types-check

To validate function arguments in R, you can use rlang's type-checking utilities and custom helpers to ensure inputs match expected types. This prevents runtime errors by verifying scalar and vector formats before the function body executes.

The best way to enforce input type checking for exported R functions is applying dedicated validation helpers that check scalar and vector inputs. This ensures type safety and generates clear error messaging when inputs fail validation.

Yes, you can validate range and specific enumerated choices for R function inputs. The validation utilities enforce constraints like minimum and maximum values or specific choice options, ensuring arguments fall within acceptable predefined boundaries.

Yes, rlang supports nullability checks for optional scalar inputs in R. The type-checking utilities allow you to define arguments as nullable, ensuring that functions can safely handle cases where an optional input is explicitly passed as NULL.

R functions crash when users pass unexpected vector formats because the code lacks robust input validation. Implementing type checking ensures that vector inputs are strictly validated against expected formats, preventing unexpected behavior and runtime crashes.

To create custom input validation helpers for an R package, use rlang's type-checking utilities to build new functions. This allows you to define specific type, range, and choice constraints, ensuring robust input validation across your package's exported functions.