Hi:
Perhaps the plyr package would be useful. It contains functions colwise(),
numcolwise() and
catcolwise() that will perform the same operation on the stated type of
object. In this case,
numcolwise() is appropriate:
> str(basicSub)
'data.frame': 5 obs. of 3 variables:
$ student: num 1 2 3 3 1
$ gender : Factor w/ 2 levels "f","m": 2 2 1 1 2
$ score : num 50 60 70 65 60
> basicSub$student <- factor(basicSub$student) # convert student to factor
> library(plyr)
# First argument is data frame, the next is the grouping variable, the
# third is the function to apply.
> ddply(basicSub, .(student), numcolwise(mean))
student score
1 1 55.0
2 2 60.0
3 3 67.5
HTH,
Dennis
On Sun, Jan 3, 2010 at 7:46 PM, david hilton shanabrook <
dhsha...@acad.umass.edu> wrote:
> I want to use aggregate with the mean function on specific columns
>
> gender <- factor(c("m", "m", "f", "f", "m"))
> student <- c(0001, 0002, 0003, 0003, 0001)
> score <- c(50, 60, 70, 65, 60)
> basicSub <- data.frame(student, gender, score)
> basicSubMean <- aggregate(basicSub, by=list(basicSub$student), FUN=mean,
> na.rm=TRUE)
>
> This doesn't work, one cannot take the mean of a factor (gender). Is there
> any way of specifying which columns to use for the mean? I want to
> aggregate by student, obtaining mean scores, and assume any other factors
> are unchanging in a specific student, ie. gender.
>
> Thanks
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>
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