MATLAB - Working with accumarray with multiple categories? - matlab

Question

I am asking a follow-up my question here, in which there was a perfect solution that did exactly what I wanted. But I'm wondering how to apply this method, or do something similar, if instead of yes/no as possible responses, I would have more than 2 responses, so yes/no/maybe, for example. Or how it would generalize to 3+ responses.
This is the answer, reformatted as my question:
Assuming my data looks like this:
responses = categorical(randi(3,1250,1),[1 2 3],{'no','yes','maybe'});
race = categorical(randi(5,1250,1),1:5,{'Asian','Black','BHispanic','White','WHispanic'});
I would like to go through and do the same thing with my yes/no data, but do this with 3 possibilities, or more. And this will not end up working anymore:
% convert everything to numeric:
yn = double(responses);
rac = double(race);
% caluculate all frequencies:
data = accumarray(rac,yn-1);
data(:,2) = accumarray(rac,1)-data;
% get the categories names:
races = categories(race);
answers = categories(responses);
% plotting:
bar(data,0.4,'stacked');
ax = gca;
ax.XTickLabel = races; % set the x-axis ticks to the race names
legend(answers) % add a legend for the colors
colormap(lines(3)) % use nicer colors (close to your example)
ylabel('YES/NO/MAYBE')% set the y-axis label
% some other minor fixes:
box off
ax.YGrid = 'on';
I'm not sure if there is even a way to use the accumarray method to do this, as it doesn't make sense from my understanding to use this with 3 possible responses. I'd like to generalize it to n possible responses too.
UPDATE: I'm currently investigating the crosstab feature which I didn't find at all until now! I think this may be the feature I'm looking for.

Answer 1

Here is a generalized version:
% the data (with even more categories):
yesno = categorical(randi(4,1250,1),1:4,{'no','yes','maybe','don''t know'});
race = categorical(randi(5,1250,1),1:5,{'Asian','Black','BHispanic','White','WHispanic'});
% convert everything to numeric:
yn = double(yesno);
rac = double(race);
% caluculate all frequencies:
data = accumarray([rac yn],1);
% get the categories names:
races = categories(race);
answers = categories(yesno);
% plotting:
bar(data,0.4,'stacked');
ax = gca;
ax.XTickLabel = races; % set the x-axis ticks to the race names
legend(answers) % add a legend for the colors
colormap(lines(numel(answers))) % use pretier colors
ylabel('YES/NO')% set the y-axis lable
% some other minor fixes:
box off
ax.YGrid = 'on';
The result:
And in a table:
T = array2table(data.','VariableNames',races,'RowNames',answers)
the output:
T =
Asian Black BHispanic White WHispanic
_____ _____ _________ _____ _________
no 58 72 69 66 62
yes 58 53 72 54 58
maybe 63 62 67 62 61
don't know 58 57 66 58 74
As you already mentioned, you can use crosstab for the same task. crosstab(rac,yn) will give you the same result as accumarray([rac yn],1). I think accumarray is faster, though I didn't check it.