The relationships between tables can be seen in a relational graph. The variables used to connect each pair of tables are called keys. A key is a variable (or set of variables) that uniquely identifies an observation.
If you have to combine data from 2 tables in a a new table, you will use `joints`.
There are several types of joints depending of what you want to get.
```{r joints, echo=FALSE, out.width='100%'}
knitr::include_graphics('img/join-venn.png')
```
## `inner_joint()`
Small concrete examples:
```{r , echo=FALSE, out.width='100%'}
knitr::include_graphics('img/overview_joins.png')
```
### `inner_joint()`
Matches pairs of observations whenever their keys are equal
keeps observations in `x` AND `y`
```{r inner_joint, eval=T}
flights2 %>%
inner_join(airlines)
```
## `left_joint()`
### `left_joint()`
keeps all observations in `x`
...
...
@@ -229,7 +244,7 @@ flights2 %>%
left_join(airlines)
```
## `right_joint()`
### `right_joint()`
keeps all observations in `y`
...
...
@@ -238,7 +253,7 @@ flights2 %>%
right_join(airlines)
```
## `full_joint()`
### `full_joint()`
keeps all observations in `x` and `y`
...
...
@@ -251,29 +266,41 @@ flights2 %>%
The default, `by = NULL`, uses all variables that appear in both tables, the so called natural join.
```{r left_join_weather, eval=T}
```{r , eval=T}
flights2 %>%
left_join(weather)
```
## Defining the key columns
If the two tables contain columns with the same names but corresponding to different things (such as `year` in `flights2` and `planes`) you have to manually define the key or the keys.
The default, `by = NULL`, uses all variables that appear in both tables, the so called natural join.
```{r left_join_tailnum, eval=T, echo = T}
```{r , eval=T, echo = T}
flights2 %>%
left_join(planes, by = "tailnum")
```
## Defining the key columns
A named character vector: `by = c("a" = "b")`. This will match variable `a` in table `x` to variable `b` in table `y`.
If you want to join by data that are in two columns with different names, you must specify the correspondence with a named character vector: `by = c("a" = "b")`. This will match variable `a` in table `x` to variable `b` in table `y`.
```{r left_join_airport, eval=T, echo = T}
```{r , eval=T, echo = T}
flights2 %>%
left_join(airports, c("dest" = "faa"))
```
If 2 columns have identical names in the input tables but are not used in the join, they are automatically renamed with the suffix `.x` and `.y` because all column names must be different in the output table.
```{r , eval=T, echo = T}
flights2 %>%
left_join(airports, c("dest" = "faa")) %>%
left_join(airports, c("origin" = "faa"))
```
You can change the suffix using the option `suffix`
```{r , eval=T, echo = T}
flights2 %>%
left_join(airports, by = c("dest" = "faa")) %>%
left_join(airports, by = c("origin" = "faa"), suffix = c(".dest",".origin"))
```
## Filtering joins
Filtering joins match observations in the same way as mutating joins, but affect the observations, not the variables. There are two types:
...
...
@@ -281,9 +308,6 @@ Filtering joins match observations in the same way as mutating joins, but affect
- `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`.
## Filtering joins
```{r top_dest, eval=T, echo = T}
top_dest <- flights %>%
count(dest, sort = TRUE) %>%
...
...
@@ -300,4 +324,8 @@ These expect the x and y inputs to have the same variables, and treat the observ
- `union(x, y)`: return unique observations in `x` and `y`.
- `setdiff(x, y)`: return observations in `x`, but not in `y`.
```{r , echo=FALSE, out.width='100%'}
knitr::include_graphics('img/overview_set.png')
```
## See you in [R.7: String & RegExp](http://perso.ens-lyon.fr/laurent.modolo/R/session_7/)
