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.

Code that runs without errors and produces plausible-looking output can still contain logical errors.
It’s important to check AI-generated code and in order to sport logical errors.
Work out the expected answer by hand on a small dataset before running the code, then compare.
Print statements let you inspect intermediate values and see what the code is actually doing.
Focus the most scrutiny on the calculations behind the results you will report, and less scrutiny on routine boilerplate such as plotting code.
Asking a chatbot to review code can be a useful extra check, but a review that finds nothing wrong is not proof of correctness, and any suggested fixes need to be re-tested.
It’s not only the code itself that needs validating. Check that suggested packages exist, are the official ones, and are not lookalikes of popular names.