Join in R: How to join (merge) data frames (inner, outer, left, right) in R We can merge two data frames in R by using the merge function or by using family of join function in dplyr package. The data frames must have same column names on which the merging happens. Merge Function in R is similar to database join operation in SQL. Merge method for sf and data.frame object. Merge (dataframe1, dataframe2, by = 'ID') If they have identifiers but the name of the column is different (I used IDdfX) in each dataframe you should use: merge (dataframe1, dataframe2, by.x = 'IDdf1', by.y = 'IDdf2') With this, you'll have a dataframe where the same ID is in both dataframes.

  1. Dataframe Join Row Values
  2. Merge In R
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:

Join dataframe in r

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.

Dataframe Join Row Values

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

  • 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

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.

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merge is a generic function whose principal method is for data frames: the default method coerces its arguments to data frames and calls the 'data.frame' method.

By default the data frames are merged on the columns with names they both have, but separate specifications of the columns can be given by by.x and by.y. The rows in the two data frames that match on the specified columns are extracted, and joined together. If there is more than one match, all possible matches contribute one row each. For the precise meaning of ‘match’, see match.

Columns to merge on can be specified by name, number or by a logical vector: the name 'row.names' or the number 0 specifies the row names. If specified by name it must correspond uniquely to a named column in the input.

If by or both by.x and by.y are of length 0 (a length zero vector or NULL), the result, r, is the Cartesian product of x and y, i.e., dim(r) = c(nrow(x)*nrow(y), ncol(x) + ncol(y)).

If all.x is true, all the non matching cases of x are appended to the result as well, with NA filled in the corresponding columns of y; analogously for all.y.

If the columns in the data frames not used in merging have any common names, these have suffixes ('.x' and '.y' by default) appended to try to make the names of the result unique. If this is not possible, an error is thrown.


If a by.x column name matches one of y, and if no.dups is true (as by default), the y version gets suffixed as well, avoiding duplicate column names in the result.

The complexity of the algorithm used is proportional to the length of the answer.

Merge In R

In SQL database terminology, the default value of all = FALSE gives a natural join, a special case of an inner join. Specifying all.x = TRUE gives a left (outer) join, all.y = TRUE a right (outer) join, and both (all = TRUE) a (full) outer join. DBMSes do not match NULL records, equivalent to incomparables = NA in R.

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