Two-table verbs

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:

library("nycflights13")
# Drop unimportant variables so it's easier to understand the join results.
flights2 <- flights %>% select(year:day, hour, origin, dest, tailnum, carrier)

flights2 %>% 
  left_join(airlines)
#> Joining by: "carrier"

year	month	day	hour	origin	dest	tailnum	carrier	name
2013	1	1	5	EWR	IAH	N14228	UA	United Air Lines Inc.
2013	1	1	5	LGA	IAH	N24211	UA	United Air Lines Inc.
2013	1	1	5	JFK	MIA	N619AA	AA	American Airlines Inc.
2013	1	1	5	JFK	BQN	N804JB	B6	JetBlue Airways
2013	1	1	5	LGA	ATL	N668DN	DL	Delta Air Lines Inc.
…	…	…	…	…	…	…	…	…

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:

NULL, the default. dplyr will will use all variables that appear in both tables, a natural join. For example, the flights and weather tables match on their common variables: year, month, day, hour and origin.

flights2 %>% left_join(weather)
#> Joining by: c("year", "month", "day", "hour", "origin")

year	month	day	hour	origin	dest	tailnum	carrier	temp	dewp	humid	wind_dir
2013	1	1	5	EWR	IAH	N14228	UA	NA	NA	NA	NA
2013	1	1	5	LGA	IAH	N24211	UA	NA	NA	NA	NA
2013	1	1	5	JFK	MIA	N619AA	AA	NA	NA	NA	NA
2013	1	1	5	JFK	BQN	N804JB	B6	NA	NA	NA	NA
2013	1	1	5	LGA	ATL	N668DN	DL	NA	NA	NA	NA
…	…	…	…	…	…	…	…	…	…	…	…

(Variables not shown: wind_speed (dbl), wind_gust (dbl), precip (dbl), pressure (dbl), visib (dbl))

A character vector, by = "x". Like a natural join, but uses only some of the common variables. For example, flights and planes have year columns, but they mean different things so we only want to join by tailnum.

flights2 %>% left_join(planes, by = "tailnum")

year.x	month	day	hour	origin	dest	tailnum	carrier	year.y
2013	1	1	5	EWR	IAH	N14228	UA	1999
2013	1	1	5	LGA	IAH	N24211	UA	1998
2013	1	1	5	JFK	MIA	N619AA	AA	1990
2013	1	1	5	JFK	BQN	N804JB	B6	2012
2013	1	1	5	LGA	ATL	N668DN	DL	1991
…	…	…	…	…	…	…	…	…

(Variables not shown: type (chr), manufacturer (chr), model (chr), engines (int), seats (int), speed (int), engine (chr))

Note that the year columns in the output are disambiguated with a suffix.

A named character vector: by = c("x" = "a"). This will match variable x in table x to variable a in table b. The variables from use will be used in the output.

Each flight has an origin and destination airport, so we need to specify which one we want to join to:

flights2 %>% left_join(airports, c("dest" = "faa"))

year	month	day	hour	origin	dest	tailnum	carrier
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 (chr), lat (dbl), lon (dbl), alt (int), tz (dbl), dst (chr))

flights2 %>% left_join(airports, c("origin" = "faa"))

year	month	day	hour	origin	dest	tailnum	carrier	name
2013	1	1	5	EWR	IAH	N14228	UA	Newark Liberty Intl
2013	1	1	5	LGA	IAH	N24211	UA	La Guardia
2013	1	1	5	JFK	MIA	N619AA	AA	John F Kennedy Intl
2013	1	1	5	JFK	BQN	N804JB	B6	John F Kennedy Intl
2013	1	1	5	LGA	ATL	N668DN	DL	La Guardia
…	…	…	…	…	…	…	…	…

(Variables not shown: lat (dbl), lon (dbl), alt (int), tz (dbl), dst (chr))

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
1	2
2	1

(df2 <- data_frame(x = c(1, 3), a = 10, b = "a"))

x	a	b
1	10	a
3	10	a

inner_join(x, y) only includes observations that match in both x and y.
```
df1 %>% inner_join(df2) %>% knitr::kable()
#> Joining by: "x"
```
x y a b

1 2 10 a
left_join(x, y) includes all observations in x, regardless of whether they match or not. This is the most commonly used join because it ensures that you don’t lose observations from your primary table.
```
df1 %>% left_join(df2)
#> Joining by: "x"
```
x y a b

1 2 10 a

2 1 NA NA
right_join(x, y) includes all obserations in y. It’s equivalent to left_join(y, x), but the columns will be ordered differently.
```
df1 %>% right_join(df2)
#> Joining by: "x"
```
x y a b

1 2 10 a

3 NA 10 a
```
df2 %>% left_join(df1)
#> Joining by: "x"
```
x a b y

1 10 a 2

3 10 a NA
full_join() includes all observations from x and y.
```
df1 %>% full_join(df2)
#> Joining by: "x"
```
x y a b

1 2 10 a

2 1 NA NA

3 NA 10 a

x	y	a	b
1	2	10	a

x	y	a	b
1	2	10	a
2	1	NA	NA

x	y	a	b
1	2	10	a
3	NA	10	a

x	a	b	y
1	10	a	2
3	10	a	NA

x	y	a	b
1	2	10	a
2	1	NA	NA
3	NA	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.

Observations

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
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:

semi_join(x, y) keeps all observations in x that have a match in y.
anti_join(x, y) drops all observaitons in x that have a match in y.

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:

library("nycflights13")
flights %>% 
  anti_join(planes, by = "tailnum") %>% 
  count(tailnum, sort = TRUE)

tailnum	n
	2512
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:

intersect(x, y): return only observations in both x and y
union(x, y): return unique observations in x and y
setdiff(x, y): return observations in x, but not in y.

Given this simple data:

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

x	y
1	1
2	1

(df2 <- data_frame(x = 1:2, y = 1:2))

x	y
1	1
2	2

The four possibilities are:

intersect(df1, df2)

x	y
1	1

# Note that we get 3 rows, not 4
union(df1, df2)

x	y
2	2
2	1
1	1

setdiff(df1, df2)

x	y
2	1

setdiff(df2, df1)

x	y
2	2

Databases

Each two-table verb has a straightforward SQL equivalent:

R	SQL
`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)`
`intersect(x, y)`	`SELECT * FROM x INTERSECT SELECT * FROM y`
`union(x, y)`	`SELECT * FROM x UNION SELECT * FROM y`
`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:

Factors with different levels are coerced to character with a warning:

df1 <- data_frame(x = 1, y = factor("a"))
df2 <- data_frame(x = 2, y = factor("b"))
full_join(df1, df2) %>% str()
#> Joining by: c("x", "y")
#> Warning: joining factors with different levels, coercing to character
#> vector
#> Classes 'tbl_df', 'tbl' and 'data.frame':    2 obs. of  2 variables:
#>  $ x: num  1 2
#>  $ y: chr  "a" "b"

Factors with the same levels in a different order are coerced to character with a warning:

df1 <- data_frame(x = 1, y = factor("a", levels = c("a", "b")))
df2 <- data_frame(x = 2, y = factor("b", levels = c("b", "a")))
full_join(df1, df2) %>% str()
#> Joining by: c("x", "y")
#> Warning: joining factors with different levels, coercing to character
#> vector
#> Classes 'tbl_df', 'tbl' and 'data.frame':    2 obs. of  2 variables:
#>  $ x: num  1 2
#>  $ y: chr  "a" "b"

Factors are preserved only if the levels match exactly:

df1 <- data_frame(x = 1, y = factor("a", levels = c("a", "b")))
df2 <- data_frame(x = 2, y = factor("b", levels = c("a", "b")))
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: Factor w/ 2 levels "a","b": 1 2

A factor and a character are coerced to character with a warning:

df1 <- data_frame(x = 1, y = "a")
df2 <- data_frame(x = 2, y = factor("a"))
full_join(df1, df2) %>% str()
#> Joining by: c("x", "y")
#> Warning: joining factor and character vector, coercing into character
#> vector
#> Classes 'tbl_df', 'tbl' and 'data.frame':    2 obs. of  2 variables:
#>  $ x: num  1 2
#>  $ y: chr  "a" "a"

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)