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Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 1

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

Ahmet Albayrak(DÜZCE ÜNİVERSİTESİ)0000-0002-2166-1102
Muammer Albayrak(KARADENİZ TEKNİK ÜNİVERSİTESİ)0000-0002-5946-6310
Metin Kaynaklı(BİTLİS EREN ÜNİVERSİTESİ)0000-0001-8372-1345
Pages
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Publication Date
2026-03-24
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Exploratory Data Analysis · Large Language Models · Shiny

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.

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Cite This Article

Albayrak, A., Albayrak, M., Kaynaklı, M. (2026). Interactive Exploratory Data Analysis with R and Shiny: An LLM-Supported Explanation and Prediction Platform. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 1. https://doi.org/10.17798/bitlisfen.1787094

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