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Show Notes
Better Data Visualizations (Jonathan Schwabish)
- Amazon USA Store: https://www.amazon.com/dp/0231193114?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Better-Data-Visualizations-Jonathan-Schwabish.html
- Apple Books: https://books.apple.com/us/audiobook/data-analytics-data-visualization-communicating-data/id1685037338?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Better+Data+Visualizations+Jonathan+Schwabish+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/0231193114/
#datavisualization #researchcommunication #chartdesign #policyanalysis #informationdesign #statisticalgraphics #visualstorytelling #BetterDataVisualizations
These are takeaways from this book.
Firstly, Clarify the message before choosing a chart, A central theme is that effective visualization begins with intent, not with templates. The book emphasizes defining the purpose of the graphic: what question it answers, what claim it supports, and what the reader should do after seeing it. This framing helps scholars avoid the common trap of plotting everything that is available rather than what is necessary. By separating exploratory analysis from explanatory communication, readers learn to treat a figure as a crafted argument that needs structure and emphasis. That means deciding which comparisons matter, which categories should be grouped, and which details can be moved to notes or appendices. Schwabish’s approach encourages a workflow that starts with a rough concept, tests it against the audience’s knowledge, and then iterates. This planning stage also includes choosing the right level of precision and choosing whether uncertainty, sample sizes, or methodological constraints must be shown prominently. The result is a visualization that is easier to read because it is organized around a single clear takeaway. For research and policy settings, this discipline reduces ambiguity and strengthens credibility because the visual aligns tightly with the analytical point being made.
Secondly, Match chart type and visual encoding to the data, The book highlights how different chart forms carry different cognitive loads and suit different analytical tasks. Readers are guided to select chart types that naturally express the structure of their data, such as comparisons across categories, trends over time, distributions, relationships, or parts of a whole. Just as important, Schwabish stresses visual encodings: position, length, angle, area, and color intensity all communicate with varying accuracy. By prioritizing more precise encodings, like aligned positions and lengths, researchers can reduce misreading and make subtle differences visible. The discussion also addresses pitfalls, such as overusing dual axes, relying on 3D effects, or choosing pie charts when comparisons are fine grained. Attention is given to scales and baselines, including when a zero baseline is essential and when it is not, and how to handle transformations and log scales responsibly. This topic reinforces that chart choice is not aesthetic preference but a methodological decision that affects interpretation. For technical audiences, the benefit is a more faithful representation of evidence and fewer opportunities for confusion, miscommunication, or unintended bias introduced by the form of the graphic.
Thirdly, Reduce clutter and strengthen hierarchy with thoughtful design, A recurring practical lesson is that most academic charts include too much nonessential ink and too little guidance. Schwabish encourages simplifying backgrounds, reducing unnecessary gridlines, limiting redundant labels, and organizing elements so the reader’s eye travels in a logical order. Visual hierarchy is treated as a tool for directing attention: stronger contrast and weight should go to the data and the key comparison, while secondary information should be lighter and quieter. The book connects these ideas to typography, spacing, alignment, and consistency across figures in a paper or report. Small choices, such as consistent axis formatting, sensible tick marks, and predictable placement of legends, can substantially improve comprehension. Color is treated as both a highlighting device and a potential source of confusion, particularly when used without a plan or when it is inaccessible to color-blind readers. The broader point is that good design is not ornament but a way to remove friction between the viewer and the message. For scholars and wonks, where readers often skim quickly, cleaner structure increases the chance that the intended evidence is noticed and remembered accurately.
Fourthly, Use annotation and storytelling to guide interpretation, Beyond clean design, the book emphasizes that explanation can be built into the figure itself. Annotation, direct labeling, callouts, and thoughtful titles can reduce the need for readers to bounce between a chart and the surrounding text. Schwabish promotes making key points explicit, such as marking notable events on a time series, labeling the most important series directly on the lines, and adding brief notes to clarify definitions or data limitations. This topic treats visualization as part of narrative communication, especially in policy and research dissemination where context matters. The reader is encouraged to consider what the audience might misunderstand and to preempt that confusion with gentle guidance. Storytelling does not mean oversimplifying; it means sequencing information and focusing the viewer on the comparisons that support the argument. This also includes being transparent about uncertainty and avoiding rhetorical tricks that exaggerate effects. When used well, annotation improves accessibility for nontechnical readers while still satisfying experts who want clarity about methods and assumptions. The outcome is a chart that not only shows data but also helps people reach the correct inference efficiently.
Lastly, Build a consistent workflow and standards for research visuals, The book’s advice supports a repeatable process that researchers can apply across projects, teams, and publication formats. Consistency is treated as a professional asset: using a coherent color palette, standardized fonts, uniform axis conventions, and stable terminology reduces cognitive load and makes a collection of figures feel trustworthy. Schwabish also encourages treating figures as objects that deserve version control, careful review, and feedback, much like code or prose. This includes checking for accessibility, verifying that labels match the underlying data, and ensuring that design choices do not distort comparisons. In academic settings, where figures may appear in journals, slides, and reports, a workflow that anticipates different sizes and contexts is valuable. Readers are guided to think about reproduction constraints, such as grayscale printing and small-screen viewing, and to design with those constraints in mind. The topic also connects visualization to ethics and rigor: clear documentation of sources, transparent handling of missing data, and honest representation of uncertainty. Taken together, these practices help scholars produce visuals that are both aesthetically coherent and analytically responsible, improving the impact and reliability of their communication.
- Amazon USA Store: https://www.amazon.com/dp/0231193114?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Better-Data-Visualizations-Jonathan-Schwabish.html
- Apple Books: https://books.apple.com/us/audiobook/data-analytics-data-visualization-communicating-data/id1685037338?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Better+Data+Visualizations+Jonathan+Schwabish+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/0231193114/
#datavisualization #researchcommunication #chartdesign #policyanalysis #informationdesign #statisticalgraphics #visualstorytelling #BetterDataVisualizations
These are takeaways from this book.
Firstly, Clarify the message before choosing a chart, A central theme is that effective visualization begins with intent, not with templates. The book emphasizes defining the purpose of the graphic: what question it answers, what claim it supports, and what the reader should do after seeing it. This framing helps scholars avoid the common trap of plotting everything that is available rather than what is necessary. By separating exploratory analysis from explanatory communication, readers learn to treat a figure as a crafted argument that needs structure and emphasis. That means deciding which comparisons matter, which categories should be grouped, and which details can be moved to notes or appendices. Schwabish’s approach encourages a workflow that starts with a rough concept, tests it against the audience’s knowledge, and then iterates. This planning stage also includes choosing the right level of precision and choosing whether uncertainty, sample sizes, or methodological constraints must be shown prominently. The result is a visualization that is easier to read because it is organized around a single clear takeaway. For research and policy settings, this discipline reduces ambiguity and strengthens credibility because the visual aligns tightly with the analytical point being made.
Secondly, Match chart type and visual encoding to the data, The book highlights how different chart forms carry different cognitive loads and suit different analytical tasks. Readers are guided to select chart types that naturally express the structure of their data, such as comparisons across categories, trends over time, distributions, relationships, or parts of a whole. Just as important, Schwabish stresses visual encodings: position, length, angle, area, and color intensity all communicate with varying accuracy. By prioritizing more precise encodings, like aligned positions and lengths, researchers can reduce misreading and make subtle differences visible. The discussion also addresses pitfalls, such as overusing dual axes, relying on 3D effects, or choosing pie charts when comparisons are fine grained. Attention is given to scales and baselines, including when a zero baseline is essential and when it is not, and how to handle transformations and log scales responsibly. This topic reinforces that chart choice is not aesthetic preference but a methodological decision that affects interpretation. For technical audiences, the benefit is a more faithful representation of evidence and fewer opportunities for confusion, miscommunication, or unintended bias introduced by the form of the graphic.
Thirdly, Reduce clutter and strengthen hierarchy with thoughtful design, A recurring practical lesson is that most academic charts include too much nonessential ink and too little guidance. Schwabish encourages simplifying backgrounds, reducing unnecessary gridlines, limiting redundant labels, and organizing elements so the reader’s eye travels in a logical order. Visual hierarchy is treated as a tool for directing attention: stronger contrast and weight should go to the data and the key comparison, while secondary information should be lighter and quieter. The book connects these ideas to typography, spacing, alignment, and consistency across figures in a paper or report. Small choices, such as consistent axis formatting, sensible tick marks, and predictable placement of legends, can substantially improve comprehension. Color is treated as both a highlighting device and a potential source of confusion, particularly when used without a plan or when it is inaccessible to color-blind readers. The broader point is that good design is not ornament but a way to remove friction between the viewer and the message. For scholars and wonks, where readers often skim quickly, cleaner structure increases the chance that the intended evidence is noticed and remembered accurately.
Fourthly, Use annotation and storytelling to guide interpretation, Beyond clean design, the book emphasizes that explanation can be built into the figure itself. Annotation, direct labeling, callouts, and thoughtful titles can reduce the need for readers to bounce between a chart and the surrounding text. Schwabish promotes making key points explicit, such as marking notable events on a time series, labeling the most important series directly on the lines, and adding brief notes to clarify definitions or data limitations. This topic treats visualization as part of narrative communication, especially in policy and research dissemination where context matters. The reader is encouraged to consider what the audience might misunderstand and to preempt that confusion with gentle guidance. Storytelling does not mean oversimplifying; it means sequencing information and focusing the viewer on the comparisons that support the argument. This also includes being transparent about uncertainty and avoiding rhetorical tricks that exaggerate effects. When used well, annotation improves accessibility for nontechnical readers while still satisfying experts who want clarity about methods and assumptions. The outcome is a chart that not only shows data but also helps people reach the correct inference efficiently.
Lastly, Build a consistent workflow and standards for research visuals, The book’s advice supports a repeatable process that researchers can apply across projects, teams, and publication formats. Consistency is treated as a professional asset: using a coherent color palette, standardized fonts, uniform axis conventions, and stable terminology reduces cognitive load and makes a collection of figures feel trustworthy. Schwabish also encourages treating figures as objects that deserve version control, careful review, and feedback, much like code or prose. This includes checking for accessibility, verifying that labels match the underlying data, and ensuring that design choices do not distort comparisons. In academic settings, where figures may appear in journals, slides, and reports, a workflow that anticipates different sizes and contexts is valuable. Readers are guided to think about reproduction constraints, such as grayscale printing and small-screen viewing, and to design with those constraints in mind. The topic also connects visualization to ethics and rigor: clear documentation of sources, transparent handling of missing data, and honest representation of uncertainty. Taken together, these practices help scholars produce visuals that are both aesthetically coherent and analytically responsible, improving the impact and reliability of their communication.
