Or “for geographies X and Y, margins of error exceed 10%”. If not, accompany your chart with a statement like “the data comes with uncertainty of up to 20% of the value”, If your data comes with uncertainty (or margins of error), use error bars to show it, if possible. Remember that being open about these things helps build credibility and accountability. Where the data came from, how it was processed and analyzed, and who created the visualization. You should add Notes, Data Sources, and Credits underneath the chart to give more context about In that case, a relevant axis can be hidden and the chart will look less cluttered.Ī legend shows symbology, such as colors and shapes used in the chart, and their meaning (usually values that they represent). You might also choose to label items directly instead of relying on axes, which is common For example,Ī line chart showing US unemployment levels between 19Ĭan have a “Great Depression” annotation around 1930s, and “Covid-19 Impact” annotation for 2020,īoth representing spikes in unemployment. Labels and annotations are often used across the chart to give more context. Horizontal (x) and vertical (y) axes define the scale and units of measure.Ī data series is a collection of observations, which is usually a row orĪ column of numbers, or data points, in your dataset. If so, the two titles above could be changed, respectively, to “Covid-19 Deaths by Race in New York City, Spring 2020” and “Tons of Plastic Entering the Ocean, 1950–2020.”Ī hybrid strategy is to combine a story-oriented title with a more technical subtitle, such as: “Pandemic Hits Black and Latino Population Hardest: Covid-19 Deaths by Race in New York City, Spring 2020.” If you follow this model, make your subtitle less prominent than your title by decreasing its font size, or changing its font style or color, or both. Sometimes your editor or audience will prefer a more technical title for your chart. Or “Millions of Tons of Plastic Enter the Ocean Every Year” are both clear titles that quickly convey a larger story. For example, “Pandemic Hits Black and Latino Population Hardest”, A good title is short, clear,Īnd tells a story on its own. To delve further into chart design, let’s start by establishing a common vocabulary about charts.Ī title is perhaps the most important element of any chart. Rost concludes that the rules we follow reflect our values, and each of us needs to ask, “What do you want your data visualizations to be judged for?”-how good the designs look, or for how truthful they are, or how they evoke emotions, inform and change minds. One example of colliding rules is the tension between creating easy-to-grasp data stories versus those that reveal the complexity of the data, as it often feels impossible to do both. Second, since rules have emerged from different “theories of data visualization,” they sometimes contradict one another. First, following rules too closely can block creativity and innovation, especially when we look for ways to overcome challenges in design work. But Rost reminds us that rules also have a downside. To better understand this tension between following and breaking rules in data visualization, see Lisa Charlotte Rost’s thoughtful reflection on “ What To Consider When Considering Data Vis Rules.” By articulating the unspoken rules behind good chart design, Rost argues that we all benefit by moving them into the public realm, where we can openly discuss and improve on them, as she had done in many Datawrapper Academy posts, which also beautifully visualize each rule. But you may be surprised to learn that some rules are less rigid than others, and can be “broken” when necessary to emphasize a point, as long as you honestly interpret the data. In this section, we’ll identify some important rules about chart design. Since creating a well-designed chart requires time and effort, make sure it enhances your data story.Īlthough not a science, data visualization comes with a set of principles and best practices that serve as a foundation for creating truthful and eloquent charts. Before creating a chart, stop and ask: Does a visualized data pattern really matter to your story? Sometimes a simple table, or even text alone, can communicate the idea more effectively to your audience. However, just because data can be made into a chart does not necessarily mean that it should be turned into one. There are so many different types of charts. Zotero and Better BibTeX for Notes and Biblio.Style Guide for Hands-On Data Visualization.GitHub Desktop and Atom Editor to Code Efficiently.Create a New Repo and Upload Files on GitHub.Copy, Edit, and Host a Simple Leaflet Map Template.Our Open-Access Web Edition: Why and How.
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