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

If the abbreviation column is complete and the lookup table is complete, could you just drop the state_name column and then join?

left_join(df1 %>% select(-state_name), lookup_df, by = 'state_abbrev') %>% 
  select(state_abbrev, state_name, value)

Another option could be to use match and if_else in a mutate call using the built in state name and abbreviation lists:

df1 %>% 
  mutate(state_name = if_else(is.na(state_name), state.name[match(state_abbrev,state.abb)], state_name))

Both give the same output:

# A tibble: 10 x 3
   state_abbrev state_name  value
                  
 1 AL           Alabama       525
 2 AK           Alaska        719
 3 AZ           Arizona      1186
 4 AR           Arkansas     1051
 5 CA           California    888
 6 CO           Colorado      615
 7 CT           Connecticut   578
 8 DE           Delaware      894
 9 FL           Florida       536
10 GA           Georgia       599