[R] creating a contingency table from a data.frame automatically (NOT BY HAND)

[Rui Barradas]

Hello,

Try the following to format nicely.


n <- 1e2
hunting.prev <- sample(c("success","fail"), n, TRUE)
groupsize <- sample(c("small", "medium", "large"), n, TRUE)
dog <- sample(c("yes", "no"), n, TRUE)
guns <- sample(c("yes", "no"), n, TRUE)

ftable(hunting.prev, groupsize, dog, guns)

As for the tests, that dependes on your needs, see the respective help 
pages. An example would be

chisq.test( table(hunting.prev, dog) )

Hope this helps,

Rui Barradas

Em 10-08-2012 11:48, Sacha Viquerat escreveu:
> Hello there!
> I am still struggling with a binomial response over all categorical 
> variables (some of them with 3 levels, most with 2 levels). After 
> initial struggles with glm's (struggle coming from the data, not the 
> actual analysis) I have decided to prefer contingency tables. I have 
> my data such as:
>
> response:
> hunting.prev=c("success","fail","success","success","success","fail",...)
>
> one of 21 surveyed variables:
> groupsize=c("small","large","small","small","small","large"...)
> ...
>
> now...
> It is intuitive to me that I will have to split up each variable by 
> its level(s), thus creating 2 new variables for groupsize (as an 
> example) holding the counts of small hunting parties when the 
> hunting.prev was a success and so on. I could write a function to do 
> that for me, however, never intend to reinvent the wheel. I would like 
> my data to look like that:
>
> hunting prev    groupsize-small    groupsize-large    dogs-yes 
> dogs-no    guns-yes    guns-no...
> success    12    2    4    14    23    12...
> failure    1    6    34    0    12    3...
>
> of course, hunting.prev would only be needed to create the index via 
> hunting.prev=="success" and is here used to indicate what each row 
> means. My questions would be:
>
> a) how to count and split each categorical variable by a response 
> variable, how to create a 2x20something (contingency) table and how 
> far a prop.test() approach or a chi² may be more appropriate to 
> actually analyze the data.
>
> b) how do you guys create R output so that it's formatted in nice 
> columns and rows?
>
> Hope to see help,
> Thanks!
>
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