Join complementary dataframes, no NAs where a value is available

An option would be to do a full_join on 'v1' and then coalesce the 'v2' columns

library(dplyr)
full_join(df1, df2, by = 'v1') %>%
    transmute(v1, v2 = coalesce(v2.x, v2.y))

Here's a simple base solution:

> df3 = df2
> df3$v2 = ifelse(is.na(df1$v2),df2$v2,df1$v2)
> df3
  v1 v2
1  a  1
2  b  2
3  c  3
4  d  4

the ifelse picks the number from one column or the other based on the NA nature of one column.

It doesn't cover the case if both or neither of the source v2 columns are NA but there's none in your sample and you don't mention it...

I am sure that I got this from @akrun but could not find the thread. Posting an answer for future reference:

library(data.table)
setDT(df1)[df2, v2i := i.v2, on='v1'][is.na(v2), v2 := v2i][,v2i:= NULL][]

#>    v1 v2
#> 1:  a  1
#> 2:  b  2
#> 3:  c  3
#> 4:  d  4

^{Created on 2019-05-13 by the reprex package (v0.2.1)}

@Henrik's comment also suggested the following:

setDT(df1)[is.na(v2), v2 := setDT(df2)[.SD, v2, on = .(v1)]]

but I personally prefer the first solution over this mostly because of the sequence of getting the answer (joining dataframes first and replacing NA later).