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 name
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 2013 1 1 5 EWR IAH N14228 UA United Air Lines Inc.
#> 2 2013 1 1 5 LGA IAH N24211 UA United Air Lines Inc.
#> 3 2013 1 1 5 JFK MIA N619AA AA American Airlines Inc.
#> 4 2013 1 1 5 JFK BQN N804JB B6 JetBlue Airways
#> 5 2013 1 1 6 LGA ATL N668DN DL Delta Air Lines Inc.
#> # … with 3.368e+05 more rowsAs 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
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2013 1 1 5 EWR IAH N14228 UA 39.0 28.0 64.4
#> 2 2013 1 1 5 LGA IAH N24211 UA 39.9 25.0 54.8
#> 3 2013 1 1 5 JFK MIA N619AA AA 39.0 27.0 61.6
#> 4 2013 1 1 5 JFK BQN N804JB B6 39.0 27.0 61.6
#> 5 2013 1 1 6 LGA ATL N668DN DL 39.9 25.0 54.8
#> # … 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 <dttm>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 type
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 2013 1 1 5 EWR IAH N14228 UA 1999 Fixe…
#> 2 2013 1 1 5 LGA IAH N24211 UA 1998 Fixe…
#> 3 2013 1 1 5 JFK MIA N619AA AA 1990 Fixe…
#> 4 2013 1 1 5 JFK BQN N804JB B6 2012 Fixe…
#> 5 2013 1 1 6 LGA ATL N668DN DL 1991 Fixe…
#> # … with 3.368e+05 more rows, and 6 more variables: 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 y. 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 15
#> year month day hour origin dest tailnum carrier name lat lon alt
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2013 1 1 5 EWR IAH N14228 UA Geor… 30.0 -95.3 97
#> 2 2013 1 1 5 LGA IAH N24211 UA Geor… 30.0 -95.3 97
#> 3 2013 1 1 5 JFK MIA N619AA AA Miam… 25.8 -80.3 8
#> 4 2013 1 1 5 JFK BQN N804JB B6 <NA> NA NA NA
#> 5 2013 1 1 6 LGA ATL N668DN DL Hart… 33.6 -84.4 1026
#> # … with 3.368e+05 more rows, and 3 more variables: tz <dbl>, dst <chr>,
#> # tzone <chr>
flights2 %>% left_join(airports, c("origin" = "faa"))
#> # A tibble: 336,776 x 15
#> year month day hour origin dest tailnum carrier name lat lon alt
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2013 1 1 5 EWR IAH N14228 UA Newa… 40.7 -74.2 18
#> 2 2013 1 1 5 LGA IAH N24211 UA La G… 40.8 -73.9 22
#> 3 2013 1 1 5 JFK MIA N619AA AA John… 40.6 -73.8 13
#> 4 2013 1 1 5 JFK BQN N804JB B6 John… 40.6 -73.8 13
#> 5 2013 1 1 6 LGA ATL N668DN DL La G… 40.8 -73.9 22
#> # … with 3.368e+05 more rows, and 3 more variables: tz <dbl>, dst <chr>,
#> # tzone <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:
inner_join(x, y) only includes observations that match in both x and y.
| 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.
right_join(x, y) includes all observations in y. It’s equivalent to left_join(y, x), but the columns will be ordered differently.
full_join() includes all observations from x and y.
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.
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 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 <- tibble(x = c(1, 1, 3, 4), y = 1:4)
df2 <- tibble(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 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 <- tibble(x = 1:2, y = c(1L, 1L)))
#> # A tibble: 2 x 2
#> x y
#> <int> <int>
#> 1 1 1
#> 2 2 1
(df2 <- tibble(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 1 1
#> 2 2 1
#> 3 2 2
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 2When 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 <- tibble(x = 1, y = factor("a"))
df2 <- tibble(x = 2, y = factor("b"))
full_join(df1, df2) %>% str()
#> Joining, by = c("x", "y")
#> Warning: Column `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 <- tibble(x = 1, y = factor("a", levels = c("a", "b")))
df2 <- tibble(x = 2, y = factor("b", levels = c("b", "a")))
full_join(df1, df2) %>% str()
#> Joining, by = c("x", "y")
#> Warning: Column `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 <- tibble(x = 1, y = factor("a", levels = c("a", "b")))
df2 <- tibble(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 <- tibble(x = 1, y = "a")
df2 <- tibble(x = 2, y = factor("a"))
full_join(df1, df2) %>% str()
#> Joining, by = c("x", "y")
#> Warning: Column `y` joining character vector and factor, 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 <- tibble(x = 1, y = 1L)
df2 <- tibble(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 <- tibble(x = 1, y = 1L)
df2 <- tibble(x = 2, y = "a")
full_join(df1, df2) %>% str()
#> Joining, by = c("x", "y")
#> Error: 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 purrr::reduce() or Reduce(), as described in Advanced R, to iteratively combine the two-table verbs to handle as many tables as you need.