Does this do what you want?

> x <- "  vara varb S  PC
+ 1  None  250 1  80
+ 2  None  250 1  70
+ 3  Some  250 1  60
+ 4  Some  250 1  70
+ 5  None 1000 1  90
+ 6  None 1000 1  90
+ 7  Some 1000 1  80
+ 8  Some 1000 1  70
+ 9  None  250 2 100
+ 10 None  250 2  80
+ 11 Some  250 2  70
+ 12 Some  250 2  70
+ 13 None 1000 2 100
+ 14 None 1000 2  90
+ 15 Some 1000 2  50
+ 16 Some 1000 2  40"
> x.in <- read.table(textConnection(x), header=TRUE)
> (x.agg <-  aggregate(x.in$PC, list(vara=x.in$vara, varb=x.in$varb, S=
x.in$S), mean))
  vara varb S  x
1 None  250 1 75
2 Some  250 1 65
3 None 1000 1 90
4 Some 1000 1 75
5 None  250 2 90
6 Some  250 2 70
7 None 1000 2 95
8 Some 1000 2 45



On 3/15/07, Christopher Brown <c-b@asu.edu> wrote:
>
> Hi,
>
> I have a data set that looks like this:
>
> > data
>    vara varb S  PC
> 1  None  250 1  80
> 2  None  250 1  70
> 3  Some  250 1  60
> 4  Some  250 1  70
> 5  None 1000 1  90
> 6  None 1000 1  90
> 7  Some 1000 1  80
> 8  Some 1000 1  70
> 9  None  250 2 100
> 10 None  250 2  80
> 11 Some  250 2  70
> 12 Some  250 2  70
> 13 None 1000 2 100
> 14 None 1000 2  90
> 15 Some 1000 2  50
> 16 Some 1000 2  40
>
> ...
>
> And so on. The last column is the dependent variable, and I have made
> the other columns factors. As you can see, there are multiple scores for
> each subject in each combination of conditions. How can I reduce the
> dataset so that there is only 1 score per subject, per condition, for
> further analysis? I can use tapply to get means, but I need a data.frame
> for analysis (aov). Any ideas?
>
> --
> Chris
>
> ______________________________________________
> R-help@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Jim Holtman
Cincinnati, OH
+1 513 646 9390

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