AgroR: An R package and a Shiny interface for agricultural experiment analysis

  • Gabriel Danilo Shimizu Universidade Estadual de Londrina
  • Rodrigo Yudi Palhaci Marubayashi Universidade Estadual de Londrina
  • Leandro Simões Azeredo Gonçalves Universidade Estadual de Londrina http://orcid.org/0000-0001-9700-9375

Resumo

Statistical analysis is central to agricultural research, but the complexity of statistical methodologies and programming languages, such as R, often poses challenges for researchers. To address these difficulties, we present AgroR, a comprehensive R package and Shiny web application (https://uel.br/fisher.uel.br/AgroR_shiny) designed to streamline the analysis of agricultural experiments. AgroR supports a wide range of experimental designs, offering tools for analysis of variance, multiple comparison tests, and assumption validation, as well as functions for exploratory data analysis and graphical representation. The package is built for accessibility, allowing users with limited programming skills to perform advanced statistical analyses using an intuitive interface. The Shiny application enhances usability by providing a graphical interface that simplifies the running of statistical tests and visualization of results. AgroR includes functions for analyzing complex experimental designs, such as factorial and split-plot designs, and offers additional tools for graphical outputs and dataset management. Available through the CRAN repository and accessible via a web browser, AgroR has been widely adopted, with thousands of downloads and citations across the scientific literature. AgroR significantly lowers the barriers to statistical analysis in agricultural research by providing a user-friendly interface and robust statistical capabilities, thereby enabling more accurate and reliable conclusions.

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Publicado
2025-03-26
Como Citar
Shimizu, G. D., Marubayashi , R. Y. P., & Gonçalves, L. S. A. (2025). AgroR: An R package and a Shiny interface for agricultural experiment analysis. Acta Scientiarum. Agronomy, 47(1), e73889. https://doi.org/10.4025/actasciagron.v47i1.73889
Seção
Biometria, Modelagem e Estatística

 

2.0
2019CiteScore
 
 
60th percentile
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2.0
2019CiteScore
 
 
60th percentile
Powered by  Scopus