Previous Next Step Data Cleaning and Preparation in R Data Cleaning and Preparation in R: Orientation Learning Objectives 15-20 minutes Applied Step 1 of 2 Flip Cards Learning Objectives 1 / 2 Flip Cards Learning Objectives Listen Objective 1 Explain the purpose of data cleaning and preparation in clinical research data management. Objective 2 Distinguish between raw data, cleaned data, analysis-ready data, derived variables, and query outputs. Objective 3 Describe a reproducible R workflow for importing REDCap exports and preparing datasets for review. Objective 4 Explain the role of the REDCap API in automated data export and why API use must be governed carefully. Objective 5 Identify and classify missing data using study-specific definitions and documentation. Objective 6 Recode categorical variables transparently while preserving traceability to original values. Objective 7 Create derived variables in R using protocol-defined rules. Objective 8 Write cleaning scripts that are readable, rerunnable, and suitable for review by another data manager. Objective 9 Produce simple cleaning logs and outputs that support query management, monitoring, and analysis preparation. Previous Lesson Next Step
Flip Cards Learning Objectives Listen Objective 1 Explain the purpose of data cleaning and preparation in clinical research data management. Objective 2 Distinguish between raw data, cleaned data, analysis-ready data, derived variables, and query outputs. Objective 3 Describe a reproducible R workflow for importing REDCap exports and preparing datasets for review. Objective 4 Explain the role of the REDCap API in automated data export and why API use must be governed carefully. Objective 5 Identify and classify missing data using study-specific definitions and documentation. Objective 6 Recode categorical variables transparently while preserving traceability to original values. Objective 7 Create derived variables in R using protocol-defined rules. Objective 8 Write cleaning scripts that are readable, rerunnable, and suitable for review by another data manager. Objective 9 Produce simple cleaning logs and outputs that support query management, monitoring, and analysis preparation.