Loop through every nth row in a data frame in a function

I want to loop through specific rows to pick up data for some program I wrote and wondering if there is an efficient way of doing that. I have my coordinates in a matrix and each row represents a subject and the colums are the different orientation coordinates for a subject (see below)

In my loop I want to go through each row (each row and pick e.g "nose" for for each subject)

%% Define coil position

S.poslist{1}.pos(1).matsimnibs = [-m0n0 m1n0 -m2n0 x; -m0n1 m1n1 -m2n1 y; -m0n2 m1n2 -m2n2 z; 0 0 0 1]

I want to achieve something like this below but selecting every 4th row

S.poslist{1}.pos(1).matsimnibs = [-TargetInfo.data(4) TargetInfo.data(7) -TargetInfo.data(10) TargetInfo.data(1); -TargetInfo.data(5) TargetInfo.data(8) -TargetInfo.data(11) TargetInfo.data(2); -TargetInfo.data(6) TargetInfo.data(9) -TargetInfo.data(12) TargetInfo.data(3); 0 0 0 1]

Answer 1

[Sulaymon Eshkabilov]

The simpler and more efficient way would be using a logical indexing option or just use necessary indices, e.g.:

% DATA input/generation as a table array
TS = categorical({'Boy'; 'Boy';'Boy';'Boy'; 'Girl';'Girl';'Girl';'Girl';});
TN = categorical({'A'; 'B'; 'C'; 'D';'A'; 'B'; 'C'; 'D'});
DATA=table(TS);
DATA.TName=TN;
M1 = randn(numel(TN),1);
M2 = randn(numel(TN),1);
M3 = randn(numel(TN),1);
M4 = randn(numel(TN),1);
M5 = randn(numel(TN),1);
M6 = randn(numel(TN),1);
M7 = randn(numel(TN),1);
M8 = randn(numel(TN),1);
M9 = randn(numel(TN),1);
M10 = randn(numel(TN),1);
M11 = randn(numel(TN),1);
M12 = randn(numel(TN),1);
DATA.M1 = M1;
DATA.M2 = M2;
DATA.M3 = M3;
DATA.x = M4;
DATA.M5 = M5;
DATA.M6 = M6;
DATA.M7 = M7;
DATA.y = M8;
DATA.M9 = M9;
DATA.M10 = M10;
DATA.M11 = M11;
DATA.z = M12

DATA = 8×14 table
     TS     TName        M1           M2           M3          x           M5          M6          M7           y           M9         M10         M11          z    
    ____    _____    __________    _________    ________    ________    ________    ________    ________    _________    ________    ________    ________    ________

    Boy       A         0.94176      0.96592     0.83127    -0.96469     -1.1138     0.95343     0.29157      -0.2933     0.13869     -1.1075     0.59653     -0.1007
    Boy       B      -0.0085146       1.4973     0.85337     -1.7562     -2.1965      1.9926    -0.10816       1.2715    -0.21628     0.23034     0.75606    0.085673
    Boy       C        -0.13105     -0.19626     0.75003    -0.97582    0.022491     -0.1896     0.80425     -0.17841     0.11202     0.77753     0.32635    -0.64259
    Boy       D        -0.18264      -2.2729    -0.39656      1.9849      0.1503     0.26151    -0.87482       1.0075     0.60166     0.43676     0.64651     0.12189
    Girl      A         0.56582      -2.5636    -0.88507    -0.95755     -1.2312     0.42936    -0.39495      -1.0043      2.1618    -0.14574     -0.8057      2.0259
    Girl      B         0.46113       0.4177     0.37114     0.12721    -0.42584    -0.19921     0.94893       1.3251    0.080742    -0.98153    0.052248     0.34372
    Girl      C        -0.78052    -0.029752     -1.2213    -0.91497    -0.18431     0.12213     0.52505     -0.22272     -1.0057     -2.6867     -1.2729     -1.9098
    Girl      D          1.0694      -1.6838     -0.2042     0.36261    -0.22849    -0.15683    -0.49298    -0.038873    -0.88004     0.76735     0.16935      1.6251

% Logical indexing is used to take only 'A' or in your exercise: 'Nose'
% Note TName is a categorical data
CA=table2array(DATA(DATA.TName=='A', 3:end));
% Take out the necessary data and make some negative and leave some
% unchanged
Nose = [reshape(CA, 6, 4); 0 0 0 1].*[-1 1 -1 1]

Nose = 7×4

-0.9418 -0.9647 -0.2916 -1.1075 -0.5658 -0.9576 0.3949 -0.1457 -0.9659 -1.1138 0.2933 0.5965 2.5636 -1.2312 1.0043 -0.8057 -0.8313 0.9534 -0.1387 -0.1007 0.8851 0.4294 -2.1618 2.0259 0 0 0 1.0000

% Alternative way:
CA2=table2array(DATA(1:4:end, 3:end));
Nose2 = [reshape(CA2, 6, 4); 0 0 0 1].*[-1 1 -1 1]

Nose2 = 7×4

-0.9418 -0.9647 -0.2916 -1.1075 -0.5658 -0.9576 0.3949 -0.1457 -0.9659 -1.1138 0.2933 0.5965 2.5636 -1.2312 1.0043 -0.8057 -0.8313 0.9534 -0.1387 -0.1007 0.8851 0.4294 -2.1618 2.0259 0 0 0 1.0000