Build Shiny Apps Faster with AI

In this introduction, you’ll discover how to move beyond static R scripts and start building interactive Shiny dashboards with clarity and confidence. I’ll share my background, what inspired this course, and how Shiny, combined with thoughtful AI support, can help you communicate insights clearly, showcase your skills, and create professional data applications you can confidently share with others.

When building Shiny apps, you often end up writing the same code repeatedly, searching for examples, or fixing small errors that stop the app from running. These interruptions may seem minor, but they add up quickly.

In this lesson, you’ll see how combining Shiny with AI changes that experience. We introduce a practical workflow based on clear prompting, building apps step by step, and using AI to reduce repetitive work and support debugging. This lesson sets the mindset and approach used throughout the course.

This lesson clarifies what this course is and what it is not.

We focus on understanding the structure of a Shiny app. You’ll learn how to break an app into clear components, inputs, server logic, and outputs, and how each part fits together.

By shifting from thinking of a Shiny app as one large script to a set of smaller, manageable pieces, apps become easier to design, easier to debug, and easier to extend.

This mindset builds confidence and sets the foundation for everything you’ll build next, with AI supporting your thinking and iteration along the way.

In this lesson, we start with a quick introduction to RStudio, including installation and a brief overview of the interface.

We then introduce the basic structure of a Shiny app by focusing on its two core parts: the UI and the server. You’ll learn to think of a Shiny app as a conversation between these two components.

The UI is responsible for what the user sees and how inputs are collected. The server handles the logic, reacts to those inputs, and updates the outputs automatically. This interaction, UI inputs, server logic, and reactive outputs, is the foundation of every Shiny app.

By the end of this lesson, you’ll understand how dynamic updates work and why this structure makes Shiny apps interactive.

In this lesson, we introduce what prompting really means, starting with a simple real-life example and then connecting it to AI and Shiny development.

You’ll learn why AI outputs should be treated as drafts, not final answers, and why your role is to guide, refine, and shape the results. We explain why AI can produce different outputs for the same request and how clear instructions reduce confusion and errors.

This lesson introduces a repeatable prompting approach used throughout the course: providing context, defining exactly what you want, specifying constraints, and asking for outputs that are easy to integrate into your project.

The goal is to build strong prompting habits before you start building your first full Shiny app.

Before we start building Shiny apps, we need data.

In this lesson, you’ll learn how to use AI to generate realistic test data, allowing you to focus on learning Shiny instead of spending time searching for datasets. This approach helps you prototype faster and practice app building with data that feels real and relevant.

In this lesson, we’ll build our first truly useful Shiny app from scratch.

We’ll start from a blank file, use prompts step by step, and end with an app that uploads a dataset and produces meaningful summary statistics.

We’ll use ChatGPT 5.2 as our AI assistant throughout the process. You’ll watch each prompt, review the generated code, and then run it in RStudio to validate the results.

I recommend following along with your own AI tool and an RStudio script open so you can copy, paste, run, and see how the app changes step by step.

This lesson continues directly from the previous one.

You’ll take the Shiny app you already built and improve it with more advanced, real dashboard features, adding tabs to organize outputs, improving tables for clearer results, and adding download buttons so users can export what they see.

We’ll keep the same step-by-step workflow: prompt, review the code, paste into RStudio, run, and validate each upgrade as the app grows.

Up to this point, we’ve been focused on building features.

In this lesson, we’re going to slow down and understand how those features actually work together.

Before we add graphs, it’s important to see how data, UI, and server are connected,

and how information flows through the app.

This lesson is about building the mental model that will make everything we add next, especially graphs, much easier to understand.