How to create nested legend

I have 20 entries in the plot, 5 under 4 heading, I want to categorise those 5 under their respective heading. I have attached the image. So, here I want to simply write h = 525 mm and its sub headings as w = 180, 150, 120, 90, 60 and then similarly for h = 450, so that my legend entry will look a little bit less clumsy.

Answer 1

[Star Strider]

One option is to put each ‘h’ in a separate column —

h = 300 : 75 : 525;
w = 60 : 30 : 180;

[H,W] = ndgrid(h,w);

figure
hold on
for k1 = 1:size(H,1)
    for k2 = 1:size(W,1)
        plot(H(:,k1), H(1,k1)+W(:,k2), 'DisplayName',["h = "+H(k1,1)+", w = "+W(k2,1)])
    end
end
hold off
legend('Location','best', 'NumColumns',numel(h))

.

[PRASHANT JHA]

Thanks for your reply.

Sorry for silly question, but I am new to matlab and so I apologize in advance. Actually, I have a table named as data3 whose data I have plotted. So, what I wanted was to categorise w = 180, 150, 120, 90, 60 mm under the headings namely 525, 450, 375, & 300. So, I wrote this code.So, could you please help me out with this.

plot(data3.Var3(1:5),data3.Var5(1:5)); hold on; 
plot(data3.Var3(6:10),data3.Var5(6:10)); hold on; 
plot(data3.Var3(11:15),data3.Var5(11:15)); hold on; 
plot(data3.Var3(16:20),data3.Var5(16:20)); hold on; 
plot(data3.Var3(1:5),data3.Var10(1:5)); hold on; 
plot(data3.Var3(6:10),data3.Var10(6:10)); hold on; 
plot(data3.Var3(11:15),data3.Var10(11:15)); hold on; 
plot(data3.Var3(16:20),data3.Var10(16:20)); hold on;
plot(data3.Var3(1:5),data3.Var15(1:5)); hold on; 
plot(data3.Var3(6:10),data3.Var15(6:10)); hold on; 
plot(data3.Var3(11:15),data3.Var15(11:15)); hold on; 
plot(data3.Var3(16:20),data3.Var15(16:20)); hold on;
plot(data3.Var3(1:5),data3.Var20(1:5)); hold on; 
plot(data3.Var3(6:10),data3.Var20(6:10)); hold on; 
plot(data3.Var3(11:15),data3.Var20(11:15)); hold on; 
plot(data3.Var3(16:20),data3.Var20(16:20)); hold off; 
xlabel('w (in mm)'); 
ylabel('Cl'); 
legend('f = 1050 mm, h = 525 mm', 'f = 1050 mm, h = 450 mm', 'f = 1050 mm, h = 375 mm', 'f = 1050 mm, h = 300 mm', 'f = 900 mm, h = 525 mm', 'f = 900 mm, h = 450 mm', 'f = 900 mm, h = 375 mm', 'f = 900 mm, h = 300 mm', 'f = 750 mm, h = 525 mm', 'f = 750 mm, h = 450 mm', 'f = 750 mm, h = 375 mm', 'f = 750 mm, h = 300 mm', 'f = 600 mm, h = 525 mm', 'f = 600 mm, h = 450 mm', 'f = 600 mm, h = 375 mm', 'f = 600 mm, h = 300 mm', 'Location', 'Best','EdgeColor', 'none','FontSize',8);lineHandles = findobj(gca, 'Type', 'Line'); set(lineHandles, 'LineWidth', 1);

[Star Strider]

I doubt that a ‘nested’ legend is possible. I presented the only option that I know to exist.

The only other option I am aware of would be:

h = 300 : 75 : 525;
w = 60 : 30 : 180;

[H,W] = ndgrid(h,w);

figure
hold on
for k1 = 1:size(H,1)
    for k2 = 1:size(W,1)
        plot(H(:,k1), W(:,k2)./H(:,k1)+rand/10, 'DisplayName',["w = "+W(k2,1)])
    end
end
hold off
hlgd = legend('Location','best', 'NumColumns',numel(h));
sspc = "                 ";
title(hlgd, [string(h(1))+sspc+string(h(2))+sspc+string(h(3))+sspc+string(h(4))])

That is the best I can come up with.

I do not have your data, so I have to improvise.

.

[PRASHANT JHA]

Thanks for you time. I have attached the matlab file. When you will load this data, you will get three tables named as data1, data2 and data3. I hope this will work.

[Star Strider]

I have no idea what to do with these —

LD = load('matlab.mat')

LD = struct with fields:
    data1: [20×20 table]
    data2: [20×20 table]
    data3: [20×20 table]
    data5: [9×6 table]
        x: [4×1 double]

data1 = LD.data1

data1 = 20×20 table
    Var1    Var2    Var3     Var4       Var5     Var6    Var7    Var8     Var9      Var10     Var11    Var12    Var13     Var14     Var15     Var16    Var17    Var18     Var19     Var20 
    ____    ____    ____    _______    ______    ____    ____    ____    _______    ______    _____    _____    _____    _______    ______    _____    _____    _____    _______    ______

    525     1050    180     0.61066    1.6549    450     1050    180     0.61083    1.6792     375     1050      180     0.61114    1.6296     300     1050      180     0.61163    1.6257
    525      900    180     0.61075    1.6594    450      900    180       0.611    1.6873     375      900      180     0.61114    1.6303     300      900      180     0.61156    1.6269
    525      750    180     0.61074    1.6677    450      750    180     0.61141    1.6808     375      750      180     0.61137    1.6329     300      750      180     0.64118    1.6456
    525      600    180     0.70535    1.6973    450      600    180     0.68547    1.6454     375      600      180     0.70427    1.6976     300      600      180     0.68976    1.6751
    525     1050    150     0.61079    1.6523    450     1050    150     0.61101    1.6911     375     1050      150     0.61128    1.6543     300     1050      150     0.61157    1.6284
    525      900    150     0.61144    1.6539    450      900    150       0.611    1.7013     375      900      150     0.61113    1.6751     300      900      150     0.61155    1.6329
    525      750    150     0.61084    1.6626    450      750    150     0.61107    1.6979     375      750      150     0.61145    1.6755     300      750      150     0.64898    1.6577
    525      600    150     0.65395     1.684    450      600    150     0.66703     1.652     375      600      150     0.71511    1.6018     300      600      150     0.69957    1.6805
    525     1050    120     0.61093    1.6724    450     1050    120     0.61117    1.6998     375     1050      120     0.61137    1.6653     300     1050      120     0.61177    1.6258
    525      900    120     0.61105    1.6734    450      900    120     0.61111    1.7103     375      900      120     0.61092     1.688     300      900      120     0.61157    1.6268
    525      750    120     0.61092     1.673    450      750    120     0.61108    1.6897     375      750      120     0.61153     1.679     300      750      120     0.64705    1.6313
    525      600    120     0.66347     1.663    450      600    120     0.66722    1.6352     375      600      120     0.72595    1.6202     300      600      120     0.71677    1.6505
    525     1050     90     0.61112    1.6701    450     1050     90     0.61122    1.6598     375     1050       90     0.61149    1.6269     300     1050       90     0.61192    1.6293
    525      900     90     0.61094    1.6706    450      900     90     0.61129    1.6576     375      900       90     0.61151    1.6258     300      900       90     0.61156    1.6292
    525      750     90     0.61118    1.6697    450      750     90     0.61124    1.6588     375      750       90     0.61162    1.6217     300      750       90     0.64726    1.6535
    525      600     90     0.66941    1.6545    450      600     90     0.67325    1.6825     375      600       90     0.73679    1.6385     300      600       90     0.72638     1.679

data2 = LD.data2

data2 = 20×20 table
    Var1    Var2    Var3     Var4       Var5     Var6    Var7    Var8     Var9      Var10     Var11    Var12    Var13     Var14     Var15     Var16    Var17    Var18     Var19     Var20 
    ____    ____    ____    _______    ______    ____    ____    ____    _______    ______    _____    _____    _____    _______    ______    _____    _____    _____    _______    ______

    1050    180     525     0.61105    1.6606    900     180     525     0.61157    1.6644     750      180      525     0.61074    1.6677     600      180      525     0.70195    1.6497
    1050    180     450     0.61103    1.6579    900     180     450     0.61156    1.6687     750      180      450     0.61141    1.6728     600      180      450     0.70169    1.6454
    1050    180     375     0.61121    1.6453    900     180     375     0.61154    1.6563     750      180      375     0.61137    1.6615     600      180      375     0.70427     1.652
    1050    180     300     0.61163    1.6226    900     180     300     0.61126    1.6269     750      180      300     0.64118    1.6456     600      180      300     0.68976    1.6751
    1050    150     525     0.61108    1.6578    900     150     525     0.61132    1.6754     750      150      525     0.61084    1.6763     600      150      525     0.69954    1.6518
    1050    150     450      0.6111    1.6606    900     150     450     0.61134    1.6819     750      150      450     0.61107    1.6798     600      150      450      0.6967     1.652
    1050    150     375     0.61128    1.6504    900     150     375     0.61142    1.6751     750      150      375     0.61145    1.6698     600      150      375     0.70022     1.662
    1050    150     300     0.61157    1.6284    900     150     300     0.61155    1.6329     750      150      300     0.64898    1.6496     600      150      300     0.68496    1.6805
    1050    120     525     0.61119    1.6626    900     120     525      0.6113    1.6785     750      120      525     0.61092    1.6753     600      120      525     0.70917    1.6596
    1050    120     450     0.61117     1.663    900     120     450     0.61129    1.6832     750      120      450     0.61108     1.679     600      120      450     0.71207    1.6603
    1050    120     375     0.61137     1.657    900     120     375     0.61131    1.6782     750      120      375     0.61153    1.6679     600      120      375     0.71259    1.6602
    1050    120     300     0.61177    1.6258    900     120     300     0.61157    1.6268     750      120      300     0.64705    1.6413     600      120      300     0.71677    1.6505
    1050     90     525     0.61112    1.6607    900      90     525     0.61131    1.6706     750       90      525     0.61118     1.663     600       90      525     0.71693    1.6545
    1050     90     450     0.61122    1.6598    900      90     450     0.61129    1.6726     750       90      450     0.61124    1.6646     600       90      450     0.71527    1.6567
    1050     90     375     0.61149    1.6527    900      90     375     0.61133    1.6626     750       90      375     0.61162    1.6622     600       90      375     0.71692    1.6694
    1050     90     300     0.61192    1.6293    900      90     300     0.61156    1.6292     750       90      300     0.64726    1.6535     600       90      300     0.70926     1.679

data3 = LD.data3

data3 = 20×20 table
    Var1    Var2    Var3     Var4       Var5     Var6    Var7    Var8     Var9      Var10     Var11    Var12    Var13     Var14     Var15     Var16    Var17    Var18     Var19     Var20 
    ____    ____    ____    _______    ______    ____    ____    ____    _______    ______    _____    _____    _____    _______    ______    _____    _____    _____    _______    ______

    1050    525     180     0.61111    1.6598    900     525     180     0.61108    1.6554     750      525      180     0.64107    1.6618     600      525      180     0.66171     1.651
    1050    525     150      0.6111    1.6615    900     525     150     0.61104    1.6554     750      525      150     0.64108    1.6626     600      525      150     0.65947    1.6495
    1050    525     120     0.61109    1.6607    900     525     120     0.61105    1.6567     750      525      120     0.64109    1.6617     600      525      120     0.66147    1.6463
    1050    525      90     0.61112    1.6596    900     525      90     0.61109     1.657     750      525       90     0.64112    1.6697     600      525       90     0.66941    1.6495
    1050    525      60     0.61131     1.671    900     525      60     0.61125    1.6721     750      525       60     0.64151    1.6685     600      525       60     0.68774    1.6615
    1050    450     180     0.61097    1.6709    900     450     180       0.611    1.6719     750      450      180     0.64114    1.6918     600      450      180     0.66855    1.6544
    1050    450     150     0.61111    1.6691    900     450     150       0.611     1.672     750      450      150     0.64111    1.6979     600      450      150     0.66703     1.652
    1050    450     120     0.61117     1.668    900     450     120     0.61111     1.672     750      450      120     0.64111    1.6897     600      450      120     0.66722    1.6494
    1050    450      90     0.61122    1.6611    900     450      90     0.61129     1.666     750      450       90     0.64112    1.6659     600      450       90     0.67325    1.6503
    1050    450      60     0.61113    1.6453    900     450      60     0.61157    1.6473     750      450       60     0.64113    1.6516     600      450       60     0.70116    1.6658
    1050    375     180     0.61121     1.667    900     375     180     0.61111    1.6693     750      375      180     0.64114    1.6833     600      375      180     0.71043     1.642
    1050    375     150     0.61128     1.667    900     375     150     0.61111    1.6702     750      375      150     0.64115    1.6876     600      375      150     0.71151     1.641
    1050    375     120     0.61137    1.6653    900     375     120     0.61115    1.6688     750      375      120     0.64115     1.679     600      375      120     0.71595     1.642
    1050    375      90     0.61149    1.6515    900     375      90     0.61148    1.6598     750      375       90     0.64116    1.6542     600      375       90     0.72368    1.6438
    1050    375      60     0.61189    1.6309    900     375      60     0.61178    1.6281     750      375       60     0.64117    1.6275     600      375       60     0.74763    1.6568
    1050    300     180      0.6115     1.658    900     300     180     0.61156    1.6513     750      300      180     0.64103    1.6595     600      300      180      0.7119    1.6498

data5 = LD.data5

data5 = 9×6 table
    Var1      Var2        Var3      Var4     Var5       Var6  
    ____    ________    ________    ____    _______    _______

    0.05    0.079527    0.072315    0.05     1.6124     1.4754
     0.1    0.065503    0.060581     0.1     1.7162     1.6127
    0.15    0.056319    0.051083    0.15     1.4266     1.2966
     0.2    0.044239    0.045688     0.2     1.2754     1.1724
    0.25     0.04054    0.042303    0.25     1.1698      1.053
     0.3     0.03749    0.039807     0.3     1.0805    0.97533
    0.35    0.034922    0.037874    0.35     1.0332    0.95852
     0.4    0.033318    0.035683     0.4    0.98215    0.91858
    0.45    0.031876    0.034601    0.45    0.94208    0.85585

x = LD.x

x = 4×1

0.6107 0.6108 0.6107 0.7054

.

[PRASHANT JHA]

yeah, i got it

i just tweaked the code a little bit here are there and got it perfectly

[Star Strider]

Thank you!