Update/Replace Values in Dataframe with Tidyverse Join

Question

What is the most efficient way to update/replace NAs in main dataset with (correct) values in a lookup table? This is such a common operation! Similar questions do not seem

Answer 1

There's currently no one-shot for trying to coalesce more than one column (which can be done by using a lookup table approach within ifelse(is.na(value), ..., value)), though there has been discussion of how such behavior may be implemented. For now, you can build it manually. If you've got a lot of columns, you can coalesce programmatically, or even put it in a function.

library(tidyverse)

df1 <- tibble(
    state_abbrev = state.abb[1:10],
    state_name = c(state.name[1:5], rep(NA, 3), state.name[9:10]),
    value = sample(500:1200, 10, replace=TRUE)
)

lookup_df <- tibble(
    state_abbrev = state.abb[6:8],
    state_name = state.name[6:8]
)

df1 %>% 
    full_join(lookup_df, by = 'state_abbrev') %>% 
    bind_cols(map_dfc(grep('.x', names(.), value = TRUE), function(x){
        set_names(
            list(coalesce(.[[x]], .[[gsub('.x', '.y', x)]])), 
            gsub('.x', '', x)
        )
    })) %>% 
    select(union(names(df1), names(lookup_df)))
#> # A tibble: 10 x 3
#>    state_abbrev state_name  value
#>                   
#>  1 AL           Alabama       877
#>  2 AK           Alaska       1048
#>  3 AZ           Arizona       973
#>  4 AR           Arkansas      860
#>  5 CA           California    938
#>  6 CO           Colorado      639
#>  7 CT           Connecticut   547
#>  8 DE           Delaware      672
#>  9 FL           Florida       667
#> 10 GA           Georgia      1142