InfoWorld: 10 tips for better R code from AI coding agents

Most generative AI tools know less about R than they do about JavaScript or Python, and that gap is mostly a function of how much training data exists for each language. That is the premise behind a practical guide on InfoWorld by Sharon Machlis, which walks through ten concrete ways to claw the difference back when you are an R user pushing code through a coding agent.

The first move is to stop using a chatbot. Machlis argues you want a real coding agent, naming Claude Code, Codex and Posit Assistant as the relevant options, on the grounds that they bring more flexibility and coding-focused tools than general-purpose chat interfaces. From there, the recipe is to feed the agent context it would not otherwise have. That means knowledge files such as CLAUDE.md, AGENTS.md or GEMINI.md that load on each session, agent skills that package recurring tasks so you stop retyping instructions, and the btw R package plus its MCP server, which gives the agent access to your actual installed packages instead of guessing against an old training snapshot.

The through line across all ten tips is closing the gap between what the model was trained on and what your project actually looks like. Plan mode before any code is written, a lessons-learned file so the agent does not keep repeating fixed mistakes, treating agent-generated tests and code reviews as a draft layer rather than the final word, and being targeted and specific in prompts. Machlis cites Ethan Mollick's line that the shift from chatbot to agent is the most important change in how people use AI since ChatGPT launched, and the post is essentially a how-to for R users who want to ride that shift without leaving their language for Python tooling.

The honest caveat is that this is a tips piece, not a benchmark. The article does not tell you how much each step actually moves the needle on R output quality, and it does not say which of the open-weight models it nods to ends up being most useful for R in practice; it just notes that smaller open models can work if you have the budget and infrastructure to host them. Read it as a checklist of plausible levers, not a ranked study.

For R practitioners who have been watching the agent boom land mostly on Python codebases, the value here is that there is now a documented set of moves that gets you most of the way without giving up the language you actually work in.