library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
# Creating datasets
customers <- tibble(
customer_id = c(1, 2, 3, 4, 5),
name = c("Alice", "Bob", "Charlie", "David", "Eve"),
city = c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix")
)
orders <- tibble(
order_id = c(101, 102, 103, 104, 105, 106),
customer_id = c(1, 2, 3, 2, 6, 7),
product = c("Laptop", "Phone", "Tablet", "Desktop", "Camera", "Printer"),
amount = c(1200, 800, 300, 1500, 600, 150)
)
# Inner Join
inner_join_result <- inner_join(customers, orders, by = "customer_id")
print(inner_join_result)
## # 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
cat("Rows in Inner Join:", nrow(inner_join_result), "\n")
## Rows in Inner Join: 4
# Left Join
left_join_result <- left_join(customers, orders, by = "customer_id")
print(left_join_result)
## # 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
cat("Rows in Left Join:", nrow(left_join_result), "\n")
## Rows in Left Join: 6
# Right Join
right_join_result <- right_join(customers, orders, by = "customer_id")
print(right_join_result)
## # 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
cat("Rows in Right Join:", nrow(right_join_result), "\n")
## Rows in Right Join: 6
# Full Join
full_join_result <- full_join(customers, orders, by = "customer_id")
print(full_join_result)
## # 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
cat("Rows in Full Join:", nrow(full_join_result), "\n")
## Rows in Full Join: 8
# Semi Join
semi_join_result <- semi_join(customers, orders, by = "customer_id")
print(semi_join_result)
## # 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
cat("Rows in Semi Join:", nrow(semi_join_result), "\n")
## Rows in Semi Join: 3
# Anti Join
anti_join_result <- anti_join(customers, orders, by = "customer_id")
print(anti_join_result)
## # A tibble: 2 × 3
## customer_id name city
## <dbl> <chr> <chr>
## 1 4 David Houston
## 2 5 Eve Phoenix
cat("Customers without orders:\n")
## Customers without orders:
print(anti_join_result)
## # A tibble: 2 × 3
## customer_id name city
## <dbl> <chr> <chr>
## 1 4 David Houston
## 2 5 Eve Phoenix
# Finding all customers (including those without orders) - Left Join
all_customers <- left_join(customers, orders, by = "customer_id")
print(all_customers)
## # 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
# Finding only customers who have placed orders - Inner Join
customers_with_orders <- inner_join(customers, orders, by = "customer_id")
print(customers_with_orders)
## # 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: Summary of customer orders
customer_summary <- left_join(customers, orders, by = "customer_id") %>%
group_by(customer_id, name, city) %>%
summarize(
total_orders = n(),
total_amount_spent = sum(amount, na.rm = TRUE),
.groups = "drop"
)
print(customer_summary)
## # A tibble: 5 × 5
## 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