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Assignment two #2

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247 changes: 247 additions & 0 deletions 02_activities/assignments/assignment2 copy.sql
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--Windowed Functions
/* 1. Write a query that selects from the customer_purchases table and numbers each customer’s
visits to the farmer’s market (labeling each market date with a different number).
Each customer’s first visit is labeled 1, second visit is labeled 2, etc.

You can either display all rows in the customer_purchases table, with the counter changing on
each new market date for each customer, or select only the unique market dates per customer
(without purchase details) and number those visits.
HINT: One of these approaches uses ROW_NUMBER() and one uses DENSE_RANK(). */

---WINDOW functions part one

SELECT
customer_id,
market_date,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY market_date
) AS visit_number
FROM (
SELECT DISTINCT customer_id, market_date
FROM customer_purchases
) AS unique_visits
ORDER BY customer_id, market_date;

--/* 2. Reverse the numbering of the query from a part so each customer’s most recent visit is labeled 1, then write another query that uses this one as a subquery (or temp table) and filters the results to only the customer’s most recent visit. */
---WINDOW functions part two

SELECT
customer_id,
market_date,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY market_date DESC
) AS reversed_visit_number
FROM customer_purchases;

/* 3. Using a COUNT() window function, include a value along with each row of the
customer_purchases table that indicates how many different times that customer has purchased that product_id. */
---WINDOW functions part three

SELECT
customer_id,
product_id,
market_date,
COUNT(*) OVER (
PARTITION BY customer_id, product_id
) AS total_purchases_for_product
FROM customer_purchases;



--String Manipulations
/* 1. Some product names in the product table have descriptions like "Jar" or "Organic".
These are separated from the product name with a hyphen.
Create a column using SUBSTR (and a couple of other commands) that captures these, but is otherwise NULL.
Remove any trailing or leading whitespaces. Don't just use a case statement for each product!

| product_name | description |
|----------------------------|-------------|
| Habanero Peppers - Organic | Organic |

Hint: you might need to use INSTR(product_name,'-') to find the hyphens. INSTR will help split the column. */

SELECT
product_name,
CASE
WHEN INSTR(product_name, '-') > 0 THEN
TRIM(SUBSTR(
product_name,
INSTR(product_name, '-') + 1
))
ELSE
NULL
END AS product_description
FROM product;

/* 2. Filter the query to show any product_size value that contain a number with REGEXP. */
SELECT
product_name,
CASE
WHEN INSTR(product_name, '-') > 0 THEN
TRIM(SUBSTR(
product_name,
INSTR(product_name, '-') + 1
))
ELSE
NULL
END AS product_description
FROM product
WHERE product_size REGEXP '[0-9]';

--Unions
/* 1. Using a UNION, write a query that displays the market dates with the highest and lowest total sales.

HINT: There are a possibly a few ways to do this query, but if you're struggling, try the following:
1) Create a CTE/Temp Table to find sales values grouped dates;
2) Create another CTE/Temp table with a rank windowed function on the previous query to create
"best day" and "worst day";
3) Query the second temp table twice, once for the best day, once for the worst day,
with a UNION binding them. */

WITH total_sales_by_date AS (
SELECT
market_date,
SUM(quantity * cost_to_customer_per_qty) AS total_sales
FROM customer_purchases
GROUP BY market_date
),
ranked_dates AS (
SELECT
market_date,
total_sales,
RANK() OVER (ORDER BY total_sales DESC) AS best_day_rank,
RANK() OVER (ORDER BY total_sales ASC) AS worst_day_rank
FROM total_sales_by_date
)
SELECT
'Highest' AS sale_type,
market_date,
total_sales
FROM ranked_dates
WHERE best_day_rank = 1

UNION

SELECT
'Lowest' AS sale_type,
market_date,
total_sales
FROM ranked_dates
WHERE worst_day_rank = 1;

--Section three cross join
/* SECTION 3 */

-- Cross Join
/*1. Suppose every vendor in the `vendor_inventory` table had 5 of each of their products to sell to **every**
customer on record. How much money would each vendor make per product?
Show this by vendor_name and product name, rather than using the IDs.

HINT: Be sure you select only relevant columns and rows.
Remember, CROSS JOIN will explode your table rows, so CROSS JOIN should likely be a subquery.
Think a bit about the row counts: how many distinct vendors, product names are there (x)?
How many customers are there (y).
Before your final group by you should have the product of those two queries (x*y). */


-- 1) Create a CTE ("vendor_products") of distinct (vendor, product) pairs,
-- pulling in the vendor_name, product_name, and original_price.
WITH vendor_products AS (
SELECT DISTINCT
v.vendor_id,
v.vendor_name,
p.product_id,
p.product_name,
vi.original_price
FROM vendor_inventory vi
JOIN vendor v
ON vi.vendor_id = v.vendor_id
JOIN product p
ON vi.product_id = p.product_id
),

-- 2) Create a CTE ("all_customers") listing every customer
all_customers AS (
SELECT c.customer_id
FROM customer c
),

-- 3) CROSS JOIN the two sets:
-- For every (vendor, product) pair, we get every customer.
vendor_product_customers AS (
SELECT
vp.vendor_id,
vp.vendor_name,
vp.product_id,
vp.product_name,
vp.original_price,
ac.customer_id
FROM vendor_products vp
CROSS JOIN all_customers ac
)

-- 4) GROUP BY vendor_name and product_name.
-- Each customer buys 5 units, so total_units_sold = 5 * count_of_customers
-- total_revenue = (5 * count_of_customers) * original_price.
SELECT
vendor_name,
product_name,
5 * COUNT(*) AS total_units_sold,
5 * COUNT(*) * original_price AS total_revenue
FROM vendor_product_customers
GROUP BY
vendor_name,
product_name,
original_price
ORDER BY
vendor_name,
product_name;

--Insert part 1
/*1. Create a new table "product_units".
This table will contain only products where the `product_qty_type = 'unit'`.
It should use all of the columns from the product table, as well as a new column for the `CURRENT_TIMESTAMP`.
Name the timestamp column `snapshot_timestamp`. */

SELECT
p.*,
CURRENT_TIMESTAMP AS snapshot_timestamp
FROM product p
WHERE p.product_qty_type = 'unit';

--- part 2
/*2. Using `INSERT`, add a new row to the product_units table (with an updated timestamp).
This can be any product you desire (e.g. add another record for Apple Pie). */
INSERT INTO product_units (
product_id,
product_name,
product_size,
product_qty_type,
snapshot_timestamp
)
VALUES (
999,
'Apple Pie Deluxe', -
'Large', -
'unit',
CURRENT_TIMESTAMP
);

-- DELETE
/* 1. Delete the older record for the whatever product you added.

HINT: If you don't specify a WHERE clause, you are going to have a bad time.*/
DELETE FROM product_units
WHERE product_id = 999;

-- UPDATE
/* 1.We want to add the current_quantity to the product_units table.
First, add a new column, current_quantity to the table using the following syntax.

--Section 4
--The article brings up important ethical issues related to how we rely on people behind both old-fashioned crafts and new technologies. For example, websites like Amazon Mechanical Turk use workers who do tiny tasks like labeling data for machine learning. These workers often earn very little money and don’t have job security, even though their work is essential for training AI systems. Just like your sewing project required effort that wasn’t always appreciated, these workers’ contributions are crucial but frequently overlooked and underpaid. Additionally, because people have their own opinions and biases, the data they label can sometimes be unfair or biased, leading to AI systems that might make mistakes or treat certain groups unfairly, such as in facial recognition or hiring processes. To fix these problems, it’s important to pay workers fairly and ensure that the data they work on is accurate and unbiased.
Furthermore, the growth of Large Language Models (LLMs) like ChatGPT brings more ethical challenges. Training these powerful AI systems uses a lot of energy, which can harm the environment. There’s also the risk that AI can create false or misleading information, which can be used to spread lies or manipulate people’s opinions. When it comes to moderating content online, human workers often have to deal with upsetting material, which can be very stressful, while automated systems might incorrectly block or allow content because of their own biases. All these issues show how much we depend on human effort to make technology work properly. As technology advances, it’s crucial to design AI systems that are fair, accountable, and inclusive, making sure that the people who build and maintain these systems are treated well and that the technology benefits everyone in a responsible way.

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