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Explaining Brexit and Trump with Tidy data graphics

CDRC Training
Wednesday 2 May 2018, 8am - 5pm
Leeds Institute for Data Analytics, Level 11, Worsley Building, University of Leeds, Clarendon Way, Leeds, LS2 9NL

2016 was an eventful year. The narrow votes in favour of Brexit in the UK and Trump in the US were a shock to many. You’ve probably heard commentators remark on the underlying causes for why people voted as they did. A familiar caricature is of blue collar disaffection (Leave and Trump) versus liberal, metropolitan values and relative affluence (Remain and Clinton). But is this true of the entirety of the UK and US?

In this workshop you’ll analyse datasets describing UK Local Authority and US county voting behaviour alongside key area-level socio-demographic variables. You’ll compare and evaluate the extent to which those demographics explain area-level variation in the vote. Importantly, you’ll explore whether explanations vary for different parts of the UK and US. You’ll do so by developing a family of data graphics (in R) that together reveal a data story behind the vote.

Learning objectives

In addition to understanding a little more about the political phenomena, you will:

  • learn how to data wranglereshape and curate Tidy data in R
  • appreciate some key principles of good data visualization design
  • confidently generate data graphics using a consistent vocabulary ggplot2
  • develop an intuitive understanding of statistical modelling procedures

Who teaches the programme?

Roger Beecham is a Lecturer in Geographic Data Science at the University of Leeds. His research demonstrates how new, passively-collected datasets can be repurposed for social research. His work spans several disciplines: spatial data analysis, information visualization, transport planning, crime, political science and market research. A current focus is around how new data and new disciplines such as ‘Data Science’ are reshaping statistical model building — and the role of data visualization in supporting this activity.


Some exposure to modern data analysis environments such as R or Python would be an advantage, but is not required. An interest in the application of statistical analysis procedures would also be beneficial.

Computers installed with the necessary software will be available for course participants. However, to really benefit from the course (and to translate learning into future workflows) it is highly recommended that participants bring their own laptops, with:

  • The latest version of R – download from here 
  • The latest version of RStudio – download from here


Further Information

For further information, please contact Kylie Norman.

Click here to book.