How To Set Up a Left Join in R (Examples of the Merge function)

Welcome to the thrilling world of joining dataframes in R, a daily fact of life for a data scientist. This builds on our earlier tutorial about the merge() function in our earlier tutorial about data manipulation. We’re going to go deeper in this tutorial, focusing on the details of setting up a “left join” operation between two tables. Get ready to dive into an adventure filled with left joins, full joins, mutating joins, and so much more!

In this overview, we’ll explore various methods to combine relational data from multiple tables using base R functions and several installed packages. Base R provides various functions to achieve this, such as the merge() function, which supports different types of joins, including left join, full join, semi join, and anti join. Left join and full join functions, in particular, are instrumental in merging dataframes based on matching column names, with the former retaining all rows from the first dataframe and the latter preserving rows from both dataframes. When faced with multiple matches, these join functions generate multiple rows in the output. Additionally, the “dplyr” package offers a more intuitive syntax for joining dataframes and introduces mutating joins, which augment the first dataframe with additional columns from the second. For users familiar with SQL queries, the “sqldf” package enables the execution of SQL statements on dataframes in R. Alternatively, the “data.table” package provides efficient methods for large datasets, such as bind_rows(), which appends observations from one dataframe to another. As a data scientist, it is essential to explore these various techniques and leverage additional resources to identify the most suitable approach for a specific task.

To set up a left join in R using the merge function, we are going to need to do two things. First, we need to set the x.all = True parameter. This configures the merge to return all records with a match on the left. Second, you will need to set up the “by statement” for the right column(s) to define how the two data frames should be matched up. See the examples below for more detail and an explanation of how the left join works in R.

See also our materials on inner joins and cross joins.

What is a Left Join In R?

A left join in R is a merge operation between two data frames where the merge returns all of the rows from one table (the left side) and any matching rows from the second table. A left join in R will NOT return values of the second table which do not already exist in the first table.

For example, let us suppose we’re going to analyze a collection of insurance policies written in Georgia, Alabama, and Florida. We want to see if they are compliant with our official state underwriting standards, which we keep in a table by state for all of the 38 states where we’re licensed to sell insurance. We’re going to need to merge these two data frames together.

The left join will return a data set consisting of all of the initial insurance policies and values for the three rows on the second table they matched to. There will not be values for states outside of the three listed (GA, FL, AL).

This is in contrast to an inner join, where you only return records which match on both tables. Suppose we had policies from a 39th state we were not allowed to operate in. If we ran this as an inner join, these records will be dropped since they were present on one table but not the other.

Left Join in R – Setting Up Merge ()

Here’s the merge function that will get this done.


  • source – the names of our two data frames
  • by – this parameter identifies the field in the dataframes to use to match records together. Trying to merge two different column names? No problem, we’ve got you covered – article covering this point.
  • all.x and all.y = Boolean which indicates if you want this to be an inner join (matches only) or an outer join (all records on one side)

Left Join in R – Code Example

We’re going to go ahead and set up the data:

# left join in R - create first data set
> policies <- data.frame(Policy = c(1:9), State=c(rep("GA",3), rep("FL", 3), rep("AL", 3)), Limit=c(rep(50000,3), rep(75000, 3), rep(85000, 3)))

> policies
  Policy State Limit
1      1    GA 50000
2      2    GA 50000
3      3    GA 50000
4      4    FL 75000
5      5    FL 75000
6      6    FL 75000
7      7    AL 85000
8      8    AL 85000
9      9    AL 85000

# left join in R - create second data set
> limits <- data.frame(State=c("FL","GA","AL"), regulatory_limit=c(75000,75000,65000))
> limits
  State regulatory_limit
1    FL            75000
2    GA            75000
3    AL            65000

So now we’re going to merge the two data frames together. After that, we can compare the amount of the policy with the acceptable limits.

# left join in R - join the dataframes! 
> scored_policies<-merge(x=policies,y=limits,by="State",all.x=TRUE)
> scored_policies
  State Policy Limit regulatory_limit
1    AL      7 85000            65000
2    AL      8 85000            65000
3    AL      9 85000            65000
4    FL      4 75000            75000
5    FL      5 75000            75000
6    FL      6 75000            75000
7    GA      1 50000            75000
8    GA      2 50000            75000
9    GA      3 50000            75000

# check to see if we're writing policies over the limit
> which(scored_policies$Limit > scored_policies$regulatory_limit)
[1] 1 2 3

# three violations...apparently there is a problem in Alabama...

The last part was an example of using the which function (tutorial link).

FAQ – Common Questions related to left join in R…

Optimizing Left Join Performance

We’re going to briefly talk about performance techniques, since Left joins can be computationally expensive, especially when dealing with large data sets. Here are some tips to consider if you notice your left join is running very slowly:

  1. Eliminate needless data! (with apologies to Strunk and White) If you know certain records will never match or satisfy your other criteria for inclusion, drop them before you merge the data frames. You can do this by filtering out unnecessary rows or columns, or by rolling up the data to a higher group.
  2. Use data.table instead of data.frame: The data.table package in R is optimized for fast data manipulation operations, including left joins. You can convert your data frames to data tables using the setDT() function, and then perform the left join using the syntax DT1[DT2, on = “key”]. This can significantly improve the speed of your left join operations.
  3. Use the dplyr package for left joins: The dplyr package in R provides a more intuitive and efficient way to perform left joins, using the join() function. You can specify the type of join and the key variable using the arguments type and by, respectively. You can use the filter() function to drop unnecessary rows.
  4. Run the operations in parallel. You can do this using the parallel package in R if you have a multi-core processor.
  5. Optimize your hardware: Finally, if you’re routinely working with large data sets, upgrade your hardware with additional memory and processing power.

By following these tips, you can optimize the performance of your left join operations in R and speed up your data manipulation workflows.

Handling Missing Values in a Left Join Operation

One of the challenges of performing a left join operation is dealing with missing values. When you perform a left join, some of the rows in the resulting data frame may have missing values for the variables that were not present in the right data frame. Here are some strategies for handling missing values in a left join operation:

  1. Remove them: You can do this using the na.omit() function in R. (assuming they don’t have predictive value for your analysis)
  2. Impute missing values: Another option is to impute the missing values using statistical techniques such as mean imputation or regression imputation. Check out the the impute() function in the mice package
  3. Consider a different type of join: an outer join includes all rows from both data frames and fills in missing values with NA.
  4. Finally, you can use a conditional statement to handle missing values. For example, the ifelse() function can replace missing values with a default value or with a value calculated from other variables in the data frame.

How would you implement a right join?

We would support a right join by changing our merge declaration to all.y = TRUE. That flips the join clause to looking the other way; it would bring in matching values for X (left table) and all values of Y (right table b). (Technically, this would be a right outer join – the same way our left join clause is a left outer join). Either way, the join function is giving you every row in one of the dataframe(s) plus any matching row in the other.

Implementation with multiple data frames is tricky; I recommend doing joins one at a time to simplify debugging.

What is a natural join?

Within databases, a natural join is a default join type where the columns have the same names in both tables. Generally speaking, this should be used as a last resort: it’s better to explicitly define the matching value / matching key column so the merge function is safe from database changes.

Using selected columns is more work but gives you far more control. A null value (na values) in the wrong place (due to a convert error) or corrupted variable names can easily generate tricky errors to debug.

What is a cross join?

The bane of database administrators everywhere, a cross join returns the Cartesian product of the two sets of matching columns. In effect, every record in the left table matched to every record in the right table. If you’re dealing with any dataset of respectable size, this will quickly return a massive amount of data. RIP your memory…

(There are actual legitimate business analytics needs for the cross join, but they are very rare. The existence of cross joins in your code design is cause for reassessment before proceeding.)

Differences Between Left Join and Merge

In R, a left join specifically combines two data frames by matching rows from the first frame with those in the second based on a common key, and preserving all rows from the first frame. On the other hand, the merge function can perform a left join, but it’s a more general function that can also handle inner, outer, and other types of joins.

Row Increase After Left Join

After a left join in R, the resulting number of rows can increase if there are multiple matches in the right data frame for the join key in the left data frame. This results in the left data frame expanding to accommodate these additional matches.

Syntax for Left Join with Different Column Names

Using dplyr for a left join with differing column names involves specifying the by argument with a named vector:

left_join(x, y, by = c("xColName" = "yColName"))

In this line, xColName is the column in the first data frame and yColName in the second.

Multiple Columns as the Key in dplyr Left Join

Yes, a left join can be performed on multiple columns by providing a character vector to the by argument:

left_join(x, y, by = c("key1", "key2"))

This would use key1 and key2 as the matching keys from both data frames.

Select Specific Columns During Left Join

While performing a left join, selecting specific columns is possible using the select function in combination with the join operation:

left_join(x, y, by = "key") %>%
  select(x.column1, x.column2, y.column1)

Here, only column1 and column2 from data frame x and column1 from data frame y are selected.

Comparison of Left, Right, and Inner Joins

Join TypeDescription
Left JoinIncludes all rows from the left data frame and matched rows from the right.
Right JoinIncludes all rows from the right data frame and matched rows from the left.
Inner JoinOnly includes rows with matching keys in both data frames.

Want to Learn more R shortcuts?

Check out our tutorial on helpful R functions. In a language where there seems to be several ways to solve any problems, this reference page can help guide you to good options for getting things done. We seek to interject a little Pythonic clarity and sustainability to the “just get it done” world of R programming.

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