Need to get hourly averages of data which have uneven frequency over a given hour. Id is the 'sample' which is repeated over and over. Here is example data:
id hour data
1 17 376.2
2 17 376.1
3 17 375.9
4 17 375.7
5 17 375.9
1 17 375.7
2 18 375.9
3 18 376.1
4 18 376.3
5 18 376.6
1 18 377.0
2 18 377.2
3 19 377.5
4 19 377.7
5 19 378.0
1 19 378.1
2 19 378.4
3 19 378.6
4 19 378.7
5 20 378.8
I want to arrive at something this this in which the hours having 2 samples get a mean value:
hour id1 id2 id3 id4 id5
17 376.0 376.1 375.9 375.7 375.9
18 377.0 376.5 376.1 376.3 376.6
19 378.1 378.4 378.0 378.2 378.0
20 379.1 379.1 379.3 378.8 379.3
I have unique value index for HH and id having already used accumarray to collect the data as shown. Is there a way to use the 'aggregation function' of unstack() to do this?
newdata = unstack(data, {'data1'}, 'id');
I can't figure out the syntax based on mathworks help. Thanks....

3 Kommentare

Colin Edgar
Colin Edgar am 13 Nov. 2015
Got a blank answer..... thanks in advance for any help!
dataout = unstack(datin, {'data'}, 'id', 'AggregationFunction', @mean);
Worked out the syntax for using unstack() in this way but doesn't solve my problem.
Colin Edgar
Colin Edgar am 14 Nov. 2015
I solved my problem using accumarray(). Will post code soon.

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Colin Edgar
Colin Edgar am 17 Dez. 2015

0 Stimmen

I adapted this approach to work here. The key is correct use of unique() to get the index that identifies the groups you are working with.

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