This repository showcases how to automate end-to-end data or dashboard refresh workflows using GitHub webhooks, an R-based Plumber API, and Bash scripting. The goal is to enable reliable, repeatable, and low-maintenance deployment processes triggered directly from GitHub events.

Automation brings consistency, reliability, and efficiency to your development and deployment processes:

• Consistency: Every execution follows the same steps, reducing human error and variability.
• Built-in Validation: Integrity checks can be embedded to catch errors early.
• Time Efficiency: Once set up, workflows run with minimal supervision, freeing up your time for higher-value tasks.

The automation is composed of three main components:

1. GitHub Webhook: Triggers the workflow upon a push event.
2. Plumber API: Acts as a secure listener for webhook payloads.
3. Bash Script: Executes the actual data or dashboard refresh.

To connect your repository to the automation pipeline:

• Go to your GitHub repo → Settings → Webhooks → Add webhook
• Payload URL: URL of the server hosting your Plumber API
• Content Type: application/json
• Secret: Add a shared secret (also configured on the server for HMAC verification)

The Plumber API acts as the orchestrator of the automation:

• Defines an HTTP POST endpoint to receive GitHub payloads
• Parses the incoming JSON and extracts key details (e.g., pushed branch)
• Validates the request signature using HMAC (see next section)

To ensure the payload is authentic and unaltered:

• GitHub signs each payload using SHA-256 and your shared secret
• The server recalculates the HMAC using the same secret
• If signatures match, the request is processed; otherwise, it's rejected

The API triggers the bash script only if the push was to the main branch:

• Validates the branch from the payload
• Logs activity for transparency and debugging
• Keeps unwanted or irrelevant updates from triggering a refresh

The Bash script contains the operational logic:

• Logs execution to a file for traceability
• Executes git pull to fetch the latest code
• Can be extended to run data ingestion, transformation, model training, or dashboard deployment

This pattern is ideal for:

• Automatically refreshing R Shiny dashboards after a GitHub push
• Updating machine learning pipelines or data preprocessing scripts
• Low-maintenance deployment of analytical apps and reports

• A Linux server running R and Plumber
• GitHub repository with push access
• Basic knowledge of Git, Bash, and R