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.

Answer 2

Yet another option could be to use the purrr::map family of functions.

If you replace rbind with dplyr::bind_rows in the get_binCI function:

library(tidyverse)  dd <- data.frame(x = c(3, 4), n = c(10, 11)) get_binCI <- function(x, n) {   bind_rows(setNames(c(binom.test(x, n)$conf.int), c("lwr", "upr"))) } 

You can use purrr::map2 with tidyr::unnest:

dd %>% mutate(result = map2(x, n, get_binCI)) %>% unnest()  #>   x  n        lwr       upr #> 1 3 10 0.06673951 0.6524529 #> 2 4 11 0.10926344 0.6920953 

Or purrr::map2_dfr with dplyr::bind_cols:

dd %>% bind_cols(map2_dfr(.$x, .$n, get_binCI))  #>   x  n        lwr       upr #> 1 3 10 0.06673951 0.6524529 #> 2 4 11 0.10926344 0.6920953