Hi Trever,
You can do it like this:
count <- function(x) {
length(na.omit(x))
}
counts <-
data.frame(aggregate(mydata[,4:5],by=list(mydata$userid),FUN="count"))
-Ista
On Fri, Apr 16, 2010 at 10:35 AM, KDT <dkaden...@gmail.com> wrote:
>
> Dear R-Users,
> I have a big data set "mydata" with repeated observation and some missing
> values. It looks like the format below:
>
> userid sex item score1 score2
> 1 0 1 1 1
> 1 0 2 0 1
> 1 0 3 NA 1
> 1 0 4 1 0
> 2 1 1 0 1
> 2 1 2 NA 1
> 2 1 3 1 NA
> 2 1 4 NA 0
> 3 0 1 1 0
> 3 0 2 1 NA
> 3 0 3 1 0
> 3 0 4 0 0
>
> I wound like to summarise the dataset such that i get something in the
> format of
>
> userid sumscore1 countscore1 meanscore1 sumscore2 countscore2
> meanscore2
> 1 2 3 0.67 3
> 4 0.75
> 2 1 2 0.5 2
> 3 0.67
> 3 3 4 0.75 0
> 3 0.00
>
> I tried using :
> means <- data.frame(aggregate(mydata[,
> 4:5],by=list(mydata$userid),FUN="mean", na.rm="TRUE"))
> and
> sums <- data.frame(aggregate(mydata[,
> 4:5],by=list(mydata$userid),FUN="sum",
> na.rm="TRUE"))
>
> so that i could merge the two data.frames later. This works quite okay but
> i
> still can not get a function that can give me a data.frame for the counts!!
> Something like this::
> counts <- data.frame(aggregate(mydata[,
> 4:5],by=list(mydata$userid),FUN="count", na.rm="TRUE")).
>
> Any advice?
>
> Trevor
> Belgium
>
> --
> View this message in context:
> http://n4.nabble.com/score-counts-in-an-aggregate-function-tp2007152p2007152.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Ista Zahn
Graduate student
University of Rochester
Department of Clinical and Social Psychology
http://yourpsyche.org
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