Aprenda Advanced Joins and Handling Mismatched Keys

Advanced joins in R allow you to merge data frames in more flexible ways, especially when your data sets do not perfectly align. While left_join() and inner_join() are commonly used, there are scenarios where you need to keep all records from one or both data frames, regardless of whether a match exists. In these cases, right_join() and full_join() become essential tools. You use these joins when you want to ensure that no information is lost from one or both tables, such as when combining transaction logs with master lists or merging survey responses with a complete sample roster.


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# Example: right_join() to keep all orders, even those without matching customers
library(dplyr)

customers <- data.frame(
  customer_id = c(1, 2, 3),
  name = c("Alice", "Bob", "Carol")
)

orders <- data.frame(
  order_id = c(101, 102, 103, 104),
  customer_id = c(1, 2, 4, 2),
  amount = c(50, 75, 100, 60)
)

right_join(customers, orders, by = "customer_id")

The right_join() function is used when you want to keep all rows from the right data frame, regardless of whether there is a matching key in the left data frame. In the example above, you keep all orders—even those where the customer_id does not exist in the customers data frame. This is useful when your main interest is in the complete set of orders, and you want to attach customer information where available. Any order with a customer_id not found in the customers list will have NA in the customer columns.


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# Example: full_join() to combine all customers and orders, including unmatched rows
library(dplyr)

customers <- data.frame(
  customer_id = c(1, 2, 3),
  name = c("Alice", "Bob", "Carol")
)

orders <- data.frame(
  order_id = c(101, 102, 103, 104),
  customer_id = c(1, 2, 4, 2),
  amount = c(50, 75, 100, 60)
)

full_join(customers, orders, by = "customer_id")

The full_join() function merges two data frames and keeps all rows from both, inserting NA for any columns where there is no match. This means every customer and every order will appear in the result, even if a customer has no orders or an order has no matching customer. Handling NA values becomes important in this situation, as they indicate missing data due to unmatched keys. You may want to filter or flag these rows for further investigation or cleaning, depending on your analysis goals.

Note

Use anti_join() to find rows in one data frame that do not have a match in the other;
Check for NA values after a join to spot missing matches quickly;
Consider whether unmatched rows should be excluded, imputed, or highlighted in your analysis.

1. When would you use a full_join() instead of a left_join()?

2. What happens to rows with no match in a full_join()?

3. How can you identify rows that do not have a match in either data frame?

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              123456789101112131415
            
# Example: right_join() to keep all orders, even those without matching customers
library(dplyr)

customers <- data.frame(
  customer_id = c(1, 2, 3),
  name = c("Alice", "Bob", "Carol")
)

orders <- data.frame(
  order_id = c(101, 102, 103, 104),
  customer_id = c(1, 2, 4, 2),
  amount = c(50, 75, 100, 60)
)

right_join(customers, orders, by = "customer_id")


              123456789101112131415
            
# Example: full_join() to combine all customers and orders, including unmatched rows
library(dplyr)

customers <- data.frame(
  customer_id = c(1, 2, 3),
  name = c("Alice", "Bob", "Carol")
)

orders <- data.frame(
  order_id = c(101, 102, 103, 104),
  customer_id = c(1, 2, 4, 2),
  amount = c(50, 75, 100, 60)
)

full_join(customers, orders, by = "customer_id")