It’s rare that a data analysis involves only a single table of data. In practice, you’ll normally have many tables that contribute to an analysis, and you need flexible tools to combine them. In dplyr, there are three families of verbs that work with two tables at a time:

(This discussion assumes that you have tidy data, where the rows are observations and the columns are variables. If you’re not familiar with that framework, I’d recommend reading up on it first.)

All two-table verbs work similarly. The first two arguments are x and y, and provide the tables to combine. The output is always a new table with the same type as x.

Mutating joins

Mutating joins allow you to combine variables from multiple tables. For example, take the nycflights13 data. In one table we have flight information with an abbreviation for carrier, and in another we have a mapping between abbreviations and full names. You can use a join to add the carrier names to the flight data:

# Drop unimportant variables so it's easier to understand the join results.
flights2 <- flights %>% select(year:day, hour, origin, dest, tailnum, carrier)

flights2 %>% 
#> Joining by: "carrier"
year month day hour origin dest tailnum carrier
(int) (int) (int) (dbl) (chr) (chr) (chr) (chr)
2013 1 1 5 EWR IAH N14228 UA
2013 1 1 5 LGA IAH N24211 UA
2013 1 1 5 JFK MIA N619AA AA
2013 1 1 5 JFK BQN N804JB B6
2013 1 1 5 LGA ATL N668DN DL

(Variables not shown: name (fctr))

Controlling how the tables are matched

As well as x and y, each mutating join takes an argument by that controls which variables are used to match observations in the two tables. There are a few ways to specify it, as I illustrate below with various tables from nycflights13:

Types of join

There are four types of mutating join, which differ in their behaviour when a match is not found. We’ll illustrate each with a simple example:

(df1 <- data_frame(x = c(1, 2), y = 2:1))
x y
(dbl) (int)
1 2
2 1
(df2 <- data_frame(x = c(1, 3), a = 10, b = "a"))
x a b
(dbl) (dbl) (chr)
1 10 a
3 10 a

The left, right and full joins are collectively know as outer joins. When a row doesn’t match in an outer join, the new variables are filled in with missing values.


While mutating joins are primarily used to add new variables, they can also generate new observations. If a match is not unique, a join will add all possible combinations (the Cartesian product) of the matching observations:

df1 <- data_frame(x = c(1, 1, 2), y = 1:3)
df2 <- data_frame(x = c(1, 1, 2), z = c("a", "b", "a"))

df1 %>% left_join(df2)
#> Joining by: "x"
x y z
(dbl) (int) (chr)
1 1 a
1 1 b
1 2 a
1 2 b
2 3 a

Filtering joins

Filtering joins match obserations in the same way as mutating joins, but affect the observations, not the variables. There are two types:

These are most useful for diagnosing join mismatches. For example, there are many flights in the nycflights13 dataset that don’t have a matching tail number in the planes table:

flights %>% 
  anti_join(planes, by = "tailnum") %>% 
  count(tailnum, sort = TRUE)
tailnum n
(chr) (int)
N725MQ 575
N722MQ 513
N723MQ 507
N713MQ 483

If you’re worried about what observations your joins will match, start with a semi_join() or anti_join(). semi_join() and anti_join() never duplicate; they only ever remove observations.

df1 <- data_frame(x = c(1, 1, 3, 4), y = 1:4)
df2 <- data_frame(x = c(1, 1, 2), z = c("a", "b", "a"))

# Four rows to start with:
df1 %>% nrow()
#> [1] 4
# And we get four rows after the join
df1 %>% inner_join(df2, by = "x") %>% nrow()
#> [1] 4
# But only two rows actually match
df1 %>% semi_join(df2, by = "x") %>% nrow()
#> [1] 2

Set operations

The final type of two-table verb is set operations. These expect the x and y inputs to have the same variables, and treat the observations like sets:

Given this simple data:

(df1 <- data_frame(x = 1:2, y = c(1L, 1L)))
x y
(int) (int)
1 1
2 1
(df2 <- data_frame(x = 1:2, y = 1:2))
x y
(int) (int)
1 1
2 2

The four possibilities are:

intersect(df1, df2)
x y
(int) (int)
1 1
# Note that we get 3 rows, not 4
union(df1, df2)
x y
(int) (int)
2 2
2 1
1 1
setdiff(df1, df2)
x y
(int) (int)
2 1
setdiff(df2, df1)
x y
(int) (int)
2 2


Each two-table verb has a straightforward SQL equivalent:

inner_join() SELECT * FROM x JOIN y ON x.a = y.a
left_join() SELECT * FROM x LEFT JOIN y ON x.a = y.a
right_join() SELECT * FROM x RIGHT JOIN y ON x.a = y.a
full_join() SELECT * FROM x FULL JOIN y ON x.a = y.a
semi_join() SELECT * FROM x WHERE EXISTS (SELECT 1 FROM y WHERE x.a = y.a)
anti_join() SELECT * FROM x WHERE NOT EXISTS (SELECT 1 FROM y WHERE x.a = y.a)
setdiff(x, y) SELECT * FROM x EXCEPT SELECT * FROM y

x and y don’t have to be tables in the same database. If you specify copy = TRUE, dplyr will copy the y table into the same location as the x variable. This is useful if you’ve downloaded a summarised dataset and determined a subset of interest that you now want the full data for. You can use semi_join(x, y, copy = TRUE) to upload the indices of interest to a temporary table in the same database as x, and then perform a efficient semi join in the database.

If you’re working with large data, it maybe also be helpful to set auto_index = TRUE. That will automatically add an index on the join variables to the temporary table.

Coercion rules

When joining tables, dplyr is a little more conservative than base R about the types of variable that it considers equivalent. This is mostly likely to surprise if you’re working factors:

Otherwise logicals will be silently upcast to integer, and integer to numeric, but coercing to character will raise an error:

df1 <- data_frame(x = 1, y = 1L)
df2 <- data_frame(x = 2, y = 1.5)
full_join(df1, df2) %>% str()
#> Joining by: c("x", "y")
#> Classes 'tbl_df', 'tbl' and 'data.frame':    2 obs. of  2 variables:
#>  $ x: num  1 2
#>  $ y: num  1 1.5

df1 <- data_frame(x = 1, y = 1L)
df2 <- data_frame(x = 2, y = "a")
full_join(df1, df2) %>% str()
#> Joining by: c("x", "y")
#> Error in eval(expr, envir, enclos): cannot join on columns 'y' x 'y': Can't join on 'y' x 'y' because of incompatible types (character / integer)

Multiple-table verbs

dplyr does not provide any functions for working with three or more tables. Instead use Reduce(), as described in Advanced R, to iteratively combine the two-table verbs to handle as many tables as you need.