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. Using dplyr within R, we can easily import our data and join these tables, using the following join types. Inner Join (innerjoin) Left Join (leftjoin) Right Join (rightjoin) Full Join (fulljoin) Semi Join (semijoin) Anti Join (antijoin) The general syntax of these joins is as follows: jointype(firstTable, secondTable, by=columnTojoinOn) e.g.

R’s data.table package provides fast methods for handling large tables of data with simplistic syntax. The following is an introduction to basic join operations using data.table.

Suppose you have two data.tables – a table of insurance policies

and a table of insurance claims.

If you want to see the policy data for each claim, you need to do a join on the policy number. In SQL terms, this is a right/left outer join. That is, you want the result to include every row from the claims table, and only rows from the policy table that are associated with a claim in the claims table. Right outer joins are the default behavior of data.table’s join method.

First we need to set the key of each table based on the column we want to use to match the rows of the tables.

Note: Technically we only need to specify the key of the policies table for this join to work, but the join runs quicker when you key both tables.

Now do the join.

How to use inner join in r

Since claim 126’s policy number, 4, was not in the policies table its effective and expiration dates are set as NA.


The important thing to remember when doing a basic X[Y] join using data.table is that the table inside of the brackets will have all of its rows in the resultant table. So, doing claims[policies] will return all policies and any matching claims.

If you want to return only claims that have a matching policy (i.e. rows where the key is in both tables), set the nomatch argument of data.table to 0.

(This is equivalent to claims[policies, nomatch = 0] and is referred to as an inner join.)

If you want to return rows in the claims table which are not in the policies table, you can do

Or, for policies with no claims…

How To Use Inner Join In R

Now suppose we add a field, Company, to each table and set all the values to “ABC”.

What would the result be if we try to join policies and claims based on the new Company field? Adobe flash mozilla.

data.table throws an error in this situation because our resultant table has more rows than the combined number of rows in each of the tables being joined. This is a common sign of a mistake, but in our case it’s desired. In this situation we need to tell data.table that this isn’t a mistake by specifying allow.cartesian = TRUE.

Next to come – rolling joins.

Source: R/join.R

spatial join, spatial filter



object of class sf


object of class sf


geometry predicate function with the same profile as st_intersects; see details


for st_join: arguments passed on to the join function or to st_intersection when largest is TRUE; for st_filter arguments passed on to the .predicate function, e.g. prepared, or a pattern for st_relate


length 2 character vector; see merge


logical; if TRUE return the left join, otherwise an inner join; see details.see also left_join


logical; if TRUE, return x features augmented with the fields of y that have the largest overlap with each of the features of x; see


geometry predicate function with the same profile as st_intersects; see details


an object of class sf, joined based on geometry


alternative values for argument join are:

  • any user-defined function of the same profile as the above

A left join returns all records of the x object with y fields for non-matched records filled with NA values; an inner join returns only records that spatially match.

Inner Join In Relational Algebra