How to use dplyr to Perform Inner Join in R In R Language

By using dplyr package we perform Inner join in R. Here are the basic syntax for Perform Inner Join in R.

Syntax: merged_df <- inner_join(dataframe1, dataframe2, by = “common_key”)

Where,

dataframe1 and dataframe2: These parameters specify the input dataframes to be merged.
by: This parameter explains the column(s) or variable(s) in the dataframes being passed in as the keys for merging.

Inner Join on a Single Column

# Load the dplyr package
library(dplyr)

# Create two example data frames
df1 <- data.frame(ID = c(1, 2, 3, 4),
                  Name = c("John", "Alice", "Bob", "Emily"))
df1
df2 <- data.frame(ID = c(2, 3, 4, 5),
                  Age = c(30, 25, 40, 35),
                  Gender = c("Male", "Female", "Male", "Female"))
df2
# Perform a simple inner join on the "ID" column using dplyr
merged_df_simple <- inner_join(df1, df2, by = "ID")

# Output the merged data frame
print(merged_df_simple)

Output:

  ID  Name
1  1  John
2  2 Alice
3  3   Bob
4  4 Emily

  ID Age Gender
1  2  30   Male
2  3  25 Female
3  4  40   Male
4  5  35 Female

  ID  Name Age Gender
1  2 Alice  30   Male
2  3   Bob  25 Female
3  4 Emily  40   Male

In this we join the dataframes by ID column.

Inner Join on Multiple Columns

# Load the dplyr package
library(dplyr)

# Create two example data frames with overlapping and non-overlapping columns
df1 <- data.frame(ID = c(1, 2, 3, 4),
                  Name = c("John", "Alice", "Bob", "Emily"),
                  Age = c(30, 25, 40, 35))
df1
df2 <- data.frame(ID = c(2, 3, 4, 5),
                  Name = c("Alice", "Bob", "Emily", "Eve"),
                  Age = c(25, 40, 35, 45), # Adjusted Age values for matches
                  Gender = c("Male", "Female", "Male", "Female"))
df2
# Perform an inner join on multiple columns using dplyr
merged_df_multiple <- inner_join(df1, df2, by = c("ID", "Name", "Age"))

# Output the merged data frame
print(merged_df_multiple)

Output:

  ID  Name Age
1  1  John  30
2  2 Alice  25
3  3   Bob  40
4  4 Emily  35

  ID  Name Age Gender
1  2 Alice  25   Male
2  3   Bob  40 Female
3  4 Emily  35   Male
4  5   Eve  45 Female

  ID  Name Age Gender
1  2 Alice  25   Male
2  3   Bob  40 Female
3  4 Emily  35   Male

In this we performed inner join on the “ID”, “Name”, and “Age” columns simultaneously. Adjust the by parameter accordingly to match the columns you want to join on.

How to Perform Inner Join in R

When working with multiple datasets in R, combining them based on common keys or variables is often necessary to derive meaningful insights. Inner join is one of the fundamental operations in data manipulation that allows you to merge datasets based on matching values. In this article, we will explore the inner join operation in R Programming Language.

Table of Content

merge() function from base R

Inner Join using merge()
Inner Join on Multiple Columns

Using dplyr to Perform Inner Join in R

Inner Join on a Single Column
Inner Join on Multiple Columns

Conclusion

There are two main two types of methods available:

merge() function from base R
inner_join() function from dplyr

The inner join operation in R can be carried out employing either the merge() function which is base R’s or the inner_join() function from dplyr. Here’s a detailed explanation of the syntax for both approaches: