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

[Sacha Viquerat]

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!