1. Graphs & Charts: The Art of Data Visualisation Alasdair Rutherford SSPC9C6University of StirlingSpring 2016

2. SPSPC9C6 University of Stirling Spring 2016 Introduction What is data visualisation, and why do we need it? Graphs and Charts: Some common forms of presenting data Developments in Data Visualisation Choosing the right visualisation “The Chart Never Lies”: How visualisation can mislead

3. SPSPC9C6 University of Stirling Spring 2016 Presenting Data Data visualisation is not just about pretty pictures; it is about selecting the best way to present complex data. It can be used to summarise data, to identify potential patterns, to present your data, or to tell a story.

4. SPSPC9C6 University of Stirling Spring 2016 Florence Nightingale & Data Data visualisation has a long history – one of the pioneers was Florence Nightingale. Watch: http://www.youtube.com/watch?v=yh X0OR1_Vfc

5. SPSPC9C6 University of Stirling Spring 2016 The right chart at the right time Commonly used graphs and charts: – Bar charts & histograms – Line graphs – Pie charts – Scatterplots Generated using: – Spread sheet, such as Microsoft Excel – Statistical software, such as SPSS or STATA – Online tools

6. SPSPC9C6 University of Stirling Spring 2016 Bar Charts Bar charts can be used to present categorical data. Categories are shown along the x-axis, while data such as averages, percentages or frequencies are represented on the y-axis. Best when you want to show proportional as well as absolute differences.

7. SPSPC9C6 University of Stirling Spring 2015 Example: Bar Chart of Twitter Activity Days of week categories on the x-axis Percentage of activity on the y-axis SOURCE: Sysomos (2009) http://www.sysomos.com/insidetwitter/appendix/

8. SPSPC9C6 University of Stirling Spring 2015 SOURCE: Sysomos (2009) http://www.sysomos.com/insidetwitter/appendix/ Example: Bar Chart showing percentages Age categories on the x-axis Height of the bars shows percentage Categories are ordered

9. SPSPC9C6 University of Stirling Spring 2016 Line Graphs Line graphs are often used to represent continuous data. They can also be used for ordered categorical data that approximates a continuous variable. Best when you want to show changes across the values of the X variable.

10. SPSPC9C6 University of Stirling Spring 2015 Example: Line Graph Continuous data on the x- axis The shape of the line gives a sense of the trend SOURCE: Sysomos (2009) http://www.sysomos.com/insidetwitter/appendix/

11. SPSPC9C6 University of Stirling Spring 2015 Example: Line graph More complex information captured Line shows the distribution SOURCE: Sysomos (2009) http://www.sysomos.com/insidetwitter/appendix/

12. SPSPC9C6 University of Stirling Spring 2016 Pie Charts Pie charts are an accessible way to represent percentages and proportions. They require categorical data, which may or may not be ordered. They also require that categories are mutually exclusive i.e. the percentages sum to 100%.

13. SPSPC9C6 University of Stirling Spring 2015 Example: Pie Chart of User Ages The area of the segment represents the percentage Easier to see group combinations e.g. two thirds under 25 SOURCE: Sysomos (2009) http://www.sysomos.com/insidetwitter/appendix/

14. SPSPC9C6 University of Stirling Spring 2015 BUT, Pie Charts can also be misleading … No. Westminster seats in the 2010 General Election by Political Party Would Labour + Lib Dem > Conservatives?

15. SPSPC9C6 University of Stirling Spring 2015 BUT, Pie Charts can also be misleading …

16. SPSPC9C6 University of Stirling Spring 2016 Scatterplots Scatterplots allow the comparison of two metric (interval or ratio) variables. They are particularly useful for identifying patterns of association ('correlations') between the two variables, and for spotting 'outliers' (cases with unusually high or low values) within data sets.

17. SPSPC9C6 University of Stirling Spring 2015 HUSBAND WIFE Example: Scatter plot of Spouse Age Each dot represents one observation e.g. couple Outliers can be identified The correlation seems quite clear SOURCE: BHPS (2008)

18. SPSPC9C6 University of Stirling Spring 2016 Box (and whisker) plots A box plot is a graphical display, based on quartiles, which help us to picture the range of values in a variable (the distribution of scores). You can use them to explore the distribution of one continuous variable or alternatively you can ask for scores to be broken down for different groups (e.g. age groups).

19. SPSPC9C6 University of Stirling Spring 2015 Example: Box Plot Contains a lot of information in one graph Helps to understand the centre, range and shape of data The statistics here will be introduced later in this course

20. SPSPC9C6 University of Stirling Spring 2015 Example: Box plot by Country Variable of interest Plots by category e.g. country Here the data for the USA has a much bigger spread than the data for France

21. SPSPC9C6 University of Stirling Spring 2016 Developments in visualisation Improvement in technology and design, alongside developments in data accessibility, have lead to the emergence of a number of tools to make presenting data more accessible. This combines statistical analysis with good design principles, using data to tell a story.

22. SPSPC9C6 University of Stirling Spring 2015 Example: Area plot of CO 2 Emissions Areas are easily compared Numerical data also included Reference point Alternative to a bar chart SOURCE: http://www.informationisbeautiful.net/

23. SPSPC9C6 University of Stirling Spring 2015 Example: Population Proportions Alternative to a pie chart Also conveys information on gender balance Each ‘person’ represents 1% SOURCE: http://www.informationisbeautiful.net/

24. SPSPC9C6 University of Stirling Spring 2016 How do you choose a visualisation? What sort of data are you using? What characteristics of the data do you need to communicate? Who is the audience?

25. SPSPC9C6 University of Stirling Spring 2016 Lies, Damn Lies and Data Visualisation Like the numbers themselves, data visualisation can be used to mislead, either accidentally or intentionally. You need to be particularly careful when using percentages, and also pay attention to scales.

26. SPSPC9C6 University of Stirling Spring 2016 Top 3 tips for BAD data visualisation i.e. what NOT to do! 1)Cram everything you can into the chart – readability is overrated 2)Choose the scale to hide the inconvenient truth 3)Emphasise the trivial and ignore the important

27. SPSPC9C6 University of Stirling Spring 2015 Example: Difficult to Read Line Graph SOURCE: Wainer (1984) “How to Display Data Badly” The American Statistician, Vol. 38, No. 2

28. SPSPC9C6 University of Stirling Spring 2016 Top 3 tips for BAD data visualisation i.e. what NOT to do! 1)Cram everything you can into the chart – readability is overrated 2)Choose the scale to hide the inconvenient truth 3)Emphasise the trivial and ignore the important

29. SPSPC9C6 University of Stirling Spring 2015 Example: Misleading Bar Chart SOURCE: http://flowingdata.com/category/statistics/mistaken-data/

30. SPSPC9C6 University of Stirling Spring 2016 Top 3 tips for BAD data visualisation i.e. what NOT to do! 1)Cram everything you can into the chart – readability is overrated 2)Choose the scale to hide the inconvenient truth 3)Emphasise the trivial and ignore the important

31. SPSPC9C6 University of Stirling Spring 2015 Example: Concealing the data SOURCE: Wainer (1984) “How to Display Data Badly” The American Statistician, Vol. 38, No. 2

32. SPSPC9C6 University of Stirling Spring 2016 Further Developments: Global health Increasingly data visualisation is being used in multimedia, animating data in order to capture more complexity and represent dynamics. This video shows changing global health and wealth over time. Watch: http://www.youtube.com/watch?v=ahp7QhbB8G4

33. SPSPC9C6 University of Stirling Spring 2015 Summary There are a wide range of data visualisations available Selecting the right graph or chart can help you describe your data Care must be taken in choosing the appropriate graph or chart depending on your data and audience Beware of misleading with your visualisations, either intentionally or accidentally