Interactive Exploratory Data Analysis with R and Shiny: An LLM-Supported Explanation and Prediction Platform

Year 2026, Volume: 15 Issue: 1 , 188 - 200 , 24.03.2026

Ahmet Albayrak , Muammer Albayrak , Metin Kaynaklı

https://doi.org/10.17798/bitlisfen.1787094

https://izlik.org/JA44CN52XP

Abstract

Exploratory Data Analysis (EDA), recognized as the initial and most critical phase of the data science workflow, plays a fundamental role in understanding the structure of datasets, performing data cleaning, and preparing data for subsequent modeling tasks. This study introduces an interactive EDA platform developed with the R programming language and the Shiny framework. The platform allows users to upload datasets and conduct essential statistical analyses and visualizations, while additionally incorporating large language models (LLMs), such as the OpenAI GPT-4-turbo model, to automatically generate explanatory insights and interpretative commentary regarding the data. By complementing traditional statistical evaluations with language model–driven perspectives, the proposed approach enriches the analytical process by enhancing user intuition and interpretive depth. The system was evaluated using sample datasets, through which both conventional EDA outputs and LLM-assisted interpretations were demonstrated. The findings suggest that the integration of LLMs within Shiny applications holds considerable potential to advance data science education, decision support systems, and automated reporting practices.

Keywords

Exploratory Data Analysis , Large Language Models , Shiny , Explainable AI , Interactive Interface

Ethical Statement

The study is complied with research and publication ethics.

Supporting Institution

TÜBİTAK

Thanks

This study was supported by the TÜBİTAK 2209/A Undergraduate Research Projects Support Program. We sincerely thank TÜBİTAK for their valuable contributions and support to our project titled "Package Development for Exploratory Data Analysis: RSEDA." The financial support provided by TÜBİTAK played a crucial role in the successful completion of this study. We hope that the results obtained will contribute to the effective use of machine learning methods in areas such as water consumption forecasting. We also extend our gratitude to the project team members, Büşra İLGEN, Gülnur PARLAK, and Miraç Can YILMAZ, for their valuable contributions throughout the project.

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