data-analysis

An end-to-end R data analysis workflow automates data loading, exploratory analysis, regression modeling, and output generation. It uses R to handle cross-sectional analysis with lm/glm and outputs publication-ready tables and figures.

To generate publication-ready regression tables in R, use the modelsummary package to format model outputs. This workflow creates LaTeX-compatible formats and saves outputs using saveRDS, ensuring reproducibility for research papers.

Yes, you can estimate policy impact on a county-level panel dataset in R using the fixest package for panel data analysis. This approach supports generating regression tables and trend plots for research paper publication.

Yes, this R data analysis workflow supports both cross-sectional and panel data. It handles cross-sectional analysis using lm and glm functions, while panel data analysis is executed using the fixest package.

To create custom figures for exploratory data analysis in R, use ggplot2 with a project theme. This workflow generates publication-ready visualizations and manages directory structures using Makefiles for reproducible outputs.

Using Makefiles and saveRDS for R data analysis enforces code conventions and ensures reproducibility. Makefiles manage directory structures while saveRDS preserves output objects, creating reliable workflows for publication-ready results.