R combine two columns with opposite values and NA values, into one column, and delete existing

I have a dataframe that looks like this:

structure(list(RFM_Score_Meaning.x = c("Dropped", "Dropped", 
"Dropped", "Dropped", "Dropped", "Dropped", "Dropped", NA, NA, 
NA, NA, NA, NA, NA, NA, NA), RFM_Score_Meaning.y = c(NA, NA, 
NA, NA, NA, NA, NA, "Need Encouragement", "Need Encouragement", 
"Need Encouragement", "Need Encouragement", "Need Encouragement", 
"Need Encouragement", "Need Encouragement", "Need Encouragement", 
"Need Encouragement")), row.names = c(NA, -16L), class = c("tidytable", 
"data.table", "data.frame"), .internal.selfref = <pointer: 0x000002470fabbfe0>)


RFM_Score_Meaning.x
<chr>
RFM_Score_Meaning.y
<chr>
Dropped NA          
Dropped NA          
Dropped NA          
Dropped NA          
Dropped NA          
Dropped NA          
Dropped NA          
NA  Need Encouragement          
NA  Need Encouragement          
NA  Need Encouragement

This dataframe is two columns. The places that are NA in the first column have values in the second column, and vice-versa.

How do I get rid of these two columns and instead have one column that that has all the values without any NA values?

>Solution :

We could use coalesce:

library(dplyr)
df %>% 
  transmute(RFM_Score_Meaning = coalesce(RFM_Score_Meaning.x, RFM_Score_Meaning.y))

     RFM_Score_Meaning
 1:            Dropped
 2:            Dropped
 3:            Dropped
 4:            Dropped
 5:            Dropped
 6:            Dropped
 7:            Dropped
 8: Need Encouragement
 9: Need Encouragement
10: Need Encouragement
11: Need Encouragement
12: Need Encouragement
13: Need Encouragement
14: Need Encouragement
15: Need Encouragement
16: Need Encouragement