Generate bar graph in a for loop

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JC 2020-7-8

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https://ww2.mathworks.cn/matlabcentral/answers/561878-generate-bar-graph-in-a-for-loop

评论： dpb 2020-7-10

So I have data in a 32*1 cell and I would like to plot them on bar graphs. The complicated part is I dont need 32 bar graphs, I would like to group these values in groups of 4. So I would have a total of 8 bar graphs. please help

clc
clear
warning('off','all')
d = dir('*.csv'); 
dm = dir('*.mat');
nm = numel(dm);
n = numel(d);
firewall = cell(32, 1);
firewall_array = cell(32, 1);
Y_Firewall_avg_4_24 = cell(32, 1);
UDT = cell(32, 1);
UDT_array = cell(32, 1);
X_UDT = cell(32, 1);
Y_UDT = cell(32, 1);
 Y_UDT_avg_4_24 = cell(32, 1);
Wan = cell(32, 1);
Wan_array = cell(32, 1);
X_Wan = cell(32, 1);
Y_Wan = cell(32, 1);
Y_Wan_avg_4_24 = cell(32, 1);
X_Firewall = cell(32, 1);
Y_Firewall = cell(32, 1);
Digitized_graphs = cell(32,1);
XDS = cell(32,1);
YDS = cell(32,1);
X_DS = cell(32,1);
Y_DS = cell(32,1);
Y_DS_avg_4_24  = cell(32,1);
y_Bar_4_24  = cell(32,1);
% X = cell(32,1);
% b = cell(32,1);
for k = 1 : 32 
    firewall{k} = readtable(sprintf('Scenario%d.csv', k));
    UDT{k} = readtable(sprintf('udt_scenario_%d.csv',k));
    Wan{k} = readtable(sprintf('WAN_Scenario%d.csv',k));
    
    firewall_array{k} = table2array(firewall{k}(:,[3,5]));
    UDT_array{k} = table2array(UDT{k}(:,[3,4]));
    Wan_array{k} = table2array(Wan{k});
   
    Y_Firewall{k} = [firewall_array{k}(:,1)]; 
    Y_UDT{k} = [UDT_array{k}(:,2)];
    Y_Wan{k} = [Wan_array{k}(:,2)]; 
    Y_Firewall_avg_4_24{k} = mean(Y_Firewall{k});
     Y_Wan_avg_4_24{k} = mean(Y_Wan{k});
    Y_DS_avg_4_24{k} = mean(Y_DS{k});
    Y_UDT_avg_4_24{k} = mean(Y_UDT{k});
bar([Y_Firewall_avg_4_24{k} Y_Wan_avg_4_24{k} Y_DS_avg_4_24{k}  Y_UDT_avg_4_24{k} ; Y_Firewall_avg_4_24{k+1} Y_Wan_avg_4_24{k+1} Y_DS_avg_4_24{k+1}  Y_UDT_avg_4_24{k+1} ])
end

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dpb 2020-7-9

编辑：dpb 2020-7-9

在 MATLAB Online 中打开

Sure, that's also easy if you create arrays instead of individual variables. In Matlab, arrays are the ticket to almost all effective use of the product.

Not having your raw data, and being too lazy to try to build a set I just took your above--

dat=[121 210 449;454 216 500;420,197,454;436 204 512];      % some sample data
sys=categorical({'Firewall','Jones WAN','Server','WAN'});   % categorical array for x axis
hBar=bar(sys,dat);                                          % grouped bar
hTxt=arrayfun(@(hB) text(hB.XEndPoints,hB.YEndPoints,compose('%d',hB.YEndPoints), ...
    'HorizontalAlignment','center','VerticalAlignment','bottom'),hBar,'UniformOutput',false);
hLg=legend(compose('Scenario %d',1:3),'Location','southoutside','Orientation','horizontal');

yields

I'd go back to the reading portion, read each of the raw data files into a large array, the exact form of which would pick based on knowing more about the file content/organization than presently do -- but statstics can be computed by grouping variables or by referencing columns by ranges or overall either by row or by column depending on the organization.

You just need to pick the orientation so that you can generate the summary statistics as arranged above -- groups are rows, columns are variables in group. So the above is four rows by three columns.

We're fortunate if you're also using at least R2019b; prior releases did not expose the X/YEndPoints properties used above -- you had to use an undocumented property whose name I could never recall...have to give kudos to TMW altho it's been 20 years in the making.

Now if they would introduce a 'BarLabel' property to set with all it's associated properties instead of having to write them explicitly...

dpb 2020-7-9

编辑：dpb 2020-7-9

Attach the .csv text files w/ the paperclip -- nobody can do anything with images.

I looked at your original code some more -- there are varying numbers of columns in the different files -- when read w/ readtable you'll get variable names -- is there some list of "who's who in the zoo" as to which should be selected from which file for the purpose of the graphs?

Need to write down the specific ground rules for what is wanted to be done so can write code to match. That's what you need to be able to define which columns count for what.

Ideal would be to zip up the lot so somebody if gets chance can actually solve your problem...

I can see enough to outline code as said but can't test much although the answer to the above Q? regarding the rules on how to know which columns to use for each is important piece of the puzzle.

Remember, we only know what you tell us; we can't know what you know about the problem so how to know what it makes sense to compare is all greek to us...

dpb 2020-7-10

Is the above image an output table/spreadsheet of computed results or the input files? I'm guessing the former as that doesn't really match up with the initial code...but it doesn't seem to make a lot of sense in comparison either with the multiple columns.

I'd be glad to help further if could just figure out what the first-source data really are we're starting from...

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