Reproducible Data Analysis With R in Laboratory Hematology

Laboratory hematology generates large, complex datasets that increasingly exceed the capabilities of traditional spreadsheet-based analytical workflows. Despite their familiarity, spreadsheets obscure analytical logic, encourage manual data manipulation, and lack durable records of analytical steps, limiting transparency, auditability, and reproducibility. These limitations present specific challenges in clinical laboratory settings subject to regulatory oversight. Script-based data analysis offers a practical alternative by encoding analytical workflows as explicit, ordered, and reusable operations. This review outlines the structural limitations of spreadsheet-centered analysis, illustrates how these constraints contribute to analytical error, and introduces script-based analysis using R as a reproducible framework suited to laboratory hematology. Practical applications are highlighted across method comparison, reference interval estimation, quality control, flow cytometry, and operational monitoring. The discussion also includes the importance of complementary technologies like integrated development environments, version control, and large language models. Adoption of reproducible, script-based analytical tools such as R represents a foundational step toward improving data integrity, analytical rigor, and readiness for advanced analytical techniques in laboratory hematology.