Here Merge Join Right Input automatically selected by the intelligence. STEP 9: Double click on Merge Join Transformation will open the Merge Join Transformation Editor to configure it. Join Type provides a drop-down list to select the Join type (such as SSIS Inner Join, Left Outer Join, and Full Outer Join) you want to perform on the source. Merge Data Frames by Two ID Columns in R (2 Examples) In this article you’ll learn how to combine multiple data frames based on more than one ID column in R. Note that the previous examples performed an inner join. However, it is also possible to apply other types of data joins such as left joins, right joins, outer joins, and so on. The sort-merge join (also known as merge join) is a join algorithm and is used in the implementation of a relational database management system. The basic problem of a join algorithm is to find, for each distinct value of the join attribute, the set of tuples in each relation which display that value. In the previous R syntax, I applied an inner join, but of cause you could also use a right, left, or full join in this step-by-step approach. Also note that there is a very smooth way to merge multiple data frames simultaneously by combining these data frames in a list. You can learn more about this approach in this tutorial.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
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):
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.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.
Using the tidyverse
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:
Full (outer) join
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
- 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
R Left Merge
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