How to sum and group data by DATE in data frame

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

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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