How to accumulate (average) data based on several criteria

Question

I have a dataset where I recorded values ​​in sets of 3 readings (to be able to get a general idea of ​​SEM). I recorded them in a list that looks like this, which I am trying to collapse into averages for each set of three points:

I want to collapse essentially every 3 lines into one line, where for this set the average data value is given. Essentially, it would look like this:

, , Excel (.. ), , MATLAB. accumarray, , . subs, . , :

subs = [1 1 1; 2 2 2; 3 3 3; 4 4 4; ...]'

, , (.. ..), , - Pivot-table-esque - , , . .

:

Subject  Flow   On/Off   Values
1        10     1        2.20
1        10     1        2.50
1        10     1        2.60
1        20     1        5.50
1        20     1        6.10
1        20     1        5.90
1        30     1        10.10
1        30     1        10.50
1        30     1        10.50
1        10     0        1.90
1        10     0        2.20
1        10     0        2.30
1        20     0        5.20
1        20     0        5.80
1        20     0        5.60
1        30     0        9.80
1        30     0        10.20
1        30     0        10.20
2        10     1        5.70
2        10     1        6.00
2        10     1        6.10
2        20     1        9.00
2        20     1        9.60
2        20     1        9.40
2        30     1        13.60
2        30     1        14.00
2        30     1        14.00
2        10     0        5.40
2        10     0        5.70
2        10     0        5.80
2        20     0        8.70
2        20     0        9.30
2        20     0        9.10
2        30     0        13.30
2        30     0        13.70
2        30     0        13.70

Answer 1

,

• ( , );
• 1, 3, 2.

, x,

[~, ~, subs] = unique(x(:, [1 3 2]), 'rows', 'sorted');
result = accumarray(subs, x(:,end), [], @mean);

result =
    2.1333
    5.5333
   10.0667
    2.4333
    5.8333
   10.3667
    5.6333
    9.0333
   13.5667
    5.9333
    9.3333
   13.8667

, unique 'rows' 'sorted'. subs . , accumarray, .

Answer 2

unique accumarray , :

[newData, ~, subs] = unique(data(:, 1:3), 'rows', 'stable');
newData(:, 4) = accumarray(subs, data(:, 4), [], @mean);

newData =

    1.0000   10.0000    1.0000    2.4333
    1.0000   20.0000    1.0000    5.8333
    1.0000   30.0000    1.0000   10.3667
    1.0000   10.0000         0    2.1333
    1.0000   20.0000         0    5.5333
    1.0000   30.0000         0   10.0667
    2.0000   10.0000    1.0000    5.9333
    2.0000   20.0000    1.0000    9.3333
    2.0000   30.0000    1.0000   13.8667
    2.0000   10.0000         0    5.6333
    2.0000   20.0000         0    9.0333
    2.0000   30.0000         0   13.5667

Answer 3

accumarray - . -, unique:

[unique_subjects, ~, ind_subjects] = unique(vect_subjects);
[unique_flows, ~, ind_flows] = unique(vect_flows);
[unique_on_off, ~, ind_on_off] = unique(vect_on_off);

, ind_subjects, ind_flows ind_on_off, [1..2], [1..3] [1..2].

[3x2x2] ( ):

mean_values = accumarray([ind_flows, ind_on_off, ind_subjects], vect_values, [], @mean);
mean_values = mean_values(:);

Nota: .

:

[ind1, ind2, ind3] = ndgrid(1:numel(unique_flows), 1:numel(unique_on_off), 1:numel(unique_subjects));
flows_summary = unique_flows(ind1(:));
on_off_summary = unique_on_off(ind2(:));
subjects_summary = unique_subjects(ind3(:));

Nota: .

Answer 4

findgroups splitapply. - , , :

 >> T = array2table(data, 'VariableNames', { 'Subject', 'Flow', 'On_Off', 'Values'});
 >> [gid,Tgrp] = findgroups(T(:,1:3));
 >> Tgrp.MeanValue = splitapply(@mean, T(:,4), gid)
 Tgrp =
   12×4 table
     Subject    Flow    On_Off    MeanValue
     _______    ____    ______    _________
     1          10      0         2.1333   
     1          10      1         2.4333   
     1          20      0         5.5333   
     1          20      1         5.8333   
     1          30      0         10.067   
     1          30      1         10.367   
     2          10      0         5.6333   
     2          10      1         5.9333   
     2          20      0         9.0333   
     2          20      1         9.3333   
     2          30      0         13.567   
     2          30      1         13.867