Separating subtables from a larger table by using the variable values in a column

I got a data folder with 19023 txt files. Each of the txt files have the data of 71 electrodes (indicated by the first column x_) like this:

As a preview, it shows only the first 7 rows.

I have created a datastore and selected the variables of interest using the codes below:

ds = datastore("data")
vars = ds.VariableNames;
idx = ismember(vars,["x_", "P1_xm_","P2_xm_","Rho","I","U","D","Time"]);
ds.SelectedVariableNames = vars(idx)
data = readall(ds)

I want to create 71 time series tables for each of electrodes, so I used the code:

e1 = data(data.x_==0,:)

And obtained the resulted table for the Electrode No.0.

However, I need to further process and analyse the individual table later on, it will be very time consuming to do repeat the same codes for 71 times every time. Is there a simpler method such as using a loop to generate 71 time series tables for individual electrodes?

Thank you in advance.

Answer 1

[Edric Ellis]

Rather than looping, you might be able to use findgroups and splitapply to do what you need. Imagine you wish to compute the maximum value of Rho for each value of x. Here's how to do that:

x = repmat([0:5]', 10, 1);
Rho = x + 10 + rand(size(x));
Time = datetime(2022, 05, 24 + (1:numel(x))', 09, 39+x, x);
% Example table
t = table(x, Rho, Time)

t = 60×3 table
    x     Rho              Time        
    _    ______    ____________________

    0    10.543    25-May-2022 09:39:00
    1    11.511    26-May-2022 09:40:01
    2    12.862    27-May-2022 09:41:02
    3    13.214    28-May-2022 09:42:03
    4     14.33    29-May-2022 09:43:04
    5    15.959    30-May-2022 09:44:05
    0    10.209    31-May-2022 09:39:00
    1    11.244    01-Jun-2022 09:40:01
    2    12.537    02-Jun-2022 09:41:02
    3    13.439    03-Jun-2022 09:42:03
    4    14.664    04-Jun-2022 09:43:04
    5    15.956    05-Jun-2022 09:44:05
    0    10.757    06-Jun-2022 09:39:00
    1    11.826    07-Jun-2022 09:40:01
    2    12.473    08-Jun-2022 09:41:02
    3    13.659    09-Jun-2022 09:42:03

% Use findgroups to group values of "x"
[g, xVal] = findgroups(t.x); % 'g' is a group index, 'xVal' is the corresponding value of x.
% Now apply the function MAX to values of Rho for each value of x
maxRho = splitapply(@max, t.Rho, g);
% Display the results
disp([xVal, maxRho])

         0   10.8994
    1.0000   11.9073
    2.0000   12.9336
    3.0000   13.8890
    4.0000   14.8112
    5.0000   15.9587

There's a more information in this example page.

[Man Lap Fung]

Thank you for the information. I guess I may need some of these later in my analysis.