You can use the following basic syntax in dplyr to perform a left join on two data frames when the columns you’re joining on have different names in each data frame:

library(dplyr)

final_df <- left_join(df_A, df_B, by = c('team' = 'team_name'))

This particular example will perform a left join on the data frames called df_A and df_B, joining on the column in df_A called team and the column in df_B called team_name.

The following example shows how to use this syntax in practice.

Example: Perform Left Join with Different Column Names in dplyr

Suppose we have the following two data frames in R:

#create first data frame
df_A <- data.frame(team=c('A', 'B', 'C', 'D', 'E'),
                   points=c(22, 25, 19, 14, 38))

df_A

  team points
1    A     22
2    B     25
3    C     19
4    D     14
5    E     38

#create second data frame
df_B <- data.frame(team=c('A', 'C', 'D', 'F', 'G'),
                   rebounds=c(14, 8, 8, 6, 9))

df_B

  team_name rebounds
1         A       14
2         C        8
3         D        8
4         F        6
5         G        9

We can use the following syntax in dplyr to perform a left join based on matching values in the team column of df_A and the team_name column of df_B:

library(dplyr)

#perform left join based on different column names in df_A and df_B
final_df <- left_join(df_A, df_B, by = c('team' = 'team_name'))

#view final data frame
final_df

  team points rebounds
1    A     22       14
2    B     25       NA
3    C     19        8
4    D     14        8
5    E     38       NA

The resulting data frame contains all rows from df_A and only the rows in df_B where the team values matched the team_name values.

Note that you can also match on multiple columns with different names by using the following basic syntax:

library(dplyr)

#perform left join based on multiple different column names
final_df <- left_join(df_A, df_B, by = c('A1' = 'B1', 'A2' = 'B2', 'A3' = 'B3'))

Note: You can find the complete documentation for the left_join() function in dplyr .