Hi Eik,
You're absolutely right. My bad.
Here is the correction of the code I sent:
apply(mydata[,-1], 2, tapply, mydata[,1], function(x) sum(x)/length(x))
Thanks for pointed this out.
Best regards,
Jorge
On Tue, Apr 7, 2009 at 5:29 PM, Eik Vettorazzi <
e.vettora...@uke.uni-hamburg.de> wrote:
> Hi Jorge,
> > apply(mydata[,-1], 2, tapply, mydata[,1], function(x)
> sum(x)/nrow(mydata))
> will calculate the total percentages of "yes" for each sex relative to the
> total size, not within sex.
>
> with your data construction, the following should work
>
> #count "yes" for each sex and question
> cnts<-aggregate(mydata[,-1],by=list(mydata[,1]),FUN=sum)
> #count gender as denumerators
> nums<-table(mydata[,1])
> cbind(sex=cnts[,1],cnts[,-1]/nums)
>
> #simple check that the solution is ok
> prop.table(table(sex=mydata[,"sex"],q1=mydata[,"q1"]),1)
> prop.table(table(sex=mydata[,"sex"],q2=mydata[,"q2"]),1)
>
>
>
>
> Jorge Ivan Velez schrieb:
>
>
> Dear Donald,
>> Assuming that your data is called "mydata", the first column represents
>> the
>> gender and the questions are the columns left, something like the
>> following
>> should do the job:
>>
>> # Some data
>> set.seed(1)
>> mydata <- matrix(rbinom(1000,1,p=0.5),ncol=10)
>> colnames(mydata)<-c('sex',paste('q',1:9,sep=""))
>>
>> # What you asked -- hopefully ;)
>> apply(mydata[,-1], 2, tapply, mydata[,1], function(x) sum(x)/nrow(mydata))
>>
>> HTH,
>>
>> Jorge
>>
>>
>> On Tue, Apr 7, 2009 at 4:33 PM, Donald Braman <dbra...@law.gwu.edu>
>> wrote:
>>
>>
>>
>>> I've been playing around with various table tools, trying to construct a
>>> fairly simple cross-tab. It shouldn't be hard, but for some reason it
>>> turning out to be (for me).
>>>
>>> If I want to see how many men and how many women agree with a
>>> agree/disagree
>>> question (coded 1,0), I can do this:
>>>
>>>
>>>
>>>> attach(mydata)
>>>> mytable <- table(male, q1.bin) # gender and a binary response variable
>>>> prop.table(mytable, 1) # row percentages
>>>>
>>>>
>>> q1.bin
>>> male 0 1
>>> 0 0.3988 0.6012
>>> 1 0.2879 0.7121
>>>
>>> I can repeat that for each of the items I want gender breakdowns for (q2,
>>> q3, q4 ....). But what I really want is a table that shows the
>>> percentage
>>> answering yes (coded as 1) across many, many binary response items.
>>> E.g.,
>>>
>>>
>>> male q1.bin q2.bin q3.bin ...
>>> 0 0.6012 0.3421 0.9871 ...
>>> 1 0.7121 0.6223 0.0198 ...
>>>
>>> I've tried various combinations of apply & cbind, but to no avail. It
>>> would
>>> be easy in SPSS crosstabs, but darnit, I want to use R!
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help@r-project.org 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.
>>>
>>>
>>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@r-project.org 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.
>>
>>
>
>
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