Welcome to "Applying SQL Aggregate Functions to Online Shop Data." In this lesson, you'll discover how to leverage SQL functions such as SUM and AVG to analyze online shopping datasets. You'll also explore how the GROUP BY clause can assist in organizing and summarizing data. Through practical examples, you'll gain insights into extracting valuable information from online shop data. Let's get started!

Great job on making it this far! Thus far, we've covered a great deal, from drilling into COUNT and DISTINCT to exploring SUM and GROUP BY. These are some of the key SQL functions required to dig deep into any dataset. In this unit, we're going to broaden our repertoire by applying these aggregate functions to analyze data related to online shopping transactions.

As you may recall from our previous lessons, aggregate functions allow us to perform calculations on a set of values to return a single scalar value. We've already seen the COUNT and SUM functions in action, but have you ever wondered if we could derive other useful insights, such as averages? That’s where the SQL AVG function comes into play.

At this juncture, the SUM function must seem pretty familiar to you. It does the heavy lifting when we need to find total values. For instance, it calculates total supports selected or total items bought in our case.

On the other hand, the AVG function might be new to you. It's a classic SQL function utilized for calculating the arithmetic mean of a set of values. Simply put, AVG can help us determine an average value, such as the average supports selected per year in the online shopping data.

SQL
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1-- Aggregate total supports selected per each year
2SELECT YEAR(Orders.order_date) as Year, SUM(OrderItems.extended_support) as TotalSupports
3FROM Orders
4JOIN OrderItems ON Orders.order_id = OrderItems.order_id
5GROUP BY YEAR(Orders.order_date);
6
7-- Output:
8-- | Year | TotalSupports |
9-- |------|---------------|
10-- | 2021 |            57 |
11-- | 2022 |            63 |
12-- | 2023 |            60 |

In the above example, we're using the SUM function to find the total supports selected per year in our online shopping data. This is achieved by joining the Orders and OrderItems tables on order_id, where OrderItems records the details of items ordered in each transaction. The GROUP BY clause ensures we get a total supports count for each year, providing a comprehensive view of the transaction trends.

SQL
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1-- Aggregate average supports selected per each year excluding 2022
2SELECT YEAR(Orders.order_date) as Year, AVG(OrderItems.extended_support) as AverageSupports
3FROM Orders
4JOIN OrderItems ON Orders.order_id = OrderItems.order_id
5WHERE YEAR(Orders.order_date) != 2022
6GROUP BY YEAR(Orders.order_date);
7
8-- Output:
9-- | Year | AverageSupports |
10-- |------|-----------------|
11-- | 2021 |          0.3000 |
12-- | 2023 |          0.2913 |

Here, we're introducing the AVG function to find the average supports selected per year in our online shopping data excluding 2022. By filtering orders based on the order_date condition (YEAR(Orders.order_date) != 2022), we focus on the other years. The AVG function calculates the arithmetic mean of supports across these years, offering insights into the trends in customer support service selections over time.

Please note that if 2023 has a lower average than 2021, it indicates that despite having a higher total of extended support selections (60 vs. 57), the proportion of extended support selections to the total number of orders is lower. This suggests there were more instances where extended support was not selected in 2023.

Aggregate functions like SUM and AVG automatically ignore NULL values. However, this can lead to misleading results if you are unaware of their presence.

Suppose the extended_support column contains NULL:

In this case, the result of the AVG would be the following:

• Total: 1 + 0 = 1
• Count of non-NULL rows: 2
• Average: 1/2 = 0.5

To handle NULL values, use COALESCE to replace them with a default value (e.g., 0):

SQL
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1SELECT AVG(COALESCE(OrderItems.extended_support, 0)) AS AverageSupports
2FROM OrderItems;

Thus, always check for NULL values in your dataset when using aggregate functions, especially if the column is not required to have a value, to ensure accuracy in your calculations.

From our past lessons, you should recall that the GROUP BY clause groups a result into subsets that share the same attribute value. It’s a vital component when using aggregate functions like SUM, COUNT, AVG, and others because it enables us to apply these functions to each group of data independently, providing us with insightful segmented data.

As you've noticed in our examples, GROUP BY plays an essential role when using aggregate functions. We use GROUP BY to return a separate sum or average for each year, allowing us to analyze the transaction trends in a structured manner.

Excellent work on learning how to use the SUM and AVG functions and mastering their symbiotic relationship with the GROUP BY clause. Using these functions isn't always straightforward, but with practice, it will become second nature.

Congratulations on completing this lesson of the course! Let's continue practicing to solidify this knowledge and enhance your SQL skills further.