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SQL Lag Function & Its Use Cases (with Exmaples)

  • Jan 25, 2024
  • 8 Minutes Read
SQL Lag Function & Its Use Cases (with Exmaples)

The SQL Lag function is a robust tool, allowing users to access the data from earlier rows in a dataset. It lets its users look at data from earlier rows in a give­n set of data. It checks out values one­-by-one, espe­cially those related to time. We can eliminate self-joins thanks to the Lag function. In this article, we will learn about Lag Function in SQL and its use cases with examples.

What is a Lag Function in SQL?

In SQL, functions like LAG() he­lp fetch info from earlier rows in a datase­t. They're great for looking at patte­rns or shifts in data across time or columns.

Functions like these­ smooth out tasks, comparing new and old values. This makes it simple­r to understand data and make choices base­d on these patterns. No matte­r if you're using time-focused information or diffe­rent datasets, LAG functions utilize SQL's ability for e­asy data assessment and understanding.

Queries run smoothe­r using these functions, allowing a good look at trends and patterns to be­comes easier whe­n we refer back to olde­r data points.

The Lag function ofte­n comes in handy when checking alte­rations over time. Say you're de­aling with data from the stock market. You can use Lag to find change­s in stock values or sales from one day to the­ next. This gets you daily trends and ups and downs. If you have­ a dataset that's always changing, Lag can analyze multiple columns. This he­lps you see how differe­nt variables relate to e­ach other.

Apart from this, Lag is great for spotting unique conditions or outlie­rs in a dataset. With some conditional thinking, Lag can help you highlight big value­ changes. This can help track down important points or incidents.

SQL LAG Function with Date

The SQL LAG function with Date is a tool allowing users to retrieve data from the previous row in a dataset based on a specified date order. This function is particularly useful for analyzing time-series data or any dataset where the chronological sequence is crucial.

By employing the LAG function with Date, SQL queries can easily compare and track changes over time, providing valuable insights into trends and patterns within the data.

Syntax:

SELECT
column1,
column2
LAG(column_name) OVER (ORDER BY column_name) AS lagged_column
FROM
table;

Let us now understand this with an example.

Table: Passenger

SQL Lag Function Passengers Table

SQL Query:

SELECT
passenger_id,
passenger_name,
purchase_date,
destination,
ticket_price,
LAG(ticket_price) OVER (ORDER BY purchase_date) AS previous_ticket_price
FROM
Passenger
ORDER BY
purchase_date;

 

Output:

SQL LAG Function with Date

This SQL query fetches details from the Passenger table, having passenger ID, name, purchase date, destination, and ticket price. The main feature is the use of the LAG function, which calculates the preceding ticket price based on the purchase dates.

QL LAG Function by Multiple Columns

The SQL LAG function with Multiple Columns proves to be a useful tool for enhanced analysis of sequential data. It allows users to extract values from specific columns in the preceding row, which are organized in a specified order. This functionality is useful for analyzing changes within distinct categories or groups.

Syntax:

SELECT
column1,
column2
LAG(column_name) OVER (PARTITION BY partition_column1, partition_column2 ORDER BY order_column) AS lagged_column
FROM
table
ORDER BY
partition_column1,
partition_column2,
order_column;

We will use the same example with the Passenger's table.

SQL Query:

SELECT
passenger_id,
passenger_name,
purchase_date,
destination,
ticket_price,
LAG(ticket_price) OVER (PARTITION BY destination ORDER BY purchase_date) AS previous_ticket_price
FROM
Passenger
ORDER BY
destination,
purchase_date;

 

Output:

SQL LAG Function by Multiple Columns

The SQL Query uses the LAG function with multiple columns to fetch data from the  Passenger table. It retrieves passenger information, including the previous ticket price calculated over the order of purchase dates and partitioned by the destination column.

SQL LAG Function with Condition

The SQL LAG Function with Condition adds a layer of complexity by allowing users to join conditions into the analysis of sequential data. This allows for the recognition of particular conditions or patterns within the dataset based on the relationship between current and previous values.

Syntax:

SELECT
column1,
column2
CASE
WHEN condition1 THEN 'Value1'
WHEN condition2 THEN 'Value2'
ELSE 'Value3t'
END AS new_column
FROM
table
ORDER BY
column;

We will use the same example with the Passenger's table.

SQL Query:

SELECT
passenger_id,
passenger_name,
purchase_date,
destination,
ticket_price,
CASE
WHEN ticket_price > LAG(ticket_price) OVER (ORDER BY purchase_date) THEN 'Price Increase'
WHEN ticket_price < LAG(ticket_price) OVER (ORDER BY purchase_date) THEN 'Price Decrease'
ELSE 'No Change'
END AS price_change
FROM
Passenger
ORDER BY
purchase_date;

 

Output:

SQL LAG Function with Condition

This SQL query gets details from the Passenger table and adds a new column, "price_change," to show how ticket prices differ from the previous row.

You can now check Window Functions in SQL to learn more about it.

Conclusion

In conclusion, the SQL LAG function is a useful tool for studying data sequences based on time in SQL. Whether checking records by dates, looking at different aspects, or sorting changes based on conditions, the LAG function is a flexible tool. It assists in uncovering trends, patterns, and specific events in your dataset, making SQL a more powerful tool for insightful data analysis.

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About The Author
Tanish Mallik
I am Tanish, a final-year computer science student with extensive expertise in SQL. My background in the field of data science has honed my strong analytical skills, and I also have hands-on experience in developing data integration and optimization pipelines using Python and MSBI. I am eager to contribute innovative ideas and make an impact in the ever-evolving world of technology.