Sum or Aggregate a Column of Spreadsheet or Table Data

This n8n workflow provides a foundational example for how to sum or aggregate data from a column within a spreadsheet or table using custom JavaScript. It demonstrates the use of the Function node to process incoming data programmatically.

What it does

This workflow is a starting point for data aggregation tasks:

1. Starts the workflow: The Start node initiates the execution.
2. Executes Custom JavaScript: A Function node is included, ready for you to add custom JavaScript code. This is where you would implement the logic to iterate over incoming data (representing rows from a spreadsheet or table), extract values from a specific column, and perform an aggregation (e.g., sum, average, count, etc.).

Prerequisites/Requirements

• n8n Instance: A running instance of n8n (cloud or self-hosted).
• Data Source (Conceptual): While not explicitly included in this basic template, you would typically connect this workflow to a node that fetches data from a source like Google Sheets, Airtable, a database, or an HTTP request returning tabular data.

Setup/Usage

1. Import the workflow: Import the provided JSON into your n8n instance.
2. Connect a data source: Add a preceding node (e.g., Google Sheets, Airtable, HTTP Request) to fetch the spreadsheet or table data you wish to aggregate. Ensure this node outputs data in a structured format (e.g., an array of objects, where each object is a row and its properties are columns).
3. Configure the Function node:
   • Open the Function node.
   • Add JavaScript code to:
     • Access the incoming items (e.g., const items = $input.all();).
     • Loop through each item.
     • Extract the value from the desired column (e.g., item.json.columnName).
     • Perform your aggregation logic (e.g., sum up the values, count occurrences, find the maximum).
     • Return the aggregated result in the desired format (e.g., return [{ json: { total: sum } }];).
4. Test the workflow: Execute the workflow to ensure your aggregation logic works as expected.
5. Add subsequent actions: Connect further nodes to utilize the aggregated data (e.g., send it to Slack, update another spreadsheet, store it in a database).