identify respective ranks of two columns in matrix and match them

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Loriann Chevalier on 14 Jul 2022
Commented: Dyuman Joshi on 14 Jul 2022
Hi everyone,
So I want to do something a bit complicated... I have a data matrix like this :
data = [1 0 0 0.0669378275070259
2 12.1056041717529 2 12.0026400602382
3 134.371655941010 9 134.704097205379
4 146.752244949341 10 139.481143043580
5 44.1991274356842 10 60.5235459177957
6 31.6353983879089 4 29.4212902375806
7 84.0974118709564 10 84.1452872130029
8 102.127936840057 7 57.8711186921884
9 104.799964427948 11 143.620002324739
10 28.8503270149232 3 15.5766566556635
11 10.8649168014526 1 10.8318218201762
12 0 0 0
13 22.3535099029541 2 22.3384987376591
14 36.9738082885742 4 36.9579155488247
15 68.8769569396973 6 68.9962084938198
16 68.3138140439987 8 64.6903763947714
17 178.282091140747 11 110.323777721892
18 52.1753841638566 6 51.2907121486635
19 120.173310279846 11 160.508708898427
20 117.115953445435 8 126.973352459287
21 79.8789970874788 12 90.5488463321847
22 113.134207248688 10 125.785059554141
23 39.8428988456726 2 66.6292778475760
24 0 0 0];
The first column is month ID (here I copied 2 months data for the example), 2nd column total rainfall (RF) observed in the month, 3rd column is the number of wet days (i.e. over how many days the RF amount of col2 was observed), and column 4 is the total rainfall amount predicted in the month according to some future climate scenario.
What I want to do is identify the rank of the value in col2, find the corresponding rank of col4, and attribute to it the same # wet days.
For example, month with ID 17 in the observations has the rank 24 (because it is the month with most RF). Its corresponding # wet days is 11. Now I want to attribute (say in a new column 5) this value 11 to month with ID 19 of the scenario (col4) because in this case this is the one with rank 24 in its respective column.
I would like to avoid sorting because in the end I need the values in the right order.
Any idea ?
Thanks
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Accepted Answer

Dyuman Joshi on 14 Jul 2022
Edited: Dyuman Joshi on 14 Jul 2022
You can perform sorting without changing the original data.
data = [1 0 0 0.0669378275070259
2 12.1056041717529 2 12.0026400602382
3 134.371655941010 9 134.704097205379
4 146.752244949341 10 139.481143043580
5 44.1991274356842 10 60.5235459177957
6 31.6353983879089 4 29.4212902375806
7 84.0974118709564 10 84.1452872130029
8 102.127936840057 7 57.8711186921884
9 104.799964427948 11 143.620002324739
10 28.8503270149232 3 15.5766566556635
11 10.8649168014526 1 10.8318218201762
12 0 0 0
13 22.3535099029541 2 22.3384987376591
14 36.9738082885742 4 36.9579155488247
15 68.8769569396973 6 68.9962084938198
16 68.3138140439987 8 64.6903763947714
17 178.282091140747 11 110.323777721892
18 52.1753841638566 6 51.2907121486635
19 120.173310279846 11 160.508708898427
20 117.115953445435 8 126.973352459287
21 79.8789970874788 12 90.5488463321847
22 113.134207248688 10 125.785059554141
23 39.8428988456726 2 66.6292778475760
24 0 0 0];
%actual rainfall
[~,iarf]=sort(data(:,2),'descend');
%predicted rainfall
[~,iprf]=sort(data(:,4),'descend');
data(iprf,5)=data(iarf,3);
data
data = 24×5
1.0000 0 0 0.0669 0 2.0000 12.1056 2.0000 12.0026 2.0000 3.0000 134.3717 9.0000 134.7041 11.0000 4.0000 146.7522 10.0000 139.4811 9.0000 5.0000 44.1991 10.0000 60.5235 6.0000 6.0000 31.6354 4.0000 29.4213 4.0000 7.0000 84.0974 10.0000 84.1453 10.0000 8.0000 102.1279 7.0000 57.8711 10.0000 9.0000 104.8000 11.0000 143.6200 10.0000 10.0000 28.8503 3.0000 15.5767 2.0000
%Not sure why MATLAB Answers is not showing full matrix
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Dyuman Joshi on 14 Jul 2022
You are welcome!

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