Overview: Generating Visual Outputs

Materials adapted from Adrien Osakwe, Larisa M. Soto and Xiaoqi Xie.

Generating Visual Outputs

By the end of this module, you will be able to:

Construct Base R Graphics: Quickly generate exploratory plots using foundational functions like plot(), hist(), and boxplot().
Master the Grammar of Graphics: Understand the ggplot2 philosophy—how to map data variables to visual aesthetics (colors, shapes, and axes).
Build Layered Visualizations: Create professional-grade scatter plots, bar charts, and line graphs by layering geometric objects (geoms).
Leverage Factors for Design: Use factor levels to control the order, grouping, and color-coding of categorical data in your plots.
Automate Reporting: Use RMarkdown (or Quarto) to weave together code, results, and narrative text into polished, reproducible PDF or HTML reports.

By the end of this module, you will be able to:

Implement Best Practices: Write clean, readable code using consistent naming conventions and logical script structures.
Standardize Documentation: Use comments and headers to document your logic, ensuring your analysis is understandable to “Future You” and your collaborators.
Avoid Common Pitfalls: Identify and steer clear of frequent R “anti-patterns,” such as mixing up = and == or failing to manage working directories.
Troubleshoot and Debug: Develop a systematic approach to reading error messages, using the RStudio debugger, and finding solutions in the R community.