Introduction to R for Clinical Data Management

Installing R and RStudio

Learning Outcomes

30-45 minutes Applied Step 2 of 9

Outcomes

Learning Outcomes

2 / 9

Explain why R is useful for clinical data management, especially for reproducible review, data cleaning, and quality control.
Distinguish between R, RStudio, R projects, scripts, packages, objects, vectors, and data frames.
Create a basic project folder structure suitable for clinical research data management work.
Install and load commonly used R packages for importing, inspecting, and preparing clinical research datasets.
Import CSV and Excel files exported from REDCap or other clinical research systems.
Use basic R commands to inspect datasets, understand their structure, and identify common data quality issues.
Apply simple quality checks for missing values, duplicate identifiers, out-of-range values, inconsistent dates, and unexpected categorical responses.
Describe how R can support auditability, reproducibility, and transparent data handling in a regulated or quality-assured clinical research environment.