Chapter 11 General

11.1 Introduction

This is an enormous topic.

But start here:

  • Alberto Cairo, The Functional Art (Cairo 2013) and The Truthful Art (Cairo 2016) are framed in the context of Cairo’s professional expertise in “data journalism”.

    – Cairo’s blog has a number of very good posts, including:

    Keep those legends (2019-07-17)

  • William S. Cleveland’s books, Visualizing Data (Cleveland 1993) and The Elements of Graphing Data (Cleveland 1994), are classics in the field of statistical graphics.

  • Kennedy Elliot, Everything we know about how humans interpret graphics, video of talk given at OpenVis conference, 2016-04-25 & 26.

  • Jill H. Larkin and Herbert A. Simon, “Why a diagram is (sometimes) worth ten thousand words”, Cognitive Science, 1987 (Larkin and Simon 1987)

  • Elijah Meeks, “What Charts Do” (2018-05-21) is a succinct summary that’s worth reading.

  • Cole Nussbaumer Knaflic, Storytelling with Data (Knaflic 2015). A great introduction to the fundamentals of good visualization. The companion blog has an on-going series of further dives into the topic.

  • Naomi Robbins, Creating More Effective Graphs. (Robbins 2013)

  • Alan Smith, “Data visualisation: it is not all about technology”, Financial Times, 2017-06-28

  • John W. Tukey, Exploratory Data Analysis (Tukey 1977) is a classic–it might seem dated with its heavy reliance on analogue methods (for example, the suggestions about graph paper (p.127)), but the concepts are timeless.

11.2 General resources

  • F.J. Anscombe, “Graphs in Statistical Analysis” – the classic paper that introduced the justifiably-famous Anscombe’s Quartet (Anscombe 1973)

RJ Andrews, 2020-01-28, “Illustration Invades Everything”: Reflections by Minard on his graphical impact.

11.2.1 Animation in Data Visualization

Jon Schwabish, 2019-08-06, Observations on Animation in Data Visualization

11.2.2 Data Visualization Society (Medium)

Medium’s Data Visualization Society “Data Visualization Society aims to collect and establish best practices, fostering a community that supports members as they grow and develop data visualization skills.”


11.3 R Resources

11.3.1 general

  • R Graph Catalog – an unbeatable resource for making good graphs in R, described by the creators as “a complement to Creating More Effective Graphs by Naomi Robbins.” (Robbins 2013)

  • The R Graph Gallery

  • Michael Friendly and David Meyer (2016) Discrete Data Analysis with R (Friendly and Meyer 2016)

  • Kieran Healy Data Visualization: A practical introduction. (Healy 2019)

  • Shiny apps for statistics – by the Statistics Department at CalPoly

11.4 {ggplot2} – the pre-eminent way to create charts and graphs in R

11.4.1 extensions

There are many extension packages that allow you to make other visualizations in {ggplot2}. Some are catalogued at www.ggplot2-exts.org.

11.4.1.1 {ggfittext}

“ggplot2 geoms to fit text into boxes”

11.4.2 tips and tricks

** Note:**

11.4.3 Plotly

Plotly for R allows you to “Create interactive, D3 and WebGL charts in R” (their words, not mine). A great resource for upping the content of online visualizations.


11.5 Colour

Colour is a vital part of good data visualization.

11.5.1 General resources

Lisa Charlotte Rost, 2018-07-31, Your Friendly Guide to Colors in Data Visualisation – An overview of color tools

11.5.1.1 General resources: R

The following links support the use of colour in R.

11.5.2 {colorspace}

“A Toolbox for Manipulating and Assessing Colors and Palettes”

11.5.3 {prismatic}

{prismatic reference site}

11.5.5 ColorBrewer

The ColorBrewer palettes were designed by Dr. Cythia Brewer – a variety of palettes designed for data visualization (including maps)

ColorBrewer 2.0 – tool for selecting colour schemes (centred on maps, but they work just as well for other forms of data visualization)

Cynthia A. Brewer (2003). A Transition in Improving Maps: The ColorBrewer Example. Cartography and Geographic Information Science (Brewer 2003)

Cynthia A. Brewer, Geoffrey W. Hatchard, and Mark A. Harrower (2003) ColorBrewer in Print: A Catalog of Color Schemes for Maps, Cartography and Geographic Information Science (Cynthia A. Brewer 2003).

11.5.5.1 the R package {RColorBrewer}

11.5.7 palettes in R

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References

Anscombe, F. J. 1973. “Graphs in Statistical Analysis.” The American Statistician 27 (1): 17–21. https://doi.org/10.1080/00031305.1973.10478966.

Brewer, Cynthia A. 2003. “A Transition in Improving Maps: The Colorbrewer Example.” Cartography and Geographic Information Science 30 (2): 159–62. https://doi.org/10.1559/152304003100011126.

Cairo, Alberto. 2013. The Functional Art: An Introduction to Information Graphics and Visualization. New Riders.

Cairo, Alberto. 2016. The Truthful Art: Data, Charts, and Maps for Communication. New Riders.

Cleveland, William S. 1993. Visualizing Data. Hobart Press.

Cleveland, William S. 1994. The Elements of Graphing Data. Hobart Press.

Cleveland, William S., and Robert McGill. 1984. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.” Journal of the American Statistical Association 79 (387): 531–54. https://doi.org/10.1080/01621459.1984.10478080.

Cynthia A. Brewer, Mark A. Harrower, Geoffrey W. Hatchard. 2003. “ColorBrewer in Print: A Catalog of Color Schemes for Maps, Cartography and Geographic Information Science.” Cartography and Geographic Information Science 30 (1): 5–32. https://doi.org/10.1559/152304003100010929.

Friendly, Michael, and David Meyer. 2016. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. CRC Press.

Healy, Kieran. 2019. Data Visualization: A Practical Introduction. Princeton. http://socviz.co/.

Knaflic, Cole Nussbaumer. 2015. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley. https://www.storytellingwithdata.com/.

Larkin, Jill H., and Herbert A. Simon. 1987. “Why a Diagram Is (Sometimes) Worth Ten Thousand Words.” Cognitive Science 11 (1): 65–100. https://doi.org/10.1111/j.1551-6708.1987.tb00863.x.

Nicolas P. Rougier, Philip E. Bourne, Michael Droettboom. 2014. “Ten Simple Rules for Better Figures.” PLOS Computational Biology 10 (9). https://doi.org/10.1371/journal.pcbi.1003833.

Robbins, Naomi B. 2013. Creating More Effective Graphs. Chart House.

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

Wainer, Howard. 1984. “How to Display Data Badly.” The American Statistician 38 (2): 137–47. https://doi.org/10.1080/00031305.1984.10483186.