Merging DataFrames in R
Introduction
Merging or joining data frames is the process of combining columns from two or more dataframes. It is a well-known operation in programming. In R we can perform join with two functions: merge() of the base package and join() of a dplyr package. Before getting into that, this guide will go through the types of joins.
Types of Joins
There are four primary types of joins:
Left Outer Join
Suppose you are joining two tables, A and B, where A is the left table and B is the right table. When you perform a left outer join on A and B, it will return all rows from A and rows that are matching in B.
All columns from A and B are returned, but the rows that do not match in B will have NA values for B columns.
Right Outer Join
A right outer join works similarly to the left outer join. It will return all matching rows from the right table in the left table. All columns from both tables are returned, and the rows that do not match in the left table will have NA values.
Inner Join
An inner join will return all the matching rows from both tables. If there are multiple matches between both tables, all combinations will be returned.
Full Join
A full join will return all values of rows and columns from both tables whether they are matching or not.
Joining Dataframes with merge()
The merge() function belongs to the base package of R. You don't need to install any additional packages to use the merge() function.
The arguments of the merge() function, along with the default values that are passed in those arguments, are given below.
1# Syntax for merge function
2merge(x, y, by = intersect(names(x), names(y)),
3 by.x = by, by.y = by, all = FALSE, all.x = all, all.y = all)
4 - The first two arguments,
xandy, are the name of the dataframes that need to be joined. - The next three arguments,
by,by.x, andby.y, decide the column used for joining the dataframes. If the name of the column that is needed for joining is the same then you don't need to pass any names. If they are different then you have to pass the names inby.xandby.y. - The next three arguments decide the type of join performed by the
merge(). The default values will perform an inner join. Ifall.xis set toTRUEthen it will perform a left outer join. Ifall.yis set toTRUEthen it will perform a right outer join. If both are set toTRUEthen it will perform a full outer join.
The dataframes used in this example are band_members and band_instruments. The column details are shared below.
1# Loading dplyr function to use the datasets present in the package
2library(dplyr)
3data(band_members)
4data(band_instruments)
5
6# Columns in band_instruments
7colnames(band_instruments)
8[1] "name" "plays"
9
10#Columns in band_members
11colnames(band_members)
12[1] "name" "band"
13
14# Lets look at the data
15view(band_instruments)
16 name plays
17 <chr> <chr>
181 John guitar
192 Paul bass
203 Keith guitar
21
22view(band_members)
23 name band
24 <chr> <chr>
251 Mick Stones
262 John Beatles
273 Paul BeatlesThe code in the next example will perform all four types of joins using the dataframes above and the merge() function.
1# Performing Left outer join
2merge(band_members, band_instruments, all.x = TRUE)
3 name band plays
41 John Beatles guitar
52 Mick Stones <NA>
63 Paul Beatles bass
7
8# Performing Right outer join
9merge(band_members, band_instruments, all.y = TRUE)
10 name band plays
111 John Beatles guitar
122 Keith <NA> guitar
133 Paul Beatles bass
14
15# Performing Inner join
16merge(band_members, band_instruments, all.y = TRUE, all.x = TRUE)
17 name band plays
181 John Beatles guitar
192 Keith <NA> guitar
203 Mick Stones <NA>
214 Paul Beatles bass
22
23# Performing Full outer join
24merge(band_members, band_instruments)
25 name band plays
261 John Beatles guitar
272 Paul Beatles bassIn the output of joins you can see that if the matching values are not there they are assigned as <NA>. In the case of an inner join, it is only showing the matching values from both dataframes.
Joining Dataframes with dplyr:: join Function
In comparison to the merge() function, dplyr has four different functions for different types of joins. It avoids confusion because you don't have to set values of the arguments. The join functions are given below:
- inner_join()
- left_join()
- right_join()
- full_join()
This example will perform all four types of joins using the above functions.
1# Performing Inner join
2inner_join(band_members, band_instruments, by = "name")
3
4 name band plays
5 <chr> <chr> <chr>
61 John Beatles guitar
72 Paul Beatles bass
8
9# Performing Left outer join
10left_join(band_members, band_instruments, by = "name")
11
12 name band plays
13 <chr> <chr> <chr>
141 Mick Stones NA
152 John Beatles guitar
163 Paul Beatles bass
17
18# Performing Right outer join
19 right_join(band_members, band_instruments, by = "name")
20
21 name band plays
22 <chr> <chr> <chr>
231 John Beatles guitar
242 Paul Beatles bass
253 Keith NA guitar
26
27# Performing Full outer join
28full_join(band_members, band_instruments, by = "name")
29
30 name band plays
31 <chr> <chr> <chr>
321 Mick Stones NA
332 John Beatles guitar
343 Paul Beatles bass
354 Keith NA guitarConclusion
When we start working with data stored in different tables or sources then we will start exploring the relationship between them. In this process, we join datasets to get a clear view. This operation happens in every project that works around data.