How to find the total value by category in a table

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julios 2019-1-2

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评论： Cris LaPierre 2019-1-23

采纳的回答： Cris LaPierre

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Answer 1

Cris LaPierre 2019-1-2

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编辑：Cris LaPierre 2019-1-2

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Use findgroups and splitapply.

G = findgroups(Table.Clase);
Result = splitapply(@sum,Table.Monto,G);
Result = table(categories(Table.Clase),Result,'VariableNames',{'Clase','Monto'})

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Cris LaPierre 2019-1-4

Ah, you are in 2017a. It looks like reordercats did not yet support the string data type.

Update the line that uses string to use cellstr instead

[cats,ia,~] = unique(cellstr(Table.Clase));

julios 2019-1-4

That's right !!!, the final result has a initial order. Thank you very much !!

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Answer 2

Peter Perkins 2019-1-23

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在 MATLAB Online 中打开

The problem with Chris' solution is that by going through findgroups in that way, you lose all the categoricalness of your grouping variable. You can work around that in splitapply in a few ways, one of which Chris shows, but here's something more direct:

>> Clase = categorical({'w'; 'w'; 'w'; 'b'; 'b'; 'y'},{'w' 'b' 'y'});
>> Monto = [1;2;3;4;5;6];
>> t = table(Clase,Monto)
t =
  6×2 table
    Clase    Monto
    _____    _____
      w        1  
      w        2  
      w        3  
      b        4  
      b        5  
      y        6  
>> tsum = varfun(@mean,t,'GroupingVariable','Clase')
tsum =
  3×3 table
    Clase    GroupCount    mean_Monto
    _____    __________    __________
      w          3              2    
      b          2            4.5    
      y          1              6      
>> categories(tsum.Clase)
ans =
  3×1 cell array
    {'w'}
    {'b'}
    {'y'}

In (very) recent versions of MATLAB, there's also groupsummary:

>> groupsummary(t,"Clase","mean")
ans =
  3×3 table
    Clase    GroupCount    mean_Monto
    _____    __________    __________
      w          3              2    
      b          2            4.5    
      y          1              6    

varfun only works for tables, whereas groupsummary is more widel applicable.

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Cris LaPierre 2019-1-23

Not sure what you mean by losing its categoricalness.

>> summary(Result)
Variables:
    Clase: 3×1 categorical
        Values:
            White     1      
            Black     1      
            Yellow    1      
    Monto: 3×1 double
        Values:
            Min       4      
            Median    5      
            Max       6      

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Answer 3

Image Analyst 2019-1-2

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You can use grpstats() to get the stats by group, if you have the Statistics and Machine Learning Toolbox. It doesn't have sum but it has count and mean so you can multiply those to get the sum. Attach your table in a .mat file, and your expected results if you need more guidance.