How to assign legend and marker to a 2D graph in Matlab?

I have an excel file consiting of multiple columns. The first column is X and the rest of columns are Y. The first row is also the titles. I am reading the excel file in Matlab and plotting them in a figure. I am using the code as below. I would like to assign different markers and legends to each Y, as it is defined in the first row. I have also attached the sample csv file. Would anyone help me in this regard? I am using the code as below. Thank You so much in advance.

csvfiles = dir('*.csv');

for file = csvfiles'

sample=readmatrix(file.name);

x=sample(1:end,1);

dataSample=sample(1:end,2:end);

figure

plot(x,dataSample(1:end,:)','--')

end

Answer 1

[Star Strider]

T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1103385/sample.csv')

T1 = 1868×33 table
     Var1        C0           C1           C2          C3          C4          C5           C6           C7           H0           H1           H2          H3           H4           H5           H6           H7          R0           R1          R2          R3          R4          R5          R6           R7           Z0          Z1           Z2           Z3           Z4           Z5           Z6           Z7    
    ______    _________    _________    ________    ________    ________    _________    _________    _________    _________    _________    ________    _________    _________    _________    _________    ________    _________    ________    ________    ________    ________    ________    _________    _________    ________    _________    _________    _________    _________    _________    _________    _________

    4000.4    0.0030977    0.0068682    0.010064    0.013078    0.012625    0.0091874      0.00734    0.0096967    0.0082328    0.0084078    0.015286    0.0034295    0.0026153    0.0036699    0.0087307    0.012609    0.0016383    0.011388    0.010033    0.012429     0.01759    0.011811    0.0078433     0.008815     0.01212     0.006157    0.0086189    0.0067927    0.0058852    0.0046381    0.0044672    0.0048745
    3998.4    0.0031007    0.0068682    0.010058    0.013089    0.012625    0.0091916     0.007349    0.0096967    0.0082328    0.0083902    0.015284    0.0034215    0.0026231    0.0036668    0.0087243    0.012611    0.0016342    0.011406    0.010041    0.012429    0.017658    0.011811    0.0078433     0.008815    0.012127     0.006157    0.0086171    0.0067927    0.0058852    0.0046435    0.0044798    0.0048745
    3996.5    0.0031007    0.0068733    0.010058     0.01309    0.012605    0.0091966      0.00734    0.0097045     0.008242    0.0083902    0.015331    0.0034215    0.0026231    0.0036447    0.0087158    0.012608    0.0016318    0.011381    0.010048    0.012511    0.017746    0.011802    0.0078433     0.008815    0.012137    0.0061542    0.0086133    0.0067927    0.0058889    0.0046435    0.0044718    0.0048527
    3994.6    0.0031041     0.006879    0.010052    0.013082    0.012605    0.0091966     0.007349    0.0097132    0.0082506    0.0084228    0.015373    0.0034295    0.0026201    0.0036402    0.0087158    0.012614    0.0016316    0.011381    0.010051    0.012511    0.017775    0.011802    0.0078388    0.0088022    0.012128    0.0061256    0.0086133    0.0067999    0.0058939    0.0046381    0.0044693    0.0048522
    3992.6    0.0031261    0.0068842    0.010049    0.013085    0.012608    0.0092082    0.0073621    0.0097193    0.0082506    0.0084438    0.015384    0.0034295    0.0026153    0.0036496    0.0087158    0.012614    0.0016485     0.01138    0.010051    0.012553     0.01777    0.011777    0.0078367    0.0088082    0.012134    0.0061542    0.0085942    0.0067999    0.0058996    0.0046435    0.0044755    0.0048522
    3990.7     0.003149    0.0068842    0.010047    0.013085    0.012612     0.009217    0.0073621    0.0097193    0.0082649    0.0084438    0.015417    0.0034344     0.002612    0.0036496    0.0087158    0.012612    0.0016529     0.01138    0.010051    0.012589     0.01777    0.011777    0.0078344    0.0088175    0.012141    0.0061256    0.0085942    0.0067999    0.0058996    0.0046883    0.0044755    0.0048547
    3988.8    0.0031371    0.0068891    0.010046     0.01309    0.012616     0.009225    0.0073927    0.0097343    0.0082706    0.0084543    0.015445    0.0034344    0.0026171    0.0036538    0.0087027    0.012607    0.0016687    0.011378    0.010056    0.012606    0.017797    0.011785    0.0078304    0.0088263    0.012147    0.0061259    0.0085919    0.0068098    0.0059348    0.0047103    0.0044869    0.0048894
    3986.9    0.0031404    0.0068916    0.010047    0.013093     0.01262    0.0092359    0.0074187    0.0097409    0.0082755    0.0084739    0.015463      0.00344    0.0026321    0.0036668    0.0086982    0.012606    0.0016367    0.011373    0.010061    0.012621    0.017806    0.011791    0.0078261    0.0088357    0.012161    0.0061266    0.0085918    0.0068175    0.0059713    0.0046883    0.0044891    0.0049132
    3984.9    0.0031625    0.0068956    0.010049    0.013097    0.012625    0.0092414    0.0074187    0.0097376    0.0082797    0.0084739     0.01548     0.003466    0.0026345    0.0036884    0.0086951    0.012607    0.0016342    0.011373    0.010067    0.012632    0.017809    0.011791    0.0078261    0.0088458    0.012171    0.0061258    0.0085935    0.0068175    0.0059878     0.004699    0.0044869    0.0049132
      3983     0.003149       0.0069     0.01005    0.013097    0.012631    0.0092414    0.0074316    0.0097459    0.0082799    0.0084784    0.015493    0.0034691    0.0026345    0.0036839    0.0086883     0.01261     0.001633    0.011375    0.010067    0.012638    0.017812      0.0118    0.0078261    0.0088576    0.012179     0.006126    0.0085961    0.0068152    0.0060109    0.0047103    0.0044869    0.0049013
    3981.1    0.0031404    0.0069047    0.010052     0.01311    0.012638    0.0092543    0.0074527     0.009755    0.0082828    0.0084643    0.015504    0.0034745    0.0026505     0.003693    0.0086932    0.012608    0.0016316    0.011376    0.010062    0.012644    0.017816     0.01181    0.0078207    0.0088576    0.012179    0.0061284    0.0085961    0.0068175    0.0059878    0.0047354    0.0044919    0.0049357
    3979.1    0.0031625     0.006909    0.010052     0.01311    0.012649    0.0092543    0.0074614     0.009755    0.0082857     0.008468    0.015515    0.0034897    0.0027131    0.0037029    0.0086944    0.012595    0.0016352    0.011376    0.010069     0.01265    0.017816    0.011822    0.0078176    0.0088695    0.012183     0.006126    0.0085989    0.0068196    0.0059713    0.0047354    0.0044883    0.0049357
    3977.2    0.0031445    0.0069117    0.010057    0.013116    0.012657    0.0092543    0.0074527     0.009755    0.0082892    0.0084715    0.015524    0.0034805    0.0026556    0.0036884    0.0086944    0.012602    0.0016402     0.01138    0.010081    0.012656    0.017822    0.011822    0.0078147    0.0088807    0.012183    0.0061284    0.0086066    0.0068196    0.0059713    0.0047354    0.0044883    0.0049013
    3975.3    0.0031445    0.0069135     0.01006    0.013118    0.012657    0.0092599    0.0074527    0.0097636    0.0082892    0.0084715    0.015524     0.003466    0.0026303    0.0036762    0.0086932    0.012602    0.0016383    0.011391    0.010081    0.012657    0.017812    0.011818    0.0078113    0.0089084    0.012165    0.0061312    0.0086116    0.0068288    0.0060029    0.0047354    0.0044936    0.0049132
    3973.4    0.0031666    0.0069148    0.010063     0.01313    0.012674    0.0092714    0.0074681    0.0097716    0.0082898    0.0084735    0.015536     0.003466    0.0026288    0.0036797    0.0086932    0.012613    0.0016633    0.011397    0.010087    0.012635    0.017818    0.011836    0.0078113    0.0089138    0.012183    0.0061272    0.0086178    0.0068326    0.0060189    0.0047547    0.0044951    0.0049357
    3971.4    0.0031415    0.0069178    0.010071    0.013136    0.012691    0.0092783    0.0074735    0.0097872    0.0082893    0.0084773    0.015535    0.0034805    0.0026345    0.0037089    0.0086951    0.012632    0.0015915    0.011397    0.010095    0.012639    0.017818    0.011855    0.0078037    0.0089199    0.012191    0.0061272    0.0086178    0.0068326    0.0060306    0.0047806    0.0044974    0.0049631

% T1.Properties.VariableNames{2:end}
linelbls = T1.Properties.VariableNames(2:end);
L = size(T1,2)-1;
cm = colormap(turbo(L))

cm = 32×3

0.1900 0.0718 0.2322 0.2258 0.1661 0.4566 0.2523 0.2573 0.6429 0.2692 0.3454 0.7912 0.2766 0.4304 0.9014 0.2746 0.5122 0.9736 0.2526 0.5930 0.9987 0.2031 0.6743 0.9708 0.1465 0.7521 0.9067 0.1035 0.8220 0.8240

mrkr = ['o+*.x_|sd^v><ph'];
Lmrkr = numel(mrkr);

hold on
for k = 1:L
    mkridx = k;
    if k > Lmrkr
        mkridx = rem(k,Lmrkr) + 1;
    end
    plot(T1{:,1},T1{:,k+1}, '-', 'Color',cm(k,:), 'Marker',mrkr(mkridx), 'DisplayName',sprintf('Variable %s',linelbls{k}))
end
hold off
legend('NumColumns',2, 'Location','eastoutside')

This does what you requested, however it might be best to use subplot (or one of its friends, such as tiledlayout or stackedplot), rather than to plot all these together in one plot.

.

[Behrad Ze]

Thank You so much. This was what I wanted exactly :)

[Star Strider]

My pleasure!

If my Answer helped you solve your problem, please Accept it!

.

[Behrad Ze]

Sorry I forgot to do it

[Star Strider]

Thank you!