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<!DOCTYPE html>
<html lang="en"><head>
<script src="slides_files/libs/clipboard/clipboard.min.js"></script>
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<link href="slides_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
<meta name="generator" content="quarto-1.4.549">
<meta name="author" content="Alexa Fredston">
<meta name="dcterms.date" content="2024-02-14">
<title>The theory and practice of effective scientific figures</title>
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
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code{white-space: pre-wrap;}
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<body class="quarto-light">
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<div class="slides">
<section id="title-slide" class="quarto-title-block center">
<h1 class="title">The theory and practice of effective scientific figures</h1>
<p class="subtitle">CORE Workshop - Ocean Sciences</p>
<div class="quarto-title-authors">
<div class="quarto-title-author">
<div class="quarto-title-author-name">
Alexa Fredston
</div>
</div>
</div>
<p class="date">2024-02-14</p>
</section>
<section id="what-am-i-looking-at-exactly" class="slide level2">
<h2>What am I looking at, exactly?</h2>
<p>I made these slides “programmatically” (in code) using a script type called <a href="https://quarto.org/docs/presentations/revealjs/">Quarto</a> in the RStudio IDE.</p>
<p>You can access the slides or the code easily in the <a href="https://github.com/afredston/core-figures">GitHub repository</a>, or navigate to <a href="https://github.com/">github.com</a>, look up the username <strong>afredston</strong>, and click on the pinned repository entitled “core-figures”.</p>
</section>
<section id="why-did-you-do-this-alexa" class="slide level2">
<h2>Why did you do this, Alexa?</h2>
<p>Great question. Partly, I wanted an excuse to use Quarto. Mostly, I wanted to show you this because it’s actually extremely useful for visual communication of scientific results, <em>especially</em> at preliminary stages. Quarto:</p>
<div>
<ul>
<li class="fragment">Is designed to integrate coding and formatting, which allows you to clearly and heavily annotate your figures / tables / results as you generate them. I often make these files to explain complicated methods and preliminary results to myself and collaborators.</li>
</ul>
</div>
</section>
<section id="why-did-you-do-this-alexa-1" class="slide level2">
<h2>Why did you do this, Alexa?</h2>
<p>Great question. Partly, I wanted an excuse to use Quarto. Mostly, I wanted to show you this because it’s actually extremely useful for visual communication of scientific results, <em>especially</em> at preliminary stages. Quarto:</p>
<div>
<ul>
<li class="fragment">Generates stuff (figures, tables, etc) programmatically: if you update the data / analysis / model / whatever, the stuff will all automatically update. This saves you time if you frequently re-visualize results. If you tell Quarto to generate text results from data (which is easy to do), it can also automatically update in-text stats.</li>
</ul>
</div>
</section>
<section id="why-did-you-do-this-alexa-2" class="slide level2">
<h2>Why did you do this, Alexa?</h2>
<p>Great question. Partly, I wanted an excuse to use Quarto. Mostly, I wanted to show you this because it’s actually extremely useful for visual communication of scientific results, <em>especially</em> at preliminary stages. Quarto:</p>
<div>
<ul>
<li class="fragment">Allows you to write up results – and potentially entire papers – that are much less prone to error (and wasting time) updating and copy-pasting statistics, tables, and figures.</li>
</ul>
</div>
</section>
<section id="none-of-that-made-sense" class="slide level2">
<h2>None of that made sense</h2>
<p>Don’t worry about it. The main goal today is to talk about figures! I just wanted to explain how I make mine, and why the slides look the way they do.</p>
</section>
<section id="todays-presentation" class="slide level2">
<h2>Today’s presentation</h2>
<p>How to get from data and results to a figure</p>
<p>Some color theory</p>
<p>When to use figures or tables</p>
<p>Examples are (mostly) from my own papers, but this is for two reasons: (1) I wanted to be able to critique them freely and (2) I ran out of time to find other people’s cooler figures</p>
</section>
<section id="is-this-really-worth-my-time" class="slide level2">
<h2>Is this really worth my time?</h2>
<p>Lots of people – <em>lots of people</em> – only look at your title, abstract, and figures</p>
<p>It is extremely obvious to reviewers who spent time on their figures and who didn’t</p>
<p>Your papers (and talks, and meetings) will be <em>so much easier for people to follow</em> if there are great figures</p>
</section>
<section id="know-your-message" class="slide level2">
<h2>Know your message</h2>
<p>Figures are like papers: they should really only contain one idea</p>
<p>If you can’t summarize the main point of a figure in a single, relatively brief sentence, it may have too much information</p>
<p>Panels are OK for dividing up results but just be sure they are clearly delineated and annotated</p>
<p>It can take a while to hone in on this message, but you should know what it is by the time you are writing up final results</p>
</section>
<section class="slide level2">
<p>##Example: one message, one figure</p>
<img data-src="burgess_2023.png" class="r-stretch quarto-figure-center"><p class="caption">Burgess <em>et al.</em> 2023</p></section>
<section id="use-your-imagination" class="slide level2">
<h2>Use your imagination</h2>
<p>Making and customizing beautiful and creative figures is much easier than it used to be</p>
<p>Don’t be constrained by the default way your personal research workflow generates plots</p>
</section>
<section id="example-simple-but-effective" class="slide level2">
<h2>Example: simple but effective</h2>
<img data-src="freeman_2018.png" class="r-stretch quarto-figure-center"><p class="caption">Freeman <em>et al.</em> 2018</p></section>
<section id="plot-types" class="slide level2 smaller">
<h2>Plot types</h2>
<p>Work backward from what you want to communicate</p>
<p>Some key questions to ask yourself:</p>
<ul>
<li>What quantities do you want to visualize – absolute values, relative values, grouped values, etc.?</li>
<li>What summary statistics (e.g., means, trends) do you want to emphasize? How can you do this while still plotting the raw data?</li>
<li>Do you need to plot your data over space? If so, does a map or an abstraction of space work better?</li>
<li>Do you need to plot your data over time?</li>
<li>How will you visualize uncertainty or variation in your data?</li>
</ul>
</section>
<section id="stay-close-to-the-data" class="slide level2">
<h2>Stay close to the data</h2>
<p>Some journals <strong>require</strong> you to plot raw data</p>
<p><em>Be very hesitant about omitting outliers</em></p>
<p>Violin plots are the new box-and-whisker plots</p>
<img data-src="fredston_2021.png" class="r-stretch quarto-figure-center"><p class="caption">Fredston <em>et al.</em> 2021</p></section>
<section id="how-do-i-match-my-idea-to-a-figure" class="slide level2">
<h2>How do I match my idea to a figure?</h2>
<p>Google is your friend, but you need to know how to describe what you’re looking for</p>
<p>For example, “best plot types for two continuous and one categorical variable”, or “how do I visualize relative change over time for many groups at once”</p>
<p>Try <a href="https://www.data-to-viz.com/">from data to viz</a></p>
</section>
<section id="example-ridge-plots-that-show-density-by-group" class="slide level2">
<h2>Example: “ridge” plots that show density by group</h2>
<img data-src="slides_files/figure-revealjs/unnamed-chunk-1-1.png" width="960" class="r-stretch"></section>
<section id="example-ribbon-plots-for-uncertainty" class="slide level2">
<h2>Example: “ribbon” plots for uncertainty</h2>
<img data-src="stat_ribbon.png" class="r-stretch quarto-figure-center"><p class="caption">Exploratory plot by Alexa</p></section>
<section id="example-tile-plots-that-abstract-space" class="slide level2">
<h2>Example: “tile” plots that abstract space</h2>
<img data-src="tileplot.png" class="r-stretch quarto-figure-center"><p class="caption">Exploratory plot by Alexa</p></section>
<section id="conceptual-figures" class="slide level2">
<h2>Conceptual figures</h2>
<p>I am a huge fan of the conceptual diagram, which can summarize a huge amount of what you did in a simple, appealing format</p>
<p>Some ways to use them:</p>
<ul>
<li>Describing an experimental procedure</li>
<li>Outlining a model workflow</li>
<li>Summarizing a collaborative process</li>
<li>Characterizing a complicated dataset</li>
</ul>
</section>
<section id="example-conceptual-diagram" class="slide level2">
<h2>Example: conceptual diagram</h2>
<img data-src="maureaud_2024.png" class="r-stretch quarto-figure-center"><p class="caption">Maureaud <em>et al.</em> 2024</p></section>
<section id="example-conceptual-diagram-1" class="slide level2">
<h2>Example: conceptual diagram</h2>
<img data-src="halpern_2022.png" class="r-stretch quarto-figure-center"><p class="caption">Halpern <em>et al.</em> 2022</p></section>
<section id="example-conceptual-diagram-2" class="slide level2">
<h2>Example: conceptual diagram</h2>
<img data-src="brodie_2023.png" class="r-stretch quarto-figure-center"><p class="caption">Brodie <em>et al.</em> 2023</p></section>
<section id="annotation" class="slide level2">
<h2>Annotation</h2>
<p>Don’t hesitate to draw on and with your plots!</p>
<p>Remember that point size, shape, color, and shading can all communicate for you</p>
<p>You can plot icons from <a href="https://thenounproject.com/">The Noun Project</a> or <a href="https://www.biorender.com/">biorender</a> or silhouettes of taxa from <a href="https://www.phylopic.org/">phylopic</a></p>
<p>I often do this in a software called <a href="https://inkscape.org/">Inkscape</a> (a little buggy on Macs though), which is also great for resizing figures to meet journal guidelines</p>
</section>
<section id="figure-captions" class="slide level2">
<h2>Figure captions</h2>
<p>Should be as short and straightforward as possible while still explaining everything in the figure</p>
<p>Try to avoid acronyms and jargon. The more intuitively you design your figure, the less you need to explain here</p>
<p>Do not make readers hunt through your manuscript to find explanations of things being plotted that are not in the figure caption!!</p>
<p>If your figure caption is getting incredibly long that is a sign your figure may need editing</p>
</section>
<section id="maps-additional-considerations" class="slide level2">
<h2>Maps: additional considerations</h2>
<p>What to plot depends on what you want the viewer to take away – could be bathymetry, country boundaries, major roads or rivers, model outputs… but don’t try to put more than one or two of these on the same map</p>
<p>Choose your projection to align with the areas you want to be most visible</p>
<p>Do not assume people know where your study region is; consider including a zoomed-out map situating it on the continent/globe</p>
<p>Be aware it is extremely difficult to plot uncertainty on a map. People often plot multiple maps side-by-side to get around this</p>
</section>
<section id="example-study-areas" class="slide level2">
<h2>Example: study areas</h2>
<img data-src="fredston_2023_map.png" class="r-stretch quarto-figure-center"><p class="caption">Fredston <em>et al.</em> 2023</p></section>
<section id="example-heatmaps-chloropleths" class="slide level2">
<h2>Example: heatmaps (“chloropleths”)</h2>
<img data-src="nyt.jpg" class="r-stretch quarto-figure-center"><p class="caption">New York Times</p></section>
<section id="color-choices" class="slide level2 smaller">
<h2>Color choices</h2>
<p>Use color only when you need color; remember people will often print papers in black and white</p>
<p>You can find color palettes or ramps online and copy over their IDs (look for “hex” or “RGB” codes)</p>
<p>Match colors to your variable types:</p>
<ul>
<li>Continuous variables –> continuous color gradients</li>
<li>Ordered variables –> discrete color gradients</li>
<li>Categorical variables –> discrete color palettes</li>
</ul>
<p><em>Make your plots colorblind-friendly.</em> Somewhere between 5-10% of the general population has some limitation to their color vision. <a href="https://mk.bcgsc.ca/colorblind/palettes.mhtml#page-container">This site</a> has some great palettes for continuous and categorical data that are colorblind-friendly, and you can read a simple explainer <a href="https://davidmathlogic.com/colorblind/#%23D81B60-%231E88E5-%23FFC107-%23004D40">here</a>.</p>
<p>Use <a href="https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3">colorbrewer</a> to explore color choices, especially for maps</p>
</section>
<section id="matching-scales-to-variables" class="slide level2">
<h2>Matching scales to variables</h2>
<img data-src="color_scales.png" class="r-stretch quarto-figure-center"><p class="caption">From the Datawrapper blog</p></section>
<section id="example-barbie-plot" class="slide level2">
<h2>Example: Barbie plot</h2>
<img data-src="fredston_2023_barbie.png" class="r-stretch quarto-figure-center"><p class="caption">Fredston <em>et al.</em> 2023</p></section>
<section id="more-on-color-choices" class="slide level2">
<h2>More on color choices</h2>
<p>Be mindful of the interpretation of your colors. For example, red colors are often interpreted as literally hotter, and blues are often interpreted as literally colder; green colors are often used for vegetation or productivity</p>
<p>If you aren’t using a variable that is commonly associated with a color try to visualize it with a neutral color that won’t be confusing</p>
<p>Be consistent across plots and use the same colors for the same variables in all your figures</p>
</section>
<section id="example-color-choices" class="slide level2">
<h2>Example: color choices</h2>
<img data-src="antao_2020.png" class="r-stretch quarto-figure-center"><p class="caption">Antão <em>et al.</em> 2020</p></section>
<section id="example-using-color-over-two-dimensions" class="slide level2">
<h2>Example: using color over two dimensions</h2>
<img data-src="https://www.joshuastevens.net/images/js_bivariateMix.gif" class="r-stretch quarto-figure-center"><p class="caption">Bivariate color ramp</p></section>
<section id="interactive-plots-gifs-etc." class="slide level2">
<h2>Interactive plots, gifs, etc.</h2>
<p>People increasingly make interactive plots to go with their papers; see <a href="https://gavinfay.shinyapps.io/ebfm-ne-teaching-eg/">Gavin Fay’s example</a></p>
<p>You can also generate gifs of your figures to embed in talks, which is really helpful for showing (for example) things evolving through time</p>
</section>
<section id="you-didnt-talk-about-tables" class="slide level2">
<h2>You didn’t talk about tables</h2>
<p>Tables in the main text of a paper can often be replaced by figures that show extra information, like variance in parameter values</p>
<p>However, sometimes tables and text boxes are important for summarizing descriptive information</p>
<p>I use them for reporting lots of p-values and models in supplementary information</p>
</section>
<section id="example-a-table-i-begrudgingly-made" class="slide level2">
<h2>Example: a table I begrudgingly made</h2>
<img data-src="fredston_2023_table.png" class="r-stretch quarto-figure-center"><p class="caption">Fredston <em>et al.</em> 2023</p></section>
<section id="more-resources-for-data-visualization" class="slide level2">
<h2>More resources for data visualization</h2>
<p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161295/">Ten Simple Rules for Better Figures</a></p>
<p><a href="https://journalofbiogeographynews.org/2020/07/15/figures-the-art-of-science/">Journal of Biogeography post</a>, a reminder to always follow the journal guidelines</p>
<p><a href="https://knowablemagazine.org/content/article/mind/2019/science-data-visualization">Betsy Mason article</a>, full of examples of how scientists can make better visualizations</p>
<p><a href="https://clauswilke.com/dataviz/">Claus Wilke’s book</a>, a deep dive into data visualization</p>
<p><a href="https://datavizcatalogue.com/">The Data Visualization Catalogue</a>, for browsing plot options</p>
<p><a href="https://www.data-imaginist.com/posts/2018-05-30-scico-and-the-colour-conundrum/index.html">Data Imaginist</a>, on color theory</p>
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