RestaurantGrowth.sql

-- Table: Customer

-- +---------------+---------+
-- | Column Name   | Type    |
-- +---------------+---------+
-- | customer_id   | int     |
-- | name          | varchar |
-- | visited_on    | date    |
-- | amount        | int     |
-- +---------------+---------+
-- In SQL,(customer_id, visited_on) is the primary key for this table.
-- This table contains data about customer transactions in a restaurant.
-- visited_on is the date on which the customer with ID (customer_id) has visited the restaurant.
-- amount is the total paid by a customer.
 
-- You are the restaurant owner and you want to analyze a possible expansion (there will be at least one customer every day).

-- Compute the moving average of how much the customer paid in a seven days window (i.e., current day + 6 days before). 
-- average_amount should be rounded to two decimal places.

-- Return the result table ordered by visited_on in ascending order.

-- The result format is in the following example.

-- Example 1:

-- Input: 
-- Customer table:
-- +-------------+--------------+--------------+-------------+
-- | customer_id | name         | visited_on   | amount      |
-- +-------------+--------------+--------------+-------------+
-- | 1           | Jhon         | 2019-01-01   | 100         |
-- | 2           | Daniel       | 2019-01-02   | 110         |
-- | 3           | Jade         | 2019-01-03   | 120         |
-- | 4           | Khaled       | 2019-01-04   | 130         |
-- | 5           | Winston      | 2019-01-05   | 110         | 
-- | 6           | Elvis        | 2019-01-06   | 140         | 
-- | 7           | Anna         | 2019-01-07   | 150         |
-- | 8           | Maria        | 2019-01-08   | 80          |
-- | 9           | Jaze         | 2019-01-09   | 110         | 
-- | 1           | Jhon         | 2019-01-10   | 130         | 
-- | 3           | Jade         | 2019-01-10   | 150         | 
-- +-------------+--------------+--------------+-------------+
-- Output: 
-- +--------------+--------------+----------------+
-- | visited_on   | amount       | average_amount |
-- +--------------+--------------+----------------+
-- | 2019-01-07   | 860          | 122.86         |
-- | 2019-01-08   | 840          | 120            |
-- | 2019-01-09   | 840          | 120            |
-- | 2019-01-10   | 1000         | 142.86         |
-- +--------------+--------------+----------------+
-- Explanation: 
-- 1st moving average from 2019-01-01 to 2019-01-07 has an average_amount of (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86
-- 2nd moving average from 2019-01-02 to 2019-01-08 has an average_amount of (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120
-- 3rd moving average from 2019-01-03 to 2019-01-09 has an average_amount of (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120
-- 4th moving average from 2019-01-04 to 2019-01-10 has an average_amount of (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86


-- Write your PostgreSQL query statement below
-- Solution
select visited_on,
       amount,
       round(average_amount::numeric, 2) as average_amount
from
(
    SELECT
        visited_on,
        SUM(amount) OVER (
            ORDER BY visited_on
            ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
        ) AS amount,
        AVG(amount) OVER (
            ORDER BY visited_on
            ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
        ) AS average_amount
    FROM
        (
            select visited_on,
                sum(amount) as amount
            from Customer 
            group by visited_on
        )
) where visited_on >= (select min(visited_on) + 6 from Customer);