How to Compare Weekly Data with SQL Expressions in PostgreSQL

When analyzing data trends over time, it’s often useful to compare metrics from different time periods, such as the current week versus the previous week. This article will guide you through the process of creating SQL expressions to compare weekly data using PostgreSQL.

Calculating Sums for the Previous Week

Consider the data presented in the table below as an illustrative example.

To calculate the sum of a column for the previous week, you can use the following SQL expression:

SUM([Paid]) FILTER (WHERE [BillDate] >= DATE_TRUNC('week', CURRENT_DATE) - INTERVAL '1 week' AND [BillDate] < DATE_TRUNC('week', CURRENT_DATE))

This expression will sum the Paid column for the dates that fall within the last week, starting from the beginning of the week up to, but not including, the current week.

Calculating Sums for the Current Week

To calculate the sum of a column for the current week, use the following SQL expression:

SUM([Paid]) FILTER (WHERE [BillDate] >= DATE_TRUNC('week', CURRENT_DATE) AND [BillDate] <= DATE_TRUNC('week', CURRENT_DATE) + INTERVAL '1 week' - INTERVAL '1 day')

This expression will sum the Paid column for the dates that fall within the current week, from the beginning of the week up to the current date.

Configuring Expression Fields in KPI Widgets

After creating the expressions, you can configure them in your KPI card widget.

Understanding the FILTER Clause in PostgreSQL

The FILTER clause in PostgreSQL is a powerful feature that allows you to apply aggregate functions to a subset of data based on a specified condition. Here’s a quick overview of its syntax:

aggregate_function(column_name) FILTER (WHERE condition)

• aggregate_function: This is the aggregate function you want to use, such as SUM(), COUNT(), AVG(), etc.
• column_name: This is the column on which you want to apply the aggregate function.
• condition: This is the condition that determines which rows to include in the aggregation. Only rows that meet this condition will be considered.

By using the FILTER clause, you can create more precise and targeted queries without the need for subqueries or complex joins.

Conclusion

Comparing data across different time periods is a common requirement in data analysis. By using the SQL expressions provided in this article, you can effectively compare weekly data to gain insights into trends and changes over time.