Abstract
<jats:p>Data visualization techniques have increasingly become the subject of interest in both scientific and technical reporting processes. There exists a significant dependence on exploratory data analysis (EDA) tools to achieve a custom presentation and examination of results in statistical techniques and data science research. Consequently, exploring data through an understanding of its structure and finding new insights is significantly supported by data visualization. Boxplots are recently becoming more popular than ever in terms of their powerful and simple glimpse into the most important robust data summaries. A newly boxplot-inspired plot called a stairboxplot was introduced in this chapter through a step-by-step construction method. Stairboxplot was robustly designed and constructed to implement multi-functionalities of classical boxplot, histogram, and dot plot in a single call to a plotting function. The chapter highlighted some justifications behind the selection of most of the important construction tools in the method, such as fence estimates, outlyingness, and quardbins. Applications of the stairboxplot were explored using real-life data and simulation. We hope this work will serve as a fundamental guide and inspire others toward the construction of visual EDA tools in general.</jats:p>