Data that has been checked against domain-specific rules and logical restrictions. Key Methodology and R Applications
Data with consistent types (e.g., numeric, character) and structures (e.g., tidy tables). Statistical Data Cleaning with Applications in R
The authors emphasize that data cleaning is not just about removing errors but about identifying them through . Statistical Data Cleaning with Applications in R Data that has been checked against domain-specific rules
Central to the authors' philosophy is the concept of the . This framework views data processing as a series of steps that increase the data’s value: Raw Data: The initial, unrefined input. character) and structures (e.g.