Visualising Uncertainty
From exploring variability in a dataset to communicating the distribution of estimates, uncertainty visualisation plays a role at every stage of data analysis. This tutorial provides an overview of uncertainty, examines how it is represented in R, and introduces a range of techniques for visualising it. The first session will introduce the concept of uncertainty and cover general visualisation approaches. The second session will focus on spatial data and the creative methods that emerge when uncertainty must be expressed using only a limited set of visual aesthetics. Wherever there are statistics and data, there is uncertainty, making the ability to visualise it effectively an essential skill for any statistician.
Dianne Cook
Dianne Cook, a Professor of Statistics at Monash University in Melbourne, Australia, is a global leader in data visualisation. She has delivered over 100 invited talks internationally and published extensively on various aspects of data visualisation. Dr. Cook is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, past editor of the Journal of Computational and Graphical Statistics, and the R Journal. She has served as a Board Member of the R Foundation and is currently the co-chair of the Statistical Computing and Visualisation Section of the Statistical Society of Australia.
Harriet Mason
Harriet Mason is a final year PhD student. She is researching how uncertainty can be effectively communicated through statistical graphics, particularly in contexts where misleading interpretations must be actively avoided. She is the author of several R packages, including cassowaryr and ggdibbler.