Basic Data Quality Checks in R

Basic data quality checks in R should be guided by the protocol, CRF, data dictionary, and data management plan. R is not deciding what is clinically valid; it is applying rules defined by the study team. A value is not "wrong" simply because it looks unusual. It is flagged because it violates a predefined expectation, is missing when required, is internally inconsistent, or requires human review. This distinction is important. R can generate flags, but data managers and investigators interpret them.