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How to Join Data Frames for different column names in R?. Using dplyr, you can connect data frames in R based on multiple columns using the following basic syntax.

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library(dplyr)
left_join(df1, df2, by=c('x1'='x2', 'y1'='y2'))

Where the following conditions are true, this syntax will perform a left join:

Df1’s x1 column corresponds to df2’s x2 column.

Df1’s y1 column corresponds to df2’s y2 column.

This syntax is demonstrated in the following example.

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Using Multiple Columns as an Example dplyr is a Python package that allows you to do a lot of things.

Assume the following two data frames are available in R:

Let’s define first data frame

df1<-data.frame(team=c('A', 'A', 'B', 'B'),
                 pos=c('X', 'F', 'F', 'X'),
                 points=c(128, 222, 129, 124))
df1
   team pos points
1    A   X    128
2    A   F    222
3    B   F    129
4    B   X    124

Now we can define the second data frame.

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df2<- data.frame(team_name=c('A', 'A', 'B', 'C', 'C'),
                 position=c('X', 'X', 'F', 'G', 'F'),
                 assists=c(224, 229, 428, 466, 525))
df2
   team_name position assists
1         A        X     224
2         A        X     229
3         B        F     428
4         C        G     466
5         C        F     525

To do a left join based on two columns, we can use the following dplyr syntax.

library(dplyr)

Let’s perform left join based on multiple columns

df3 <- left_join(df1, df2, by=c('team'='team_name', 'pos'='position'))

now we can view the result

df3
   team pos points assists
1    A   X    128     224
2    A   X    128     229
3    A   F    222      NA
4    B   F    129     428
5    B   X    124      NA

The resulting data frame comprises all of the rows from df1 as well as only the rows from df2 when the team and position values were identical.

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Also, if the two data frames have identical column names, you can join multiple columns with the following syntax.

library(dplyr)
df3 <- left_join(df1, df2, by=c('team', 'position'))

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