Construct dataframe columns based on a function of other columns in R - r

Question

I am trying to figure out a way to do this in R, ideally with something in the apply() family of functions (i.e. not with a for loop).
I want to use a function based on four other columns in my data frame and I want to save the results of that function in three new columns of the data frame.
For example if I have (with test data):
x <- c("var1","var2","var3","var4")
A_x <- c(5,4,3,2)
A_notx <- c(5,6,7,8)
B_x <- c(10,10,5,15)
B_notx <- c(10,10,15,5)
example <- data.frame(A_x,A_notx,B_x,B_notx)
rownames(example) <- x
A_x A_notx B_x B_notx
var1 5 5 10 10
var2 4 6 10 10
var3 3 7 5 15
var4 2 8 15 5
And I want to use oddsratio() from the epitools library on these counts, how could I save the odds ratio as well as the upper and lower bounds as 3 new columns? I would like example$odds, example$upper, and example$lower to exist in my dataframe.
I have messed around a bit with apply() and and by() but can't seem to figure it out. With apply it changes the structure of the row from data frame to matrix, and it is outside of the scope of the function to set column values within the function. Perhaps this whole thing is better served by a list object than a data frame? In the end I want to have all the information on hand (counts, statistics, etc.) for a given variable name, with a variable in each column.

Answer 1

Perhaps this is what you're looking for?
example <- cbind(example,
t(apply(example,1,function(x){
oddsratio(as.table(rbind(x[1:2],x[3:4])))$measure[2,]
}
)))
example
A_x A_notx B_x B_notx estimate lower upper
var1 5 5 10 10 0.99999998 0.20537812 4.8690679
var2 4 6 10 10 0.68116864 0.13043731 3.2586139
var3 3 7 5 15 1.28836297 0.20019246 7.2563905
var4 2 8 15 5 0.09603445 0.01039156 0.5446693