Automate end-to-end R data analysis with dplyr and fixest.
npx skills add https://github.com/mgaldino/agents-workflow --skill data-analysis-r
Please help me install this Agent Skill. Skill: data-analysis-r Source: https://github.com/mgaldino/agents-workflow/tree/main/skills-docs/data-analysis-r Command: npx skills add https://github.com/mgaldino/agents-workflow --skill data-analysis-r
Este Skill facilita a análise de dados em R do início ao fim para pesquisas em ciência política, relações internacionais e econometria aplicada, reduzindo o tempo de preparação e aumentando a reprodutibilidade.
Automated data import & cleaning: importa dados de formatos CSV, Excel e fontes diversas, e realiza limpeza com dplyr e tidyr para deixar o dataset pronto para análise.
Econometric modeling: executa regressões com fixest (feols, feglm, fepois), difference-in-differences com did/did2s e outras técnicas conforme necessidade.
Results presentation: gera tabelas prontas para publicação com modelsummary ou etable, e cria gráficos com ggplot2 com estilos padrões.
Use case: ao trabalhar com dados de painel e variáveis de controle, aplique efeitos fixos e apresente os resultados de forma clara.
Execute uma análise end-to-end em R em um conjunto de dados fornecido, incluindo importação, limpeza, modelagem econométrica com fixest e saída pronta para publicação.
High-intent search queries and answers about installing and using this skill.
To run an end-to-end econometric analysis in R, use an automated workflow that imports data, cleans it with dplyr, runs regressions with fixest functions like feols, and presents results using modelsummary for reproducible research outputs.
Estimating difference-in-differences models in R for political science is best handled using did or did2s packages, integrated into an automated workflow that cleans data with tidyverse and formats results into publication-ready tables.
Yes, you can import and clean data from CSV and Excel formats before running econometric models in R, using dplyr and tidyr to transform diverse data sources into a structured dataset ready for analysis.
You generate publication-ready tables for econometrics results in R using the modelsummary or etable packages, which format regression outputs from models like feols or feglm into clear, standardized academic tables.
Yes, this R data analysis workflow supports reproducible research practices by setting global options, using set.seed for randomization, and documenting methodological decisions throughout the data import and modeling process.
You should use fixest for econometric analysis in R when working with panel data and fixed effects, using specialized functions like feols, feglm, and fepois that efficiently handle high-dimensional data.
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