Cascading sort order that restarts count at each subsequent column
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I'm trying to sort a table by multiple columns (easy) and obtain a specialized sort order (not-so-easy).
Rather than a sortIndex vector I'm trying to get a sortIndex matrix where each column's count restarts from 1 as a preceding column changes. This is kind of like a set of cascading counters that restart back at 1 as any of the preceding counters tick forward.
Here's some data:
DAT = array2table([
"A" "a" "1"
"A" "a" "2"
"B" "a" "3"
"C" "a" "3"
"C" "b" "5"
"C" "a" "4"
"C" "b" "6"
"C" "b" "7"
], 'Var', ["ColA" "ColB" "ColC"])
And here's a clunky nested loop that achieves what I'm looking for in the specific case of having 3 columns to my data:
Omat = zeros(height(DAT),3);
[~,~,grpA] = unique(DAT.ColA,'stable');
Omat(:,1) = grpA;
for noA = 1:max(grpA)
maskA = grpA==noA;
subIndsA = find(maskA);
[~,~,subGrpB] = unique(DAT.ColB(maskA),'stable');
Omat(subIndsA,2) = subGrpB;
for noB = 1:max(subGrpB)
subMaskB = subGrpB==noB;
[~,~,subSubGrpC] = unique(DAT.ColC(subIndsA(subMaskB)),'stable');
Omat(subIndsA(subMaskB),3) = subSubGrpC;
end
end
And here's the output next to the original data:
Omat =
1 1 1 % "A" "a" "1"
1 1 2 % "A" "a" "2"
2 1 1 % "B" "a" "3"
3 1 1 % "C" "a" "3"
3 2 1 % "C" "b" "5"
3 1 2 % "C" "a" "4"
3 2 2 % "C" "b" "6"
3 2 3 % "C" "b" "7"
Can anyone think of a cleaner implementation that can generalize to N columns?
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Answers (1)
KSSV
on 29 Jun 2022
DAT = array2table([
"A" "a" "1"
"A" "a" "2"
"B" "a" "3"
"C" "a" "3"
"C" "b" "5"
"C" "a" "4"
"C" "b" "6"
"C" "b" "7"
], 'Var', ["ColA" "ColB" "ColC"]) ;
for i = 1:3
DAT.(i) = grp2idx(DAT.(i)) ;
end
DAT
3 Comments
KSSV
on 29 Jun 2022
Also:
DAT = array2table([
"A" "a" "1"
"A" "a" "2"
"B" "a" "3"
"C" "a" "3"
"C" "b" "5"
"C" "a" "4"
"C" "b" "6"
"C" "b" "7"
], 'Var', ["ColA" "ColB" "ColC"]) ;
for i = 1:3
[~,~,DAT.(i)] = unique(DAT.(i)) ;
end
DAT
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