This is a continuation of the dplyr basic joins post. In that data toolkit article I covered the most basic joins: left, right, and inner. In this section, I’ll be covering full, semi, and anti joins.
library('tidyverse')
library('dplyr')
To effectively demonstrate dplyr’s join functions, I’ll again have to prepare some data.
tib_1 <- tibble(x = 1:2, y = 2:1)
tib_1
## # A tibble: 2 x 2
## x y
## <int> <int>
## 1 1 2
## 2 2 1
tib_2 <- tibble(x = c(1,3), a = 10, b = "a")
tib_2
## # A tibble: 2 x 3
## x a b
## <dbl> <dbl> <chr>
## 1 1 10 a
## 2 3 10 a
Full Join
The most inclusive join of all, full_join() joins every observation
from both tables, as the name suggests. If there is no match, the cell
is filled in with NA.
full_join(tib_1, tib_2, 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 a
Semi Join
The first of two filter joins, semi_join(), keeps all observations in
the x argument that have a match in the y argument. In this example,
the key is the “x” column of tib_1; the only row that matches in
tib_2 is the observation x = 1. Thus, the resulting semi_join only
has one observation filtered from tib_1.
semi_join(tib_1, tib_2, by = "x")
## # A tibble: 1 x 2
## x y
## <int> <int>
## 1 1 2
Anti Join
anti_join() is the antithesis of semi_join(), the function only
returns observations that are not present in the y dataset.
anti_join(tib_1, tib_2)
## Joining, by = "x"
## # A tibble: 1 x 2
## x y
## <int> <int>
## 1 2 1
That’s it for joining! Really an easy concept once you map out what exactly you’re looking for.
Thanks for reading!
- Fisher
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