Examples @examples provides executable R code showing how to use the function in practice. This is a very important part of the documentation because many people look at the examples before reading anything. Example code must work without errors as it is run automatically as part of R CMD check. For the purpose of illustration, it’s often useful to include code that causes an error. You can do this by wrapping the code in try() or using \dontrun{} to exclude from the executed example code. For finer control, you can use @examplesIf: #' @examplesIf interactive() #' browseURL("https://roxygen2.r-lib.org")

Instead of including examples directly in the documentation, you can put them in separate files and use @example path/relative/to/package/root to insert them into the documentation. All functions must have examples for initial CRAN submission.

Impact of AI on Developer Productivity: Faster development cycles: Code suggestions and automation reduce time spent on repetitive tasks. Improved code quality: AI tools identify bugs or security risks that may go unnoticed by manual reviews. Enhanced learning: Engineers can receive real-time feedback or even ask AI for code explanations to learn new patterns or frameworks.

GitHub Copilot is a game-changer for writing code. Powered by OpenAI’s Codex model, Copilot suggests lines of code based on the context of what you’re writing. It’s especially useful when you’re working with repetitive tasks or writing boilerplate code.

ChatGPT, an AI chatbot developed by OpenAI, is not just a tool for casual conversations. It can be used to ask technical questions, explain difficult code, or even generate ideas for solving specific coding problems. Developers often use it for quick consultations — whether it’s about debugging or understanding the intricacies of a particular algorithm.

AI-Assisted System Architecture and Design As AI becomes more sophisticated, it may start to play a role in designing system architectures. Currently, system design is one of the more complex tasks that engineers handle, requiring a deep understanding of the trade-offs between different architectural patterns (monolithic vs. microservices, synchronous vs. asynchronous communication, etc.). Future AI tools could help design optimal architectures by analyzing the specific needs of a project, performance goals, and scalability requirements. AI could suggest which patterns, frameworks, or technologies are best suited for a given application. It could even generate architecture diagrams, API designs, or database schemas based on historical data from similar projects. This would revolutionize system design, making it faster and more accessible to engineers of all levels. While experienced architects would still be needed to make judgment calls, AI could drastically reduce the time spent on initial design phases, especially in large and complex systems.