Data Cleaning and Preparation in R: Summary and Assessment

Data cleaning and preparation in R should be understood as part of the clinical data management quality system. R can import REDCap exports, connect to approved API workflows, classify missingness, recode values, create derived variables, generate query listings, and prepare datasets for reporting or analysis. The value of R lies not only in speed, but in reproducibility and transparency. The chapter emphasized the importance of distinguishing raw data, cleaned data, analysis-ready data, derived variables, query outputs, and cleaning logs. Raw data should be preserved. Corrections should usually occur in the source database through the approved query and audit trail workflow. R scripts should document transformations and generate outputs that can be reviewed and rerun. Missing data require careful interpretation. R can count and list missing values, but the study team must decide whether a value is not yet due, not applicable, pending, unknown, accidentally omitted, or structurally absent from the export. Recoding and derived variables must be based on the data dictionary, protocol, CRF guidance, and analysis plan. Scripts should preserve traceability to original values and avoid silent transformations. Readable cleaning scripts are central to professional practice. A good script can be reviewed by another data manager, rerun when a new export arrives, and used as documentation of the workflow. The ultimate goal is to create data outputs that are credible, auditable, and fit for their intended use.