16_Data visualisation.pdf

BEHL 2005 / BEHL 2019 (UO) Introductory Research Methods DATA VISUALISATION Professor Hannah Keage What are we going to cover? Why visualise data? Principles of data visualisation Common ways to visualise data Content from this lecture references: The story of Florence Nightingale • Led a team...

BEHL 2005 / BEHL 2019 (UO) Introductory Research Methods DATA VISUALISATION Professor Hannah Keage What are we going to cover? Why visualise data? Principles of data visualisation Common ways to visualise data Content from this lecture references: The story of Florence Nightingale • Led a team of nurses to Istanbul in 1854 to assist in the care of British soldiers fighting in the Crimean war. • Collected data on cause of death and invented the “polar” or “artic” plot. • Linked deaths to poor sanitisation. https://99percentinvisible.org/episode/florence-nightingale-data-viz-pioneer/ The story of Dr John Snow • Mapped cholera outbreaks from nineteenth century London. • Cholera appeared to cluster around one water pump, which turned out to be contaminated by sewerage. https://www.theguardian.com/news/datablog/2013/mar/15/john-snow-cholera-map Why visualise data? • It enables us to discover patterns. • It enables us to thinking deeply about the data. • It is enables us to see differences between groups and individuals. • It’s all about communication. • It tells a story. • To us, in terms of understanding. • To the reader, in terms of presentation. Principles of data visualisation • Should be guided by scientific questions. Don’t go fishing. • Aesthetics – colours, be careful with yellow and similar colours. Note, colour blindness effects. • Don’t just show summary statistic (e.g. mean), show the distribution (variance). • Think about looking at individuals, to really understand your data and find the hidden stories. Examples of bad data visualisation Time is going backwards…? https://viz.wtf/ Examples of bad data visualisation 28 is smaller than 25 and 13..? https://vciba.springeropen.com/articles/10.1186/s42492-021-00092-y Examples of bad data visualisation 23% bigger than 33%..? Examples of bad data visualisation Y-axis does not reach 0. https://badvisualisations.tumblr.com/ Common ways to visualise data: histograms Represents distribution of one factor/variable. Common ways to visualise data: density plot A “smoothed” histogram. Common ways to visualise data: box plot Displays the median, the interquartile range, and the range of the data from one factor/variable. Can also detect outliers. Common ways to visualise data: violin plot Similar to a box plot but they also display the density of data for one variable/factor. BEHL 2005 / BEHL 2019 (UO) Introductory Research Methods DATA VISUALISATION Professor Hannah Keage