Inner Join (3 points) Perform an inner join between the customers and orders datasets.

q1 <- inner_join(customers , orders , by = 'customer_id')

How many rows are in the result?

There are 4 rows

Why are some customers or orders not included in the result?

They did not have a match in the other table

Display the result

q1
## # A tibble: 4 × 6
##   customer_id name    city        order_id product amount
##         <dbl> <chr>   <chr>          <dbl> <chr>    <dbl>
## 1           1 Alice   New York         101 Laptop    1200
## 2           2 Bob     Los Angeles      102 Phone      800
## 3           2 Bob     Los Angeles      104 Desktop   1500
## 4           3 Charlie Chicago          103 Tablet     300

Left Join (3 points) Perform a left join with customers as the left table and orders as the right table.

q2 <- left_join (customers , orders , by = 'customer_id')

How many rows are in the result?

there are 6 rows

Explain why this number differs from the inner join result.

Because all customers are included, even those without matching orders

Display the result

q2
## # A tibble: 6 × 6
##   customer_id name    city        order_id product amount
##         <dbl> <chr>   <chr>          <dbl> <chr>    <dbl>
## 1           1 Alice   New York         101 Laptop    1200
## 2           2 Bob     Los Angeles      102 Phone      800
## 3           2 Bob     Los Angeles      104 Desktop   1500
## 4           3 Charlie Chicago          103 Tablet     300
## 5           4 David   Houston           NA <NA>        NA
## 6           5 Eve     Phoenix           NA <NA>        NA

Right Join (3 points) Perform a right join with customers as the left table and orders as the right table.

q3 <- right_join (customers , orders , by = 'customer_id')

How many rows are in the result?

there are 6 rows

Which customer_ids in the result have NULL for customer name and city? Explain why.

Customers 6 and 7 because they are present in the Orders table but not in the Customers table

Display the result

q3
## # A tibble: 6 × 6
##   customer_id name    city        order_id product amount
##         <dbl> <chr>   <chr>          <dbl> <chr>    <dbl>
## 1           1 Alice   New York         101 Laptop    1200
## 2           2 Bob     Los Angeles      102 Phone      800
## 3           2 Bob     Los Angeles      104 Desktop   1500
## 4           3 Charlie Chicago          103 Tablet     300
## 5           6 <NA>    <NA>             105 Camera     600
## 6           7 <NA>    <NA>             106 Printer    150

Full Join (3 points) Perform a full join between customers and orders.

q4 <- full_join (customers, orders, by = 'customer_id')

How many rows are in the result?

there are 8 rows

Identify any rows where there’s information from only one table. Explain these results.

Customers 4 and 5 with NA in order_id, product, and amount because they have no orders. Orders 105 and 106 with NA in name and city because there are no matching customers.

Display the result

q4
## # A tibble: 8 × 6
##   customer_id name    city        order_id product amount
##         <dbl> <chr>   <chr>          <dbl> <chr>    <dbl>
## 1           1 Alice   New York         101 Laptop    1200
## 2           2 Bob     Los Angeles      102 Phone      800
## 3           2 Bob     Los Angeles      104 Desktop   1500
## 4           3 Charlie Chicago          103 Tablet     300
## 5           4 David   Houston           NA <NA>        NA
## 6           5 Eve     Phoenix           NA <NA>        NA
## 7           6 <NA>    <NA>             105 Camera     600
## 8           7 <NA>    <NA>             106 Printer    150

Semi Join (3 points) Perform a semi join with customers as the left table and orders as the right table.

q5 <- semi_join (customers, orders, by = 'customer_id')

How many rows are in the result?

there are 3 rows

How does this result differ from the inner join result?

Only includes customers with orders, but no order details are shown.

Display the result

q5
## # A tibble: 3 × 3
##   customer_id name    city       
##         <dbl> <chr>   <chr>      
## 1           1 Alice   New York   
## 2           2 Bob     Los Angeles
## 3           3 Charlie Chicago

Anti Join (3 points) Perform an anti join with customers as the left table and orders as the right table.

q6 <- anti_join (customers, orders, by = 'customer_id')

Which customers are in the result?

there are 2 rows

Explain what this result tells you about these customers.

David and Eve (customers 4 and 5) have no orders.

Display the result

q6
## # A tibble: 2 × 3
##   customer_id name  city   
##         <dbl> <chr> <chr>  
## 1           4 David Houston
## 2           5 Eve   Phoenix

Practical Application (4 points) Imagine you’re analyzing customer behavior.

Which join would you use to find all customers, including those who haven’t placed any orders? Why?

left join because it keeps all customers from the left table (customers), even if they have no matching orders in the right table. # Which join would you use to find only the customers who have placed orders? Why? Inner join because it only includes customers who have matching entries in the orders table. # Write the R code for both scenarios.

q7a <- left_join (customers, orders, by = "customer_id")
q7b <- inner_join(customers, orders, by = "customer_id")

Display the result

q7a
## # A tibble: 6 × 6
##   customer_id name    city        order_id product amount
##         <dbl> <chr>   <chr>          <dbl> <chr>    <dbl>
## 1           1 Alice   New York         101 Laptop    1200
## 2           2 Bob     Los Angeles      102 Phone      800
## 3           2 Bob     Los Angeles      104 Desktop   1500
## 4           3 Charlie Chicago          103 Tablet     300
## 5           4 David   Houston           NA <NA>        NA
## 6           5 Eve     Phoenix           NA <NA>        NA
q7b
## # A tibble: 4 × 6
##   customer_id name    city        order_id product amount
##         <dbl> <chr>   <chr>          <dbl> <chr>    <dbl>
## 1           1 Alice   New York         101 Laptop    1200
## 2           2 Bob     Los Angeles      102 Phone      800
## 3           2 Bob     Los Angeles      104 Desktop   1500
## 4           3 Charlie Chicago          103 Tablet     300

Challenge Question (3 points) Create a summary that shows each customer’s name, city, total number of orders, and total amount spent. Include all customers, even those without orders. Hint: You’ll need to use a combination of joins and group_by/summarize operations.

q8 <- full_join(customers, orders, by = "customer_id") %>%
  group_by(customer_id , name, city) %>%
  summarize(
    total_orders = n(),
    total_amount_spent = sum(amount, na.rm = TRUE)
  )
## `summarise()` has grouped output by 'customer_id', 'name'. You can override
## using the `.groups` argument.
print(q8)
## # A tibble: 7 × 5
## # Groups:   customer_id, name [7]
##   customer_id name    city        total_orders total_amount_spent
##         <dbl> <chr>   <chr>              <int>              <dbl>
## 1           1 Alice   New York               1               1200
## 2           2 Bob     Los Angeles            2               2300
## 3           3 Charlie Chicago                1                300
## 4           4 David   Houston                1                  0
## 5           5 Eve     Phoenix                1                  0
## 6           6 <NA>    <NA>                   1                600
## 7           7 <NA>    <NA>                   1                150