Join Function In R

1. Join Function In React
2. Join Function In Report Builder
3. Left Join Function In R
4. Join Function In Ruby
5. Full_join Function In R

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.

An inner join in R is a merge operation between two data frames where the merge returns all of the rows that match from both tables. You are going to need to specify a common key for R use to use to match the data elements. The join method is a string method and returns a string in which the elements of sequence have been joined by str separator. Syntax: stringname.join(iterable) stringname: It is the name of string in which joined elements of iterable will be stored.

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):

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:

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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

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

Join Function In React

• 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

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Join Function In Report Builder

Adding Columns

Left Join Function In R

To merge two data frames (datasets) horizontally, use the merge function. In most cases, you join two data frames by one or more common key variables (i.e., an inner join).

# merge two data frames by ID
total <- merge(data frameA,data frameB,by='ID')

# merge two data frames by ID and Country
total <- merge(data frameA,data frameB,by=c('ID','Country'))

Adding Rows

To join two data frames (datasets) vertically, use the rbind function. The two data frames must have the same variables, but they do not have to be in the same order.

total <- rbind(data frameA, data frameB)

If data frameA has variables that data frameB does not, then either:

1. Delete the extra variables in data frameA or
2. Create the additional variables in data frameB and set them to NA (missing)

Join Function In Ruby

before joining them with rbind( ).

Going Further

Full_join Function In R

To practice manipulating data frames with the dplyr package, try this interactive course on data frame manipulation in R.