Looker Studio’s Sneaky Behavior: Uncovering the Mystery of Duplicate Ignoring in Averages

If you’re a data enthusiast working with Looker Studio, you might have stumbled upon a curious phenomenon: it appears that Looker Studio ignores duplicates when calculating averages. But is this really the case? In this in-depth article, we’ll delve into the world of Looker Studio’s averaging mechanism, exploring the reasons behind this behavior and providing you with actionable tips to overcome this ” limitation” and get accurate results.

The Mystery Unfolds: What’s Happening Behind the Scenes

To understand why Looker Studio seemingly ignores duplicates, let’s first examine how the platform processes data. When you create a measure in Looker Studio, the platform performs the following steps:

1. SELECT the desired columns from your dataset.
2. FILTER the data based on the specified conditions.
3. GROUP the data by the chosen dimensions.
4. AGGREGATE the data using the selected aggregate function (e.g., AVERAGE).

During the aggregation step, Looker Studio applies a default behavior, which we’ll explore in the next section.

Unraveling the Default Behavior: Averaging with Distinct Values

By default, Looker Studio calculates averages using distinct values, meaning that duplicate values are ignored in the calculation. This behavior is rooted in the platform’s SQL foundation, specifically in the AVG function. When you use AVG, Looker Studio automatically applies the DISTINCT keyword, which removes duplicates from the calculation.

SELECT AVG(DISTINCT column_name) FROM table_name;

This default behavior can lead to unexpected results, especially when working with datasets containing duplicate values.

Why Ignoring Duplicates Might Be a Problem

In many cases, ignoring duplicates can lead to inaccurate or misleading results. Here are some scenarios where this default behavior can cause issues:

• Data duplication due to data entry errors: If your dataset contains duplicate rows due to data entry mistakes, ignoring these duplicates might mask the actual values.
• Multiple measurements per entity: When you have multiple measurements for a single entity (e.g., multiple sales transactions for a customer), ignoring duplicates can lead to incorrect averages.
• Weighted averages: If you need to calculate weighted averages, ignoring duplicates can skew the results.

Overcoming the Limitation: Techniques for Accurate Averages

Fear not, dear data analyst! There are ways to circumvent Looker Studio’s default behavior and get accurate averages. Here are some techniques to help you overcome the limitation:

Using the COUNT Function

One way to include duplicates in your average calculation is to use the COUNT function in conjunction with the SUM function. This approach allows you to calculate the total value and divide it by the total count, including duplicates.

SELECT SUM(column_name) / COUNT(column_name) FROM table_name;

This method ensures that duplicates are accounted for in the calculation, providing a more accurate average.

Creating a Derived Table

Another approach is to create a derived table that removes duplicates and then calculates the average on the resulting table. This method involves creating a nested query:

WITH duplicate_free AS (
  SELECT DISTINCT column_name FROM table_name
)
SELECT AVG(column_name) FROM duplicate_free;

By removing duplicates in the inner query, you can ensure that the average calculation is performed on the distinct values only.

Using the ROW_NUMBER() Function

In cases where you need to preserve the original dataset and still calculate accurate averages, you can use the ROW_NUMBER() function to assign a unique identifier to each row. Then, you can use this identifier to calculate the average:

WITH numbered_rows AS (
  SELECT column_name, ROW_NUMBER() OVER () AS row_num FROM table_name
)
SELECT AVG(column_name) FROM numbered_rows;

This method allows you to maintain the original dataset while still accounting for duplicates in the average calculation.

Real-World Scenarios: Putting the Techniques into Practice

To demonstrate the effectiveness of these techniques, let’s explore some real-world scenarios:

        +---------+---------+-------+
        | Customer| Order ID| Amount|
        +---------+---------+-------+
        | John    | 1      | 100  |
        | John    | 1      | 100  |
        | Jane    | 2      | 200  |
        | Jane    | 3      | 300  |
        +---------+---------+-------+
      

        +--------+---------+-------+
        | Region  | Sales  | Date  |
        +--------+---------+-------+
        | North  | 100   | 2022-01-01|
        | North  | 100   | 2022-01-01|
        | South  | 200   | 2022-01-15|
        | East   | 300   | 2022-02-01|
        +--------+---------+-------+
      

        +----------+---------+-------+
        | Customer| Rating  | Date  |
        +----------+---------+-------+
        | John    | 4      | 2022-01-01|
        | John    | 4      | 2022-01-01|
        | Jane    | 5      | 2022-01-15|
        | Jane    | 5      | 2022-01-15|
        +----------+---------+-------+
      

These scenarios demonstrate how the techniques outlined above can be applied to real-world problems, ensuring accurate averages in the presence of duplicates.

Conclusion: Mastering Looker Studio’s Averaging Mechanism

In conclusion, while Looker Studio’s default behavior of ignoring duplicates when calculating averages might seem counterintuitive, understanding the underlying mechanics and applying the techniques outlined in this article can help you overcome this limitation. By using the COUNT function, creating derived tables, or leveraging the ROW_NUMBER() function, you can ensure accurate averages in your data analysis.

Remember, as a data analyst, it’s essential to stay vigilant and aware of the subtleties in your tools and data. By doing so, you can unlock the full potential of Looker Studio and uncover insights that might have otherwise gone unnoticed.

Now, go forth and conquer the world of data analysis with your newfound understanding of Looker Studio’s averaging mechanism!

Frequently Asked Question

Get the scoop on Looker studio’s behavior when calculating averages!