types-check

You can validate function inputs in R by adding check_* helpers like check_string or check_number_whole at the start of your exported functions. This enforces valid arguments and generates clear, actionable error messages for users.

To check scalar types in R, use check_string, check_name, check_number_whole, check_number_decimal, and check_bool helpers. These validators enforce argument types at function entry points to ensure predictable code behavior.

When validating R function inputs, you can handle optional or null arguments by applying the allow_null parameter within your check_* helpers. This accommodates optional values while still enforcing type constraints when arguments are provided.

Yes, you can validate data frames and vectors in R using standalone check_character, check_data_frame, and related validators. These helpers ensure your data structures maintain the proper shapes and content required by your functions.

The tidymodels approach to input validation in R package development supports rlang-style checkers for scalars, vectors, and data structures. It helps build reliable package APIs by enforcing type and value constraints with documented error messages.

You should use standalone check_* utilities when writing exported R functions that need input validation with clear, actionable error messages. They are designed to review existing validation code and create robust input validation helpers in package development workflows.