[R] hairy indexing problem

[Ian.Saunders@csiro.au]

There's probably a better way, but ...

	apply(outer(subject,subject,FUN="=="),1,sum)

will give you a vector of the counts for each value of subject, so would be 
	2 2 4 4 4 4 2 2 ...
in your example.

You could add this as a column of the data frame and use gsummary to get the
summary statistics.

Ian.

> -----Original Message-----
> From: Russell Senior [mailto:seniorr at aracnet.com]
> Sent: Wednesday, 5 June 2002 10:31 AM
> To: R-help at stat.math.ethz.ch
> Subject: [R] hairy indexing problem
> 
> 
> 
> I've got a data frame that looks like this:
> 
>    subject   foo   bar
>       2      1.7   3.2
>       2      2.3   4.1
>       3      7.6   2.3
>       3      7.1   3.3
>       3      7.3   2.3
>       3      7.4   1.3
>       5      6.2   6.1
>       5      3.4   6.9
>      ...
> 
> That is, I've got multiple rows per subject.  I need to compute
> summaries within categories where the subject has the same number of
> rows.  For example, subject 2 and 5 both have two rows.  I need to
> compute mean for those four values of foo.  This looks like a good
> candidate for index vectors, but I need some help.  I've tried
> something like:
> 
>   table(data) -> tmp
>  
> and:
> 
>   tmp[tmp == 2]
> 
> and even:
> 
>   as.numeric(attr(tmp[tmp == 2],"names"))
> 
> to get a vector of subject numbers that have two rows in the original
> data frame.  But I am getting stuck there.  I want some kind of
> "is.member" function to use in a subsequent index vector expression,
> like:
> 
>   i <- as.numeric(attr(tmp[tmp == 2],"names"))
>   data[is.member($subject,i)]$foo
> 
> but there isn't an is.member() function.  Can someone please give me a
> pointer on the canonical way to do this?
> 
> Thanks!
> 
> -- 
> Russell Senior         ``The two chiefs turned to each other.        
> seniorr at aracnet.com      Bellison uncorked a flood of horrible       
>                          profanity, which, translated meant, `This is
>                          extremely unusual.' ''                      
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