Introducing num_dist: A Revolutionary Visualization R function for Enhancing Data Science Explorations

DOI

10.2991/978-94-6239-598-5_19How to use a DOI?

Keywords

Exploratory Data Analysis; Data Visualization; heavy-tailed; RShiny

Abstract

Exploratory Data Analysis (EDA) is a fundamental practice in data science, enabling practitioners to uncover patterns, identify anomalies, and generate hypotheses without preconceived assumptions. However, traditional EDA tools often fall short in handling datasets with skewed or heavy-tailed distributions, where key insights are often hidden in the tail regions. num_dist, a function within the developing SmartVisual R package, addresses this gap by providing an interactive, Shiny-based tool that focuses specifically on the tail behavior of numerical distributions. By allowing users to zoom into the tail regions, adjust data ranges dynamically, and compare subgroups, num_dist enhances the exploration of outliers, rare events, and extreme values. Through real-time statistical summaries and intuitive faceting, it empowers data scientists to generate deeper insights, refine hypotheses, and make data-driven decisions. This paper demonstrates how num_dist promotes a more effective and efficient approach to EDA by facilitating a detailed examination of critical data segments often overlooked by traditional methods. With its focus on interactive exploration and hypothesis generation, num_dist serves as a valuable tool in modern data science, particularly for datasets with complex distributions and extreme values.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuqing Xue
PY  - 2026
DA  - 2026/02/26
TI  - Introducing num_dist: A Revolutionary Visualization R function for Enhancing Data Science Explorations
BT  - Proceedings of the 2025 6th  International Conference on Big Data and Social Sciences (ICBDSS 2025)
PB  - Atlantis Press
SP  - 181
EP  - 191
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-598-5_19
DO  - 10.2991/978-94-6239-598-5_19
ID  - Xue2026
ER  -