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    <p>You are almost all the way there. All you need to do now is create a zoo-isch version of that data and map it to the aggregate.zoo code. Since you want to categorize by both time and by type your second argument to aggregate.zoo must be a bit more complex and you want counts rather than means so your should use length(). I do not think that <code>count</code> is a base R or zoo function and the only <code>count</code> function I see in my workspace comes from pkg:plyr so I don't know how well it would play with aggregate.zoo. <code>length</code> works as most people expect for vectors but is often surprises people when working with data.frames. If you do not get what you want with <code>length</code>, then you should see if <code>NROW</code> works instead (and with your data layout they both succeed): With the new data object it is necessary to put the type argument first. AND it surns out the aggregate/zoo only handles single category classifiers so you need to put in the as.vector to remove it zoo-ness:</p> <pre><code>with(cured_data, aggregate(as.vector(x), list(type = type, interval=as.factor(time(x) - as.numeric(time(x)) %% 360)), FUN=NROW) ) # interval x #1 2011-09-22 09:24:00 12 #2 2011-09-22 09:24:00 11 </code></pre> <p>This is an example modified from where you got the code (an example on SO by WizaRd Dirk): <a href="https://stackoverflow.com/questions/7528571/aggregate-count-occurences-of-values-over-arbitrary-timeframe">Aggregate (count) occurences of values over arbitrary timeframe</a></p> <pre><code>tt &lt;- seq(10, 2000, 10) x &lt;- zoo(tt, structure(tt, class = c("POSIXt", "POSIXct"))) aggregate(as.vector(x), by=list(cat=as.factor(x), tms = as.factor(index(x) - as.numeric(index(x)) %% 600)), length) cat tms x 1 1 1969-12-31 19:00:00 26 2 2 1969-12-31 19:00:00 22 3 3 1969-12-31 19:00:00 11 4 1 1969-12-31 19:10:00 17 5 2 1969-12-31 19:10:00 28 6 3 1969-12-31 19:10:00 15 7 1 1969-12-31 19:20:00 17 8 2 1969-12-31 19:20:00 16 9 3 1969-12-31 19:20:00 27 10 1 1969-12-31 19:30:00 8 11 2 1969-12-31 19:30:00 4 12 3 1969-12-31 19:30:00 9 </code></pre>
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    1. POAggregate (count) occurences of values over arbitrary timeframe
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    1. COHey, that doesn't look too bad so far, but it only shows me aggregated data for type=1: https://gist.github.com/8049f54780cf0f18147b Hmmmmm! I'll look more into it.
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      1. USAlexander Janssen
    2. CODo a better job presenting your data and you will get faster, better, tested answers. Look at the function `dput`.
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    3. COSorry for being not precise enough, I appreciate your efforts. I added dput's output to my original posting.
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