Chapter 14 Chart types
14.1 Chart types: theory and methods
Naomi Robbins (2013), Creating More Effective Graphs, Chart House.
14.2 Bar charts (and their variants)
Andy Kirk (2019-07-19) Five Ways To…Present Bar Charts – first in a series of “Five Ways To…”
- Thomas Mock: {ggplot2} code for the article
14.3 Box plots (a way to visualize distributions)
R package boxplot
Laura DeCicco, 2018-08-10, Exploring ggplot2 boxplots - Defining limits and adjusting style (via The Wayback Machine at web.archive.org)
Ron Pearson, 2011-01-29, Boxplots and Beyond – Part I (first in a multi-part series on various distribution plots)
More papers should use this plot type!! pic.twitter.com/iEglulaMyM
— Johanna Rickne (/@/johannarickne) August 11, 2018
14.4 Bump plot
A line chart that shows changes in ranking over time (not the absolute value).
{ggbump} – “A geom for {ggplot} to create bump plots”
Kaiser Fung, 2019-04-16, The Bumps come to the NBA, courtesy of 538 – some good examples of bump charts, with links to other examples.
14.5 Density plot
- Jodie Burchell, 2016-03-16, Creating plots in R using ggplot2 - part 8: density plots
14.6 Dot plot (Cleveland dot plot, lollipop plot)
UC Business Analytics R Programming Guide, Cleveland Dot Plots
Nina Zumel (2013-02-18) Revisiting Cleveland’s The Elements of Graphing Data in ggplot2
Datavis with R: Drawing a Cleveland dot plot with ggplot2 (via The Wayback Machine at web.archive.org)
14.7 Dynamite plots
A.K.A. bar and line graphs. Don’t use them!
Rafael Irizarry, 2019/02/21, “Open letter to journal editors: dynamite plots must die”
GB Drummond and SL Vowler, 2011, “Show the data, don’t conceal them”, British Journal of Pharmacology, 2011 May; 163(2): 208–210.
14.8 Eikosograms
an eikosogram is a picture of probability. It visually partitions a unit square into rectangular regions whose areas give the numerical values of various probabilities. The construction is such that each rectangular region is identified with the value of one or more categorical variates. R.W. Oldford
- Wayne Oldford, Erle Holgersen, Ben Lafreniere, Tianlu Zhu (2018-08-22) {eikosograms} CRAN page
14.9 Flow visualizations
1. Circle plots
a.k.a. Circos plot
David Smith, Global Migration, animated with R, 2018-06-29
2. Sankey plots
Interactive flow visualization in R; Kyle Walker, 2016-06-26
14.10 Genealogical data
14.10.0.1 {ggeneology}
Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham (2019) “ggenealogy: An R Package for Visualizing Genealogical Data”, Journal of Statistical Software, Vol 89 (13)
CRAN page: ggenealogy: Visualization Tools for Genealogical Data
14.11 Heatmaps
The Heatmap function in the R Graph Gallery
Rebecca L. Barter & Bin Yu, 2017-01-30, “Superheat: An R package for creating beautiful and extendable heatmaps for visualizing complext data”
Heatmaps in R, from ploty
Mick Watson, 2015-04-05, You probably don’t understand heatmaps (via The Wayback Machine at web.archive.org)
14.12 Histograms
see also Mosaic (a.k.a. Marimekko) charts
Variable width column charts (in ggplot2)
Aran Lunzer and Amelia McNamara, What’s so hard about histograms?
14.13 Lexis diagrams
Tim RiffeEmail author, Jonas Schöley and Francisco Villavicencio (2017) “A unified framework of demographic time”, Genus: Journal of Population Sciences, 2017 73:7
14.14 Mosaic (a.k.a. Marimekko) charts
Mosaic plots are a variant of Histograms
Haley Jeppson and Heike Hofmann (2018-09-12) Mosaic plots with ggplot2
Hadley Wickham and Heike Hofmann, [“Product Plots”](Wickham and Hofmann 2011)
Mosaic or Marimekko charts (in {ggplot2})
Perceptual Edge, A Design Problem
Alberto Cairo (2019-07-09) A mosaic plot that exemplifies good design practices
14.15 Network graphs
14.15.0.1 {DiagramR}
{DiagramR}: Graph and network visualization using tabular data in R
ggnet2: network visualization with ggplot2 – part of the GGally package
14.16 Pie Charts
Over-used and often mis-used and poorly designed, the pie chart is frequently the subject of ridicule and scorn. But this format does have utility (if done well) and supporters.
Robert Kosara, 2016, “A Pair of Pie Chart Papers” (Kosara 2016a)
Robert Kosara, 2016, “An Illustrated Tour of the Pie Chart Study Results” (Kosara 2016b)
Elizabeth Ricks, 2020, “What is a Pie Chart” (Ricks 2020)
14.17 Population Pyramids
A common visualization in demography to show the age and gender distribution of a population.
Lauren Boucher (2016-03-10) What are the different types of population pyramids?
14.17.2 R
Population Pyramids for Select Canadian Provinces, 2015-2035
- uses {ggplot2} to create pyramids
Simpler population pyramid in ggplot2
** Kyle Walker’s stuff **
Kyle Walker (2014-06-07) Interactive international population pyramids with R, rCharts
Kyle Walker (2014-07-06) International population pyramids with ggplot2
Kyle Walker (2015-04-07) Animated population pyramid of India with rcdimple
Kyle Walker (2016-06-06) idbr: access the US Census Bureau International Data Base in R (http://personal.tcu.edu/kylewalker/)
- Example: “Japan’s aging population in R”
Arizona pyramids:
Ilya Kashnitsky, 2017-03-31, “Who is old? Visualizing the concept of prospective ageing with animated population pyramids”
acarioli (2016-01-11) Population pyramids in ggplot
14.18 Raincloud plot
Micah Allen, Davide Poggiali, Kirstie Whitaker, Tom Rhys Marshall, Jordy van Langen, Rogier A. Kievit, 2021-01-21, “Raincloud plots: a multi-platform tool for robust data visualization”, Wellcome Open Res 2021, 4:63
RainCloudPlots – “Code and tutorials to visualise your data that is both beautiful and statistically valid”
David Zhao, 2019-09-02, The ultimate EDA visualization in R
14.19 Ridgeline plot
ridgeline plots in R
14.19.0.1 {ggridges}
{ggridges} package by Claus Wilke – CRAN page
Alex Whan, 2016-03-24, ggplot2 and Joy Division - at Incrutable Errors
Mauricio Vargas S., 2016-11-08, Joy Division’s Unknown PleasuRes - at R-Bloggers
Henrik Lindberg, Sports: Time of Day
Unknown Pleasures
The over of Joy Division’s debut album Unknown Pleasures (1979) is perhaps the most famous ridgeline plot.
Jen Christiansen, 2015-02-18, Pop Culture Pulsar: Origin Story of Joy Division’s Unknown Pleasures Album Cover - Scientific American, SA Visual – a great piece that traces the Joy Division album cover image directly back to the PhD dissertation of Harold D. Craft, Jr.
Adam Cap, 2011-05-19 - 2016-02-13, The History of Joy Division’s “Unknown Pleasures” Album Art
Data Visualization, Reinterpreted by VISUALIZED: Peter Saville on the Design + Effect of Joy Division’s “Unknown Pleasures” - (from Visualized)
Andrew B. Collier, 2019-07-15, Recreating ‘Unknown Pleasures’ graphic – using {ggplot} and {gganimate}
14.20 Slopegraphs
A common visualization to show relative change between two time periods across different categories.
14.20.2 R
Kyle Walker, 2015-05-17, Global population change with a slopegraph in ggplot2
14.20.2.1 CGPfunctions
“Using newggslopegraph” – CRAN vignette
14.21 Ternary plots
14.21.0.1 {ggtern}
Nicholas Hamilton, ggtern: An Extension to ‘ggplot2’, for the Creation of Ternary Diagrams: CRAN page
14.22 Violin plots
- “It is similar to a box plot, with the addition of a rotated kernel density plot on each side.”
14.22.0.1 within {ggplot2}
- “A violin plot is a compact display of a continuous distribution. It is a blend of geom_boxplot() and geom_density(): a violin plot is a mirrored density plot displayed in the same way as a boxplot.”
14.22.0.2 {vioplot}
Package
CRAN page: vioplot: Violin Plot: “A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.”
GitHub page: {vioplot}(https://github.com/TomKellyGenetics/vioplot): “This package allows extensive customisation of violin plots.”
14.23 Waffle plots
14.23.0.1 {waffle}
Package
CRAN page: waffle: Create Waffle Chart Visualizations in R
GitHub page: {waffle}: Make waffle (square pie) charts in R
articles
14.24 Unit visualization
Antoine Béland and Thomas Hurtut (2020) Unit Visualizations for Visual Storytelling, 2020, research paper presented at the 2020 Computation + Journalism Symposium (2020-03-20 to 2020-03-21)
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