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proportion of gender having health issue

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I have a table that has Gender vector (1=Boy, 2=Girl) and Healtissue (based on score). I would like to know the proportion of girls only have a health issue if their score more than 17?

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dpb
dpb on 31 May 2020
Look at findgroups, groupsummary with the Subject "Split-Apply-Combine" workflow under splitapply
Many examples...

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Accepted Answer

Image Analyst
Image Analyst on 31 May 2020
Assume t is your table and you have a column for gender, a column with HealthScore (true or false), and a column for their score (continously-valued number). Then try
% Find rows with girls who have a score more than 17.
rows1 = (t.Gender == 2) & (t.Score > 17)
% Now find girls with a score more than 17 who ALSO have a health issue (may not be 100% - may only be part of them).
rows2 = (t.Gender == 2) & (t.Score > 17) & t.HealthIssue % Assuming this is a logical column.
proportion = rows2 / rows1

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Doaa Alamoudi
Doaa Alamoudi on 31 May 2020
Great that give good out put
now if you want to compare the result of boys who are under the smae condition with the girls who have same problem how can I do that?
Image Analyst
Image Analyst on 31 May 2020
I'd do
maleRows = (sex == 1); % To get male students only.
femaleRows = (sex == 2); % To get female students only.
Doaa Alamoudi
Doaa Alamoudi on 31 May 2020
I rewrite based on your suggestion and I think it looks bettern now. However I have problem in creating figure showing the result
score=sdq;
BoyGender= (sex == 1);
GirlGender=(sex==2);
BoysMentalHealthProblem = sum(score > 17 & BoyGender)
GirlsMentalHealthProblem = sum(score > 17 & GirlGender)

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More Answers (1)

dpb
dpb on 31 May 2020
Some other things to explore...
hScore=randi(100,100,1)/4; % An artificial dataset...
sex=(rand(size(hScore))>0.5)+1; % 50:50 roughly
tMH=table(sex,hScore,'VariableNames',{'Gender','HealthIssue'}); % make a table
tMH.Gender=categorical(tMH.Gender,[1,2],{'Boy','Girl'}); % turn into categorical instead
tMH.AtRisk=categorical(tMH.HealthIssue>17); % compute the risk factor
heatmap(tMH,'Sex','AtRisk') % one way to look at results...
Results will vary for a random sample, but for the particular dataset generated here:
>> groupsummary(tMH,'Gender')
ans =
2×2 table
Gender GroupCount
______ __________
Boy 51
Girl 49
>> groupsummary(tMH,'AtRisk')
ans =
2×2 table
AtRisk GroupCount
______ __________
false 68
true 32
>> groupsummary(tMH,{'Gender','AtRisk'})
ans =
4×3 table
Gender AtRisk GroupCount
______ ______ __________
Boy false 37
Boy true 14
Girl false 31
Girl true 18
>>
You can compute percentages from the GroupCounts depending upon whether want by Gender or overall...the above produces

  1 Comment

Doaa Alamoudi
Doaa Alamoudi on 31 May 2020
That was Great answer
Thanks for your help

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