group sum with matrix
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With below matrix, I would like to have a sum of the third column and a sum of the fourth column by groups that are the combination of the first and second column.
1 1 1 0.5
1 1 2 0.5
1 1 3 0.5
1 1 4 0.5
1 1 5 0.5
1 2 6 0.5
1 2 7 0.5
1 2 8 0.5
1 2 8 0.5
1 2 9 0.5
2 1 0.1 2
2 1 0.2 2
2 1 0.3 2
2 1 0.4 2
2 1 0.5 2
2 2 0.6 2
2 2 0.7 2
2 2 0.8 2
2 2 0.8 2
2 2 0.9 2
So, here is what I am desiring for
1 1 15 2.5
1 2 38 2.5
2 1 1.5 10
2 2 3.8 10
1 comentario
Guillaume
el 1 de Mayo de 2018
This is very similar to the question you asked yesterday. The same method can be used for both, so please read and learn from the answers you're given as otherwise people will start ignoring your questions.
Respuestas (1)
Ameer Hamza
el 1 de Mayo de 2018
The following code will summarise the table as described in question
groups = findgroups(A(:, 1), A(:, 2));
[~, uniqueGroupsIndex] = unique(groups);
uniqueGroups = A(uniqueGroupsIndex, 1:2);
s1 = splitapply(@(x) sum(x), A(:, 3:end), findgroups(A(:, 1), A(:, 2)) );
final = [uniqueGroups s1];
4 comentarios
Guillaume
el 1 de Mayo de 2018
You can simplify the whole
groups = findgroups(A(:, 1), A(:, 2));
[~, uniqueGroupsIndex] = unique(groups);
uniqueGroups = A(uniqueGroupsIndex, 1:2);
with
[uniqeGroups, ~, groups] = unique(A(:, [1 2]), 'rows')
and there's no need to call findgroups in the splitapply call. You've already calculated the result as group, so:
s1 = splitapply(@(x) sum(x), A(:, 3:end), groups);
Note: In my answer to the near identical question, I use accumarray instead of splitapply. The two are more or less equivalent, with splitapply being newer and slightly more versatile (needed here but not in the other question) but generally a lot slower than accumarray.
Ameer Hamza
el 1 de Mayo de 2018
@Guillaume Thanks for further elaborating. Properly using unique() can really make code simple sometimes.
Boram Lim
el 1 de Mayo de 2018
Boram Lim
el 1 de Mayo de 2018
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