Apr 30, 2014 Using the merge function in R on big tables can be time consuming. Luckily the join functions in the new package dplyr are much faster. The package offers four different joins: innerjoin (similar to merge with all.x=F and all.y=F).
R merge data frames Inner join. An inner join (actually a natural join), is the most usual join of data sets that you can perform. Full (outer) join. As not all rows in the first data frame match all the rows in the second, the output is filled with. Left (outer) join in R. Apr 10, 2021 Merge with dplyr leftjoin rightjoin innerjoin fulljoin Multiple keys; Data Cleaning functions; gather spread separate unite Merge with dplyr dplyr provides a nice and convenient way to combine datasets. We may have many sources of input data, and at some point, we need to combine them.
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
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
Now, we show how to perform the 4 merges (joins):
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.ywith the names of the columns in the respective data frames. The
byargument 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.y- default to
FALSEand 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
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
yto 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
xto the result for
all = FALSE
all = TRUE- gives a full (outer) join. This is a shorthand for
all.x = TRUEand
all.y = TRUE
Other arguments include
TRUE(default), results are sorted on the
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
xmatching a value in this vector is assigned the
nomatchvalue (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
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
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.
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:
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:
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.
No time for reading? Click here to get just the code with commentary
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.