Preserve raw evidence while creating cleaned fields for use.
The cleaning target is not a pretty table. It is data fit for a specific use. The mechanism Tidy data emphasizes structure: variables, observations, and values should be represented consistently. In professional cleaning, structure also means separating raw input, cleaned representation, and decision-ready output. The raw field records what arrived. The cleaned field applies a documented rule. The decision field may combine cleaned values with business logic. This separation works because cleaning is not only correction; it is interpretation. Trimming whitespace is low risk, but parsing names, merging duplicates, or inferring missing values changes meaning. Keeping raw values allows review,…
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