Previous Next Step Introduction to R for Clinical Data Management Introduction to R for Clinical Data Management: 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 why R is useful for clinical data management, especially for reproducible review, data cleaning, and quality control. Objective 2 Distinguish between R, RStudio, R projects, scripts, packages, objects, vectors, and data frames. Objective 3 Create a basic project folder structure suitable for clinical research data management work. Objective 4 Install and load commonly used R packages for importing, inspecting, and preparing clinical research datasets. Objective 5 Import CSV and Excel files exported from REDCap or other clinical research systems. Objective 6 Use basic R commands to inspect datasets, understand their structure, and identify common data quality issues. Objective 7 Apply simple quality checks for missing values, duplicate identifiers, out-of-range values, inconsistent dates, and unexpected categorical responses. Objective 8 Describe how R can support auditability, reproducibility, and transparent data handling in a regulated or quality-assured clinical research environment. Previous Lesson Next Step
Flip Cards Learning Objectives Listen Objective 1 Explain why R is useful for clinical data management, especially for reproducible review, data cleaning, and quality control. Objective 2 Distinguish between R, RStudio, R projects, scripts, packages, objects, vectors, and data frames. Objective 3 Create a basic project folder structure suitable for clinical research data management work. Objective 4 Install and load commonly used R packages for importing, inspecting, and preparing clinical research datasets. Objective 5 Import CSV and Excel files exported from REDCap or other clinical research systems. Objective 6 Use basic R commands to inspect datasets, understand their structure, and identify common data quality issues. Objective 7 Apply simple quality checks for missing values, duplicate identifiers, out-of-range values, inconsistent dates, and unexpected categorical responses. Objective 8 Describe how R can support auditability, reproducibility, and transparent data handling in a regulated or quality-assured clinical research environment.