Rolling Averages in SQL: Enhancing Data Analysis
Data trends can be unpredictable, and without proper analysis, it is difficult to retrieve relevant insights. With rolling averages, data can be analyzed in a more comprehensive and accurate manner.
Rolling averages, also known as moving averages, are a fundamental statistic tool used to analyze time series data. This article will dive into rolling averages, how to calculate them using SQL window functions, and their importance in data analysis.
What is a Rolling Average? A rolling average is a statistic tool used to analyze multiple data points by calculating the average of a fixed number of values over a certain period of time.
It gives insight into a trend or pattern that might not be visible with individual data points. Rolling averages smooth out the noise present in shortterm fluctuations, making it easier to identify longterm trends.
This tool is widely used in fields like finance, economics, and healthcare to analyze trends over time.
Calculating Rolling Averages in SQL with Window Functions
SQL window functions are a powerful tool for working with data. They compute a calculation across a set of rows that are related to the current row.
Using window functions in SQL, one can calculate rolling averages with ease. The commonly used AVG() function is used along with the ROWS BETWEEN command that analyses data between the current row and a defined range of rows.
To apply a rolling average to a set of data, the following code is implemented:
SELECT date_column,
AVG(value_column)
OVER (
ORDER BY date_column
ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
) AS rolling_avg
FROM table_name;
In this query, the window function will return the average of a specified number of values and the preceding rows of a table. The calculation is carried out every time a new row is processed.
The result is that the rolling average is continuously updated and changes with new data. Example 1: Rolling Average for Stock Prices
Rolling averages are widely used in finance and are a great way to identify trends in stock prices.
By calculating the moving mean of a stock over a specified period, one can determine the trend of the stock. A 50day rolling average calculates the average stock price over the last 50 days.
Using the SQL window function, a query to calculate the rolling average for a specific stock would be:
SELECT date,
AVG(close_price)
OVER (
ORDER BY date
ROWS BETWEEN 49 PRECEDING AND CURRENT ROW
) AS rolling_avg
FROM stock_table
WHERE stock_name = ‘Microsoft’;
This query calculates the rolling average of Microsoft’s stock price over the last 50 days. Example 2: Using Rolling Averages to Discover Trends in New Users
Rolling averages can be used to analyze user activity on a website or application.
For instance, tracking new registered users over a set period using rolling averages can reveal useful insights. By analyzing trends in the number of new users over a given timeline, developers can adjust their products to meet user needs and increase traffic.
Using Common Table Expressions (CTEs) in SQL, the following query can be used:
WITH user_activity AS
(
SELECT date_trunc(‘day’, registration_date) AS day, COUNT(*) AS count
FROM user_table
GROUP BY date_trunc(‘day’, registration_date)
)
SELECT day,
AVG(count)
OVER (
ORDER BY day
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
) AS rolling_avg
FROM user_activity;
The query identifies user activity by grouping users who registered on a specific day, then evaluates the rolling average for a run of 7day periods. Example 3: Rolling Average in Economics
The use of rolling averages in economics is extensive.
One way it’s applied is calculating the Gross Domestic Product (GDP) of a country over a particular period. The GDP can be analyzed using rolling averages to calculate trends and identify growth and contraction periods.
A popular way of calculating rolling averages in economics is by using a 10year or 20year window. For instance, to calculate the 10year rolling average of a country’s GDP, a SQL query would be implemented as follows:
SELECT year, AVG(gdp)
OVER (
ORDER BY year
ROWS BETWEEN 9 PRECEDING AND CURRENT ROW
) AS rolling_avg
FROM gdp_table;
This query calculates the 10year rolling average of a country’s GDP by grouping the GDP values by year and computing the moving average across the data.
Understanding Rolling Averages
Rolling averages are an essential tool in analyzing data trends across different fields. They are particularly relevant in time series data, and they are achieved through the use of SQL window functions.
The importance of rolling averages lies in their ability to smoothen curves and reveal underlying trends that might not be visible in individual data points. They offer a comprehensive and accurate analysis of the data, making them invaluable in various fields.
Employing rolling averages in data analysis provides insights on shortlived tendencies, facilitating better decisionmaking processes.
Calculating Rolling Averages in SQL for Data Analysis
Rolling averages, also known as moving averages, are a powerful tool used in data analysis. They can be used to identify trends, reduce noise in data, and evaluate fluctuations in data over a given period.
In this article, we will explore how to calculate rolling averages in SQL using window functions and look at an example of calculating the rolling average for stock prices.
Using Window Functions in SQL
Window functions are commonly used in SQL to analyze data. Window functions organize rows into partitions based on a specific column and allow calculations to be performed on those partitions individually.
For instance, the average of a column can be calculated for each partition, providing useful insights on the data set.
Window Function Syntax
Window functions in SQL use the following syntax:
SELECT column_name, window_function(column_name)
OVER (
[PARTITION BY partition_column]
ORDER BY column_name
ROWS BETWEEN number PRECEDING AND number FOLLOWING
);
Explanation of window function syntax:
– column_name: represents the column you want to calculate the rolling average for. – partition_column: partitions the data into groups based on a specific column.
– ORDER BY column_name: organizes the data based on the specified column. – ROWS BETWEEN number PRECEDING AND number FOLLOWING: includes a specified number of rows in the calculation.
For example, to calculate the threeday rolling average, the syntax would be ROWS BETWEEN 2 PRECEDING AND 0 FOLLOWING.
Rolling Average Formula
The rolling average calculation is achieved by dividing the sum of a fixed number of values by the number of values. For example, to calculate the 3day rolling average for a set of stock prices, the calculation would be:
(Price of day 1 + Price of day 2 + Price of day 3) / 3
This formula is used to calculate the rolling average for each set of values across the data set.
Example 1: Rolling Average for Stock Prices
To illustrate how to calculate the rolling average in SQL, let’s consider a table of stock values for a particular company over a period of several weeks. The table has the following columns:
– Date: the date the stock price was recorded.
– Price: the price of the stock on a particular date. The data set contains daily stock prices for the past 30 days.
Stock Values Data Set
 Date  Price 
———————
 20210701  43.21 
 20210702  54.32 
 20210703  67.12 
 …  …

 20210730  75.43 
SQL Query for Rolling Average Calculation
To calculate the rolling average for these stock prices, we can use the following SQL query:
SELECT date,
TRUNC(AVG(price)
OVER (
ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
), 2) AS rolling_average
FROM stock_values
ORDER BY date;
In this query, we use the TRUNC function to round the rolling average to two decimal places. We then calculate the rolling average using the AVG function in combination with the OVER and ROWS BETWEEN commands.
Result Set and Rolling Average Calculation Formula
The result set shows the date and the corresponding rolling average for each day:
 Date  Rolling Average 
—————————
 20210701  43.21 
 20210702  48.77 
 20210703  54.22 
 …  …

 20210730  72.96 
The rolling average calculation for July 2nd is:
(43.21 + 54.32 + 67.12) / 3 = 48.77
This shows that the rolling average for July 2nd is $48.77, which provides a better indication of the stock’s overall trend.
Conclusion
In conclusion, rolling averages are a powerful tool in data analysis, enabling data scientists to evaluate trends and make informed decisions based on data. SQL window functions make it easy to calculate rolling averages in a large dataset, providing valuable insights to decisionmakers.
By calculating rolling averages for stock prices, user activity, GDP, and other time series data, organizations can get a better understanding of trends and fluctuations in their data. Example 2: Using Rolling Averages to Discover Trends in New Users
Rolling averages are a valuable tool in analyzing user activity data, and they can help discover trends that might not be immediately apparent.
In this section, we will explore a simple user activity data set and how to calculate a rolling average of registered users using SQL.
User Activity Data Set
The user activity data set well be using contains information about user registration on a website, and it includes the following columns:
– Date: the date a user registered on the website. – Count: the number of users who registered on that particular date.
Transforming Data Format with CTEs
Before calculating the rolling average, we need to transform the data from daily counts to weekly counts. We can accomplish this using Common Table Expressions (CTEs), also called Pseudotables, to group the data by week.
WITH weekly_users AS (
SELECT DATE_TRUNC(‘week’, date) AS week,
SUM(count) AS count
FROM user_activity
GROUP BY week
)
This CTE groups the user registration data by week, calculating the sum of users who registered in each week.
SQL Query for Rolling Average Calculation
Once the data is aggregated by week, we can calculate the rolling average, similar to how we calculated the rolling average for stock prices.
SELECT week,
TRUNC(AVG(count)
OVER (
ORDER BY week ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
), 2) AS rolling_average
FROM weekly_users
ORDER BY week;
In this query, we use the TRUNC function to round the rolling average to two decimal places. The rolling average is calculated using the AVG function in combination with the OVER and ROWS BETWEEN commands.
Result Set and Rolling Average Curves
The result set shows the rolling average of registered users by week:
 Week  Rolling Average 
—————————
 20210103  12.33 
 20210110  15.00 
 20210117  14.33 
 …  …

 20211226  31.00 
The rolling average curve shows the general trend of user registration over the past year. In this example, the average number of weekly registered users has increased since the start of the year.
Example 3: Rolling Average in Economics
Rolling averages are frequently used in the field of economics to analyze data trends in GDP, stock prices, and other economic indicators. In this section, we will look at how to calculate the 10year rolling average of GDP growth rate using SQL.
Yearly GDP Data Set
The yearly GDP data set well be using contains information about a country’s GDP from the last 30 years and includes the following columns:
– Year: the year for which the GDP was recorded. – GDP: the GDP value for the particular year.
SQL Query for Rolling Average Calculation
To calculate the 10year rolling average of GDP growth rate using SQL, we can use a similar query to the ones we used for the previous examples:
SELECT year,
TRUNC(AVG((gdp – LAST_VALUE(gdp)
OVER (
ORDER BY year ROWS BETWEEN 9 PRECEDING AND CURRENT ROW
))/ LAST_VALUE(gdp) * 100
OVER (
ORDER BY year ROWS BETWEEN 9 PRECEDING AND CURRENT ROW
)),
2) AS rolling_average
FROM yearly_gdp
ORDER BY year;
In this query, we calculate the rolling average of GDP growth rate using the TRUNC function to round the result to two decimal places. The rolling average is calculated using AVG() and OVER() functions with the ROWS BETWEEN command.
Result Set and Rolling Average Curves
The result set shows the rolling average of GDP growth rate for each year.  Year  Rolling Average 
———————
 1992  NaN 
 1993  NaN 
 1994  NaN 
 …
 … 
 2020  0.45 
The rolling average curve shows us the general trend of GDP growth rate in the country over the past 30 years.
In this example, the GDP growth rate has been stable, with a few spikes and dips in the economy.
Conclusion
Rolling averages are a powerful tool in data analysis, and SQL window functions make it easy to apply them to a variety of data sets. Whether it’s analyzing stock prices, user activity, or GDP growth rates, the rolling average can help identify trends, smoothing out the noise in a dataset and providing valuable insights to data analysts.
Conclusion
In conclusion, rolling averages in SQL are a powerful tool in data analysis that can provide valuable insights into trends and fluctuations in data over time. Whether it’s identifying stock trends, analyzing user activity, or evaluating economics indicators like GDP growth rate, rolling averages can help analyses gain a more comprehensive understanding of the data.
Benefits of Using Rolling Averages in SQL
Using rolling averages in SQL has several benefits that make it an invaluable tool in data analysis. Some of these benefits include:
– Identifying trends: Rolling averages can help data analysts identify trends and patterns in data that might not be immediately apparent by working with individual data points.
– Filtering out noise: By calculating rolling averages, analysts can smooth out shortterm fluctuations or random variations in the data, allowing for a more accurate analysis of longterm trends. – SQL Skills: Applying rolling averages in SQL requires a proficient knowledge of the SQL language, which is an important skill for data analysts.
Rolling averages help to improve ones proficiency with SQL database management and analytical skills.
Further Learning on Advanced SQL Techniques
SQL window functions have become an essential tool in data analysis. If youre looking to refine your SQL skills further, consider taking an advanced SQL course.
LearnSQL.com is an online learning platform that provides courses in advanced SQL, data modeling, and ETL for both beginners and advanced learners.