Iterate over data frame by elements of a list

I am trying to merge two data frames based on a subset on common IDs. Let me demonstrate:

library(tidyverse)
set.seed(42)

df = list(id = c(1,2,3,4,1,2,2,2,1,1),
       group = c("A","A","A","A","B","B","B","B","C","C"),
       val = c(round(rnorm(10,6,6),0))
       ) %>% 
        tbl_df()

df_na = list(id = c(1,1,1,2,3,3,4,5,5,5),
       group = c(rep(NA,10)),
       val = c(rep(NA,10))
       ) %>% 
        tbl_df()

df contains data and ids, while df_na only contains ids and NAs. I would like to create a combined data frame that contains all the information of df and add the NAs by group and id, i.e. for each group in df find which ids are present in both df and df_na and merge.

If I was doing this manually, i.e. group for group, I would use something like this:

A_dist = df %>% filter(group=="A") %>%
  distinct(id) %>%
  pull() 

df_A_comb = df_na %>% 
  filter(id %in% A_dist) %>%
  bind_rows(filter(df, group=="A"))

# A tibble: 11 x 3
      id group   val
   <dbl> <chr> <dbl>
 1     1 NA       NA
 2     1 NA       NA
 3     1 NA       NA
 4     2 NA       NA
 5     3 NA       NA
 6     3 NA       NA
 7     4 NA       NA
 8     1 A        14
 9     2 A         3
10     3 A         8
11     4 A        10

But obvioulsy, I would rather automate this. As an emerging fan of the tidyverse, I'm trying to get my head around purrr::map. I can create a vector of ids for each group.

df_dist = df %>% 
        split(.$group) %>% 
        map(distinct, id) %>% 
        map("id")

> df_dist
$A
[1] 1 2 3 4

$B
[1] 1 2

$C
[1] 1

But translating my dplyr approach is more complicated and produces an error message earlier on.

###this approach doesn't work...
df_comb = df_na %>% 
        map(filter, id %in% df_dist)# %>% 
        ...

Error in UseMethod("filter_") : no applicable method for 'filter_' applied to an object of class "c('double', 'numeric')"

Any help will be much appreaciated!