What is Anti-Join?
The anti-join operation in R is used to identify observations that exist in the first dataset but not in the second dataset. In other words, it returns the rows from the first dataset that have no matching keys in the second dataset.
The syntax of the anti_join() function is as follows:
Syntax:
anti_join(x, y, by = NULL, copy = FALSE)
- x: The first dataset.
- y: The second dataset.
- by: Variables to join by. If NULL, the function will use all variables that appear in both datasets.
- copy: Logical value indicating whether to make a copy of `x` before performing the operation. Default is FALSE.
Here is one basic example for Anti Join in R.
# Installing and loading the dplyr package
install.packages("dplyr")
library(dplyr)
# Creating the first data frame
df1 <- data.frame(
id = 1:5,
name = c("Alice", "Bob", "Charlie", "David", "Eva")
)
# Creating the second data frame
df2 <- data.frame(
id = c(2, 4, 6),
name = c("Bob", "David", "Frank")
)
# Printing the data frames
print(df1)
print(df2)
# Performing the anti join
result <- anti_join(df1, df2, by = "id")
# Printing the result
print(result)
Output:
id name
1 1 Alice
2 2 Bob
3 3 Charlie
4 4 David
5 5 Eva
id name
1 2 Bob
2 4 David
3 6 Frank
id name
1 1 Alice
2 3 Charlie
3 5 Eva
Let’s walk through some examples to better understand how the anti join operation works.
Identifying Non-Matching Observations using Anti Join
In data analysis and manipulation, identifying non-matching observations between two datasets is often crucial. This process allows analysts to pinpoint differences, gaps, or errors in the data, enabling them to make informed decisions and take appropriate actions.
library(dplyr)
# Creating the first dataset
df1 <- data.frame(ID = c(1, 2, 3, 4),
Value = c("A", "B", "C", "D"))
df1
# Creating the second dataset
df2 <- data.frame(ID = c(2, 4),
Value = c("B", "D"))
df2
# Performing the anti join
result <- anti_join(df1, df2, by = "ID")
# Viewing the result
print(result)
Output:
ID Value
1 1 A
2 2 B
3 3 C
4 4 D
ID Value
1 2 B
2 4 D
ID Value
1 1 A
2 3 C
This result indicates that the observations with IDs 1 and 3 from df1 do not have a matching ID in df2.
Joining Multiple Variables using Anti Join
Now we will joining datasets based on multiple variables is a common task, especially when dealing with complex datasets or relational data.
# Installing and loading the dplyr package
install.packages("dplyr")
library(dplyr)
# Creating the first data frame
employees <- data.frame(
employee_id = c(101, 102, 103, 104, 105),
name = c("John", "Jane", "Doe", "Smith", "Emily"),
department = c("HR", "Finance", "IT", "Marketing", "HR")
)
employees
# Creating the second data frame
terminated_employees <- data.frame(
employee_id = c(102, 104, 106),
name = c("Jane", "Smith", "Andrew"),
department = c("Finance", "Marketing", "IT")
)
terminated_employees
# Printing the data frames
print(employees)
print(terminated_employees)
# Performing the anti join
active_employees <- anti_join(employees, terminated_employees,
by = c("employee_id", "name", "department"))
# Printing the result
print(active_employees)
Output:
employee_id name department
1 101 John HR
2 102 Jane Finance
3 103 Doe IT
4 104 Smith Marketing
5 105 Emily HR
employee_id name department
1 102 Jane Finance
2 104 Smith Marketing
3 106 Andrew IT
employee_id name department
1 101 John HR
2 103 Doe IT
3 105 Emily HR
This output shows that the rows with (employee_id, name, department) combinations of (101, John, HR), (103, Doe, IT), and (105, Emily, HR) from employees do not have matching rows in terminated_employees.
Anti Join in R
The anti-join operation in R Programming Language provided by the anti_join() function in the dplyr package, allows us to perform this comparison efficiently. This article will explore the anti-join operation in detail, providing explanations and examples to illustrate its usage.
Anti Join in R