Emile Dorion refactored the attitudes.R data pipeline in the clessn/datagotchi_federal_2024 repository to standardize the mapping of raw survey responses to a numerical scale, improving the consistency and reliability of survey data. Using R and leveraging data cleaning and transformation techniques, Emile introduced table() and attributes() calls for rapid data inspection, which enhanced quality assurance during processing. The systematic refresh of the data_clean dataset ensured uniformity across issue-related columns, making the data more reproducible and ready for downstream analytics. This work demonstrated a focused approach to data quality and reproducibility, addressing a core need in survey data workflows.