5. Drawing graphs

This chapter explores the fundamental role of data visualisation in the analytical process, highlighting its dual purposes: as a tool for presenting information in an accessible and visually compelling manner, and as an aid to enhance understanding of the data during analysis. Beginning with an iconic example, the chapter revisits John Snow’s 1854 cholera map, illustrating the transformative power of effective visualisation in uncovering insights and driving impactful decisions. The discussion then transitions to practical guidance on creating graphs using the software jamovi, with an emphasis on simplicity and clarity. While jamovi offers robust default plotting capabilities, the chapter acknowledges its limitations for advanced customisation, directing readers towards tools like ggplot2 in R for more complex needs.

The chapter systematically introduces key visualisation types—histograms, boxplots, violin plots, and bar graphs—outlining their specific applications, strengths, and limitations. Detailed, step-by-step instructions are provided for generating each type of graph in jamovi, including tips for filtering data and refining visual outputs. The importance of interpreting these plots to detect patterns, distributions, or outliers is emphasised, alongside practical advice on exporting high-quality images for reports or presentations. Ultimately, the chapter underscores the centrality of visualisation in data analysis, offering readers both foundational knowledge and practical skills to effectively “show the data” and derive meaningful insights.