There are two common ways to perform a right join in R:
Method 1: Use Base R
merge(df1, df2, by='column_to_join_on', all.y=TRUE)
Method 2: Use dplyr
library(dplyr) right_join(df1, df2, by='column_to_join_on')
Both methods will return all rows from df2 and any rows with matching keys from df1.
It’s also worth noting that both methods will produce the same result, but the dplyr method will tend to work faster on extremely large datasets.
The following examples show how to use each of these functions in practice with the following data frames:
#define first data frame df1 = data.frame(team=c('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'), points=c(18, 22, 19, 14, 14, 11, 20, 28)) df1 team points 1 A 18 2 B 22 3 C 19 4 D 14 5 E 14 6 F 11 7 G 20 8 H 28 #define second data frame df2 = data.frame(team=c('A', 'B', 'C', 'D', 'L', 'M'), assists=c(4, 9, 14, 13, 10, 8)) df2 team assists 1 A 4 2 B 9 3 C 14 4 D 13 5 L 10 6 M 8
Example 1: Right Join Using Base R
We can use the merge() function in base R to perform a right join, using the ‘team’ column as the column to join on:
#perform right join using base R df3 team', all.y=TRUE) #view result df3 team points assists 1 A 18 4 2 B 22 9 3 C 19 14 4 D 14 13 5 L NA 10 6 M NA 8
Notice that all of the rows from df2 were included in the final data frame, but only the rows from df1 that had a matching team name were included in the final data frame.
Example 2: Right Join Using dplyr
We can use the right_join() function from the dplyr package to perform a right join, using the ‘team’ column as the column to join on:
library(dplyr) #perform right join using dplyr df3 team') #view result df3 team points assists 1 A 18 4 2 B 22 9 3 C 19 14 4 D 14 13 5 L NA 10 6 M NA 8
Notice that this matches the result we obtained from using the merge() function in base R.
Additional Resources
The following tutorials explain how to perform other common operations in R:
How to Do a Left Join in R
How to Add a Column to Data Frame in R
How to Drop Columns from Data Frame in R