data-analysis

An end-to-end R data analysis workflow covers data exploration, regression modeling, and generation of publication-ready tables and figures. This process uses libraries like tidyverse for exploration, fixest for panel data regressions, and modelsummary for formatted outputs.

Yes, regression analysis with fixed effects on panel data is supported using the fixest package. This allows you to model complex datasets with strict deterministic execution and adhere to standard R coding conventions for statistical modeling.

You can generate publication-ready tables and figures using the modelsummary library for tables and ggplot2 for visualizations. The workflow enforces a project theme and saves intermediate objects to ensure outputs are suitable for reports and publications.

Reproducible R data analysis is achieved by enforcing strict code conventions, structured script organization, and saving intermediate objects. This deterministic approach ensures that data loading, statistical modeling, and output generation can be consistently replicated.

Yes, the workflow supports exploratory data analysis on CSV datasets, such as analyzing county panel data or tweet data. It utilizes tidyverse libraries to load, clean, and explore datasets before progressing to regression analysis and output generation.