vis.bib

@Article{augustin_quantile_2012,
  Title                    = {On quantile quantile plots for generalized linear models},
  Author                   = {Augustin, Nicole H. and Sauleau, Erik-André and Wood, Simon N.},
  Journal                  = {Computational Statistics \& Data Analysis},
  Year                     = {2012},

  Month                    = aug,
  Number                   = {8},
  Pages                    = {2404--2409},
  Volume                   = {56},

  Doi                      = {10.1016/j.csda.2012.01.026},
  ISSN                     = {01679473},
  Url                      = {http://linkinghub.elsevier.com/retrieve/pii/S0167947312000692},
  Urldate                  = {2013-07-01}
}

@InProceedings{bateman_useful_2010,
  Title                    = {Useful junk?: the effects of visual embellishment on comprehension and memorability of charts},
  Author                   = {Bateman, Scott and Mandryk, Regan L. and Gutwin, Carl and Genest, Aaron and McDine, David and Brooks, Christopher},
  Booktitle                = {Proceedings of the {SIGCHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
  Year                     = {2010},
  Pages                    = {2573--2582},
  Publisher                = {ACM},

  Shorttitle               = {Useful junk?}
}

@Misc{beus_redesign_2018,
  Title                    = {Redesign of a truly bananas chart},

  Author                   = {de Beus, Thomas},
  Month                    = jan,
  Year                     = {2018},

  Abstract                 = {On Twitter, I stumbled upon this horrendous 3D bar chart. When looking at the data, it might have been made in 2005. Data visualisation as…},
  Journal                  = {Colourful Facts},
  Url                      = {https://medium.com/tdebeus/redesign-of-a-truly-bananas-chart-1617f930808d},
  Urldate                  = {2018-01-09}
}

@Misc{debeus_contribute_2017,
  Title                    = {colourful-facts repository},

  Author                   = {de Beus, Thomas},
  Month                    = jul,
  Note                     = {original-date: 2017-07-24T11:35:18Z},
  Year                     = {2017},

  Url                      = {https://github.com/thomasdebeus/colourful-facts},
  Urldate                  = {2018-01-09}
}

@Article{borkin_beyond_2016,
  Title                    = {Beyond {Memorability}: {Visualization} {Recognition} and {Recall}},
  Author                   = {Borkin, Michelle A. and Bylinskii, Zoya and Kim, Nam Wook and Bainbridge, Constance May and Yeh, Chelsea S. and Borkin, Daniel and Pfister, Hanspeter and Oliva, Aude},
  Journal                  = {IEEE Transactions on Visualization and Computer Graphics},
  Year                     = {2016},

  Month                    = jan,
  Number                   = {1},
  Pages                    = {519--528},
  Volume                   = {22},

  Doi                      = {10.1109/TVCG.2015.2467732},
  ISSN                     = {1077-2626},
  Shorttitle               = {Beyond {Memorability}},
  Url                      = {http://ieeexplore.ieee.org/document/7192646/},
  Urldate                  = {2018-02-09}
}

@Misc{broman_creating_2017,
  Title                    = {Creating effective figures and tables},

  Author                   = {Broman, Karl W},
  Year                     = {2017},

  Url                      = {https://www.biostat.wisc.edu/~kbroman/presentations/graphs2017.pdf},
  Urldate                  = {2017-10-26}
}

@TechReport{broman_data_2017,
  Title                    = {Data organization in spreadsheets},
  Author                   = {Broman, Karl W and Woo, Kara H.},
  Institution              = {PeerJ Preprints},
  Year                     = {2017},
  Month                    = aug,
  Note                     = {DOI: 10.7287/peerj.preprints.3183v1},

  Abstract                 = {Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this paper offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYY-MM-DD, don't leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, don't include calculations in the raw data files, don't use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text file.},
  Language                 = {eng},
  Url                      = {https://peerj.com/preprints/3183v1/},
  Urldate                  = {2017-08-31}
}

@Article{buja_statistical_2009,
  Title                    = {Statistical inference for exploratory data analysis and model diagnostics},
  Author                   = {Buja, A. and Cook, D. and Hofmann, H. and Lawrence, M. and Lee, E.-K. and Swayne, D. F. and Wickham, H.},
  Journal                  = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  Year                     = {2009},

  Month                    = oct,
  Number                   = {1906},
  Pages                    = {4361--4383},
  Volume                   = {367},

  Doi                      = {10.1098/rsta.2009.0120},
  ISSN                     = {1364-503X},
  Url                      = {http://rsta.royalsocietypublishing.org/cgi/doi/10.1098/rsta.2009.0120}
}

@Misc{camoes_stephen_2010,
  Title                    = {Stephen {Few}, {Data} {Visualization}, {Eye} {Candy} and {Pie} {Charts}},

  Author                   = {Camoes, Jorge},

  Url                      = {http://www.excelcharts.com/blog/stephen-few-data-visualization-eye-candy-and-the-pie/},
  Urldate                  = {2010-09-30}
}

@Misc{camoes_what-would-tufte-say_nodate,
  Title                    = {The "what-would-{Tufte}-say" syndrome},

  Author                   = {Camoes, Jorge},

  Abstract                 = {An alarming level of the "what-would-Tufte-say" syndrome can be found in this post and some of its comments discussing a New York Times's infographic. This syndrome has some recognizable features like the extensive use of "chart junk", "lie factor" or other terms and expressions coined by Tufte that reveal a somewhat},
  Journal                  = {The Excel Charts Blog},
  Url                      = {http://www.excelcharts.com/blog/the-what-would-tufte-say-syndrome/},
  Urldate                  = {2017-01-10}
}

@Book{cleveland_visualizing_1993,
  Title                    = {Visualizing {Data}},
  Author                   = {Cleveland, William},
  Publisher                = {Hobart Press},
  Year                     = {1993},

  Address                  = {Summit, NJ}
}

@Article{cleveland_graphical_1987,
  Title                    = {Graphical {Perception}: {The} {Visual} {Decoding} of {Quantitative} {Information} on {Graphical} {Displays} of {Data}},
  Author                   = {Cleveland, William S. and McGill, Robert},
  Journal                  = {Journal of the Royal Statistical Society. Series A (General)},
  Year                     = {1987},

  Month                    = jan,
  Note                     = {ArticleType: research-article / Full publication date: 1987 / Copyright © 1987 Royal Statistical Society},
  Number                   = {3},
  Pages                    = {192--229},
  Volume                   = {150},

  Abstract                 = {Studies in graphical perception, both theoretical and experimental, provide a scientific foundation for the construction area of statistical graphics. From these studies a paradigm that has important applications for practice has begun to emerge. The paradigm is based on elementary codes: Basic geometric and textural aspects of a graph that encode the quantitative information. The methodology that can be invoked to study graphical perception is illustrated by an investigation of the shape parameter of a two-variable graph, a topic that has had much discussion, but little scientific study, for at least 70 years.},
  Doi                      = {10.2307/2981473},
  ISSN                     = {00359238},
  Shorttitle               = {Graphical {Perception}},
  Url                      = {http://www.jstor.org/stable/2981473},
  Urldate                  = {2011-01-03}
}

@Article{cleveland_graphical_1984,
  Title                    = {Graphical {Perception}: {Theory}, {Experimentation}, and {Application} to the {Development} of {Graphical} {Methods}},
  Author                   = {Cleveland, William S. and McGill, Robert},
  Journal                  = {Journal of the American Statistical Association},
  Year                     = {1984},
  Note                     = {ArticleType: research-article / Full publication date: Sep., 1984 / Copyright © 1984 American Statistical Association},
  Number                   = {387},
  Pages                    = {531--554},
  Volume                   = {79},

  Abstract                 = {The subject of graphical methods for data analysis and for data presentation needs a scientific foundation. In this article we take a few steps in the direction of establishing such a foundation. Our approach is based on graphical perception-the visual decoding of information encoded on graphs-and it includes both theory and experimentation to test the theory. The theory deals with a small but important piece of the whole process of graphical perception. The first part is an identification of a set of elementary perceptual tasks that are carried out when people extract quantitative information from graphs. The second part is an ordering of the tasks on the basis of how accurately people perform them. Elements of the theory are tested by experimentation in which subjects record their judgments of the quantitative information on graphs. The experiments validate these elements but also suggest that the set of elementary tasks should be expanded. The theory provides a guideline for graph construction: Graphs should employ elementary tasks as high in the ordering as possible. This principle is applied to a variety of graphs, including bar charts, divided bar charts, pie charts, and statistical maps with shading. The conclusion is that radical surgery on these popular graphs is needed, and as replacements we offer alternative graphical forms-dot charts, dot charts with grouping, and framed-rectangle charts.},
  Doi                      = {10.2307/2288400},
  ISSN                     = {01621459},
  Shorttitle               = {Graphical {Perception}},
  Url                      = {http://www.jstor.org/stable/2288400},
  Urldate                  = {2011-01-03}
}

@Article{dawson_how_2011,
  Title                    = {How significant is a boxplot outlier},
  Author                   = {Dawson, Robert},
  Journal                  = {Journal of Statistics Education},
  Year                     = {2011},
  Number                   = {2},
  Pages                    = {1--12},
  Volume                   = {19}
}

@Book{doumont_trees_2009,
  Title                    = {Trees, {Maps}, and {Theorems} {Effective} {Communication} for {Rational} {Minds}},
  Author                   = {Doumont, Jean-Luc},
  Publisher                = {Principiae},
  Year                     = {2009},

  Address                  = {Kraainem},

  Abstract                 = {The book comprises five parts: first, fundamentals, then the written documents, oral presentations, and graphical displays, and finally the application of these ideas to more specific types of document.},
  ISBN                     = {978-90-813677-0-7},
  Language                 = {English}
}

@Misc{drang_drum_2017,
  Title                    = {Drum and {Tufte} and grids and axes - {All} this},

  Author                   = {{Dr. Drang}},

  Url                      = {http://leancrew.com/all-this/2017/12/drum-tufte-grids-axes/},
  Urldate                  = {2018-01-01}
}

@Misc{drum_yet_2017,
  Title                    = {Yet more chart geekery},

  Author                   = {Drum, Kevin},

  Journal                  = {Mother Jones},
  Url                      = {http://www.motherjones.com/kevin-drum/2017/12/yet-more-chart-geekery/},
  Urldate                  = {2018-01-01}
}

@Misc{elliott_39_2016,
  Title                    = {39 studies about human perception in 30 minutes},

  Author                   = {Elliott, Kennedy},
  Month                    = may,
  Year                     = {2016},

  Abstract                 = {These are my speaker notes from a talk I gave at OpenVis in April 2016. Originally this talk was supposed to be called “Everything we know…},
  Journal                  = {Medium},
  Url                      = {https://medium.com/@kennelliott/39-studies-about-human-perception-in-30-minutes-4728f9e31a73},
  Urldate                  = {2018-01-16}
}

@Book{few_now_2009,
  Title                    = {Now {You} {See} {It}: {Simple} {Visualization} {Techniques} for {Quantitative} {Analysis}},
  Author                   = {Few, Stephen},
  Publisher                = {Analytics Press},
  Year                     = {2009},
  Edition                  = {1st},
  Month                    = apr,

  ISBN                     = {0-9706019-8-0},
  Shorttitle               = {Now {You} {See} {It}}
}

@Article{friendly_effect_2003,
  Title                    = {Effect ordering for data displays},
  Author                   = {Friendly, Michael and Kwan, Ernest},
  Journal                  = {Computational Statistics \& Data Analysis},
  Year                     = {2003},

  Month                    = aug,
  Number                   = {4},
  Pages                    = {509--539},
  Volume                   = {43},

  Abstract                 = {This paper outlines a general framework for ordering information in visual displays (tables and graphs) according to the effects or trends which we desire to see. This idea, termed effect-ordered data displays, applies principally to the arrangement of unordered factors for quantitative data and frequency data, and to the arrangement of variables and observations in multivariate displays (star plots, parallel coordinate plots, and so forth).

As examples of this principle, we present several techniques for ordering items, levels or variables “optimally”, based on some desired criterion. All of these may be based on eigenvalue or singular-value decompositions.

Along the way, we tell some stories about data display, illustrated by graphs—some surprisingly bad, and some surprisingly good—for showing patterns, trends, and anomalies in data. We hope to raise more questions than we can provide answers for.},
  Doi                      = {10.1016/S0167-9473(02)00290-6},
  File                     = {ScienceDirect Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/C79AU4QH/S0167947302002906.html:text/html},
  ISSN                     = {0167-9473},
  Series                   = {Data {Visualization}},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0167947302002906},
  Urldate                  = {2016-01-23}
}

@Misc{gelman_infovis_2011,
  Title                    = {Infovis, infographics, and data visualization: {Where} {I}'m coming from, and where {I}'d like to go},

  Author                   = {Gelman, Andrew},
  Month                    = aug,
  Year                     = {2011},

  Abstract                 = {I continue to struggle to convey my thoughts on statistical graphics so I’ll try another approach, this time giving my own story. For newcomers to this discussion: the background is that Antony Unwin and I wrote an article on the different goals embodied in information visualization and statistical graphics, but I have difficulty communicating on …},
  Journal                  = {Statistical Modeling, Causal Inference, and Social Science},
  Language                 = {en-US},
  Shorttitle               = {Infovis, infographics, and data visualization},
  Url                      = {http://andrewgelman.com/2011/08/29/infovis-infographics-and-data-visualization-where-im-coming-from-and-where-id-like-to-go/},
  Urldate                  = {2018-02-12}
}

@Article{gelman_why_2011,
  Title                    = {Why {Tables} {Are} {Really} {Much} {Better} {Than} {Graphs}},
  Author                   = {Gelman, Andrew},
  Journal                  = {Journal of Computational and Graphical Statistics},
  Year                     = {2011},

  Month                    = mar,
  Number                   = {1},
  Pages                    = {3--7},
  Volume                   = {20},

  Doi                      = {10.1198/jcgs.2011.09166},
  ISSN                     = {1061-8600},
  Url                      = {http://pubs.amstat.org/doi/abs/10.1198/jcgs.2011.09166},
  Urldate                  = {2011-08-15}
}

@Article{gelman_scaling_2008,
  Title                    = {Scaling regression inputs by dividing by two standard deviations},
  Author                   = {Gelman, Andrew},
  Journal                  = {Statistics in Medicine},
  Year                     = {2008},

  Month                    = jul,
  Number                   = {15},
  Pages                    = {2865--2873},
  Volume                   = {27},

  Doi                      = {10.1002/sim.3107},
  ISSN                     = {02776715, 10970258},
  Language                 = {en},
  Url                      = {http://doi.wiley.com/10.1002/sim.3107},
  Urldate                  = {2018-02-04}
}

@Article{gelman_exploratory_2004,
  Title                    = {Exploratory {Data} {Analysis} for {Complex} {Models}},
  Author                   = {Gelman, Andrew},
  Journal                  = {Journal of Computational and Graphical Statistics},
  Year                     = {2004},

  Month                    = dec,
  Number                   = {4},
  Pages                    = {755--779},
  Volume                   = {13},

  Doi                      = {10.1198/106186004X11435},
  ISSN                     = {1061-8600, 1537-2715},
  Language                 = {en},
  Url                      = {http://www.tandfonline.com/doi/abs/10.1198/106186004X11435},
  Urldate                  = {2018-01-24}
}

@Article{gelman_lets_2002,
  Title                    = {Let's practice what we preach: turning tables into graphs},
  Author                   = {Gelman, Andrew and Pasarica, Cristian and Dodhia, Rahul},
  Journal                  = {The American Statistician},
  Year                     = {2002},
  Number                   = {2},
  Pages                    = {121--130},
  Volume                   = {56},

  Shorttitle               = {Let's practice what we preach},
  Url                      = {http://www.tandfonline.com/doi/abs/10.1198/000313002317572790},
  Urldate                  = {2016-02-01}
}

@Article{gelman_all_1999,
  Title                    = {All maps of parameter estimates are misleading},
  Author                   = {Gelman, Andrew and Price, Phillip N. and {others}},
  Journal                  = {Statistics in medicine},
  Year                     = {1999},
  Number                   = {23},
  Pages                    = {3221--3234},
  Volume                   = {18},

  Url                      = {http://www.stat.columbia.edu/~gelman/research/published/allmaps.pdf},
  Urldate                  = {2017-08-09}
}

@Article{gelman_infovis_2013,
  Title                    = {Infovis and {Statistical} {Graphics}: {Different} {Goals}, {Different} {Looks}},
  Author                   = {Gelman, Andrew and Unwin, Antony},
  Journal                  = {Journal of Computational and Graphical Statistics},
  Year                     = {2013},
  Number                   = {1},
  Pages                    = {2--28},
  Volume                   = {22},

  Abstract                 = {The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey's Exploratory Data Analysis and Tufte's books in the succeeding decades. But statistical graphics still occupy an awkward in-between position: within statistics, exploratory and graphical methods represent a minor subfield and are not well integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) are huge, but their purveyors and enthusiasts appear largely to be uninterested in statistical principles. We present here a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. We recognize that we offer only one perspective and intend this article to be a starting point for a wide-ranging discussion among graphic designers, statisticians, and users of statistical methods. The purpose of this article is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization.},
  Doi                      = {10.1080/10618600.2012.761137},
  File                     = {Full Text PDF:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/FPZGWWSB/Gelman and Unwin - 2013 - Infovis and Statistical Graphics Different Goals,.pdf:application/pdf;Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/JD3PKJ4X/10618600.2012.html:text/html},
  ISSN                     = {1061-8600},
  Shorttitle               = {Infovis and {Statistical} {Graphics}},
  Url                      = {http://www.tandfonline.com/doi/abs/10.1080/10618600.2012.761137},
  Urldate                  = {2013-07-01}
}

@Article{gelman_tradeoffs_2013,
  Title                    = {Tradeoffs in {Information} {Graphics}},
  Author                   = {Gelman, Andrew and Unwin, Antony},
  Journal                  = {Journal of Computational and Graphical Statistics},
  Year                     = {2013},
  Number                   = {1},
  Pages                    = {45--49},
  Volume                   = {22},

  Doi                      = {10.1080/10618600.2012.761141},
  ISSN                     = {1061-8600},
  Url                      = {http://www.tandfonline.com/doi/abs/10.1080/10618600.2012.761141},
  Urldate                  = {2013-06-03}
}

@Article{goldberg_bivariate_1992,
  Title                    = {Bivariate {Extensions} of the {Boxplot}},
  Author                   = {Goldberg, Kenneth M. and Iglewicz, Boris},
  Journal                  = {Technometrics},
  Year                     = {1992},

  Month                    = aug,
  Number                   = {3},
  Pages                    = {307--320},
  Volume                   = {34},

  Abstract                 = {The boxplot has proven to be a very useful tool for summarizing univariate data. Several options of bivariate boxplot-type constructions are discussed. These include both elliptic and asymmetric plots. An inner region contains 50\% of the data, and a fence identifies potential outliers. Such a robust plot shows location, scale, correlation, and a resistant regression line. Alternative constructions are compared in terms of efficiency of the relevant parameters. Additional properties are given and recommendations made. Emphasis is given to the bivariate biweight M estimator. Several practical examples illustrate that standard least squares ellipsoids can give graphically misleading summaries.},
  Doi                      = {10.1080/00401706.1992.10485280},
  ISSN                     = {0040-1706},
  Keywords                 = {Ellipsoid, Outlier, Resistant line, Robust estimators},
  Url                      = {https://www.tandfonline.com/doi/abs/10.1080/00401706.1992.10485280},
  Urldate                  = {2019-09-11}
}

@InProceedings{heer_crowdsourcing_2010,
  Title                    = {Crowdsourcing graphical perception: using mechanical turk to assess visualization design},
  Author                   = {Heer, Jeffrey and Bostock, Michael},
  Booktitle                = {Proceedings of the {SIGCHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
  Year                     = {2010},
  Pages                    = {203--212},
  Publisher                = {ACM},

  Shorttitle               = {Crowdsourcing graphical perception}
}

@Article{hosmer_comparison_1997,
  Title                    = {A comparison of goodness-of-fit tests for the logistic regression model},
  Author                   = {Hosmer, David W. and Hosmer, Trina and Le Cessie, Saskia and Lemeshow, Stanley},
  Journal                  = {Statistics in medicine},
  Year                     = {1997},
  Number                   = {9},
  Pages                    = {965--980},
  Volume                   = {16}
}

@Book{huff_how_1993,
  Title                    = {How to lie with statistics},
  Author                   = {Huff, Darrell},
  Publisher                = {WW Norton \& Company},
  Year                     = {1993},
  Note                     = {originally published 1954}
}

@Misc{hullman_how_2019,
  Title                    = {How to {Get} {Better} at {Embracing} {Unknowns}: {Interpreting} uncertainty through data visualizations},

  Author                   = {Hullman, Jessica},
  Month                    = sep,
  Year                     = {2019},

  Abstract                 = {Data-visualization techniques can clarify the uncertainty in information or make it more confusing if not implemented well.
 For example, even though error bars seem exact, people often misunderstand them. 
 Quantile dot plots can be effective because they present uncertainty as discrete probabilities that people can readily see. 
 Effective visualizations of uncertainty can help us make judgments, analytically and emotionally, about the likelihood of future events.},
  Journal                  = {Scientific American},
  Url                      = {https://www.scientificamerican.com/article/how-to-get-better-at-embracing-unknowns/},
  Urldate                  = {2019-09-03}
}

@Misc{irizarry_you_2019,
  Title                    = {You can replicate almost any plot with {R} · {Simply} {Statistics}},

  Author                   = {Irizarry, Rafael A.},
  Month                    = aug,
  Year                     = {2019},

  Abstract                 = {Although R is great for quickly turning data into plots, it is not widely used for making publication ready figures. But, with enough tinkering you can make almost any plot in R.},
  Journal                  = {Simply Statistics},
  Url                      = {https://simplystatistics.org/2019/08/28/you-can-replicate-almost-any-plot-with-ggplot2/},
  Urldate                  = {2019-08-30}
}

@Misc{johnston_ggplot_2015,
  Title                    = {ggplot fish},

  Author                   = {Johnston, Myfanwy},
  Month                    = mar,
  Year                     = {2015},

  Abstract                 = {guys. GUYS. I'm diving in Palau this week and I've found the \#ggplot2 fish. \#rstats},
  Journal                  = {Twitter},
  Language                 = {en},
  Shorttitle               = {ggplot fish},
  Url                      = {https://twitter.com/voovarb/status/580722323843596288/photo/1},
  Urldate                  = {2019-09-10}
}

@Article{kahle_ggmap_2013,
  Title                    = {ggmap : {Spatial} {Visualization} with ggplot2},
  Author                   = {Kahle, David and Wickham, Hadley},
  Journal                  = {The R Journal},
  Year                     = {2013},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {144--162},
  Volume                   = {5},

  Url                      = {http://journal.r-project.org/archive/2013-1/RJournal_2013-1_kahle-wickham.pdf}
}

@Article{kosara_infovis_2013,
  Title                    = {{InfoVis} {Is} {So} {Much} {More}: {A} {Comment} on {Gelman} and {Unwin} and an {Invitation} to {Consider} the {Opportunities}},
  Author                   = {Kosara, Robert},
  Journal                  = {Journal of Computational and Graphical Statistics},
  Year                     = {2013},

  Month                    = jan,
  Number                   = {1},
  Pages                    = {29--32},
  Volume                   = {22},

  Doi                      = {10.1080/10618600.2012.755465},
  File                     = {Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/UTYES3JA/10618600.2012.html:text/html},
  ISSN                     = {1061-8600},
  Shorttitle               = {{InfoVis} {Is} {So} {Much} {More}},
  Url                      = {https://doi.org/10.1080/10618600.2012.755465},
  Urldate                  = {2018-02-12}
}

@Article{levine_editorial_2010,
  Title                    = {Editorial: {Publishing} {Animations}, 3D {Visualizations}, and {Movies} in {JCGS}},
  Author                   = {Levine, Richard A. and Tierney, Luke and Wickham, Hadley and Sampson, Eric and Cook, Dianne and van Dyk, David A.},
  Journal                  = {Journal of Computational and Graphical Statistics},
  Year                     = {2010},

  Month                    = jan,
  Number                   = {1},
  Pages                    = {1--2},
  Volume                   = {19},

  Doi                      = {10.1198/jcgs.2010.191ed},
  ISSN                     = {1061-8600, 1537-2715},
  Shorttitle               = {Editorial},
  Url                      = {http://amstat.tandfonline.com/doi/abs/10.1198/jcgs.2010.191ed},
  Urldate                  = {2012-05-11}
}

@Article{li_judging_2008,
  Title                    = {Judging correlation from scatterplots and parallel coordinate plots},
  Author                   = {Li, Jing and Martens, Jean-Bernard and van Wijk, Jarke J},
  Journal                  = {Information Visualization},
  Year                     = {2008},

  Month                    = may,

  Doi                      = {10.1057/palgrave.ivs.9500179},
  File                     = {Information Visualization - Abstract of article\: Judging correlation from scatterplots and parallel coordinate plots:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/IPH4KZVV/9500179a.html:text/html},
  ISSN                     = {1473-8716},
  Url                      = {http://www.palgrave-journals.com/ivs/journal/vaop/ncurrent/abs/9500179a.html},
  Urldate                  = {2009-11-11}
}

@Misc{lindberg_viz_2017,
  Title                    = {viz-pub: {A} place to publish data-vizes},

  Author                   = {Lindberg, Henrik},
  Month                    = nov,
  Note                     = {original-date: 2017-06-08T08:40:42Z},
  Year                     = {2017},

  File                     = {Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/T4J3DPJF/viz-pub.html:text/html},
  Shorttitle               = {viz-pub},
  Url                      = {https://github.com/halhen/viz-pub},
  Urldate                  = {2017-11-07}
}

@Misc{machlis_best_2015,
  Title                    = {Best {R} packages for data import, data wrangling \& data visualization},

  Author                   = {Machlis, Sharon},
  Month                    = nov,
  Year                     = {2015},

  Abstract                 = {Useful R packages in a handy searchable table},
  File                     = {Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/8B6B4ZUG/great-r-packages-for-data-import-wrangling-visualization.html:text/html},
  Journal                  = {Computerworld},
  Url                      = {http://www.computerworld.com/article/2921176/business-intelligence/great-r-packages-for-data-import-wrangling-visualization.html},
  Urldate                  = {2016-05-16}
}

@Article{mccallum_situ_2017,
  Title                    = {In situ exposure to wastewater effluent reduces survival but has little effect on the behaviour or physiology of an invasive {Great} {Lakes} fish},
  Author                   = {McCallum, Erin S. and Du, Sherry N. N. and Vaseghi-Shanjani, Maryam and Choi, Jasmine A. and Warriner, Theresa R. and Sultana, Tamanna and Scott, Graham R. and Balshine, Sigal},
  Journal                  = {Aquatic Toxicology},
  Year                     = {2017},

  Month                    = mar,
  Pages                    = {37--48},
  Volume                   = {184},

  Abstract                 = {Treated effluents from wastewater treatment plants (WWTP) are a significant source of anthropogenic contaminants, such as pharmaceuticals, in the aquatic environment. Although our understanding of how wastewater effluent impacts fish reproduction is growing, we know very little about how effluent affects non-reproductive physiology and behaviours associated with fitness (such as aggression and activity). To better understand how fish cope with chronic exposure to wastewater effluent in the wild, we caged round goby (Neogobius melanostomus) for three weeks at different distances from a wastewater outflow. We evaluated the effects of this exposure on fish survival, behaviour, metabolism, and respiratory traits. Fish caged inside the WWTP and close to the outfall experienced higher mortality than fish from the reference site. Interestingly, those fish that survived the exposure performed similarly to fish caged at the reference site in tests of aggressive behaviour, startle-responses, and dispersal. Moreover, the fish near WWTP outflow displayed similar resting metabolism (O2 consumption rates), hypoxia tolerance, haemoglobin concentration, haematocrit, and blood-oxygen binding affinities as the fish from the more distant reference site. We discuss our findings in relation to exposure site water quality, concentrations of pharmaceutical and personal care product pollutants, and our test species tolerance.},
  Doi                      = {10.1016/j.aquatox.2016.12.017},
  ISSN                     = {0166-445X},
  Keywords                 = {Round goby, Activity, Caging, Cootes Paradise Marsh, PPCPs, Respirometry},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0166445X16303757},
  Urldate                  = {2018-02-06}
}

@Article{mcgill_variations_1978,
  Title                    = {Variations of {Box} {Plots}},
  Author                   = {McGill, Robert and Tukey, John W. and Larsen, Wayne A.},
  Journal                  = {The American Statistician},
  Year                     = {1978},
  Number                   = {1},
  Pages                    = {12--16},
  Volume                   = {32},

  Abstract                 = {Box plots display batches of data. Five values from a set of data are conventionally used; the extremes, the upper and lower hinges (quartiles), and the median. Such plots are becoming a widely used tool in exploratory data analysis and in preparing visual summaries for statisticians and nonstatisticians alike. Three variants of the basic display, devised by the authors, are described. The first visually incorporates a measure of group size; the second incorporates an indication of rough significance of differences between medians; the third combines the features of the first two. These techniques are displayed by examples.},
  Doi                      = {10.2307/2683468},
  ISSN                     = {0003-1305},
  Url                      = {http://www.jstor.org/stable/2683468},
  Urldate                  = {2018-01-10}
}

@Misc{meeks_color_2018,
  Title                    = {Color {Advice} for {Data} {Visualization} with {D}3.js},

  Author                   = {Meeks, Elijah},
  Month                    = jan,
  Year                     = {2018},

  Abstract                 = {Using color in a post-category20 world},
  Journal                  = {Elijah Meeks},
  Url                      = {https://medium.com/@Elijah_Meeks/color-advice-for-data-visualization-with-d3-js-33b5adc41c90},
  Urldate                  = {2018-02-02}
}

@Book{meeks_d3_2017,
  Title                    = {D3.js in Action: Data visualization with JavaScript},
  Author                   = {Elijah Meeks},
  Publisher                = {Manning},
  Year                     = {2017},
  Edition                  = {2},

  ISBN                     = {9781617294488},
  Url                      = {https://www.manning.com/books/d3js-in-action-second-edition}
}

@Article{murrell_infovis_2013,
  Title                    = {{InfoVis} and {Statistical} {Graphics}: {Comment}},
  Author                   = {Murrell, Paul},
  Journal                  = {Journal of Computational and Graphical Statistics},
  Year                     = {2013},

  Month                    = jan,
  Number                   = {1},
  Pages                    = {33--37},
  Volume                   = {22},

  Doi                      = {10.1080/10618600.2012.751875},
  File                     = {Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/RPZE7DQ8/10618600.2012.html:text/html},
  ISSN                     = {1061-8600},
  Shorttitle               = {{InfoVis} and {Statistical} {Graphics}},
  Url                      = {https://doi.org/10.1080/10618600.2012.751875},
  Urldate                  = {2018-02-12}
}

@Misc{noren_cost_2011,
  Title                    = {Cost of {Health} {Care} by {Country}},

  Author                   = {Noren, Laura},
  Month                    = apr,
  Year                     = {2011},

  Abstract                 = {The Society Pages (TSP) is an open-access social science project headquartered in the Department of Sociology at the University of Minnesota},
  Journal                  = {Graphic Sociology},
  Language                 = {en},
  Url                      = {https://thesocietypages.org/graphicsociology/2011/04/26/cost-of-health-care-by-country-national-geographic/},
  Urldate                  = {2018-02-12}
}

@Article{nunez_optimizing_2017,
  Title                    = {Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data},
  Author                   = {Nuñez, Jamie R. and Anderton, Chris R. and Renslow, Ryan S.},
  Journal                  = {arXiv preprint arXiv:1712.01662},
  Year                     = {2017}
}

@Article{peltonen_visualizations_2009,
  Title                    = {Visualizations for assessing convergence and mixing of {Markov} chain {Monte} {Carlo} simulations},
  Author                   = {Peltonen, Jaakko and Venna, Jarkko and Kaski, Samuel},
  Journal                  = {Computational Statistics \& Data Analysis},
  Year                     = {2009},

  Month                    = oct,
  Number                   = {12},
  Pages                    = {4453--4470},
  Volume                   = {53},

  Doi                      = {10.1016/j.csda.2009.07.001},
  ISSN                     = {01679473},
  Url                      = {http://linkinghub.elsevier.com/retrieve/pii/S0167947309002461}
}

@Article{peltonen_visualizations_2009,
  Title                    = {Visualizations for assessing convergence and mixing of {Markov} chain {Monte} {Carlo} simulations},
  Author                   = {Peltonen, Jaakko and Venna, Jarkko and Kaski, Samuel},
  Journal                  = {Computational Statistics \& Data Analysis},
  Year                     = {2009},

  Month                    = oct,
  Number                   = {12},
  Pages                    = {4453--4470},
  Volume                   = {53},

  Doi                      = {10.1016/j.csda.2009.07.001},
  ISSN                     = {01679473},
  Url                      = {http://linkinghub.elsevier.com/retrieve/pii/S0167947309002461}
}

@Book{pugin_true_1853,
  Title                    = {The true principles of pointed or {Christian} architecture: set forth in two lectures delivered at {St}. {Marie}'s, {Oscott}},
  Author                   = {Pugin, Augustus Welby Northmore},
  Publisher                = {H. G. Bohn},
  Year                     = {1853},
  Note                     = {Google-Books-ID: QphZAAAAYAAJ},

  Keywords                 = {Architecture / General, Architecture / History / General, Architecture / Religious Buildings, Architecture, Gothic},
  Language                 = {en},
  Shorttitle               = {The true principles of pointed or {Christian} architecture},
  Url                      = {https://books.google.ca/books?id=QphZAAAAYAAJ}
}

@Book{QuinnKeough2002,
  Title                    = {Experimental Design and Data Analysis for Biologists},
  Author                   = {Gerry P. Quinn and Michael J. Keough},
  Publisher                = {Cambridge University Press},
  Year                     = {2002},

  Address                  = {Cambridge, England},

  ISBN                     = {0521009766}
}

@Misc{rauser_how_2016,
  Title                    = {How {Humans} {See} {Data}},

  Author                   = {Rauser, John},
  Month                    = dec,
  Year                     = {2016},

  Abstract                 = {John Rauser explains a few of the most important results from research into the functioning of the human visual system and the question of how humans decode information presented in graphical form. By understanding and applying this research when designing statistical graphics, you can simplify difficult analytical tasks as much as possible.},
  Keywords                 = {data, PSYCHOLOGY, statistics, Visualization, graphs, monitoring, velocity},
  Url                      = {https://www.youtube.com/watch?v=fSgEeI2Xpdc},
  Urldate                  = {2017-07-24}
}

@Article{rougier_ten_2014,
  Title                    = {Ten {Simple} {Rules} for {Better} {Figures}},
  Author                   = {Rougier, Nicolas P. and Droettboom, Michael and Bourne, Philip E.},
  Journal                  = {PLOS Computational Biology},
  Year                     = {2014},

  Month                    = sep,
  Number                   = {9},
  Pages                    = {e1003833},
  Volume                   = {10},

  Doi                      = {10.1371/journal.pcbi.1003833},
  ISSN                     = {1553-7358},
  Keywords                 = {data visualization, Research Design, Software tools, Vision, Software design, Radii, Eye movements, Seismic signal processing},
  Url                      = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833},
  Urldate                  = {2016-12-21}
}

@Article{rousseeuw_bagplot_1999,
  Title                    = {The {Bagplot}: {A} {Bivariate} {Boxplot}},
  Author                   = {Rousseeuw, Peter J. and Ruts, Ida and Tukey, John W.},
  Journal                  = {The American Statistician},
  Year                     = {1999},

  Month                    = nov,
  Number                   = {4},
  Pages                    = {382--387},
  Volume                   = {53},

  Abstract                 = {We propose the bagplot, a bivariate generalization of the univariate boxplot. The key notion is the half space location depth of a point relative to a bivariate dataset, which extends the univariate concept of rank. The “depth median” is the deepest location, and it is surrounded by a “bag” containing the n/2 observations with largest depth. Magnifying the bag by a factor 3 yields the “fence” (which is not plotted). Observations between the bag and the fence are marked by a light gray loop, whereas observations outside the fence are flagged as outliers. The bagplot visualizes the location, spread, correlation, skewness, and tails of the data. It is equivariant for linear transformations, and not limited to elliptical distributions. Software for drawing the bagplot is made available for the S-Plus and MATLAB environments. The bagplot is illustrated on several datasets—for example, in a scatterplot matrix of multivariate data.},
  Doi                      = {10.1080/00031305.1999.10474494},
  ISSN                     = {0003-1305},
  Keywords                 = {Algorithms, Depth contours, Graphical display, Location depth, Ranks},
  Shorttitle               = {The {Bagplot}},
  Urldate                  = {2019-09-11}
}

@Book{sarkar_lattice_2008,
  Title                    = {Lattice: {Multivariate} {Data} {Visualization} with {R}},
  Author                   = {Sarkar, Deepayan},
  Publisher                = {Springer},
  Year                     = {2008},
  Edition                  = {1},
  Month                    = mar,

  ISBN                     = {0-387-75968-9},
  Shorttitle               = {Lattice}
}

@Article{schielzeth_simple_2010,
  Title                    = {Simple means to improve the interpretability of regression coefficients},
  Author                   = {Schielzeth, Holger},
  Journal                  = {Methods in Ecology and Evolution},
  Year                     = {2010},
  Pages                    = {103-113},
  Volume                   = {1},

  Abstract                 = {1. Linear regression models are an important statistical tool in evolutionary and ecological studies. Unfortunately, these models often yield some uninterpretable estimates and hypothesis tests, especially when models contain interactions or polynomial terms. Furthermore, the standard errors for treatment groups, although often of interest for including in a publication, are not directly available in a standard linear model. 2. Centring and standardization of input variables are simple means to improve the interpretability of regression coefficients. Further, refitting the model with a slightly modified model structure allows extracting the appropriate standard errors for treatment groups directly from the model. 3. Centring will make main effects biologically interpretable even when involved in interactions and thus avoids the potential misinterpretation of main effects. This also applies to the estimation of linear effects in the presence of polynomials. Categorical input variables can also be centred and this sometimes assists interpretation. 4. Standardization (z-transformation) of input variables results in the estimation of standardized slopes or standardized partial regression coefficients. Standardized slopes are comparable in magnitude within models as well as between studies. They have some advantages over partial correlation coefficients and are often the more interesting standardized effect size. 5. The thoughtful removal of intercepts or main effects allows extracting treatment means or treatment slopes and their appropriate standard errors directly from a linear model. This provides a simple alternative to the more complicated calculation of standard errors from contrasts and main effects. 6. The simple methods presented here put the focus on parameter estimation (point estimates as well as confidence intervals) rather than on significance thresholds. They allow fitting complex, but meaningful models that can be concisely presented and interpreted. The presented methods can also be applied to generalised linear models {(GLM)} and linear mixed models.},
  Doi                      = {10.1111/j.2041-210X.2010.00012.x},
  Url                      = {http://dx.doi.org/10.1111/j.2041-210X.2010.00012.x}
}

@Article{schultz_evidence_2016,
  Title                    = {Evidence for a trophic cascade on rocky reefs following sea star mass mortality in {British} {Columbia}},
  Author                   = {Schultz, Jessica A. and Cloutier, Ryan N. and Côté, Isabelle M.},
  Journal                  = {PeerJ},
  Year                     = {2016},

  Month                    = apr,
  Pages                    = {e1980},
  Volume                   = {4},

  Abstract                 = {Echinoderm population collapses, driven by disease outbreaks and climatic events, may be important drivers of population dynamics, ecological shifts and biodiversity. The northeast Pacific recently experienced a mass mortality of sea stars. In Howe Sound, British Columbia, the sunflower star Pycnopodia helianthoides—a previously abundant predator of bottom-dwelling invertebrates—began to show signs of a wasting syndrome in early September 2013, and dense aggregations disappeared from many sites in a matter of weeks. Here, we assess changes in subtidal community composition by comparing the abundance of fish, invertebrates and macroalgae at 20 sites in Howe Sound before and after the 2013 sea star mortality to evaluate evidence for a trophic cascade. We observed changes in the abundance of several species after the sea star mortality, most notably a four-fold increase in the number of green sea urchins, Strongylocentrotus droebachiensis, and a significant decline in kelp cover, which are together consistent with a trophic cascade. Qualitative data on the abundance of sunflower stars and green urchins from a citizen science database show that the patterns of echinoderm abundance detected at our study sites reflected wider local trends. The trophic cascade evident at the scale of Howe Sound was observed at half of the study sites. It remains unclear whether the urchin response was triggered directly, via a reduction in urchin mortality, or indirectly, via a shift in urchin distribution into areas previously occupied by the predatory sea stars. Understanding the ecological implications of sudden and extreme population declines may further elucidate the role of echinoderms in temperate seas, and provide insight into the resilience of marine ecosystems to biological disturbances.},
  Doi                      = {10.7717/peerj.1980},
  ISSN                     = {2167-8359},
  Language                 = {en},
  Url                      = {https://peerj.com/articles/1980},
  Urldate                  = {2018-02-06}
}

@Misc{sciani_cividis_2018,
  Title                    = {cividis: {Implementation} of the {Matplotlib} 'viridis' color map in {R} (lite version)},

  Author                   = {Sciani, Marco},
  Month                    = jan,
  Note                     = {original-date: 2018-01-15T07:56:06Z},
  Year                     = {2018},

  Shorttitle               = {cividis},
  Url                      = {https://github.com/marcosci/cividis},
  Urldate                  = {2018-01-15}
}

@Book{steele_beautiful_2010,
  Title                    = {Beautiful {Visualization}: {Looking} at {Data} through the {Eyes} of {Experts}},
  Author                   = {Steele, Julie and Iliinsky, Noah},
  Publisher                = {O'Reilly Media},
  Year                     = {2010},
  Edition                  = {1},
  Month                    = jun,

  ISBN                     = {1-4493-7986-9},
  Shorttitle               = {Beautiful {Visualization}}
}

@Article{su_its_2008,
  Title                    = {It’s easy to produce chartjunk using {Microsoft}®{Excel} 2007 but hard to make good graphs},
  Author                   = {Su, Yu-Sung},
  Journal                  = {Computational Statistics \& Data Analysis},
  Year                     = {2008},

  Month                    = jun,
  Number                   = {10},
  Pages                    = {4594--4601},
  Volume                   = {52},

  Abstract                 = {The purpose of default settings in a graphic tool is to make it easy to produce good graphics that accord with the principles of statistical graphics, e.g., [Tufte, E.R., 1990. Envisioning Information. Graphics Press, Cheshire, Conn, Tufte, E.R., 1997. Visual Explanations: Images and Quantities, Evidence and Narrative, 2nd Edition. Graphics Press, Cheshire, Conn, Cleveland, W.S., 1993. Visualizing Data. Hobart Press, N.J Cleveland, W.S., 1994. The Elements of Graphing Data, rev. edition. AT\&T Bell Laboratories, Murray Hill, N.J, Wainer, H., 1997. Visual revelations: Graphical tales of fate and deception from Napoleon to Ross Perot. Copernicus, New York, Spence, R., 2001. Information Visualization. ACM Press \& AddisonWesley, New York, and Few, S., 2004. Show Me the Numbers. Analytic Press, Hillsdale, NJ]. If the defaults do not embody these principles, then the only way to produce good graphics is to be sufficiently familiar with the principles of statistical graphics. This paper shows that Excel graphics defaults do not embody the appropriate principles. Users who want to use Excel are advised to know the principles of good graphics well enough so that they can choose the appropriate options to override the defaults. Microsoft® should overhaul the Excel graphics engine so that its defaults embody the principles of statistical graphics and make it easy for non-experts to produce good graphs.},
  Doi                      = {10.1016/j.csda.2008.03.007},
  File                     = {ScienceDirect Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/T6N4TMUQ/science.html:text/html},
  Url                      = {http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4S1S6FC-6&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=d3e57dcf23c1adccee012110b71fefcf},
  Urldate                  = {2008-07-04}
}

@InProceedings{swayne_exploratory_2004,
  Title                    = {Exploratory visual analysis of graphs in {GGobi}},
  Author                   = {Swayne, Deborah F. and Buja, Andreas and Lang, Duncan Temple},
  Booktitle                = {Proceeds of the 3d International Workshop on Distributed Statistical Computing (DSC 2003)},
  Year                     = {2004},
  Editor                   = {K. Hornik and F. Leisch},
  Pages                    = {477--488},
  Publisher                = {Springer},

  Url                      = {https://link.springer.com/chapter/10.1007/978-3-7908-2656-2_39}
}

@Article{thompson_graphical_2010,
  Title                    = {Graphical {Comparison} of {MCMC} {Performance}},
  Author                   = {Thompson, Madeleine B},
  Journal                  = {1011.4457},
  Year                     = {2010},

  Month                    = nov,

  Abstract                 = {This paper presents a graphical method for comparing performance of Markov Chain Monte Carlo methods. Most researchers present comparisons of MCMC methods using tables of figures of merit; this paper presents a graphical alternative. It first discusses the computation of autocorrelation time, then uses this to construct a figure of merit, log density function evaluations per independent observation. Then, it demonstrates how one can plot this figure of merit against a tuning parameter in a grid of plots where columns represent sampling methods and rows represent distributions. This type of visualization makes it possible to convey a greater depth of information without overwhelming the user with numbers, allowing researchers to put their contributions into a broader context than is possible with a textual presentation.},
  File                     = {1011.4457 PDF:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/VHPNUKD5/Thompson - 2010 - Graphical Comparison of MCMC Performance.pdf:application/pdf;arXiv.org Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/Q2R69HTJ/1011.html:text/html},
  Keywords                 = {Statistics - Computation, G.3},
  Url                      = {http://arxiv.org/abs/1011.4457},
  Urldate                  = {2010-11-22}
}

@Article{tibshirani_estimating_1987,
  Title                    = {Estimating optimal transformations for regression},
  Author                   = {Rob Tibshirani},
  Journal                  = {Journal of the American Statistical Association},
  Year                     = {1987},
  Pages                    = {394},
  Volume                   = {83}
}

@Book{tufte_visual_2001,
  Title                    = {The {Visual} {Display} of {Quantitative} {Information}},
  Author                   = {Tufte, Edward},
  Publisher                = {Graphics Press},
  Year                     = {2001},
  Edition                  = {2d}
}

@Book{tufte_beautiful_2006,
  Title                    = {Beautiful evidence},
  Author                   = {Tufte, Edward R},
  Publisher                = {Graphics Press},
  Year                     = {2006},

  Address                  = {Cheshire, Conn.},

  ISBN                     = {0-9613921-7-7 978-0-9613921-7-8},
  Language                 = {English}
}

@Book{tufte_visual_1997,
  Title                    = {Visual explanations: images and quantities, evidence and narrative},
  Author                   = {Tufte, Edward R},
  Publisher                = {Graphics Press},
  Year                     = {1997},

  Address                  = {Cheshire, Conn.},

  ISBN                     = {0-9613921-2-6 978-0-9613921-2-3},
  Language                 = {English},
  Shorttitle               = {Visual explanations}
}

@Book{tufte_envisioning_1995,
  Title                    = {Envisioning information},
  Author                   = {Tufte, Edward R},
  Publisher                = {Graphics Press},
  Year                     = {1995},

  Address                  = {Cheshire, Conn.},

  ISBN                     = {0-9613921-1-8 978-0-9613921-1-6},
  Language                 = {English}
}

@Article{tufte_dataink_1990,
  Title                    = {Data-{Ink} {Maximization} and {Graphical} {Design}},
  Author                   = {Tufte, E. R.},
  Journal                  = {Oikos},
  Year                     = {1990},

  Month                    = jun,
  Note                     = {ArticleType: research-article / Full publication date: Jun., 1990 / Copyright © 1990 Nordic Society Oikos},
  Number                   = {2},
  Pages                    = {130--144},
  Volume                   = {58},

  Copyright                = {Copyright © 1990 Nordic Society Oikos},
  Doi                      = {10.2307/3545420},
  ISSN                     = {0030-1299},
  Url                      = {http://www.jstor.org/stable/3545420},
  Urldate                  = {2013-01-03}
}

@PhdThesis{wei_extending_2017,
  Title                    = {Extending {Growth} {Mixture} {Models} and {Handling} {Missing} {Values} via {Mixtures} of {Non}-{Elliptical} {Distributions}},
  Author                   = {Wei, Yuhong},
  Year                     = {2017},
  Type                     = {Thesis},

  Abstract                 = {Growth mixture models (GMMs) are used to model intra-individual change and inter-individual differences in change and to detect underlying group structure in longitudinal studies. Regularly, these models are fitted under the assumption of normality, an assumption that is frequently invalid. To this end, this thesis focuses on the development of novel non-elliptical growth mixture models to better fit real data. Two non-elliptical growth mixture models, via the multivariate skew-t distribution and the generalized hyperbolic distribution, are developed and applied to simulated and real data. Furthermore, these two non-elliptical growth mixture models are extended to accommodate missing values, which are near-ubiquitous in real data.

Recently, finite mixtures of non-elliptical distributions have flourished and facilitated the flexible clustering of the data featuring longer tails and asymmetry. However, in practice, real data often have missing values, and so work in this direction is also pursued. A novel approach, via mixtures of the generalized hyperbolic distribution and mixtures of the multivariate skew-t distributions, is presented to handle missing values in mixture model-based clustering context. To increase parsimony, families of mixture models have been developed by imposing constraints on the component scale matrices whenever missing data occur. Next, a mixture of generalized hyperbolic factor analyzers model is also proposed to cluster high-dimensional data with different patterns of missing values. Two missingness indicator matrices are also introduced to ease the computational burden. The algorithms used for parameter estimation are presented, and the performance of the methods is illustrated on simulated and real data.},
  Language                 = {en},
  Url                      = {https://macsphere.mcmaster.ca/handle/11375/21987},
  Urldate                  = {2018-02-04}
}

@Book{wickham_ggplot2_2009,
  Title                    = {ggplot2: {Elegant} {Graphics} for {Data} {Analysis}},
  Author                   = {Wickham, Hadley},
  Publisher                = {Springer},
  Year                     = {2009},
  Edition                  = {2nd Printing.},
  Month                    = aug,

  ISBN                     = {0-387-98140-3},
  Shorttitle               = {ggplot2}
}

@Article{wickham_graphical_2010,
  Title                    = {Graphical inference for infovis},
  Author                   = {Wickham, H. and Cook, D. and Hofmann, H. and Buja, Andreas},
  Journal                  = {IEEE Transactions on Visualization and Computer Graphics},
  Year                     = {2010},

  Month                    = nov,
  Number                   = {6},
  Pages                    = {973--979},
  Volume                   = {16},

  Abstract                 = {How do we know if what we see is really there? When visualizing data, how do we avoid falling into the trap of apophenia where we see patterns in random noise? Traditionally, infovis has been concerned with discovering new relationships, and statistics with preventing spurious relationships from being reported. We pull these opposing poles closer with two new techniques for rigorous statistical inference of visual discoveries. The "Rorschach" helps the analyst calibrate their understanding of uncertainty and "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure.},
  Doi                      = {10.1109/TVCG.2010.161},
  File                     = {IEEE Xplore Abstract Record:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/6U3IB5QM/abs_all.html:text/html;Wickham et al_2010_Graphical inference for infovis.pdf:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/KGAEDMF8/Wickham et al_2010_Graphical inference for infovis.pdf:application/pdf},
  ISSN                     = {1077-2626},
  Keywords                 = {Testing, Models, Statistical, Neoplasms, null hypotheses, Computer Graphics, infovis, data visualization, visual testing, Protocols, Accuracy, data visualisation, Humans, Databases, Factual, Histograms, data plot, Data Interpretation, Statistical, graphical inference, Tag clouds, statistics, permutation tests, Visualization, Statistical inference}
}

@TechReport{boxplots,
  Title                    = {40 years of boxplots},
  Author                   = {Hadley Wickham and Lisa Stryjewski},
  Institution              = {had.co.nz},
  Year                     = {2012},

  Url                      = {http://vita.had.co.nz/papers/boxplots.pdf}
}

@Book{Wilkinson1999,
  Title                    = {The grammar of graphics},
  Author                   = {L. Wilkinson},
  Publisher                = {Springer},
  Year                     = {1999},

  Address                  = {New York}
}

@Article{you_heres_2017,
  Title                    = {Here’s the visual proof of why vaccines do more good than harm},
  Author                   = {Jia You},
  Journal                  = {Science},
  Year                     = {2017},

  Month                    = apr,

  Abstract                 = {Vaccines have beat back infectious diseases. Bubbles represent reported U.S. cases, but not all diseases were notifiable in all years. For example, mumps was not reported until 1968, the year after a vaccine was licensed. Click or hover on a bubble or a dot on the timeline to view more details.},
  Url                      = {http://www.sciencemag.org/news/2017/04/here-s-visual-proof-why-vaccines-do-more-good-harm}
}

@Misc{zeileis_was_2019,
  Title                    = {Was {Trump} confused by rainbow color map?},

  Author                   = {Zeileis, Achim},
  Month                    = sep,
  Year                     = {2019},

  Abstract                 = {In the controversy over Hurricane Dorian, US President Donald Trump repeatedly claimed that early forecasts showed a high probability of Alabama being hit. We demonstrate that a potentially confusing rainbow color map in the official weather forecasts may have attributed to Trump overestimating the risk.},
  Journal                  = {Achim Zeileis},
  Language                 = {en},
  Url                      = {https://eeecon.uibk.ac.at/~zeileis//news/dorian_rainbow/},
  Urldate                  = {2019-09-09}
}

@Article{zeileis_object_2006,
  Title                    = {Object-Oriented Computation of Sandwich Estimators},
  Author                   = {Achim Zeileis},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2006},
  Number                   = {9},
  Pages                    = {1-16},
  Volume                   = {16},

  Url                      = {http://www.jstatsoft.org/v16/i09/}
}

@article{zeileis_escaping_2009,
  Title                    = {Escaping {RGBland}: {Selecting} colors for statistical graphics},
  Author                   = {Zeileis, Achim and Hornik, Kurt and Murrell, Paul},
  Journal                  = {Computational Statistics \& Data Analysis},
  Year                     = {2009},

  Month                    = jul,
  Number                   = {9},
  Pages                    = {3259--3270},
  Volume                   = {53},

  Abstract                 = {Statistical graphics are often augmented by the use of color coding information contained in some variable. When this involves the shading of areas (and not only points or lines)—e.g., as in bar plots, pie charts, mosaic displays or heatmaps—it is important that the colors are perceptually based and do not introduce optical illusions or systematic bias. Based on the perceptually-based Hue-Chroma-Luminance (HCL) color space suitable color palettes are derived for coding categorical data (qualitative palettes) and numerical variables (sequential and diverging palettes).},
  Doi                      = {10.1016/j.csda.2008.11.033},
  ISSN                     = {0167-9473},
  Shorttitle               = {Escaping {RGBland}},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0167947308005549},
  Urldate                  = {2019-09-11}
}


@Article{zuur_protocol_2016,
  Title                    = {A protocol for conducting and presenting results of regression-type analyses},
  Author                   = {Zuur, Alain F. and Ieno, Elena N.},
  Journal                  = {Methods in Ecology and Evolution},
  Year                     = {2016},

  Month                    = jun,
  Number                   = {6},
  Pages                    = {636--645},
  Volume                   = {7},

  Abstract                 = {* Scientific investigation is of value only insofar as relevant results are obtained and communicated, a task that requires organizing, evaluating, analysing and unambiguously communicating the significance of data. In this context, working with ecological data, reflecting the complexities and interactions of the natural world, can be a challenge. Recent innovations for statistical analysis of multifaceted interrelated data make obtaining more accurate and meaningful results possible, but key decisions of the analyses to use, and which components to present in a scientific paper or report, may be overwhelming.


* We offer a 10-step protocol to streamline analysis of data that will enhance understanding of the data, the statistical models and the results, and optimize communication with the reader with respect to both the procedure and the outcomes. The protocol takes the investigator from study design and organization of data (formulating relevant questions, visualizing data collection, data exploration, identifying dependency), through conducting analysis (presenting, fitting and validating the model) and presenting output (numerically and visually), to extending the model via simulation. Each step includes procedures to clarify aspects of the data that affect statistical analysis, as well as guidelines for written presentation. Steps are illustrated with examples using data from the literature.


* Following this protocol will reduce the organization, analysis and presentation of what may be an overwhelming information avalanche into sequential and, more to the point, manageable, steps. It provides guidelines for selecting optimal statistical tools to assess data relevance and significance, for choosing aspects of the analysis to include in a published report and for clearly communicating information.},
  Doi                      = {10.1111/2041-210X.12577},
  ISSN                     = {2041-210X},
  Keywords                 = {Visualization, effective communication, protocol, statistical analysis},
  Language                 = {en},
  Url                      = {http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12577/abstract},
  Urldate                  = {2016-06-17}
}

@Misc{noauthor_data_nodate,
  Title                    = {Data visualization: {A} view of every {Points} of {View} column : {Methagora}},

  File                     = {Data visualization\: A view of every Points of View column \: Methagora:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/24IZIWDV/data-visualization-points-of-view.html:text/html},
  Url                      = {http://blogs.nature.com/methagora/2013/07/data-visualization-points-of-view.html},
  Urldate                  = {2015-05-22}
}

@Book{noauthor_data_nodate-1,
  Title                    = {Data {Visualization} for {Social} {Science}},

  Abstract                 = {A practical introduction with R and ggplot2.},
  File                     = {Snapshot:/home/bolker/.mozilla/firefox/f2nw6467.default/zotero/storage/MTXFEB3J/socviz.co.html:text/html},
  Url                      = {http://socviz.co/},
  Urldate                  = {2017-09-09}
}

@Misc{annkemery_data_2014,
  Title                    = {Data {Visualization} {Checklist}},
  Month                    = may,
  Year                     = {2014},

  Abstract                 = {Stephanie Evergreen and I created the Data Visualization Checklist to provide guidance on how, exactly, to make effective graphs},
  Journal                  = {Ann's Blog},
  Url                      = {http://annkemery.com/portfolio/dataviz-checklist/},
  Urldate                  = {2016-07-09}
}

@Article{noauthor_chance_2008,
  Title                    = {{CHANCE} {Graphic} {Display} {Contest}: {Burtin}'s {Antibiotic} {Data}},
  Journal                  = {CHANCE},
  Year                     = {2008},

  Month                    = sep,
  Number                   = {4},
  Pages                    = {62--62},
  Volume                   = {21},

  Doi                      = {10.1080/09332480.2008.10722935},
  ISSN                     = {0933-2480},
  Shorttitle               = {{CHANCE} {Graphic} {Display} {Contest}},
  Url                      = {https://doi.org/10.1080/09332480.2008.10722935},
  Urldate                  = {2018-01-15}
}



@article{leonhardt_rich_2019,
	chapter = {Opinion},
	title = {The {Rich} {Really} {Do} {Pay} {Lower} {Taxes} {Than} {You}},
	issn = {0362-4331},
	url = {https://www.nytimes.com/interactive/2019/10/06/opinion/income-tax-rate-wealthy.html},
	abstract = {The 400 wealthiest Americans now pay a lower rate than the middle class.},
	language = {en-US},
	urldate = {2019-10-25},
	journal = {The New York Times},
	author = {Leonhardt, David},
	month = oct,
	year = {2019},
	keywords = {Corporate Taxes, Federal Taxes (US), High Net Worth Individuals, Income Inequality, Income Tax, Saez, Emmanuel, Tax Credits, Deductions and Exemptions, Tax Evasion, Taxation, United States Economy, United States Politics and Government, Zucman, Gabriel}
}


@article{koo_geovisualizing_2018,
	title = {Geovisualizing attribute uncertainty of interval and ratio variables: {A} framework and an implementation for vector data},
	volume = {44},
	shorttitle = {Geovisualizing attribute uncertainty of interval and ratio variables},
	journal = {Journal of Visual Languages \& Computing},
	author = {Koo, Hyeongmo and Chun, Yongwan and Griffith, Daniel A.},
	year = {2018},
	pages = {89--96}
}


@misc{hohle_cartograms_2016,
	title = {Cartograms with {R}},
	url = {http://staff.math.su.se/hoehle/blog/2016/10/10/cartograms.html},
	abstract = {We show how to create cartograms with R by illustrating the population and age-distribution of the planning regions of Berlin by static plots and animations.},
	urldate = {2019-10-27},
	journal = {Theory meets practice},
	author = {Höhle, Michael},
	month = oct,
	year = {2016}
}

@misc{perrier_topogram_2019,
	title = {{dreamRs}/topogram},
	url = {https://github.com/dreamRs/topogram},
	abstract = {R htmlwidget for cartogram-chart.},
	urldate = {2019-10-27},
	publisher = {dreamRs},
	author = {Perrier, Victor},
	month = oct,
	year = {2019},
	note = {original-date: 2016-10-10T20:29:36Z},
	keywords = {htmlwidgets, maps, r}
}


@article{maceachren_visualizing_2005,
	title = {Visualizing geospatial information uncertainty: {What} we know and what we need to know},
	volume = {32},
	shorttitle = {Visualizing geospatial information uncertainty},
	number = {3},
	journal = {Cartography and Geographic Information Science},
	author = {MacEachren, Alan M. and Robinson, Anthony and Hopper, Susan and Gardner, Steven and Murray, Robert and Gahegan, Mark and Hetzler, Elisabeth},
	year = {2005},
	pages = {139--160}
}


@book{bivand_applied_2013,
	address = {New York},
	edition = {2nd},
	title = {Applied {Spatial} {Data} {Analysis} with {R}},
	isbn = {978-1-4614-7617-7},
	abstract = {This book first presents R packages, functions, classes and methods for handling spatial data. It then showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics and disease mapping.},
	language = {English},
	publisher = {Springer},
	author = {Bivand, Roger S. and Pebesma, Edzer and Gómez-Rubio, Virgilio},
	month = jun,
	year = {2013}
}


@article{buja_interactive_1996,
	title = {Interactive {High}-{Dimensional} {Data} {Visualization}},
	volume = {5},
	issn = {1061-8600},
	url = {http://www.jstor.org/stable/1390754},
	doi = {10.2307/1390754},
	abstract = {We propose a rudimentary taxonomy of interactive data visualization based on a triad of data analytic tasks: finding Gestalt, posing queries, and making comparisons. These tasks are supported by three classes of interactive view manipulations: focusing, linking, and arranging views. This discussion extends earlier work on the principles of focusing and linking and sets them on a firmer base. Next, we give a high-level introduction to a particular system for multivariate data visualization--XGobi. This introduction is not comprehensive but emphasizes XGobi tools that are examples of focusing, linking, and arranging views; namely, high-dimensional projections, linked scatterplot brushing, and matrices of conditional plots. Finally, in a series of case studies in data visualization, we show the powers and limitations of particular focusing, linking, and arranging tools. The discussion is dominated by high-dimensional projections that form an extremely well-developed part of XGobi. Of particular interest are the illustration of asymptotic normality of high-dimensional projections (a theorem of Diaconis and Freedman), the use of high-dimensional cubes for visualizing factorial experiments, and a method for interactively generating matrices of conditional plots with high-dimensional projections. Although there is a unifying theme to this article, each section--in particular the case studies--can be read separately.},
	number = {1},
	urldate = {2018-01-01},
	journal = {Journal of Computational and Graphical Statistics},
	author = {Buja, Andreas and Cook, Dianne and Swayne, Deborah F.},
	year = {1996},
	pages = {78--99}
}


@book{holmes_modern_2019,
	address = {Cambridge},
	edition = {1 edition},
	title = {Modern {Statistics} for {Modern} {Biology}},
	isbn = {978-1-108-70529-5},
	abstract = {If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you 'cooking from scratch', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.},
	language = {English},
	publisher = {Cambridge University Press},
	author = {Holmes, Susan and Huber, Wolfgang},
	month = mar,
	year = {2019}
}


@article{mason_why_2019,
	title = {Why scientists need to be better at data visualization},
	url = {https://www.knowablemagazine.org/article/mind/2019/science-data-visualization},
	doi = {10.1146/knowable-110919-1},
	abstract = {The scientific literature is riddled with bad charts and graphs, leading to misunderstanding and worse. Avoiding design missteps can improve understanding of research.},
	urldate = {2019-11-18},
	journal = {Knowable Magazine},
	author = {Mason, Betsy},
	month = nov,
	year = {2019}
}



@article{zeileis_colorspace_2019,
	title = {colorspace: {A} {Toolbox} for {Manipulating} and {Assessing} {Colors} and {Palettes}},
	shorttitle = {colorspace},
	url = {http://arxiv.org/abs/1903.06490},
	abstract = {The R package colorspace provides a flexible toolbox for selecting individual colors or color palettes, manipulating these colors, and employing them in statistical graphics and data visualizations. In particular, the package provides a broad range of color palettes based on the HCL (Hue-Chroma-Luminance) color space. The three HCL dimensions have been shown to match those of the human visual system very well, thus facilitating intuitive selection of color palettes through trajectories in this space. Using the HCL color model general strategies for three types of palettes are implemented: (1) Qualitative for coding categorical information, i.e., where no particular ordering of categories is available. (2) Sequential for coding ordered/numeric information, i.e., going from high to low (or vice versa). (3) Diverging for coding ordered/numeric information around a central neutral value, i.e., where colors diverge from neutral to two extremes. To aid selection and application of these palettes the package also contains scales for use with ggplot2, shiny (and tcltk) apps for interactive exploration, visualizations of palette properties, accompanying manipulation utilities (like desaturation and lighten/darken), and emulation of color vision deficiencies.},
	urldate = {2019-11-18},
	journal = {arXiv:1903.06490},
	author = {Zeileis, Achim and Fisher, Jason C. and Hornik, Kurt and Ihaka, Ross and McWhite, Claire D. and Murrell, Paul and Stauffer, Reto and Wilke, Claus O.},
	month = mar,
	year = {2019},
	note = {arXiv: 1903.06490 [cs, stat]},
	keywords = {Computer Science - Graphics, Statistics - Computation}
}


@article{cook_foundation_2021,
	title = {The {Foundation} is {Available} for {Thinking} about {Data} {Visualization} {Inferentially}},
	issn = {,},
	url = {https://hdsr.mitpress.mit.edu/pub/mpdasaqt/release/1},
	doi = {10.1162/99608f92.8453435d},
	language = {en},
	urldate = {2021-07-30},
	journal = {Harvard Data Science Review},
	author = {Cook, Dianne and Reid, Nancy and Tanaka, Emi},
	month = jul,
	year = {2021},
	note = {Publisher: PubPub}
}


@article{hullman_design_2021,
	title = {To design interfaces for exploratory data analysis, we need theories of graphical inference},
	url = {http://arxiv.org/abs/2104.02015},
	abstract = {Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern finding and data exposure. But design philosophies that emphasize exploration over other phases of analysis risk confusing a need for flexibility with a conclusion that exploratory visual analysis is inherently model-free and cannot be formalized. We describe how without a grounding in theories of human statistical inference, research in exploratory visual analysis can lead to contradictory interface objectives and representations of uncertainty that can discourage users from drawing valid inferences. We discuss how the concept of a model check in a Bayesian statistical framework unites exploratory and confirmatory analysis, and how this understanding relates to other proposed theories of graphical inference. Viewing interactive analysis as driven by model checks suggests new directions for software and empirical research around exploratory and visual analysis. For example, systems should enable specifying and explicitly comparing data to null and other reference distributions and better representations of uncertainty. Implications of Bayesian and other theories of graphical inference should be tested against outcomes of interactive analysis by people to drive theory development.},
	urldate = {2021-07-30},
	journal = {arXiv:2104.02015 [cs]},
	author = {Hullman, Jessica and Gelman, Andrew},
	month = jul,
	year = {2021},
	note = {arXiv: 2104.02015},
	keywords = {Computer Science - Human-Computer Interaction, H.5.2}
}

@misc{bryan_project-oriented_2017,
	title = {Project-oriented workflow},
	url = {https://www.tidyverse.org/blog/2017/12/workflow-vs-script/},
	abstract = {Advice on workflows for developing R scripts. How to think about whether an action belongs in the script or elsewhere.},
	language = {en-us},
	urldate = {2021-01-14},
	journal = {Tidyverse},
	author = {Bryan, Jenny},
	month = dec,
	year = {2017}
}


@inproceedings{matejka_datasaurus_2017,
	title = {The {Datasaurus} Dozen - Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing},
	url = {https://www.autodeskresearch.com/publications/samestats},
	doi = {10.1145/3025453.3025912},
	urldate = {2018-11-14},
	author = {Matejka, Justin and Fitzmaurice, George},
	year = {2017},
      booktitle = {ACM SIGCHI Conference on Human Factors in Computing Systems}
}


@article{sailynoja_graphical_2021,
	title = {Graphical {Test} for {Discrete} {Uniformity} and its {Applications} in {Goodness} of {Fit} {Evaluation} and {Multiple} {Sample} {Comparison}},
	url = {http://arxiv.org/abs/2103.10522},
	abstract = {Assessing goodness of fit to a given distribution plays an important role in computational statistics. The Probability integral transformation (PIT) can be used to convert the question of whether a given sample originates from a reference distribution into a problem of testing for uniformity. We present new simulation and optimization based methods to obtain simultaneous confidence bands for the whole empirical cumulative distribution function (ECDF) of the PIT values under the assumption of uniformity. Simultaneous confidence bands correspond to such confidence intervals at each point that jointly satisfy a desired coverage. These methods can also be applied in cases where the reference distribution is represented only by a finite sample. The confidence bands provide an intuitive ECDF-based graphical test for uniformity, which also provides useful information on the quality of the discrepancy. We further extend the simulation and optimization methods to determine simultaneous confidence bands for testing whether multiple samples come from the same underlying distribution. This multiple sample comparison test is especially useful in Markov chain Monte Carlo convergence diagnostics. We provide numerical experiments to assess the properties of the tests using both simulated and real world data and give recommendations on their practical application in computational statistics workflows.},
	urldate = {2021-04-07},
	journal = {arXiv:2103.10522 [stat]},
	author = {Säilynoja, Teemu and Bürkner, Paul-Christian and Vehtari, Aki},
	month = mar,
	year = {2021},
	note = {arXiv: 2103.10522},
	keywords = {Statistics - Methodology}
}



@article{talts_validating_2020,
	title = {Validating {Bayesian} {Inference} {Algorithms} with {Simulation}-{Based} {Calibration}},
	url = {http://arxiv.org/abs/1804.06788},
	abstract = {Verifying the correctness of Bayesian computation is challenging. This is especially true for complex models that are common in practice, as these require sophisticated model implementations and algorithms. In this paper we introduce {\textbackslash}emph\{simulation-based calibration\} (SBC), a general procedure for validating inferences from Bayesian algorithms capable of generating posterior samples. This procedure not only identifies inaccurate computation and inconsistencies in model implementations but also provides graphical summaries that can indicate the nature of the problems that arise. We argue that SBC is a critical part of a robust Bayesian workflow, as well as being a useful tool for those developing computational algorithms and statistical software.},
	urldate = {2021-10-01},
	journal = {arXiv:1804.06788 [stat]},
	author = {Talts, Sean and Betancourt, Michael and Simpson, Daniel and Vehtari, Aki and Gelman, Andrew},
	month = oct,
	year = {2020},
	note = {arXiv: 1804.06788},
	keywords = {Statistics - Methodology},
	annote = {Comment: 19 pages, 13 figures}
}


@article{cairo_ethical_2014,
	title = {Ethical infographics},
	volume = {37},
	url = {https://www.dropbox.com/s/pqgmg02yz0pgju4/EthicalInfographics.pdf},
	number = {2},
	journal = {IRE Journal},
	author = {Cairo, Alberto},
	year = {2014},
	pages = {25--27}
}

@article{cairo_if_2020,
	title = {If {Anything} on {This} {Graphic} {Causes} {Confusion}, {Discard} the {Entire} {Product}},
	volume = {40},
	issn = {1558-1756},
	doi = {10.1109/MCG.2019.2961716},
	abstract = {Reading a visualization is often an experience mediated by people other than designers themselves. In more than two decades designing and teaching visualization, I have learned to embrace the mantra "what you design isn't what people see." It applies not only to visualizations, but to most artifacts, tools, and technologies.},
	number = {2},
	journal = {IEEE Computer Graphics and Applications},
	author = {Cairo, Alberto},
	month = mar,
	year = {2020},
	note = {Conference Name: IEEE Computer Graphics and Applications},
	keywords = {Cognitive science, Data visualization, Hurricanes, Uncertainty, Visualization},
	pages = {91--97}
}




@article{bolin_quantifying_2017,
	title = {Quantifying the {Uncertainty} of {Contour} {Maps}},
	volume = {26},
	issn = {1061-8600},
	url = {https://doi.org/10.1080/10618600.2016.1228537},
	doi = {10.1080/10618600.2016.1228537},
	abstract = {Contour maps are widely used to display estimates of spatial fields. Instead of showing the estimated field, a contour map only shows a fixed number of contour lines for different levels. However, despite the ubiquitous use of these maps, the uncertainty associated with them has been given a surprisingly small amount of attention. We derive measures of the statistical uncertainty, or quality, of contour maps, and use these to decide an appropriate number of contour lines, which relates to the uncertainty in the estimated spatial field. For practical use in geostatistics and medical imaging, computational methods are constructed, that can be applied to Gaussian Markov random fields, and in particular be used in combination with integrated nested Laplace approximations for latent Gaussian models. The methods are demonstrated on simulated data and an application to temperature estimation is presented.},
	number = {3},
	urldate = {2021-10-21},
	journal = {Journal of Computational and Graphical Statistics},
	author = {Bolin, David and Lindgren, Finn},
	month = jul,
	year = {2017},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/10618600.2016.1228537},
	keywords = {Excursions, Isolines, Kriging, Latent Gaussian processes, Spatial uncertainty visualization},
	pages = {513--524},
	file = {Snapshot:/home/bolker/Documents/zotero_new/storage/6ZGG3R2W/10618600.2016.html:text/html;Submitted Version:/home/bolker/Documents/zotero_new/storage/V956KGP7/Bolin and Lindgren - 2017 - Quantifying the Uncertainty of Contour Maps.pdf:application/pdf},
}

@inproceedings{correll_value-suppressing_2018,
	address = {Montreal QC Canada},
	title = {Value-{Suppressing} {Uncertainty} {Palettes}},
	isbn = {978-1-4503-5620-6},
	url = {https://dl.acm.org/doi/10.1145/3173574.3174216},
	doi = {10.1145/3173574.3174216},
	abstract = {Understanding uncertainty is critical for many analytical tasks. One common approach is to encode data values and uncertainty values independently, using two visual variables. These resulting bivariate maps can be difficult to interpret, and interference between visual channels can reduce the discriminability of marks. To address this issue, we contribute ValueSuppressing Uncertainty Palettes (VSUPs). VSUPs allocate larger ranges of a visual channel to data when uncertainty is low, and smaller ranges when uncertainty is high. This non-uniform budgeting of the visual channels makes more economical use of the limited visual encoding space when uncertainty is low, and encourages more cautious decisionmaking when uncertainty is high. We demonstrate several examples of VSUPs, and present a crowdsourced evaluation showing that, compared to traditional bivariate maps, VSUPs encourage people to more heavily weight uncertainty information in decision-making tasks.},
	language = {en},
	urldate = {2021-10-21},
	booktitle = {Proceedings of the 2018 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {ACM},
	author = {Correll, Michael and Moritz, Dominik and Heer, Jeffrey},
	month = apr,
	year = {2018},
	pages = {1--11},
	file = {Correll et al. - 2018 - Value-Suppressing Uncertainty Palettes.pdf:/home/bolker/Documents/zotero_new/storage/7P4Y7L7M/Correll et al. - 2018 - Value-Suppressing Uncertainty Palettes.pdf:application/pdf},
}

@incollection{schermann_ethics_2019,
	title = {Ethics (chapter 5)},
	date = {2019},
	url = {https://mschermann.github.io/data_viz_reader/ethics.html},
	abstract = {This is the class reader for MSIS 2629.},
	urldate = {2021-11-01},
	booktitle = {A {Reader} on {Data} {Visualization}},
	author = {Schermann, Michael},
	file = {Snapshot:/home/bolker/Documents/zotero_new/storage/Z3MW8F4B/ethics.html:text/html},
}

@misc{skau_code_2012,
	title = {A {Code} of {Ethics} for {Data} {Visualization} {Professionals}},
	url = {https://rockcontent.com/blog/a-code-of-ethics-for-data-visualization-professionals/},
	abstract = {A Code of Ethics for Data Visualization Professionals. Insights to help you grow your business.},
	language = {en-US},
	urldate = {2021-11-01},
	journal = {Rock Content},
	author = {Skau, Drew},
	month = feb,
	year = {2012},
	file = {Snapshot:/home/bolker/Documents/zotero_new/storage/KUPWFCNR/a-code-of-ethics-for-data-visualization-professionals.html:text/html},
}

@article{lundgard_accessible_2022,
	title = {Accessible {Visualization} via {Natural} {Language} {Descriptions}: {A} {Four}-{Level} {Model} of {Semantic} {Content}},
	shorttitle = {Accessible {Visualization} via {Natural} {Language} {Descriptions}},
	url = {http://vis.csail.mit.edu/pubs/vis-text-model/},
	doi = {10.1109/TVCG.2021.3114770},
	language = {en},
	urldate = {2021-10-29},
	author = {Lundgard, Alan and Satyanarayan, Arvind},
	month = jan,
	year = {2022}
}


@misc{okabe_color_2008,
	title = {Color {Universal} {Design} ({CUD}): {How} to make figures and presentations that are friendly to {Colorblind} people},
	url = {https://jfly.uni-koeln.de/color/},
	urldate = {2021-11-03},
	author = {Okabe, Masataka and Ito, Kei},
	month = sep,
	year = {2008},
	file = {Color Universal Design (CUD) / Colorblind Barrier Free:/home/bolker/Documents/zotero_new/storage/RRC249KI/color.html:text/html},
}

@article{bergstrom_why_2018,
	title = {Why scatter plots suggest causality, and what we can do about it},
	url = {http://arxiv.org/abs/1809.09328},
	abstract = {Scatter plots carry an implicit if subtle message about causality. Whether we look at functions of one variable in pure mathematics, plots of experimental measurements as a function of the experimental conditions, or scatter plots of predictor and response variables, the value plotted on the vertical axis is by convention assumed to be determined or influenced by the value on the horizontal axis. This is a problem for the public understanding of scientific results and perhaps also for professional scientists' interpretations of scatter plots. To avoid suggesting a causal relationship between the x and y values in a scatter plot, we propose a new type of data visualization, the diamond plot. Diamond plots are essentially 45 degree rotations of ordinary scatter plots; by visually jarring the viewer they clearly indicate that she should not draw the usual distinction between independent/predictor variable and dependent/response variable. Instead, she should see the relationship as purely correlative.},
	urldate = {2021-12-27},
	journal = {arXiv:1809.09328 [cs]},
	author = {Bergstrom, Carl T. and West, Jevin D.},
	month = sep,
	year = {2018},
	note = {arXiv: 1809.09328},
	keywords = {Computer Science - Human-Computer Interaction}
}

@book{wilke_fundamentals_2019,
	address = {Sebastopol, CA},
	edition = {1st edition},
	title = {Fundamentals of {Data} {Visualization}: {A} {Primer} on {Making} {Informative} and {Compelling} {Figures}},
	isbn = {978-1-4920-3108-6},
	shorttitle = {Fundamentals of {Data} {Visualization}},
	language = {English},
	publisher = {O'Reilly Media},
	author = {Wilke, Claus O.},
	month = apr,
	year = {2019},
}


@inproceedings{correll_rosschernoff_2018,
  title = {Ross-{{Chernoff}} Glyphs Or: How Do We Kill Bad Ideas in Visualization?},
  shorttitle = {Ross-{{Chernoff Glyphs Or}}},
  booktitle = {Extended {{Abstracts}} of the 2018 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
  author = {Correll, Michael},
  year = {2018},
  month = apr,
  pages = {1--10},
  publisher = {{ACM}},
  address = {{Montreal QC Canada}},
  doi = {10.1145/3170427.3188398},
  abstract = {As data increases in dimensionality or complexity, it becomes difficult to graphically represent data items or series in a straightforward way. Chernoff faces encode data values as features of a human face, but afford only a handful of dimensions, and can be difficult to decode. In this paper, we extend and improve Chernoff faces by merging them with the work of landscape painter Bob Ross, creating datalandscapes glyphs that directly encode data as three series with arbitrary numbers of data items per series. This is pretty obviously a bad idea, yet it is difficult to precisely articulate why, given the current state of the art in academic visualization. We propose and evaluate this technique as a way of highlighting these gaps in our ontology of bad visualization ideas, with the goal of being able to dismiss future bad ideas right out of the gate.},
  isbn = {978-1-4503-5621-3},
  langid = {english},
  note = {https://dl.acm.org/doi/10.1145/3170427.3188398},
  file = {/home/bolker/Documents/zotero_new/storage/I24MB923/Correll - 2018 - Ross-Chernoff Glyphs Or How Do We Kill Bad Ideas .pdf}
}



@misc{rogers_visualizing_2020,
  title = {Visualizing {{Science}}: {{How Color Determines What We See}}},
  shorttitle = {Visualizing {{Science}}},
  author = {Rogers, S. and Zeller, D.},
  year = {2020},
  month = may,
  journal = {Eos},
  abstract = {Color plays a major role in the analysis and communication of scientific information. New tools are helping to improve how color can be applied more accurately and effectively to data.},
  howpublished = {http://eos.org/features/visualizing-science-how-color-determines-what-we-see},
  langid = {american},
  file = {/home/bolker/Zotero/storage/BCBR2X84/visualizing-science-how-color-determines-what-we-see.html}
}

@inproceedings{stewartExamination2010,
	address = {Berlin, Heidelberg},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {An {Examination} of {Cleveland} and {McGill}’s {Hierarchy} of {Graphical} {Elements}},
	isbn = {978-3-642-14600-8},
	doi = {10.1007/978-3-642-14600-8_46},
	abstract = {Two experiments were conducted to examine Cleveland and McGills’ theory of graph perception. In Experiment 1 participants made judgments about the individual perceptual elements. In Experiment 2, participants were presented with graphs that isolated specific perceptual elements. Although the original hierarchy included ten elements, the current research suggested that, depending on the task, there may be no more than three or four individual rankings. This research presents the first attempt at a comprehensive examination of the relationship between the perception of isolated graph elements and how these elements affect graph reading.},
	language = {en},
	booktitle = {Diagrammatic {Representation} and {Inference}},
	publisher = {Springer},
	author = {Stewart, Brandie M. and Best, Lisa A.},
	editor = {Goel, Ashok K. and Jamnik, Mateja and Narayanan, N. Hari},
	year = {2010},
	keywords = {graph perception, hierarchy of graph elements, psychophysics},
	pages = {334--337},
	file = {Full Text PDF:/home/bolker/Documents/zotero_new/storage/G6LMZ6UL/Stewart and Best - 2010 - An Examination of Cleveland and McGill’s Hierarchy.pdf:application/pdf},
}