Key Points

Introducing LLMs as a Tool to Learn R

Choosing which and when to use Generative AI tools require thoughtful goal-setting
R and RStudio provide a reasonable entry point with a large and welcoming community
The Gapminder dataset contains global metrics for data analysis

Providing context such as programming language, dataset, and goals can lead to better AI responses
Follow-up questions help personalize learning and deepen understanding
Modify code by starting with code, asking for changes, reviewing the code, and testing the code

“Reverse-engineering” existing code and inserting print statements can help you develop your understanding of how code works.
Develop your own expertise by comparing a chatbot’s explanations to your own understanding and finding practical ways to test the explanations.

Being specific about what you know already and what exactly you need to be explained will increase your chances of getting a helpful response.
Avoid the illusion of competence by putting your knowledge and skills into practice instead of passively consuming responses from a chatbot.

Following established principles to organise and document your programs makes them easier to maintain.
When instructing a chatbot to change a program, provide relevant context and be precise about what you want to change and why.
Be reflective about how you use a chatbot and test your understanding of the code it generates to keep building your own expertise.
Approaches that work well for novices working with small programs may not scale well to larger software and bigger projects.