R中的三因素列联表

我希望为三个主要影响建立一个列联表。这些是犯罪、性别和先前定罪。反应变量是是否准予从宽判决

这是我目前为止最好的一次

     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

它是由以下代码创建的

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))

不幸的是，这不是很优雅，因此我想知道是否有其他方法可以更清楚地呈现数据

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谢谢。

对于意外情况，您可以使用示例运算符并将其放入函数中，以更改字符串的数量，如

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)

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可能会调查XTAB和/或ftable@DominicComtois我喜欢xtabs的工作。我试着用xtabscbindYes，No~犯罪+性别+Priorconv，data=table1Glad to know！也许你可以把代码和输出作为你自己问题的答案。我相信这对你这种情况下的其他人是有用的。这里的表格有两个条目，不是吗？

              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