📅 2023-10-17 — Session: Structured RMarkdown and Causal Analysis Session
🕒 20:30–21:05
🏷️ Labels: Rmarkdown, Causal Analysis, Modular Design, Code Organization, Data Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and refine the structure of RMarkdown files for data projects, conduct causal analysis using PanelMatch in R, and discuss modular code structures for large projects.
Key Activities
- RMarkdown File Structure: Reviewed the overall structure of an .Rmd file, including metadata, R code chunks, and LaTeX generation. Proposed a modular file structure to enhance maintainability and collaboration.
- Causal Analysis: Utilized the PanelMatch package in R to perform causal analysis on panel data, focusing on estimating average treatment effects with covariate balance.
- Modular Code Structure: Discussed the benefits of organizing code into separate files based on functionality, with examples for database operations, business logic, and UI components.
- Report Organization: Revised the table of contents for analysis reports to improve clarity and organization.
- Code Commenting in R: Established conventions for commenting in R and RMarkdown to improve code readability.
Achievements
- Developed a comprehensive understanding of RMarkdown file structures and their application in data projects.
- Successfully applied causal analysis techniques using PanelMatch.
- Formulated a modular approach to code organization for large projects.
Pending Tasks
- Implement the proposed modular RMarkdown structure in ongoing data projects.
- Further refine the table of contents template for complex analysis reports.