There are two common ways to perform an inner join in R:

Method 1: Use Base R

merge(df1, df2, by='column_to_join_on')

Method 2: Use dplyr

library(dplyr)

inner_join(df1, df2, by='column_to_join_on')

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', 'G', 'H'),
                 assists=c(4, 9, 14, 13, 10, 8))

df2

  team assists
1    A       4
2    B       9
3    C      14
4    D      13
5    G      10
6    H       8

Example 1: Inner Join Using Base R

We can use the merge() function in base R to perform an inner join, using the ‘team’ column as the column to join on:

#perform inner join using base R
df3 <- merge(df1, df2, by='team')

#view result
df3

  team points assists
1    A     18       4
2    B     22       9
3    C     19      14
4    D     14      13
5    G     20      10
6    H     28       8

Notice that only the teams that were in both datasets are kept in the final dataset.

Example 2: Inner Join Using dplyr

We can use the inner_join() function from the package to perform an inner join, using the ‘team’ column as the column to join on:

library(dplyr)

#perform inner join using dplyr 
df3 <- inner_join(df1, df2, by='team')

#view result
df3

  team points assists
1    A     18       4
2    B     22       9
3    C     19      14
4    D     14      13
5    G     20      10
6    H     28       8

Notice that this matches the result we obtained from using the merge() function in base R.

Additional Resources