Chapter 13 General

13.1 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.

13.2 General resources

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

See, people think Anscombe's quartet is all artificial extreme cases and no one would actually make such a stupid mistake with regression, and then you get figures like Fig 8C https://t.co/LrPn1fjShp pic.twitter.com/sEG3BXpQYj

— Mark Baxter #ITMFA ((markgbaxter?)) February 3, 2020

• Enrico Bertini’s homepage – a plethora of articles and resources on data visualization

• Data Visualization Inventors, Founders, and Developers – credit where credit is due

• Richard Brath & Ebad Banissi, 2016, “Using Typography to Expand the Design Space of Data Visualization”, She Ji: The Journal of Design, Economics, and Innovation, Vol. 2, Issue 1, Spring 2016

• Karl Broman, The top ten worst graphs

• William Cleveland & Robert McGill, “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods” (Cleveland and McGill 1984)

• Keith Collins (2015-12-23), The most misleading charts of 2015, fixed; good discussion of problematic choices with axis scales.

• Datawrapper, Chartable – blog including weekly charts and how-to advice

• Financial Times Visual Vocabulary, “A poster (available in English, Japanese, traditional Chinese and simplified Chinese) and web site to assist designers and journalists to select the optimal symbology for data visualisations, by the Financial Times Visual Journalism Team.”

• Steve Franconeri, Chart Chooser

  • background: “Multiple views on how to choose a visualization”, 2019-04-24, at medium.com

• Michael Friendly, DataVis.ca – a variety of resources, including the “Milestones Project” (significant events in the history of data visualization), books, courses, papers, and R materials.

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

  • online version

• Kate Moran, 2017-08-06, “How to Present Scientific Findings Online” (file under: Know Your Audience)

• Tamara Munzer, list and links to talks

• PolicyViz, DataViz Cheatsheet (2018-08-07)

• Elizabeth Ricks, “How do I know which graph to use?”—blog article at storytellingwithdata.com

• Rougier NP, Droettboom M, Bourne PE (2014) “Ten Simple Rules for Better Figures”. (Nicolas P. Rougier 2014)

• Howard Wainer, “How to Display data Badly” (Wainer 1984)

• Claus O. Wilke (2019, full draft) Fundamentals of Data Visualization

  • Important: Wilke notes that this is a platform-agnostic book, but it was written in R’s {bookdown} and uses {ggplot2} for all of the figures.

  • the github repo for the book (in case you want to plagiarize the code for a specific figure)

• Google, Data Visualization, part of Google’s Material

  • Mark Wilson, 2019-06-28, “Google’s six rules for great data design” (at FastCompany)

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

13.2.1 Animation in Data Visualization

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

13.2.2 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.”

• Allen Hillery, 2019-05-06, W.E.B Du Bois and Four Essential Steps to Effective Persuasion

• Michael Correll (2018-11-20) What Does “Visualization Literacy” Mean, Anyway?

• Kennedy Elliot (2016-05-02) 39 studies about human perception in 30 minutes

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13.3 R Resources

13.3.1 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)

  • online version

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

13.3.2 Packages

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

• {ggplot2}: part of the tidyverse – reference materials, examples, etc etc. Start here.

• Winston Chang, R Graphics Cookbook, 2nd edition(Chang 2018)

• Top 50 {ggplot2} Visualizations - The Master List (With Full R Code)

• A Compendium of Clean Graphs in R, by Eric-Jan Wagenmakers and Quentin F. Gronau

• BBC Visual and Data Journalism cookbook for R graphics

  • {bbplot}, a package with their templates etc.

  • “How the BBC Visual and Data Journalism team works with graphics in R”

• Gina Reynolds, 2019-01-31, the ggplot flipbook

• Will Chase, 2019-05-29, R you ready to make charts?, Philly dataviz meetup

• Dana Paige Seidel, Default ggplot2 aesthetics by geom

• Henry Wang, ggplot2 Theme Elements Demonstration

• {ggplot2}: A complete guide to scales “There are numerous scales in ggplot2. Too many to memorize. This app makes it easy for you to find the right scales and arguments for your variable types and aesthetics.”

13.4.1 extensions

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

• Gallery of {ggplot2} extensions:

13.4.1.1 {ggfittext}

“ggplot2 geoms to fit text into boxes”

• GitHub page: wilkox/ggfittext

• Reference page: ggfittext

13.4.1.2 {patchwork} – The Composer of Plots

“The goal of patchwork is to make it ridiculously simple to combine separate ggplots into the same graphic.”

• CRAN page

• Reference page

13.4.1.3 {see} – Model Visualization Toolbox for ’easystatsand 'ggplot2

• Reference page

13.4.2 themes

{ggplot2} has a number of themes in the core package, and here are some to extend the range:

• ggthemes – includes various publications (e.g. Wall Street Journal, Economist), software (e.g. Excel, Stata), dataviz celebrities (e.g. Tufte, Few), and others

• ggpomological: A Pomological ggplot2 Theme

13.4.3 tips and tricks

• Simon Jackson, 2016-08-11, Plotting background data for groups with {ggplot2}

• Laura Ellis, 2018-08-01, Highlighting with {ggplot2}: The Old School and New School Way

• Sharon Machlis, 2019-07-24, How to write your own ggplot2 functions in R

• Isabella R. Ghement, 2020-05-18, twitter thread that shows step-by-step how to build and customize scatterplots in R.

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13.5 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.

• Carson Sievert, Plotly for R

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13.6 Colour

Colour is a vital part of good data visualization.

13.6.1 General resources

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

Lisa Charlotte Rost, 2020-09-04, How to pick more beautiful colors for your data visualizations: Common color mistakes and how to avoid them

13.6.1.1 General resources: R

The following links support the use of colour in R.

• R color cheatsheet

13.6.2 colour (or color) blindness

• Masataka Okabe and Kei Ito, 2008-09-24, Color Universal Design (CUD) - How to make figures and presentations that are friendly to Colorblind people

• Tom Jager (2018-06-25) How to Optimize Charts For Color Blind Readers Using Color Blind Friendly Palettes

• Natalie Houston, “Creating Color-Blind Accessible Figures”, ProfHacker, The Chronicle of Higher Education, 2015-02-09

13.6.2.1 R packages to address colour blindness

{ggokabeito}: ‘Okabe-Ito’ Scales for ‘ggplot2’ and ‘ggraph’

• “ggokabeito provides ggplot2 and ggraph scales to easily use the discrete, colorblind-friendly ‘Okabe-Ito’ palette in your data visualizations”

• {ggokabeito} reference page

• CRAN page

functions in the {see} package:

• Okabe-Ito color palette

{colorblindr}: An R package to simulate colorblindness on R figures.

• github repo

• Graphical User Interface to Check Images for Color Constraints

13.6.3 {colorspace}

“A Toolbox for Manipulating and Assessing Colors and Palettes”

• {colorspace} homepage

Looking forward to my (UseR2019_Conf?) presentation about color palettes with #rstats pkg #colorspace #dataviz #useR2019 #endrainbow

When: 14:00
Where: Saint-Exupéry

Web: https://t.co/pdjmkYiDkV
Interactive apps: https://t.co/YFC7gjKCKT
Slides: https://t.co/KOiC1NmrMF pic.twitter.com/cPF9ZF8Cl1

— Achim Zeileis ((AchimZeileis?)) July 11, 2019

13.6.4 {prismatic}

{prismatic reference site}

13.6.5 {fishualize}

GitHub: fishualize: Color Palettes Based on Fish Species

13.6.6 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).

13.6.6.1 the R package {RColorBrewer}

• RColorBrewer’s Palettes from The R Graph Gallery

• Stewart MacArthur, 2010-12-08, “R: Using RColorBrewer to colour your figures in R”

13.6.7 other colour notes and guides

• Alberto Cairo, 2019-06-26, “Our understanding of rainbow colour schemes remains incomplete”

• Robert Simmon, “Use of Color in Data Visualization” {pdf}

• Alan Wilson, 2017-02-27, “The Power of The Palette: Why Color is Key in Data Visualization and How to Use It” at Adobe Blog

13.6.8 palettes in R

{paletteer}

• “a comprehensive collection of color palettes in R using a common interface. Think of it as the “caret of palettes”.”

{MetBrewer}

• “Color palette package in R inspired by works at the Metropolitan Museum of Art in New York” by BlakeRMills

Other palette resources:

• Clay Ford, 2016-06-10, “Setting up Color Palettes in R”

• Joachim Goedhart, 2019-08-29, Data Visualization with Flying Colors, at thenode.biologists.com

• R color cheatsheet

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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.

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

Chang, Winston. 2018. R Graphics Cookbook. Second. O’Reilly. https://r-graphics.org/.

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

———. 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.