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

Here are some possibilities with rowwise and nesting.

library("dplyr")
library("tidyr")

data frame with repeated x/n combinations, for fun

dd <- data.frame(x=c(3, 4, 3), n=c(10, 11, 10))

a versions of the CI function that returns a data frame, like @Joran's

get_binCI_df <- function(x,n) {
  binom.test(x, n)$conf.int %>% 
    setNames(c("lwr", "upr")) %>% 
    as.list() %>% as.data.frame()
}

Grouping by x and n as before, removes the duplicate.

dd %>% group_by(x,n) %>% do(get_binCI_df(.$x,.$n))
# # A tibble: 2 x 4
# # Groups:   x, n [2]
#       x     n       lwr       upr
#   <dbl> <dbl>     <dbl>     <dbl>
# 1     3    10 0.1181172 0.8818828
# 2     4    11 0.1092634 0.6920953

Using rowwise keeps all the rows but removes x and n unless you put them back using cbind(. (like Ben does in his OP).

dd %>% rowwise() %>% do(cbind(., get_binCI_df(.$x,.$n)))
# Source: local data frame [3 x 4]
# Groups: <by row>
#   
# # A tibble: 3 x 4
#       x     n        lwr       upr
# * <dbl> <dbl>      <dbl>     <dbl>
# 1     3    10 0.06673951 0.6524529
# 2     4    11 0.10926344 0.6920953
# 3     3    10 0.06673951 0.6524529

It feels like nesting could work more cleanly, but this is as good as I can get. Using mutate means I can use x and n directly instead of .$x and .$n, but mutate expects a single value, so it needs to be wrapped in list.

dd %>% rowwise() %>% mutate(ci=list(get_binCI_df(x, n))) %>% unnest()
# # A tibble: 3 x 4
#       x     n        lwr       upr
#   <dbl> <dbl>      <dbl>     <dbl>
# 1     3    10 0.06673951 0.6524529
# 2     4    11 0.10926344 0.6920953
# 3     3    10 0.06673951 0.6524529

Finally, looks like something like this is an open issue (as of 5 Oct 2017) for dplyr; see https://github.com/tidyverse/dplyr/issues/2326; if something like that is implemented then that will be the easiest way!

Answer 3

This uses a "standard" dplyr workflow, but as @BenBolker notes in the comments, it requires calling get_binCI twice:

dd %>% group_by(x,n) %>%
  mutate(lwr=get_binCI(x,n)[1],
         upr=get_binCI(x,n)[2])

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

Answer 4

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

Answer 5

Here's a quick solution using data.table package instead

First, a little change to the function

get_binCI <- function(x,n) as.list(setNames(binom.test(x,n)$conf.int, c("lwr", "upr")))

Then, simply

library(data.table)
setDT(dd)[, get_binCI(x, n), by = .(x, n)]
#    x  n        lwr       upr
# 1: 3 10 0.06673951 0.6524529
# 2: 4 11 0.10926344 0.6920953