330027200, 1330090200, 1330092000,
> 1330093800, 1330095600, 1330097400, 1330099200, 1330101000, 1330102800,
> 1330104600, 1330106400, 1330108200, 1330110000, 1330111800, 1330113600,
> 1330349400, 1330351200, 1330353000, 1330354800, 1330356600, 1330358400,
> 1330360200, 1330362000, 1330363800, 1330365600, 1330367400, 1330369200,
> 1330371000, 1330372800, 1330435800, 1330437600, 1330439400, 1330441200,
> 1330443000, 1330444800, 1330446600, 1330448400, 1330450200, 1330452000,
> 1330453800, 1330455600, 1330457400, 1330459200, 1330522200, 1330524000,
> 1330525800, 1330527600, 1330529400, 1330531200, 1330533000, 1330534800,
> 1330536600, 1330538400, 1330540200, 1330542000, 1330543800, 1330545600,
> 1330608600, 1330610400, 1330612200, 1330614000, 1330615800, 1330617600,
> 1330619400, 1330621200, 1330623000, 1330624800, 1330626600, 1330628400,
> 1330630200, 1330632000, 1330695000, 1330696800, 1330698600, 1330700400,
> 1330702200, 1330704000, 1330705800, 1330707600, 1330709400, 1330711200,
> 1330713000, 1330714800, 1330716600, 1330718400, 1330954200, 1330956000,
> 1330957800, 1330959600, 1330961400, 1330963200, 1330965000, 1330966800,
> 1330968600, 1330970400, 1330972200, 1330974000, 1330975800, 1330977600,
> 1331040600, 1331042400, 1331044200, 1331046000, 1331047800, 1331049600,
> 1331051400, 1331053200, 1331055000, 1331056800, 1331058600, 1331060400,
> 1331062200, 1331064000, 1331127000, 1331128800, 1331130600, 1331132400,
> 1331134200, 1331136000, 1331137800, 1331139600, 1331141400, 1331143200,
> 1331145000, 1331146800, 1331148600, 1331150400, 1331213400, 1331215200,
> 1331217000, 1331218800, 1331220600, 1331222400, 1331224200, 1331226000,
> 1331227800, 1331229600, 1331231400, 1331233200, 1331235000, 1331236800,
> 1331299800, 1331301600, 1331303400, 1331305200, 1331307000, 1331308800,
> 1331310600, 1331312400, 1331314200, 1331316000, 1331317800, 1331319600,
> 1331321400, 1331323200, 1331555400, 1331557200, 1331559000, 1331560800,
> 1331562600, 1331564400, 1331566200, 1331568000, 1331569800, 1331571600,
> 1331573400, 1331575200, 1331577000, 1331578800, 1331641800, 1331643600,
> 1331645400, 1331647200, 1331649000, 1331650800, 1331652600, 1331654400,
> 1331656200, 1331658000, 1331659800, 1331661600, 1331663400, 1331665200,
> 1331728200, 1331730000, 1331731800, 1331733600, 1331735400, 1331737200,
> 1331739000, 1331740800, 1331742600, 1331744400, 1331746200, 1331748000,
> 1331749800, 1331751600, 1331814600, 1331816400, 1331818200, 1331820000,
> 1331821800, 1331823600, 1331825400, 1331827200, 1331829000, 1331830800,
> 1331832600, 1331834400, 1331836200, 1331838000, 1331901000, 1331902800,
> 1331904600, 1331906400, 1331908200, 1331910000, 1331911800, 1331913600,
> 1331915400, 1331917200, 1331919000, 1331920800, 1331922600, 1331924400,
> 1332160200, 1332162000, 1332163800, 1332165600, 1332167400, 1332169200,
> 1332171000, 1332172800, 1332174600, 1332176400, 1332178200, 1332180000,
> 1332181800, 1332183600, 1332246600, 1332248400, 1332250200, 1332252000,
> 1332253800, 1332255600, 1332257400, 1332259200, 1332261000, 1332262800,
> 1332264600, 1332266400, 1332268200, 1332270000, 1332333000, 1332334800,
> 1332336600, 1332338400, 1332340200, 1332342000, 1332343800, 1332345600,
> 1332347400, 1332349200, 1332351000, 1332352800, 1332354600, 1332356400,
> 1332419400, 1332421200, 1332423000, 1332424800, 1332426600, 1332428400,
> 1332430200, 1332432000, 1332433800, 1332435600, 1332437400, 1332439200,
> 1332441000, 1332442800, 1332505800, 1332507600, 1332509400, 1332511200,
> 1332513000, 1332514800, 1332516600, 1332518400, 1332520200, 1332522000,
> 1332523800, 1332525600, 1332527400, 1332529200, 1332768600, 1332770400,
> 1332772200, 1332774000, 1332775800, 1332777600, 1332779400, 1332781200,
> 1332783000, 1332784800, 1332786600, 1332788400, 1332790200, 1332792000,
> 1332855000, 1332856800, 1332858600, 1332860400, 1332862200, 1332864000,
> 1332865800, 1332867600, 1332869400, 1332871200, 1332873000, 1332874800,
> 1332876600, 1332878400, 1332941400, 1332943200, 1332945000, 1332946800,
> 1332948600, 1332950400, 1332952200, 1332954000, 1332955800, 1332957600,
> 1332959400, 1332961200, 1332963000, 1332964800, 1333027800, 1333029600,
> 1333031400, 1333033200, 1333035000, 1333036800, 1333038600, 1333040400,
> 1333042200, 1333044000, 1333045800, 1333047600, 1333049400, 1333051200,
> 1333114200, 1333116000, 1333117800, 1333119600, 1333121400, 1333123200,
> 1333125000, 1333126800, 1333128600, 1333130400, 1333132200, 1333134000,
> 1333135800, 1333137600, 1333373400, 1333375200, 1333377000, 1333378800,
> 1333380600, 1333382400, 1333384200, 1333386000, 1333387800, 1333389600,
> 1333391400, 1333393200, 1333395000, 1333396800, 1333459800, 1333461600,
> 1333463400, 1333465200, 1333467000, 1333468800, 1333470600, 1333472400,
> 1333474200, 1333476000, 1333477800, 1333479600, 1333481400, 1333483200,
> 1333546200, 1333548000, 1333549800, 1333551600, 1333553400, 1333555200,
> 1333557000, 1333558800, 1333560600, 1333562400, 1333564200, 1333566000,
> 1333567800, 1333569600, 1333632600, 1333634400, 1333636200, 1333638000,
> 1333639800, 1333641600, 1333643400, 1333645200, 1333647000, 1333648800,
> 1333650600, 1333652400, 1333654200, 1333656000, 1333978200, 1333980000,
> 1333981800, 1333983600, 1333985400, 1333987200, 1333989000, 1333990800,
> 1333992600, 1333994400, 1333996200, 1333998000, 1333999800, 1334001600,
> 1334064600, 1334066400, 1334068200, 1334070000, 1334071800, 1334073600,
> 1334075400, 1334077200, 1334079000, 1334080800, 1334082600, 1334084400,
> 1334086200, 1334088000, 1334151000, 1334152800, 1334154600, 1334156400,
> 1334158200, 1334160000, 1334161800, 1334163600, 1334165400, 1334167200,
> 1334169000, 1334170800, 1334172600, 1334174400, 1334237400, 1334239200,
> 1334241000, 1334242800, 1334244600, 1334246400, 1334248200, 1334250000,
> 1334251800, 1334253600, 1334255400, 1334257200, 1334259000, 1334260800,
> 1334323800, 1334325600, 1334327400, 1334329200, 1334331000, 1334332800,
> 1334334600, 1334336400, 1334338200, 1334340000, 1334341800, 1334343600,
> 1334345400, 1334347200, 1334583000, 1334584800, 1334586600, 1334588400,
> 1334590200, 1334592000, 1334593800, 1334595600, 1334597400, 1334599200,
> 1334601000, 1334602800, 1334604600, 1334606400, 1334669400, 1334671200,
> 1334673000, 1334674800, 1334676600, 1334678400, 1334680200, 1334682000,
> 1334683800, 1334685600, 1334687400, 1334689200, 1334691000, 1334692800,
> 1334755800, 1334757600, 1334759400, 1334761200, 1334763000, 1334764800,
> 1334766600, 1334768400, 1334770200, 1334772000, 1334773800, 1334775600,
> 1334777400, 1334779200, 1334842200, 1334844000, 1334845800, 1334847600,
> 1334849400, 1334851200, 1334853000, 1334854800, 1334856600, 1334858400,
> 1334860200, 1334862000, 1334863800, 1334865600, 1334928600, 1334930400,
> 1334932200, 1334934000, 1334935800, 1334937600, 1334939400, 1334941200,
> 1334943000, 1334944800, 1334946600, 1334948400, 1334950200, 1334952000,
> 1335187800, 1335189600, 1335191400, 1335193200, 1335195000, 1335196800,
> 1335198600, 1335200400, 1335202200, 1335204000, 1335205800, 1335207600,
> 1335209400, 1335211200, 1335274200, 1335276000, 1335277800, 1335279600,
> 1335281400, 1335283200, 1335285000, 1335286800, 1335288600, 1335290400,
> 1335292200, 1335294000, 1335295800, 1335297600, 1335360600, 1335362400,
> 1335364200, 1335366000, 1335367800, 1335369600, 1335371400, 1335373200,
> 1335375000, 1335376800, 1335378600, 1335380400, 1335382200, 1335384000,
> 1335447000, 1335448800, 1335450600, 1335452400, 1335454200, 1335456000,
> 1335457800, 1335459600, 1335461400, 1335463200, 1335465000, 1335466800,
> 1335468600, 1335470400, 1335533400, 1335535200, 1335537000, 1335538800,
> 1335540600, 1335542400, 1335544200, 1335546000, 1335547800, 1335549600,
> 1335551400, 1335553200, 1335555000, 1335556800, 1335792600, 1335794400,
> 1335796200, 1335798000, 1335799800, 1335801600, 1335803400, 1335805200,
> 1335807000, 1335808800, 1335810600, 1335812400, 1335814200, 1335816000,
> 1335879000, 1335880800, 1335882600, 1335884400, 1335886200, 1335888000,
> 1335889800, 1335891600, 1335893400, 1335895200, 1335897000, 1335898800,
> 1335900600, 1335902400, 1335965400, 1335967200, 1335969000, 1335970800,
> 1335972600, 1335974400, 1335976200, 1335978000, 1335979800, 1335981600,
> 1335983400, 1335985200, 1335987000, 1335988800, 1336051800, 1336053600,
> 1336055400, 1336057200, 1336059000, 1336060800, 1336062600, 1336064400,
> 1336066200, 1336068000, 1336069800, 1336071600, 1336073400, 1336075200,
> 1336138200, 1336140000, 1336141800, 1336143600, 1336145400, 1336147200,
> 1336149000, 1336150800, 1336152600, 1336154400, 1336156200, 1336158000,
> 1336159800, 1336161600, 1336397400, 1336399200, 1336401000, 1336402800,
> 1336404600, 1336406400, 1336408200, 1336410000, 1336411800, 1336413600,
> 1336415400, 1336417200, 1336419000, 1336420800, 1336483800, 1336485600,
> 1336487400, 1336489200, 1336491000, 1336492800, 1336494600, 1336496400,
> 1336498200, 1336500000, 1336501800, 1336503600, 1336505400, 1336507200,
> 1336570200, 1336572000, 1336573800, 1336575600, 1336577400, 1336579200,
> 1336581000, 1336582800, 1336584600, 1336586400, 1336588200, 1336590000,
> 1336591800, 1336593600, 1336656600, 1336658400, 1336660200, 1336662000,
> 1336663800, 1336665600, 1336667400, 1336669200, 1336671000, 1336672800,
> 1336674600, 1336676400, 1336678200, 1336680000, 1336743000, 1336744800,
> 1336746600, 1336748400, 1336750200, 1336752000, 1336753800, 1336755600,
> 1336757400, 1336759200, 1336761000, 1336762800, 1336764600, 1336766400,
> 1337002200, 1337004000, 1337005800, 1337007600, 1337009400, 1337011200,
> 1337013000, 1337014800, 1337016600, 1337018400, 1337020200, 1337022000,
> 1337023800, 1337025600, 1337088600, 1337090400, 1337092200, 1337094000,
> 1337095800, 1337097600, 1337099400, 1337101200, 1337103000, 1337104800,
> 1337106600, 1337108400, 1337110200, 1337112000, 1337175000, 1337176800,
> 1337178600, 1337180400, 1337182200, 1337184000, 1337185800, 1337187600,
> 1337189400, 1337191200, 1337193000, 1337194800, 1337196600, 1337198400
> )
>    , format = "%Y-%m-%d %H:%M:%S"
>    , FinCenter = "GMT"
>    , recordIDs = structure(list(), .Names = character(0), row.names =
> integer(0), class = "data.frame")
>    , title = "Time Series Object"
>    , documentation = "Thu May 17 08:44:10 2012"
> )
>
> Those ten-digits numbers are read by R from Excel spreadsheet and their
> original source they're dates in format "%m-%d-%y %hour:%min"
>
> --
> View this message in context: http://r.789695.n4.nabble.com/How-to-sum-and-group-data-by-DATE-in-data-frame-tp903708p4630340.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
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> 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.

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and provide commented, minimal, self-contained, reproducible code.