A practical, step-by-step guide to building dynamic dashboards, models, and workflows in R using AI support
β±οΈ Length: 1.5 total hours
π₯ 81 students
π February 2026 update
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- Course Overview
- Discover the transformative power of integrating Generative Artificial Intelligence with the R Shiny ecosystem to revolutionize your data science development lifecycle. This comprehensive guide moves beyond traditional coding paradigms, teaching you how to act as an architect of information rather than just a writer of scripts. Throughout this 1.5-hour intensive journey, you will explore the synergy between Large Language Models (LLMs) and R programming, focusing on how to drastically reduce the time spent on boilerplate code and complex reactive debugging. The course is designed to bridge the gap between raw data analysis and interactive web deployment, ensuring that you can produce professional-grade dashboards with unprecedented speed and efficiency. By focusing on AI-assisted workflows, you will learn to conceptualize, draft, and refine sophisticated applications that include dynamic data visualizations, automated reporting features, and sleek user interfaces that adapt to various user inputs in real-time.
- Requirements / Prerequisites
- Participants should possess a foundational understanding of R, including familiarity with common data structures such as vectors, data frames, and lists, to effectively interpret the code generated by AI.
- Access to a modern Large Language Model interface, such as ChatGPT (GPT-4), Claude 3.5 Sonnet, or GitHub Copilot, is essential for following the practical prompting exercises throughout the curriculum.
- A working installation of RStudio (or VS Code with R support) along with the latest version of the ‘shiny’ package and associated libraries like ‘bslib’ or ‘shinywidgets’ for UI enhancements.
- Basic knowledge of data manipulation libraries, specifically the Tidyverse (dplyr, ggplot2), will help you better direct the AI when requesting specific data transformations or aesthetic plotting adjustments.
- A stable internet connection and a creative problem-solving mindset are required to navigate the iterative process of refining AI-generated outputs into production-ready software solutions.
- Skills Covered / Tools Used
- Advanced Prompt Engineering: Master the art of crafting specific, context-aware prompts designed to generate error-free R Shiny code, including UI layouts, server logic, and reactive triggers.
- Rapid Prototyping Workflows: Learn to use AI to generate wireframes and mockups in minutes, allowing for immediate stakeholder feedback and faster iteration cycles compared to manual coding.
- AI-Driven Debugging: Develop the ability to utilize LLMs for interpreting R console errors and logs, turning cryptic warning messages into actionable code corrections and structural improvements.
- Custom UI/UX Enhancement: Leverage AI to write custom CSS and JavaScript snippets that integrate seamlessly with Shiny, providing your apps with a modern, high-end feel without needing deep front-end expertise.
- Modular Programming: Understand how to instruct AI to build reusable Shiny modules, ensuring your applications are scalable, organized, and much easier to maintain over the long term.
- Integration of Interactive Libraries: Use AI support to implement complex features from Plotly, Leaflet, and DT (DataTables), handling the intricate parameter tuning that usually takes hours of documentation diving.
- Automated Documentation: Learn techniques for using AI to comment code and generate comprehensive user manuals or technical documentation for your finished Shiny applications.
- Benefits / Outcomes
- Massive Productivity Gains: Achieve a significant reduction in development time, moving from initial concept to a fully functional dashboard in a fraction of the time required by traditional methods.
- Enhanced Portfolio Value: Build a series of impressive, dynamic web applications that demonstrate your ability to leverage cutting-edge AI tools to solve real-world data communication challenges.
- Lower Barrier to Entry: Empower yourself to build complex architectural features (like multi-page dashboards or secure login screens) that might have previously felt beyond your current coding level.
- Future-Proof Skillset: Position yourself at the forefront of the data science market by mastering the hybrid “Human + AI” coding model that is becoming the industry standard.
- Improved Code Quality: Benefit from AI’s ability to suggest optimized R code and best practices, leading to apps that are not only faster to build but also perform better under heavy data loads.
- Creative Freedom: Spend less time worrying about syntax errors and more time focusing on the logic, design, and insights that make your data application truly valuable to users.
- Professional Versatility: Gain the confidence to take on freelance projects or corporate assignments that demand quick turnarounds for interactive data products and analytical tools.
- PROS
- Highly Practical: Focuses on real-world implementation rather than theory, ensuring you have tangible assets to show after only 90 minutes of instruction.
- Up-to-Date Content: Reflects the latest 2026 AI advancements, ensuring the prompting techniques and library recommendations are current and effective.
- Efficiency-First Approach: Specifically tailored for busy professionals who need to deliver results quickly without spending weeks learning the intricacies of web development.
- Scalable Techniques: The methods taught are platform-agnostic, meaning they can be applied to any LLM or future AI tool that enters the market.
- CONS
- Conceptual Gaps: Students may face a learning curve in understanding the underlying R logic if they rely too heavily on AI without reviewing the generated code for educational purposes.
Learning Tracks: English,Personal Development,Career Development
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