From Data to Visualization

 Ugly, Bad and Wrong Figures

Data visualization is a powerful tool for communicating information, but it can also be misused or executed poorly, leading to figures that are misleading, confusing, or just plain ugly. Here are some examples of "ugly," "bad," and "wrong" figures in data visualization

UGLY:

              


  • Pie charts with too many slices: Pie charts are best for showing the composition of a whole with a few major categories. When you have too many slices (more than 6-8), it becomes difficult to compare them visually and the chart becomes visually cluttered.

BAD:

Line chart with 3D elements and gradient colors 3D elements and excessive visual effects: 3D charts and heavy use of gradients, textures, and other visual effects can distract from the data and make the chart difficult to read. 

Missing labels or unclear legends: Charts should have clear labels for axes and data points, as well as a legend that explains what each symbol or color represents. Without these elements, it's impossible to understand the data.


WRONG:

Bar chart with manipulated scale to exaggerate a difference.

Manipulated scales: Changing the scale of an axis to make a difference appear larger or smaller than it actually is is a form of data manipulation. It's dishonest and can be very misleading.

Misleading chart types: Choosing the wrong type of chart for your data can lead to misinterpretations. For example, using a pie chart to show trends over time is not a good idea, as pie charts are not designed to show change.



Core Elements of a Good Data Visualization

  • Accuracy: The most important aspect of a good data visualization is that it must represent the data truthfully, without distortions or misleading scales.
  • Clarity: The viewer should be able to understand the data and the message behind it quickly and easily. Avoid any excess clutter or overly complex visuals.
  • Relevance: The visualization should address the question or speak to the issue at hand, highlighting the most important insights.
  • Appropriate Chart Type: You can go for famous charts like line graphs for trends, bar charts for comparisons, scatter plots for relationships... Choosing the right chart is essential for clarity.
  • Focus: Guide the viewer's eye to the key takeaways using color, size, annotations, and a clear visual hierarchy.

Elevating Good to Great

  • Aesthetics: Well-designed animated data visualizations are easier to process and more appealing. Pay attention to color schemes, fonts, and spacing.
  • Storytelling: Don't just show the data, guide the viewer through a narrative with clear titles, annotations, and logical transitions between visuals.
  • Audience Focus: Tailor complexity, language, and visuals to your audience's knowledge level and goals.
  • Interactivity (When Appropriate): Allowing users to explore, filter, and drill down into the data can unlock deeper insights.
  • Context: Provide background information and labels where needed to ensure proper interpretation.

Common Pitfalls in Data Visualization

Data visualizations can fall victim to issues such as misleading scales, too much visual trickery, or overwhelming visual clutter. Being able to spot these issues takes skill and attention. Always make sure you run your data visualizations past some unbiased viewers before presenting them. If your test viewers are left scratching their heads or seem overwhelmed, then you know that it isn’t working as intended.

Some data visualization basics to watch out for include:

  • Charts with chopped-off starting points
  • Explosions of color that distract from the data
  • 3D effects that skew proportions
  • Unclear or inconsistent scale

To avoid these common pitfalls, always focus on the fundamentals: clarity and accuracy. Choose the right chart type for the data story you're telling and keep things as simple as possible, prioritizing clear communication over visual flair. 


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