r-error-handling

You can handle errors in an R data pipeline using rlang by adopting structured conditions with `rlang::abort` to signal custom errors, and using `withCallingHandlers` to catch and recover from failures gracefully without stopping execution.

The best way to add custom error classes for R package development is using `rlang::abort` to enforce error constructors, which provides precise, chainable errors and context-rich diagnostics to reduce debugging time.

Yes, you can chain errors and trace root causes in R by attaching a parent error via `rlang::abort`, allowing you to propagate context-rich diagnostics and maintain a clear hierarchy of failure conditions across complex interactions.

Yes, this approach supports both classic `tryCatch` and modern `withCallingHandlers` patterns in R, allowing you to integrate structured condition handling into existing codebases while upgrading to precise, chainable error messaging.

You validate inputs and log errors in production R applications by integrating input validation helper templates and using `rlang::abort` to propagate structured conditions, ensuring failures are diagnosed and recovered gracefully with consistent messaging.

You should use `withCallingHandlers` instead of `tryCatch` in R when you need to handle conditions globally or log warnings without interrupting the execution flow, whereas `tryCatch` is better suited for localized error recovery and halting.