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'))
