data-analysis

This Skill automates R data analysis workflows by executing scripts that handle data loading, exploratory data analysis, regression modeling, and publication-ready output generation in one pipeline. It manages the entire process from data exploration to final results using R.

Yes, publication-ready tables and figures can be generated directly from R regressions. The Skill uses packages like modelsummary and fixest to format regression outputs and exploratory data analysis results into numbered R scripts suitable for academic publication.

The required R packages for this data analysis workflow are tidyverse, fixest, and modelsummary. Tidyverse handles data cleaning and exploration, while fixest and modelsummary manage regression modeling and formatting publication-ready tables.

To run a regression with fixed effects, you provide a command specifying your dataset and fixed effect variables. The Skill uses the fixest package to execute the regression and modelsummary to produce the final summary tables.

Yes, this approach supports reproducible research through automated script creation. It generates numbered R scripts and outputs during the data analysis pipeline, ensuring that exploratory data analysis and regression steps are documented and ready for publication.