Full Outer Join In R

FULL JOIN returns all matching records from both tables whether the other table matches or not. Be aware that a FULL JOIN can potentially return very large datasets. These two: FULL JOIN and FULL OUTER JOIN are the same. Join Description. Join two DataFrames based on the given join expression. Usage ## S4 method for signature 'DataFrame,DataFrame' join(x, y, joinExpr = NULL, joinType = NULL).

  1. Full Outer Join Sqldf In R
  2. Inner Join In R
  3. R Join Two Tables
  4. Merge Data In R
  5. Full Outer Join In R Programming

At times, you may need to create a full outer join in Access. Yet, one of the limitations of Access is that you cannot create such a join.

Well at least not directly…

You can create a full outer join by “tricking” the system.

And what do I mean by saying “tricking the system”?


You can separately apply Left and Right Joins. Then, you can use Union to get your desired full outer join.

Here is the general template that you may use to create your full outer join in Access:

In the next section, I’ll review an example to show you how to create a full outer join in Access.

The Example

Let’s say that you have two tables in Access:

  • Client_First_Name that contains a list of first names
  • Client_Last_Name which contains a list of last names

The Client_First_Name table would look like this:

Client IDClient First Name

While the Client_Last_Name table would look like this:

Client IDClient Last Name

You’ll notice that each of the tables contains a ‘Client ID‘ field. The goal here is to connect the two tables using that Client ID field.

You will also notice that some Client IDs exist in the first table, but not in the second table (and vice versa).

This is when a full outer join becomes useful. It will allow you to get ALL the records from both tables.

Let’s now look at the steps to create a full outer join using the above example.

Steps to Create a Full Outer Join in Access

(1) First thing first, create the above two tables in Access.

Save the first table as Client_First_Name:

And save the second table as Client_Last_Name:

(2) Now create the Left Join portion of the query:

(3) Then, create the Right Join portion of the query:

(4) Finally, to create your full outer join, place a ‘UNION‘ in between the Left Join portion and the Right Join portion:

Ta-da! you just created your full outer join in Access!

All the records from both tables would now appear (even if the Client ID key does not exist in both of the tables).

That way, you’ll make sure that all of your records are fully captured.

In the example reviewed, you can observe that some values are missing under both the Client First and Last Names. By using a full outer join, you can recognize those missing values, and then add them into the appropriate tables.

That’s it for this tutorial! You may wish to check the following source for additional tutorials on MS Access.

October 27, 2018

In this post in the R:case4base series we will look at one of the most common operations on multiple data frames - merge, also known as JOIN in SQL terms.

We will learn how to do the 4 basic types of join - inner, left, right and full join with base R and show how to perform the same with tidyverse’s dplyr and data.table’s methods. A quick benchmark will also be included.

To showcase the merging, we will use a very slightly modified dataset provided by Hadley Wickham’s nycflights13 package, mainly the flights and weather data frames. Let’s get right into it and simply show how to perform the different types of joins with base R.

First, we prepare the data and store the columns we will merge by (join on) into mergeCols:

Now, we show how to perform the 4 merges (joins):

Left (outer) join

Full (outer) join

The key arguments of base merge data.frame method are:

  • x, y - the 2 data frames to be merged
  • by - names of the columns to merge on. If the column names are different in the two data frames to merge, we can specify by.x and by.y with the names of the columns in the respective data frames. The by argument can also be specified by number, logical vector or left unspecified, in which case it defaults to the intersection of the names of the two data frames. From best practice perspective it is advisable to always specify the argument explicitly, ideally by column names.
  • all, all.x, all.y - default to FALSE and can be used specify the type of join we want to perform:
    • all = FALSE (the default) - gives an inner join - combines the rows in the two data frames that match on the by columns
    • all.x = TRUE - gives a left (outer) join - adds rows that are present in x, even though they do not have a matching row in y to the result for all = FALSE
    • all.y = TRUE - gives a right (outer) join - adds rows that are present in y, even though they do not have a matching row in x to the result for all = FALSE
    • all = TRUE - gives a full (outer) join. This is a shorthand for all.x = TRUE and all.y = TRUE

Other arguments include

  • sort - if TRUE (default), results are sorted on the by columns
  • suffixes - length 2 character vector, specifying the suffixes to be used for making the names of columns in the result which are not used for merging unique
  • incomparables - for single-column merging only, a vector of values that cannot be matched. Any value in x matching a value in this vector is assigned the nomatch value (which can be passed using ...)

For this example, let us have a list of all the data frames included in the nycflights13 package, slightly updated such that they can me merged with the default value for by, purely for this exercise, and store them into a list called flightsList:

Since merge is designed to work with 2 data frames, merging multiple data frames can of course be achieved by nesting the calls to merge:

We can however achieve this same goal much more elegantly, taking advantage of base R’s Reduce function:

Note that this example is oversimplified and the data was updated such that the default values for by give meaningful joins. For example, in the original planes data frame the column year would have been matched onto the year column of the flights data frame, which is nonsensical as the years have different meanings in the two data frames. This is why we renamed the year column in the planes data frame to yearmanufactured for the above example.

Using the tidyverse

The dplyr package comes with a set of very user-friendly functions that seem quite self-explanatory:

We can also use the “forward pipe” operator %>% that becomes very convenient when merging multiple data frames:

Using data.table

The data.table package provides an S3 method for the merge generic that has a very similar structure to the base method for data frames, meaning its use is very convenient for those familiar with that method. In fact the code is exactly the same as the base one for our example use.

One important difference worth noting is that the by argument is by default constructed differently with data.table.

We however provide it explicitly, therefore this difference does not directly affect our example:

Alternatively, we can write data.table joins as subsets:

For a quick overview, lets look at a basic benchmark without package loading overhead for each of the mentioned packages:

Inner join

Full (outer) join

Full Outer Join Sqldf In R

Visualizing the results in this case shows base R comes way behind the two alternatives, even with sort = FALSE.

Note: The benchmarks are ran on a standard droplet by DigitalOcean, with 2GB of memory a 2vCPUs.

Inner Join In R

Full outer join example in relational algebra

No time for reading? Click here to get just the code with commentary

R Join Two Tables

  • Animated inner join, left join, right join and full join by Garrick Aden-Buie for an easier understanding
  • Joining Data in R with dplyr by Wiliam Surles
  • Join (SQL) Wikipedia page
  • The nycflights13 package on CRAN

Merge Data In R

Exactly 100 years ago tomorrow, October 28th, 1918 the independence of Czechoslovakia was proclaimed by the Czechoslovak National Council, resulting in the creation of the first democratic state of Czechs and Slovaks in history.

Full Outer Join In R Programming

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