A full outer join is useful when you want to combine two datasets and retain all rows from both datasets, even if there are no matching values in the join key.
For example, let's say you have two datasets orders and products, and you want to combine them to create a single dataset containing all orders and their associated product information. However, some orders may not have a matching product record, and some products may not have been ordered yet. In this case, a full outer join can be used to combine the two datasets and retain all orders and products, even if there are no matching values in the join key.
In R, you can use the data.table package to perform a full outer join using the "merge" operation. Here's an example of how to do it: First, let's create two data tables. We'll call them table1 and table2:
library(data.table)
dt1 <- data.table(id = c(1, 2, 3), name = c("John", "Mary", "Bob"))
dt2 <- data.table(id = c(2, 3, 4), age = c(30, 40, 50))
To perform a full outer join on these data.tables, you can use the merge() function with the all = TRUE argument:
dt_full <- merge(dt1, dt2, by = "id", all = TRUE)
The by argument specifies the columns to merge on, which in this case is the id column. The all = TRUE argument tells merge() to perform a full outer join.
The resulting dt_full data.table will contain all rows from both dt1 and dt2, with missing values (NA) in the columns that do not have a matching value in the other data.table.
> dt_full
id name age
1: 1 John NA
2: 2 Mary 30
3: 3 Bob 40
4: 4 NA 50