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.
Sessions
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.
Adding uncertainty representation in a data visualisation can help in decision-making. There is an existing wealth of software designed to visualise uncertainty as a distribution or probability. These visualisations are excellent for helping understand the uncertainty in our data, but they may not be effective at incorporating uncertainty to prevent false conclusions. Successfully preventing false conclusions requires us to communicate the estimate and its error as a single “validity of signal” variable, and doing so proves to be difficult with current methods. In this talk, we introduce ggdibbler, a ggplot extension that makes it easier to visualise uncertainty in plots for the purposes of preventing these “false signals”. We illustrate how ggdibbler can be seamlessly integrated into existing visualisation workflows and highlight the effect of these changes by showing the alternative visualisations ggdibbler produces for a choropleth map.