Data Engineering in R: Foundations, Mindset, and First Pipelines

Many R users are comfortable exploring data, cleaning datasets, and producing analyses — but what happens when a script needs to run again tomorrow, or be reused by someone else?
This session introduces the foundations of data engineering from the perspective of an R user.
Rather than focusing on tools or infrastructure, we’ll focus on mindset, structure, and reliability:

• How data engineering differs from analysis
• What a “data pipeline” really means in practice
• How to turn a familiar R script into a simple, repeatable workflow
• Why structure and clarity matter more than scale

We’ll walk through a small, realistic example using plain R scripts to show how data flows from raw inputs to reusable outputs — safely and predictably.
This is not a deep dive into packages, cloud tools, or big data systems. It’s about learning how to think like a data engineer using tools you already know.
Audience

• R users with basic familiarity
• No prior data engineering experience required

Format

• Online
• 45–60 minutes
• Includes a short live demo

What you’ll take away

• A clear mental model for data engineering
• Practical patterns you can apply to your existing R scripts
• Confidence to make your data workflows more reliable and reusable