How to do Left Join in R? Tips and Tricks


This tutorial will walk you through the fundamentals of performing a left join in R, using concise explanations and examples to help you understand this critical skill. Data analysis and manipulation frequently require joining datasets, with the “left join” being one of the most common.

What is a Left Join in R?

A relational join operation known as a left join joins two datasets together according to a shared column or variable. All of the rows from the left dataset and any matching rows from the right dataset are included in the outcome of a left join. The resulting dataset will still have all of the rows from the left dataset with missing values (NA) in the columns from the right dataset if there are no matches in the right dataset.

Importance of Left Join in R

A Left Join in R is an essential tool for data manipulation and analysis, providing a strong means of merging datasets in a way that improves understanding and generates new insights. Its significance comes from its capacity to maintain the original dataset’s integrity while allowing data to be enriched with extra attributes from a different dataset.

Real-world scenarios are full of practical applications. Take a retail company that wants to study customer behavior, for instance. Analysts can determine the purchasing habits of active customers and highlight those who haven’t made any purchases by executing a Left Join on the customer dataset (A) and the transactions dataset (B).

See also  How to Reverse String in Javascript?

Example Code in R:

  • # Assuming data.frames customer data and transaction data
  • # with a common key ‘customerID’
  • merged data <- merge(customer data, transaction data, by = ‘customerID’, all.x = TRUE)

This sample of code shows how to use the merge function in R to perform a Left Join, which effectively combines customerData with transactionData based on the customerID. The utility of the all.x = TRUE parameter in providing a comprehensive view of the data is highlighted by ensuring that all entries from the customer data are retained in the merged dataset.

Step-by-Step Guide to Left Join with Merge ()

Left Join in R via the merge() Function

R has a flexible function called `merge()` that can be used to combine datasets. To execute a left join, utilize the subsequent syntax:



`x` is the left dataset.

`y` is the right dataset.

`by` specifies the common column(s) to join on.

`all. x = TRUE` ensures that all rows from the left dataset are retained.


Left Join in R via the left_join() Function from dplyr

A more expressive and user-friendly method for completing data manipulation tasks, such as left joins, is offered by the Dplyr package. You must first load the dplyr package to use the `left_join()` function. This is the fundamental syntax:



result <- left_join(x, y, by = “common_column”)

Practical Examples in R

Left Join with merge()

Let’s say you have two datasets: salaries and employees. To obtain a comprehensive list of employees along with their corresponding salaries, you want to join these datasets using the common column “employee_id”.


employees <- data.frame(

  employee_id = c(1, 2, 3, 4),

See also  Java Tutorial for Beginners: A Comprehensive Guide

  employee_name = c(“Alice”, “Bob”, “Charlie”, “David”)



salaries <- data.frame(

  employee_id = c(1, 2, 5),

  salary = c(60000, 70000, 80000)



result_merge <- merge(employees, salaries, by = “employee_id”, all.x = TRUE)




Left Join with left_join() from dplyr

Let’s use the `left_join()` function from the dplyr package to accomplish the same goal.




result_dplyr <- left_join(employees, salaries, by = “employee_id”)




Left Join with Multiple Columns

Assume you have datasets named ‘customers’ and ‘orders’. To create a comprehensive list of orders with customer information, combine these datasets using the two common columns “customer_id” and “order_year”.


orders <- data.frame(

  order_id = c(1, 2, 3, 4, 5),

  customer_id = c(101, 102, 103, 101, 104),

  order_year = c(2021, 2022, 2021, 2022, 2022),

  product = c(“A”, “B”, “C”, “D”, “E”)


customers <- data.frame(

  customer_id = c(101, 102, 103, 105),

  customer_name = c(“Alice”, “Bob”, “Charlie”, “Eve”),

  city = c(“New York”, “Los Angeles”, “Chicago”, “Houston”)


result_merge_multi <- merge(orders, customers, by = c(“customer_id”, “order_year”), all.x = TRUE)



R’s left join is a critical tool for effective data analysis and manipulation. This comprehensive tutorial includes examples of how to merge datasets without sacrificing integrity, as well as an explanation of the purpose and significance of left joins. With clear instructions and examples, analysts can quickly integrate multiple datasets and derive insightful information.

Read more

Share This Article
I'm a tech enthusiast and content writer at With a passion for simplifying complex tech concepts, delivers engaging content to readers. Follow for insightful updates on the latest in technology.
Leave a comment

Leave a Reply

Your email address will not be published. Required fields are marked *