Structured RMarkdown and PanelMatch Analysis Session

• Day: 2023-10-17
• Time: 20:30 to 21:05
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Rmarkdown, Panelmatch, Modular Design, Data Analysis

Description

Session Goal: The session aimed to explore and enhance understanding of RMarkdown file structures, causal analysis using PanelMatch in R, and modular code organization for data projects.

Key Activities:

• Reflected on the structure of .Rmd files, detailing metadata, R code chunks, and LaTeX file generation.
• Executed causal analysis using the PanelMatch package in R, focusing on estimating average treatment effects with covariate balance.
• Planned modular code structures for large projects, emphasizing separation of code by functionality.
• Developed a modular RMarkdown file structure to improve maintainability and collaboration.
• Revised the table of contents structure for analysis reports, detailing sections and subsections for clarity.
• Introduced R Markdown and RStudio, explaining code chunk formatting and interactive editing benefits.
• Outlined code commenting conventions in R for enhanced readability.

Achievements:

• Gained insights into the structured breakdown of RMarkdown files and data flows.
• Successfully applied PanelMatch for causal analysis in R.
• Proposed a modular design framework for both RMarkdown and large project code structures.

Pending Tasks:

• Further exploration of modular design in RMarkdown for specific data analysis workflows.
• Implementation of revised table of contents in ongoing analysis projects.

Evidence

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