How to process the data that have same row name

Hello. Currently I am doing a testing codes. But then, I found a loop problem which is too challenging for me since I am already facing it more than ten times (I am still a newbie). Below is my coding. My goals is to process the raw data and extract the features as stated in the code. Basically the problem that I am facing is that:

Error using tabular/vertcat Duplicate table row name: '2004.03.15.12.0'.

Error in Third_Test (line 55) Time_feature_matrix1 = [Time_feature_matrix1; df];

The file is too big so with that I share my link to GDrive: https://drive.google.com/drive/folders/1MltnJyAUh1BJpoPdNpqOUiHfhdbGF4wV?usp=drive_link

Here the Code:

% Initialize empty tables for each bearing
Time_feature_matrix1 = table();
Time_feature_matrix2 = table();
Time_feature_matrix3 = table();
Time_feature_matrix4 = table();
% Specify the test set and bearing numbers
test_set = 3;
bearing_numbers = [1, 2, 3, 4];
% Set the path to the directory containing the data files
path = 'file directory';
% Get list of files in the directory
files = dir(fullfile(path, '*'));
% Loop through the files in the directory
for j = 1:length(files)
    filename = files(j).name;
    if files(j).isdir % Skip directories
        continue;
    end
    file_path = fullfile(path, filename); % Full path of the file
    dataset = readtable(file_path, 'Delimiter', '\t', 'FileType', 'text'); % Read the dataset
    % Loop through each bearing number
    for j = 1:length(bearing_numbers)
        bearing_no = bearing_numbers(j);
        % Extract the bearing data
        bearing_data = dataset{:, bearing_no};
        % Calculate features
        feature_matrix = [
            max(bearing_data),  % Max
            min(bearing_data),  % Min
            mean(bearing_data), % Mean
            std(bearing_data, 1), % Std
            sqrt(mean(bearing_data.^2)), % RMS
            compute_skewness(bearing_data), % Skewness
            compute_kurtosis(bearing_data), % Kurtosis
            max(bearing_data) / sqrt(mean(bearing_data.^2)), % CrestFactor
            sqrt(mean(bearing_data.^2)) / mean(bearing_data) % FormFactor
            ];
        df = array2table(feature_matrix.'); % Transpose for correct orientation
        df.Properties.VariableNames = {'Max', 'Min', 'Mean', 'Std', 'RMS', 'Skewness', 'Kurtosis', 'CrestFactor', 'FormFactor'};
        df.Properties.RowNames = {[filename(1:end-4)]}; % Append bearing number
        df.Properties.RowNames = {rowName};
        % Append the table to the corresponding bearing's feature matrix
        switch bearing_no
            case 1
                if ~ismember(rowName, Time_feature_matrix1.Properties.RowNames)
                    Time_feature_matrix1 = [Time_feature_matrix1; df];
                end
            case 2
                if ~ismember(rowName, Time_feature_matrix2.Properties.RowNames)
                    Time_feature_matrix2 = [Time_feature_matrix2; df];
                end
            case 3
                if ~ismember(rowName, Time_feature_matrix3.Properties.RowNames)
                    Time_feature_matrix3 = [Time_feature_matrix3; df];
                end
            case 4
                if ~ismember(rowName, Time_feature_matrix4.Properties.RowNames)
                    Time_feature_matrix4 = [Time_feature_matrix4; df];
                end
                % case 1
                %     Time_feature_matrix1 = [Time_feature_matrix1; df];
                % case 2
                %     Time_feature_matrix2 = [Time_feature_matrix2; df];
                % case 3
                %     Time_feature_matrix3 = [Time_feature_matrix3; df];
                % case 4
                %     Time_feature_matrix4 = [Time_feature_matrix4; df];
        end
    end
end
% Define function to compute skewness
function skewness = compute_skewness(x)
n = length(x);
third_moment = sum((x - mean(x)).^3) / n;
s_3 = std(x, 1)^3;
skewness = third_moment / s_3;
end
% Define function to compute kurtosis
function kurtosis = compute_kurtosis(x)
n = length(x);
fourth_moment = sum((x - mean(x)).^4) / n;
s_4 = std(x, 1)^4;
kurtosis = fourth_moment / s_4 - 3;
end

[Infinite_king]

The access to the input file is currently restricted. Could you please share the file with open access ?

[Rusyaidi Yusri]

Hi, sorry about that. Already open for everyone with the link. It should available right now.

[Infinite_king]

Still showing 'request to access' screen.

Answer 1

[Steven Lord]

Rather than trying to create a table with duplicate RowNames (which as you see from the error message is not allowed) I'd store that information in a variable in the table itself (I'll call this bearingID based on the comment "% Append bearing number") Then you could probably just use one of the grouping functions from Grouping and Binning Data section on this documentation page (like groupsummary) with the bearingID variable as the grouping variable.

[Rusyaidi Yusri]

Hi Steven, thank you for the advice. Ill try it right away.