This will work if you are using matrices (if you have data frames, convert
them to matrix):
 > table1
q1 q3 q4 q8 q9
A 5 2 0 1 3
B 2 0 2 4 4
> table2
q1 q2 q3 q4 q5 q6 q7 q8 q9
C 10 7 4 2 6 9 3 1 2
> index <- match(colnames(table2), colnames(table1), nomatch=0)
> t(t(table1[,index]) / table2[index != 0, drop=FALSE])
q1 q3 q4 q8 q9
A 0.5 0.5 0 1 1.5
B 0.2 0.0 1 4 2.0


 On 11/12/05, Claus Atzenbeck <claus.atzenbeck@freenet.de> wrote:
>
> Hello,
>
> I have a table (1) of the form
>
> q1 q3 q4 q8 q9
> A 5 2 0 1 3
> B 2 0 2 4 4
>
> I have another table (2):
>
> q1 q2 q3 q4 q5 q6 q7 q8 q9
> C 10 7 4 2 6 9 3 1 2
>
> I would like to divide the numbers in table (1) by the number of the
> appropriate column in table (2):
>
> q1 q3 q4 q8 q9
> A 5/10 2/4 0/2 1/1 3/2
> B 2/10 0/4 2/2 4/1 4/2
>
> The result would look lie this:
>
> q1 q3 q4 q8 q9
> A 0.5 0.5 0 1 1.5
> B 0.2 0 1 4 2
>
> BACKGROUND: I have a data frame with measured times for answering
> questions. I want to know how many PERCENT of the answers are wrong,
> caused by reason A or B.
>
> This gives me the subset of false answers. The table looks like table (1):
>
> fail <- subset(questions, type=="wrong")
> fail$qid <- factor(fail$qid)
> failtab <- table(fail$failtype, fail$qid)
>
> The following gives me information about how often a specific question
> was asked. This is similar to table (2) above.
>
> count <- table(questions$failtype, questions$qid)
> count <- colSums(count)
>
> One solution would be to delete the line that calls factor(...) on the
> subset and calculate failtab/count. However, then I have the problem
> that I have to get rid of all columns of the table that have '0' in all
> rows.
>
> Thanks for any hint.
> Claus
>
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Jim Holtman
Cincinnati, OH
+1 513 247 0281

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