r - dplyr::mutate to add multiple values

Question

There are a couple of issues about this on the dplyr Github repo already, and at least one related SO question, but none of them quite covers my question -- I think.

• Adding multiple columns in a dplyr mutate call is more or less what I want, but there's a special-case answer for that case (tidyr::separate) that doesn't (I think) work for me.
• This issue ("summarise or mutate with functions returning multiple values/columns") says "use do()".

Here's my use case: I want to compute exact binomial confidence intervals

dd <- data.frame(x=c(3,4),n=c(10,11))
get_binCI <- function(x,n) {
    rbind(setNames(c(binom.test(x,n)$conf.int),c("lwr","upr")))
}
with(dd[1,],get_binCI(x,n))
##             lwr       upr
## [1,] 0.06673951 0.6524529

I can get this done with do() but I wonder if there's a more expressive way to do this (it feels like mutate() could have a .n argument as is being discussed for summarise() ...)

library("dplyr")
dd %>% group_by(x,n) %>%
    do(cbind(.,get_binCI(.$x,.$n)))

## Source: local data frame [2 x 4]
## Groups: x, n
## 
##   x  n        lwr       upr
## 1 3 10 0.06673951 0.6524529
## 2 4 11 0.10926344 0.6920953

Answer 1

Yet another variant, although I think we're all splitting hairs here.

> dd <- data.frame(x=c(3,4),n=c(10,11))
> get_binCI <- function(x,n) {
+   as_data_frame(setNames(as.list(binom.test(x,n)$conf.int),c("lwr","upr")))
+ }
> 
> dd %>% 
+   group_by(x,n) %>%
+   do(get_binCI(.$x,.$n))
Source: local data frame [2 x 4]
Groups: x, n

  x  n        lwr       upr
1 3 10 0.06673951 0.6524529
2 4 11 0.10926344 0.6920953

Personally, if we're just going by readability, I find this preferable:

foo  <- function(x,n){
    bi <- binom.test(x,n)$conf.int
    data_frame(lwr = bi[1],
               upr = bi[2])
}

dd %>% 
    group_by(x,n) %>%
    do(foo(.$x,.$n))

...but now we're really splitting hairs.