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 here.
The following tutorials explain how to perform other common operations in R: