How do I pull out from a table, the total mean of one item based on a particular condition (for several subjects), and then compute the difference in mean values?

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Monique Chatterton
Monique Chatterton 2021-10-22
评论： Monique Chatterton 2021-10-23
I have a large table of data (example below).
For each "Subject", I need to determine 1) the total mean value of all "Item As" that are 'High' in "Condition Z", and 2) the total mean value of all "Item As" that are 'Low' in "Condition Z". For example, Subject 1 the answers would be: 25 (i.e. The mean for Item A for 'High' "Condition Z" items); and 50 (i.e. The mean for Item A for 'Low' "Condition Z" items)
I then need to generate a table that, for each "Subject", lists the difference in these two values as follows: ("total mean value of all "Item As" that are 'High' in "Condition Z"") - ("the total mean value of all "Item As" that are 'Low' in "Condition Z"). Thank you very much!
Subject Item A Condition Z Restudied Class
1 30 High Y A
1 20 High N B
1 45 Low N A
1 55 Low Y B
2 17 High Y A
2 45 Low N B
2 13 High Y A
3 56 High Y A
3 12 Low N B
3 34 Low N B
3 15 High N A

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Dave B
Dave B 2021-10-22
编辑：Dave B 2021-10-22
Ran in:
You can do the first part with groupsummary
Subject = [1 1 1 1 2 2 2 3 3 3 3]';
ItemA=[30 20 45 55 17 45 13 56 12 34 15]';
ConditionZ=["High" "High" "Low" "Low" "High" "Low" "High" "High" "Low" "Low" "High"]';
t=table(Subject,ItemA,ConditionZ)
t = 11×3 table
Subject ItemA ConditionZ _______ _____ __________ 1 30 "High" 1 20 "High" 1 45 "Low" 1 55 "Low" 2 17 "High" 2 45 "Low" 2 13 "High" 3 56 "High" 3 12 "Low" 3 34 "Low" 3 15 "High"
tsummary=groupsummary(t,["Subject" "ConditionZ"],"mean","ItemA")
tsummary = 6×4 table
Subject ConditionZ GroupCount mean_ItemA _______ __________ __________ __________ 1 "High" 2 25 1 "Low" 2 50 2 "High" 2 15 2 "Low" 1 45 3 "High" 2 35.5 3 "Low" 2 23
You might be tempted to do the second part by just subtracting tsummary.mean_ItemA(tsummary.ConditionZ=="High") - tsummary.mean_ItemA(tsummary.ConditionZ=="low")...but i think a nicer solution is to make two separate tables and join them which will make sure that all of the indices line up.
thigh = tsummary(tsummary.ConditionZ=="High",:)
thigh = 3×4 table
Subject ConditionZ GroupCount mean_ItemA _______ __________ __________ __________ 1 "High" 2 25 2 "High" 2 15 3 "High" 2 35.5
tlow = tsummary(tsummary.ConditionZ=="Low",:)
tlow = 3×4 table
Subject ConditionZ GroupCount mean_ItemA _______ __________ __________ __________ 1 "Low" 2 50 2 "Low" 1 45 3 "Low" 2 23
tdiff=join(tlow,thigh,'Keys','Subject')
tdiff = 3×7 table
Subject ConditionZ_tlow GroupCount_tlow mean_ItemA_tlow ConditionZ_thigh GroupCount_thigh mean_ItemA_thigh _______ _______________ _______________ _______________ ________________ ________________ ________________ 1 "Low" 2 50 "High" 2 25 2 "Low" 1 45 "High" 2 15 3 "Low" 2 23 "High" 2 35.5
tdiff.difference = tdiff.mean_ItemA_thigh - tdiff.mean_ItemA_tlow
tdiff = 3×8 table
Subject ConditionZ_tlow GroupCount_tlow mean_ItemA_tlow ConditionZ_thigh GroupCount_thigh mean_ItemA_thigh difference _______ _______________ _______________ _______________ ________________ ________________ ________________ __________ 1 "Low" 2 50 "High" 2 25 -25 2 "Low" 1 45 "High" 2 15 -30 3 "Low" 2 23 "High" 2 35.5 12.5
tdiff=tdiff(:,[1 end]) % just for display, no reason to eliminate the intermediate columns
tdiff = 3×2 table
Subject difference _______ __________ 1 -25 2 -30 3 12.5
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dpb
dpb 2021-10-23
Yeah, this (relatively) recent penchant to introduce so many (almost) overlapping functionalities in common toolboxes or even in base product is maddening in causing so much overhead and bloat in the dictionary space; much of which then ends up being deprecated but never going away. The early incarnation of the table as the Statistics TB dataset and its implementation of a different categorical class is a prime example.
While such were indeed needed, it would have been far better for the TB and the base product to have been integrated together even if it meant delaying some TB functionality for a while.
The seeming lack of an overall comprehensive design and direction is becoming a real issue in my mind; the disparate user interfaces and inconsistencies continue to multiply.
$0.02, imo, ymmv, etc., etc., ...

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dpb
dpb 2021-10-22
tMEANS=rowfun(@mean,tData,'GroupingVariables'{'ConditionZ'},'InputVariables','ItemA','OutputVariableNames','GroupedMeans');
See the doc for all the skinny on grouping variables and rowfun and friends...you might also find groupsummary of interest.

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