Monthly maximum in data

I am trying to write a function that finds all the monthly maxima for a whole dataset. I am very new to programming and MATLAB. This is an example of a loop similar to the one i am doing

for i=1:100
    year(i)=max(d(i))
end

I tried to use this code and copy and paste it for every year in the dataset, because I don't know how to use timetable or double 'for' loops, but I received an error message telling me that the left and right sides have different numbers of elements. How could I rewrite this to give me the function I'm looking for?

[dpb]

Don't beat head against a wall, convert to a timetable and then use retime

That way will be about two or three lines of code -- there are clear examples in the doc -- give it a go and post back your efforts and where get stuck.

Answer 1

[Kelly Kearney]

While you could do this with find, using the second output of max is a bit quicker.

% Some fake data similar to yours

t = datetime(2018,1,1:500)';
rsq = [rand(500,1) day(t) month(t) year(t)];
 
% Find max...

imax = zeros(12,1);
for im = 1:12
    tmp = rsq(:,1);           % temporary copy of the data
    tmp(rsq(:,3)~=im) = -Inf; % anything not matching this month set low
    [~,imax(im)] = max(tmp);  % returns index of max value
end
 
maxval = rsq(imax,:)

Answer 2

[dpb]

t = datetime(2018,1,1:500)';
rsq = [rand(500,1) day(t) month(t) year(t)];

Alternatively for the same data structure above:

maxVals=splitapply(@max,rsq(:,1),findgroups(rsq(:,3)));

This solution and the one above both will be a global max over all data in the array of the given month -- if there are multiple years of data and it isn't desired to analyze all months of all years as a composite, then must group by both year and month:

[g,idy,idm]=findgroups(rsq(:,4),rsq(:,3));
maxVals=splitapply(@max,rsq(:,1),g)];

For a given set of values here, this resulted in:

[idm idy maxVals]
ans =
          1.00       2018.00          0.98
          2.00       2018.00          0.97
          3.00       2018.00          0.99
          4.00       2018.00          0.99
          5.00       2018.00          1.00
          6.00       2018.00          0.92
          7.00       2018.00          0.99
          8.00       2018.00          0.91
          9.00       2018.00          0.97
         10.00       2018.00          0.96
         11.00       2018.00          0.95
         12.00       2018.00          0.99
          1.00       2019.00          0.93
          2.00       2019.00          0.96
          3.00       2019.00          1.00
          4.00       2019.00          0.99
          5.00       2019.00          0.96
>> 

in a tabular form with the grouping id variables from findgroups. I used format bank to avoid the automatic scaling of default format but that does add the two decimal points onto the integer values...

A table isn't really any harder--starting from the array above,

trsq=array2table(rsq,'VariableNames',{'Data','Day','Month','Year'});
tmaxVals=rowfun(@max,trsq,'InputVariables','Data', ...
                          'GroupingVariables',{'Year','Month'}, ,,, ...
                          'OutputVariableNames',{'GroupMax'});

for the same realization of the random data as above this gives:

>> format short,format  compact
>> head(trsq)
ans =
  8×4 table
     Data      Day    Month    Year
    _______    ___    _____    ____
    0.30596     1       1      2018
    0.91988     2       1      2018
    0.80037     3       1      2018
    0.66754     4       1      2018
    0.25832     5       1      2018
    0.95717     6       1      2018
    0.40822     7       1      2018
    0.51435     8       1      2018
>> tmaxVals =
  17×4 table
    Year    Month    GroupCount    GroupMax
    ____    _____    __________    ________
    2018      1          31        0.98046 
    2018      2          28        0.96657 
    2018      3          31        0.99294 
    2018      4          30        0.99388 
    2018      5          31        0.99522 
    2018      6          30        0.92159 
    2018      7          31        0.98775 
    2018      8          31        0.91045 
    2018      9          30         0.9694 
    2018     10          31        0.95942 
    2018     11          30         0.9504 
    2018     12          31        0.98866 
    2019      1          31        0.92582 
    2019      2          28        0.95508 
    2019      3          31        0.99525 
    2019      4          30        0.99393 
    2019      5          15        0.95888 
>>