How to do pivot from table-type variable

Hi,
I have this table-type data
tdata.yearID=[1950;1950;1950;1951;1951;1951;1951;1952;1952;1952];
tdata.cluster=[1;3;4;1;2;3;4;1;2;3];
tdata.GroupCount=[5;2;6;2;2;4;2;1;2;2];
tdata= struct2table( tdata)
tdata =
10×3 table
yearID cluster GroupCount
______ _______ __________
1950 1 5
1950 3 2
1950 4 6
1951 1 2
1951 2 2
1951 3 4
1951 4 2
1952 1 1
1952 2 2
1952 3 2
Currently I am using this code below to do pivot, but it was very slow for large data. Is there a way to vectorize this code?
[uniqYr, ~, JYr]= unique( tdata.yearID);
nEventData.yearID= [1: 2000]';
[ nEventData.cnt]= zeros( length(nEventData.yearID), 4);
for runYr= 1: length( uniqYr)
indxYr= uniqYr( runYr);
tloc = (JYr== runYr);
tt = tdata( tloc, :);
nEventData.cnt( indxYr, tt.cluster')= tt.GroupCount';
end % for runYr
So results look like this for rows 1949-1955. Other rows have zeros.
nEventData.cnt (1949:1955,:)
ans =
0 0 0 0
5 0 2 6
2 2 4 2
1 2 2 0
0 0 0 0
0 0 0 0
0 0 0 0

2 件のコメント

Peter Perkins
Peter Perkins 2018 年 10 月 5 日
Pete, your code does not run and it's not at all clear what you want as your result. You are going to need to be much more clear.
Peter Perkins
Peter Perkins 2018 年 10 月 5 日
The updated question and output clarify that unstack does what you need, as Steve suggests.

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 採用された回答

Steven Lord
Steven Lord 2018 年 10 月 5 日

1 投票

The output you described looks somewhat like the first example in the documentation for the unstack function. I think what you want is something close to the pivotedTdata variable created by this example:
tdata.yearID=[1950;1950;1950;1951;1951;1951;1951;1952;1952;1952];
tdata.cluster=[1;3;4;1;2;3;4;1;2;3];
tdata.GroupCount=[5;2;6;2;2;4;2;1;2;2];
tdata= struct2table( tdata)
pivotedTdata = unstack(tdata, 'GroupCount', 'cluster')
You can retrieve data for a particular year this way:
pivotedTdata(pivotedTdata.yearID == 1950, :)

その他の回答 (1 件)

Lola Davidson
Lola Davidson 2023 年 3 月 17 日

3 投票

As of R2023a, you can also use the pivot function.
pivotedTable = pivot(tdata, Rows="yearID", Columns="cluster", DataVariable="GroupCount");

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2018 年 10 月 4 日

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2023 年 3 月 17 日

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