Three factor contingency table in R

Question

I am looking to construct a contingency table for three main effects. These are crime, gender and prior conviction. The response variable is whether or not a lenient sentence was granted.

This is the best I have so far.

     Crime Gender Priorconv Yes No
1      Shoplifting    Men         N  24  1
2 Other Theft Acts    Men         N  52  9
3      Shoplifting  Women         N  48  3
4 Other Theft Acts  Women         N  22  2
5      Shoplifting    Men         P  17  6
6 Other Theft Acts    Men         P  60 34
7      Shoplifting  Women         P  15  6
8 Other Theft Acts  Women         P   4  3

which was created by the following code

table1<-expand.grid(Crime=factor(c("Shoplifting","Other Theft Acts")),Gender=factor(c("Men","Women")),
Priorconv=factor(c("N","P")))

table1<-data.frame(table1,Yes=c(24,52,48,22,17,60,15,4),No=c(1,9,3,2,6,34,6,3))

Unfortunately this is not very elegant and so I was wondering if there is another way to present the data more clearly.

Thank you.

Answer 1

For contingency you can use sample operator and put it inside function to change the number of strings like

factory <- function(i) {
    crime <- sample(c("Shoplifting","Other Theft Acts"),i, replace = TRUE)
    gender <- sample(c("Men","Women"),i,replace = TRUE)
    priorconv <- sample(c("P","N"),i, replace = TRUE)
    table <- data.frame(crime,gender,priorconv)
    return(table)
}
table1 <- factory(20)

result:

              crime gender priorconv
1       Shoplifting    Men         N
2       Shoplifting  Women         P
3  Other Theft Acts    Men         P
4       Shoplifting    Men         P
5  Other Theft Acts  Women         N
6       Shoplifting  Women         N
7       Shoplifting  Women         P
8       Shoplifting    Men         P
9  Other Theft Acts  Women         P
10      Shoplifting    Men         P
11 Other Theft Acts    Men         N
12 Other Theft Acts    Men         P
13      Shoplifting    Men         P
14      Shoplifting  Women         N
15 Other Theft Acts    Men         N
16 Other Theft Acts    Men         P
17 Other Theft Acts  Women         P
18      Shoplifting  Women         P
19 Other Theft Acts    Men         N
20      Shoplifting  Women         N