Extracting data based on categories

I have a table (z) with two columns, year and annual precipitation. I have a separate vector (E) of three categories (0,1,2) that correspond to the years in the first table. The categories are unevenly spaced. I would like to create three new tables of year and annual precipitation, one for each category. How do I extract the correct year and precipitation data for each category and populate new tables?

z=['01-Jan-1973'	0.0114583333333333
'01-Jan-1974'	0.0918518518518519
'01-Jan-1975'	0.0529032258064516
'01-Jan-1976'	0.213571428571429
'01-Jan-1977'	0.0857692307692308]
E=[0 1 0 2 1]

I would like to create three new tables in the same format as z, like below.

A=['01-Jan-1973'	0.0114583333333333
   '01-Jan-1975'	0.0529032258064516]
B=['01-Jan-1974'	0.0918518518518519
   '01-Jan-1977'	0.0857692307692308]
C=['01-Jan-1976'	0.213571428571429]

My actual tables and vectors are 1x79, making extracting the three new tables challenging to do by hand.

[Guillaume]

Can you give an example of inputs and desired output. Preferably using valid matlab syntax.

[Allie]

I updated the question. Do you need more information still?

Answer 1

[Star Strider]

The splitapply function (R2015b and later releases) is perfect for this!

z = {'01-Jan-1973'	0.0114583333333333
    '01-Jan-1974'	0.0918518518518519
    '01-Jan-1975'	0.0529032258064516
    '01-Jan-1976'	0.213571428571429
    '01-Jan-1977'	0.0857692307692308};

E=[0 1 0 2 1];

Out = splitapply(@(x){x}, z, E(:)+1);
A = Out{1}
B = Out{2}
C = Out{3}

producing:

A =

  2×2 cell array

    {'01-Jan-1973'}    {[0.0114583333333333]}
    {'01-Jan-1975'}    {[0.0529032258064516]}

B =

  2×2 cell array

    {'01-Jan-1974'}    {[0.0918518518518519]}
    {'01-Jan-1977'}    {[0.0857692307692308]}

C =

  1×2 cell array

    {'01-Jan-1976'}    {[0.213571428571429]}

[Star Strider]

This can also be done with accumarray:

Out = accumarray(E(:)+1, (1:size(z,1))', [], @(x){z(x,:)})
A = Out{1}
B = Out{2}
C = Out{3}

The result is the same as with splitapply.

[Allie]

When I used both codes on my data, split array only produced single column arrays with the precipitation data and not the associated dates. Could this be because the dates are in datetime format?

The accumarry function produced both columns I wanted!

[Star Strider]

I did not know your dates were in datetime format, so that is likely the reason the splitapply code failed.

I added the accumarray code later when I realised that you may not have splitapply. I’m glad it worked!

[Guillaume]

Note: If you are actually using a table (or a timetable, which would be better probably), the best solution might be not to split the table at all and just add the E as an extra table variable.

Matlab has plenty of aggregation functions (rowfun, varfun, splitapply as shown here, groupsummary) which allows you to apply the same function to each group of a table all at once. More often, the code is actually simpler and faster if the data is not split beforehand.

e.g:

%demo table
T = table(datetime({'01-Jan-1973'; '01-Jan-1974'; '01-Jan-1975'; '01-Jan-1976'; '01-Jan-1977'}), ...
          [0.0114583333333333; 0.0918518518518519; 0.0529032258064516; 0.213571428571429; 0.0857692307692308], ...
          'VariableNames', {'Date', 'Value'});
T.group = [0; 1; 0; 2; 1]

groupsummary(T, 'group', {'mean', 'sum', 'std'}, 'Value')

outputs:

ans =
  3×5 table
    group    GroupCount        mean_Value            sum_Value              std_Value     
    _____    __________    __________________    __________________    ___________________
      0          2         0.0321807795698924    0.0643615591397849     0.0293059645132893
      1          2         0.0888105413105414     0.177621082621083    0.00430106261490964
      2          1          0.213571428571429     0.213571428571429                      0