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:
Mutating joins, which add new variables to one table from matching rows in another.
Filtering joins, which filter observations from one table based on whether or not they match an observation in the other table.
Set operations, which combine the observations in the data sets as if they were set elements.
(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 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"
#> # A tibble: 336,776 x 9
#> year month day hour origin dest tailnum carrier
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr>
#> 1 2013 1 1 5 EWR IAH N14228 UA
#> 2 2013 1 1 5 LGA IAH N24211 UA
#> 3 2013 1 1 5 JFK MIA N619AA AA
#> 4 2013 1 1 5 JFK BQN N804JB B6
#> 5 2013 1 1 6 LGA ATL N668DN DL
#> ... with 3.368e+05 more rows, and 1 more variables: name <chr>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")
#> # A tibble: 336,776 x 18
#> year month day hour origin dest tailnum carrier temp dewp humid
#> <dbl> <dbl> <int> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2013 1 1 5 EWR IAH N14228 UA NA NA NA
#> 2 2013 1 1 5 LGA IAH N24211 UA NA NA NA
#> 3 2013 1 1 5 JFK MIA N619AA AA NA NA NA
#> 4 2013 1 1 5 JFK BQN N804JB B6 NA NA NA
#> 5 2013 1 1 6 LGA ATL N668DN DL 39.92 26.06 57.33
#> ... with 3.368e+05 more rows, and 7 more variables: wind_dir <dbl>,
#> wind_speed <dbl>, wind_gust <dbl>, precip <dbl>, pressure <dbl>,
#> visib <dbl>, time_hour <time>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")
#> # A tibble: 336,776 x 16
#> year.x month day hour origin dest tailnum carrier year.y
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <int>
#> 1 2013 1 1 5 EWR IAH N14228 UA 1999
#> 2 2013 1 1 5 LGA IAH N24211 UA 1998
#> 3 2013 1 1 5 JFK MIA N619AA AA 1990
#> 4 2013 1 1 5 JFK BQN N804JB B6 2012
#> 5 2013 1 1 6 LGA ATL N668DN DL 1991
#> ... with 3.368e+05 more rows, and 7 more variables: 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"))
#> # A tibble: 336,776 x 14
#> year month day hour origin dest tailnum carrier
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr>
#> 1 2013 1 1 5 EWR IAH N14228 UA
#> 2 2013 1 1 5 LGA IAH N24211 UA
#> 3 2013 1 1 5 JFK MIA N619AA AA
#> 4 2013 1 1 5 JFK BQN N804JB B6
#> 5 2013 1 1 6 LGA ATL N668DN DL
#> ... with 3.368e+05 more rows, and 6 more variables: name <chr>, lat <dbl>,
#> lon <dbl>, alt <int>, tz <dbl>, dst <chr>
flights2 %>% left_join(airports, c("origin" = "faa"))
#> # A tibble: 336,776 x 14
#> year month day hour origin dest tailnum carrier name
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 2013 1 1 5 EWR IAH N14228 UA Newark Liberty Intl
#> 2 2013 1 1 5 LGA IAH N24211 UA La Guardia
#> 3 2013 1 1 5 JFK MIA N619AA AA John F Kennedy Intl
#> 4 2013 1 1 5 JFK BQN N804JB B6 John F Kennedy Intl
#> 5 2013 1 1 6 LGA ATL N668DN DL La Guardia
#> ... with 3.368e+05 more rows, and 5 more variables: lat <dbl>, lon <dbl>,
#> alt <int>, tz <dbl>, dst <chr>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))
#> # A tibble: 2 x 2
#> x y
#> <dbl> <int>
#> 1 1 2
#> 2 2 1
(df2 <- data_frame(x = c(1, 3), a = 10, b = "a"))
#> # A tibble: 2 x 3
#> x a b
#> <dbl> <dbl> <chr>
#> 1 1 10 a
#> 2 3 10 ainner_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"
#> # A tibble: 2 x 4
#> x y a b
#> <dbl> <int> <dbl> <chr>
#> 1 1 2 10 a
#> 2 2 1 NA <NA>right_join(x, y) includes all observations in y. It’s equivalent to left_join(y, x), but the columns will be ordered differently.
df1 %>% right_join(df2)
#> Joining, by = "x"
#> # A tibble: 2 x 4
#> x y a b
#> <dbl> <int> <dbl> <chr>
#> 1 1 2 10 a
#> 2 3 NA 10 a
df2 %>% left_join(df1)
#> Joining, by = "x"
#> # A tibble: 2 x 4
#> x a b y
#> <dbl> <dbl> <chr> <int>
#> 1 1 10 a 2
#> 2 3 10 a NAfull_join() includes all observations from x and y.
df1 %>% full_join(df2)
#> Joining, by = "x"
#> # A tibble: 3 x 4
#> x y a b
#> <dbl> <int> <dbl> <chr>
#> 1 1 2 10 a
#> 2 2 1 NA <NA>
#> 3 3 NA 10 aThe 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"
#> # A tibble: 5 x 3
#> x y z
#> <dbl> <int> <chr>
#> 1 1 1 a
#> 2 1 1 b
#> 3 1 2 a
#> 4 1 2 b
#> 5 2 3 aFiltering 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 observations 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)
#> # A tibble: 722 x 2
#> tailnum n
#> <chr> <int>
#> 1 <NA> 2512
#> 2 N725MQ 575
#> 3 N722MQ 513
#> 4 N723MQ 507
#> 5 N713MQ 483
#> ... with 717 more rowsIf 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] 2The 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 yunion(x, y): return unique observations in x and ysetdiff(x, y): return observations in x, but not in y.Given this simple data:
(df1 <- data_frame(x = 1:2, y = c(1L, 1L)))
#> # A tibble: 2 x 2
#> x y
#> <int> <int>
#> 1 1 1
#> 2 2 1
(df2 <- data_frame(x = 1:2, y = 1:2))
#> # A tibble: 2 x 2
#> x y
#> <int> <int>
#> 1 1 1
#> 2 2 2The four possibilities are:
intersect(df1, df2)
#> # A tibble: 1 x 2
#> x y
#> <int> <int>
#> 1 1 1
# Note that we get 3 rows, not 4
union(df1, df2)
#> # A tibble: 3 x 2
#> x y
#> <int> <int>
#> 1 2 2
#> 2 2 1
#> 3 1 1
setdiff(df1, df2)
#> # A tibble: 1 x 2
#> x y
#> <int> <int>
#> 1 2 1
setdiff(df2, df1)
#> # A tibble: 1 x 2
#> x y
#> <int> <int>
#> 1 2 2Each 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.
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 in full_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): 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 in full_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): 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 2A 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 in full_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): 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): Can't join on 'y' x 'y' because of incompatible types (character / integer)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.