References and resources

Texts referenced in the Guide

Battle-Baptiste, W., and B. Rusert. 2018. W.e.b. Du Bois’s Data Portraits: Visualizing Black America: The Color Line at the Turn of the Twentieth Century. The W.E.B. Du Bois Center at the University of Massachusetts.

Becker, Richard A., and John M. Chambers. 1984. S: An Interactive Environment for Data Analysis and Graphics. Pacific Grove, CA: Wadsworth & Brooks/Cole.

Becker, Richard A., and William S. Cleveland. 1996. S-PLUS Trellis Graphics User’s Manual. Seattle, WA: MathSoft.

Beecham, Roger, Jason Dykes, Layik Hama, and Nik Lomax. 2021. “On the Use of ‘Glyphmaps’ for Analysing the Scale and Temporal Spread of COVID-19 Reported Cases.” ISPRS International Journal of Geo-Information 10 (4). https://doi.org/10.3390/ijgi10040213.

Cesal, Amy. 2020. “Writing Alt Text for Data Visualization.” Nightingale. 2020. https://medium.com/nightingale/writing-alt-text-for-data-visualization-2a218ef43f81.

“Chart Titles and Text.” n.d. Office for National Statistics. Accessed July 10, 2023. https://style.ons.gov.uk/data-visualisation/titles-and-text/annotation-and-footnotes/.

Cleveland, William S. 1993. Visualizing Data. Summit, NJ: Hobart Press.

———. 1994. The Elements of Graphing Data. Summit, NJ: Hobart Press.

“Coblis — Color Blindness Simulator.” n.d. Colblindor. Accessed July 10, 2023. https://www.color-blindness.com/coblis-color-blindness-simulator/.

Corbett, J. 2001. “Charles Joseph Minard, Mapping Napoleon’s March, 1861.” CSISS Class 2001. 2001. https://escholarship.org/uc/item/4qj8h064.

D’Ignazio, C., and L. F. Klein. 2020. Data Feminism. MIT Press. https://mitpress.mit.edu/9780262547185/data-feminism/.

“Documentation.” n.d. Statistical Analysis System. Accessed June 19, 2023. https://support.sas.com/en/documentation.html.

Du Bois, W. E. B. 1900. The Exhibit of American Negroes. Paris.

Few, Stephen. 2004. Show Me the Numbers. Burlingame, CA: Analytics Press.

Friendly, M. 2018. “A Very Brief History of Visualization: Visions, Stories and Pictures.” Chicago, IL. 2018. http://datavis.ca/papers/CHF-2x2.pdf.

———. 2022. “Remembrances of Things EDA.” 2022. https://www.researchgate.net/publication/361191335_Rememberances_of_Things_EDA.

Friendly, M., and D. Denis. 2005. “The Early Origins and Development of the Scatterplot.” J. Hist. Behav. Sci. 41 (2): 103–30. https://doi.org/10.1002/jhbs.20078.

Garland, Kevin. 1994. Mr Beck’s Underground Map. Capital Transport.

Green, Nathan. 2023. “Why your data viz needs alt text.” Significance 20 (1): 38–39. https://doi.org/10.1093/jrssig/qmad011.

Hedley, Alison. 2020. “Florence Nightingale and Victorian Data Visualisation.” Significance 17 (2): 26–30. https://doi.org/10.1111/1740-9713.01376.

Kent, AJ. 2021. “When Topology Trumped Topography: Celebrating 90 Years of Beck’s Underground Map.” The Cartographic Journal 58 (1): 1–12. https://doi.org/10.1080/00087041.2021.1953765.

Krause, Andreas. 2013. “Concepts and Principles of Clinical Data Graphics.” In A Picture Is Worth a Thousand Tables: Graphics in Life Sciences, 3–21. Springer. https://doi.org/10.1007/978-1-4614-5329-1_1.

Krause, Andreas, and Michael O’Connell, eds. 2013. A Picture Is Worth a Thousand Tables: Graphics in Life Sciences. Springer. https://doi.org/10.1007/978-1-4614-5329-1.

Muth, Lisa. 2018. “An Alternative to Pink & Blue: Colors for Gender Data.” Datawrapper. 2018. https://blog.datawrapper.de/gendercolor/.

Nightingale, Florence. 1859. “A Contribution to the Sanitary History of the British Army During the Late War with Russia.” London, UK: Harrison; Sons. 1859. https://iiif.lib.harvard.edu/manifests/view/drs:7420433$24b.

Norman, Donald A. 1990. The Design of Everyday Things. New York, NY: Currency Doubleday.

R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Rello, Luz, and Ricardo Baeza-Yates. 2016. “The Effect of Font Type on Screen Readability by People with Dyslexia.” ACM Trans. Access. Comput. 8 (4). https://doi.org/10.1145/2897736.

Robbins, Naomi B. 2006. Creating More Effective Graphs. Hoboken, NJ: Wiley.

Robinson, A. H. 1967. “The Thematic Maps of Charles Joseph Minard.” Imago Mundi 21: 95–108.

Sarkar, Deepan. 2008. Lattice: Multivariate Data Visualization with r. New York, NY: Springer.

Schwabish, Jonathan. 2021. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press. http://www.jstor.org/stable/10.7312/schw19310.

Setlur, V., and B. Cogley. 2022. Functional Aesthetics for Data Visualization. Wiley. https://www.functionalaestheticsbook.com/.

Snow, John. 1854. “Mode of Communication of Cholera.” Piccadilly (London), UK: John Churchill. 1854. https://archive.org/details/b28985266/page/52/mode/2up?view=theater.

“Styling Base r Graphics.” 2018. Jumping Rivers. 2018. https://www.jumpingrivers.com/blog/styling-base-r-graphics/.

Tennekes, Martijn, and Marco J. H. Puts. 2023. “cols4all: a Color Palette Analysis Tool.” In EuroVis 2023 - Short Papers, edited by Thomas Hoellt, Wolfgang Aigner, and Bei Wang. The Eurographics Association. https://doi.org/10.2312/evs.20231040.

Tol, Paul. 2021. “Introduction to Colour Schemes.” 2021. https://personal.sron.nl/~pault/.

Tufte, Edward R. 1990. Envisioning Information. Graphics Press.

———. 2001. The Visual Display of Quantitative Information. 2nd ed. Cheshire, CT: Graphics Press.

———. 2004. Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, CT: Graphics Press.

———. 2006. Beautiful Evidence. Cheshire, CT: Graphics Press.

Tukey, John W. 1977. Exploratory Data Analysis. Reading, MA: Addison-Wesley.

van Weert, Julia C. M., Monique C. Alblas, Liset van Dijk, and Jesse Jansen. 2021. “Preference for and Understanding of Graphs Presenting Health Risk Information. The Role of Age, Health Literacy, Numeracy and Graph Literacy.” Patient Education and Counseling 104 (1): 109–17. https://doi.org/10.1016/j.pec.2020.06.031.

Wery, J. J., and J. A. Diliberto. 2017. “The Effect of a Specialized Dyslexia Font, OpenDyslexic, on Reading Rate and Accuracy.” Ann. Of Dyslexia. 67: 114–27. https://doi.org/10.1007/s11881-016-0127-1.

Wickham, Hadley. 2011. “Ggplot2.” Wiley Interdisciplinary Reviews: Computational Statistics 3: 180–85. https://onlinelibrary.wiley.com/doi/10.1002/wics.147.

———. 2016a. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. Vol. 2. Use r! Springer International Publishing. https://doi.org/10.1007/978-3-319-24277-4.

———. 2016b. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

Wikipedia contributors. 2023. “1854 Broad Street Cholera Outbreak.” 2023. https://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak.

Wilkinson, Leland. 2005. The Grammar of Graphics. Statistics and Computing. New York: Springer-Verlag. https://doi.org/10.1007/0-387-28695-0.

Williams, T. A., David R. Anderson, and Dennis J. Sweeney. 2023. “Statistics.” Encyclopedia Britannica. https://www.britannica.com/science/statistics.

Further reading

Functional Aesthetics for Data Visualization.

Setlur and Cogley (2022)

Why we recommend: This book connects research and practice, combining ideas from cognitive psychology, data literacy, and visual design. Its ideas go beyond simple charts, and can be extending to complex visual displays including interactive dashboards and visualisations.

Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks.

Schwabish (2021)

Why we recommend: This book guides you towards which types of data visualisations may best depending on what you’re trying to show. Not only that, but it uses visual perception theory to explain why some charts are easier to read than others.

Data Feminism.

D’Ignazio and Klein (2020)

Why we recommend: We often hear the term “storytelling” when we’re talking about data visualisation, and we should remember the role of the storyteller in this. In this book, the authors make several critical points, including that when we work with data we must also think about why it was collected, and what it could be used for.

Preference for and understanding of graphs presenting health risk information. The role of age, health literacy, numeracy and graph literacy.

van Weert et al. (2021)

Why we recommend: Patients are often presented with data about health risk information in the form in charts and tables. This article examines how well patients understand the information presented to them across a range of different chart types. It finds that the types of charts that patients prefer, is not always the type of chart that they understand best.

Additional resources

Training courses

Presenting Data (RSS)

This online course is the foundation to all presentations of statistical information. The basic principles of presenting information in tables, charts, maps and text are explained. These are illustrated and then reinforced through practical exercises.

Data Exploration in Tableau (RSS)

Tableau is more than just a simple data visualisation tool. It also gives people the capability to manipulate multiple data sources, create custom charts, build predictive models, and turn their plots into interactive dashboards and presentations.

This virtual course will run over two afternoons. It will guide you through the process of wrangling data, performing data analysis, and visually communicating outputs. By the end of this course you will be able to manipulate your own data to build custom charts. You will learn how to work with geographic data in Tableau, use predictive models to make forecasts, and create interactive dashboards and stories to share your work.

Power BI for Data Exploration and Basic Statistics (RSS)

Power BI is rapidly becoming a standard tool for producing interactive reports and dashboards and this course will provide a systematic introduction to Power BI, focusing on the workflow, from data preparation through to building and sharing visuals. There will be a focus on design issues, helping to achieve the right level of functionality and usability based on the intended audience. Particular attention will also be paid to the use of basic statistics.

Publishing Quality Charts in R with ggplot2 (RSS)

This tutor-led virtual course will introduce how the tidyverse and ggplot2 can be used to reproducibly create publication quality charts from R.

Low-code/no-code visualisation tools

Datawrapper

https://www.datawrapper.de/

Flourish

https://flourish.studio/