With Halloween just around the corner, ghosts, witches, and skeletons may not be a daily concern in the ag world, but there’s one thing that can scare anyone: poorly designed charts. A confusing, poorly constructed, or misleading chart can become a nightmare for any agronomist or analyst. So today, we’re diving into the best practices for data visualization in agriculture to help you avoid unnecessary scares and get the most out of your analyses.
1. Choosing the Right Chart: Not All Charts Are What They Seem
The first step to prevent your charts from frightening your colleagues is to choose the right type. Not all charts work for every data set, and using an inappropriate one can make the data even more confusing than before. We explored this in detail in a previous article, which you can check out here
Common Mistakes: A terrifying mistake is using overly complex charts for simple data. Sometimes, people go for radar charts, bubble charts, or 3D graphs to impress, but the result is often confusion. A good chart is clear, not overwhelming.
2. Size and Spacing: Don’t Make It a Cluttered Nightmare
Another common mistake is overcrowding the chart with too much data or too many elements.
Give your chart some breathing room (or at least give the reader some). White space is your friend; it helps highlight the important points. Don’t try to cram everything into a single chart; it’s better to extend it a bit and create two charts rather than piling on data and causing confusion.
Proportion and clarity: If your chart is too small, nobody will be able to read it correctly. If it’s too big, it can lose impact and become tedious. Always seek a balance with the other elements in your composition and the medium you’re using—whether it’s a poster, a slide, or a vertical page layout.
Simplicity: Less is more. Don’t fill your chart with unnecessary decorations, excessive text, or clashing colors. Every detail you include should add value, not distract. Use concise, relevant labels, and avoid unnecessary visual effects like shadows, glows, or perspectives.
Common Mistakes: A classic horror story is overloading the chart with secondary data, like extra lines or legends that don’t add value. A good test is to remove one element at a time and see if the story still holds.
3. Scales and Axes: Don’t Distort the Truth
One of the most “spine-chilling” mistakes you can make is distorting data by manipulating scales. Charts are powerful tools, but they can also mislead if not presented accurately.
Truncated scales: If you cut off the Y-axis to exaggerate a trend, you can make a small variation look much more dramatic than it actually is. Avoid truncating axes unless absolutely necessary, and always clarify when you do. A helpful alternative is to add a “zoomed-in” chart to emphasize what you want to highlight.
Consistent scales: If you’re comparing two charts, ensure that both use the same scales. This is especially important in reports where you’re analyzing multiple plots or time periods. Inconsistent scales can confuse readers and lead to incorrect conclusions.
4. Use of Color: How to Avoid a Chromatic Disaster
This is where many charts fail, turning what should be a clear visualization into a visual mess. Colors should not only make the chart look good, but they should also aid interpretation.
Color Palettes: How to Choose Them to Avoid Confusion
Discrete palettes: These palettes are used when working with clearly defined categories, such as crop types or different fields. Each color represents a different category, and it’s crucial that the chosen colors are easy to distinguish. Try to use colors that are quite distinct from each other, like oranges, blacks, and greens.
Continuous palettes: When showing a scale of values, such as soil moisture over time, continuous palettes are the way to go. In these cases, a gradient from light to dark (e.g., from blue to light blue to white) can clearly show increases or decreases in the variable.
Diverging palettes: These are useful when working with values that have an important midpoint. For example, if you want to show anomalies in yields, you could use a palette that goes from red (for low values) to green (for high values), with yellow representing the midpoint.
Converging palettes: Use these when you want to show how a phenomenon is concentrated around a single point, such as moisture variability around an irrigation well. They typically use a strong color in the center, with softer colors towards the edges.
Color Mistakes to Avoid
Poorly differentiated colors: Don’t use colors that are hard to tell apart, such as similar shades of green or blue. This can make the chart unreadable, especially for those with visual impairments.
Don’t use more colors than necessary: A rainbow-like chart isn’t necessarily a good one. Too many colors can distract and complicate interpretation. Opt for limited, clear, and well-contrasted palettes.
Avoid combining colors of similar intensity and high saturation, especially complementary colors (such as red and green, red and blue, etc.), as this can create an optical vibration effect. This effect makes colors appear to move or vibrate, creating visual tension that can make the chart difficult to focus on and uncomfortable for the viewer.
Best Practices for Using Color in Your Charts
Less is more: Use a reduced palette with colors that really contrast and are designed to highlight key points.
Use accessible colors: Consider people with color vision deficiencies. Tools like Color Universal Design can help you choose colors that are accessible to everyone.
Clear contrast: Ensure there’s enough contrast between the colors you choose, especially in charts like bar or pie charts, where distinguishing between categories is crucial.
5. Fonts and Labels: Don't Let Your Words Become Ghostly Whispers
Your font choices and labels are crucial to the readability of your charts. An eerie font may be fun for Halloween decorations, but in professional charts, it could be a nightmare to interpret. Using clear and accessible fonts, like Arial or Helvetica, ensures that your audience understands your data rather than struggling to read it.
Font Size Matters: Keep titles large enough to grab attention, while labels should be easily readable without being too bold. Avoid decorative or overly complex fonts, as they can be hard to read and distract from the data—and please, keep away from using Comic Sans!
Label Placement: Be strategic about where you place labels. For instance, place axis labels close enough to the axes to guide the reader easily, but not so close that they crowd the data. Clear, concise labels make the chart easier to navigate, preventing the audience from having to ‘dig’ for understanding.
Common Mistakes: Using a tiny font size for secondary details or legends can make your chart look cleaner, but it could also make important details vanish into the background. If labels are crucial, ensure they’re readable and well-positioned.
6. Chart Composition: Don’t Summon the ‘Frankenstein’ Chart
Compositions that blend too many chart types together—like a bar chart with line plots and scatter points all in one—often result in a disorganized mess that’s hard to interpret. In agricultural data, where clarity and precision are key, use only one or two types per chart to avoid confusion.
Focus on One Story Per Chart: Choose a main message for each visualization and build around it. For example, if you’re showing yield comparisons across regions, adding too many variables like soil types and climatic factors in the same chart could be overwhelming.
Use Panels: When you have a multi-faceted story to tell, split it into panels or small multiple charts rather than cluttering a single visualization. This lets viewers grasp individual aspects without feeling overwhelmed.
Common Mistakes: A “Frankenstein chart” might combine pie slices with line markers and bar heights in a single view, which is visually overwhelming and hard to follow. Try to keep each chart limited to one data visualization type or use small multiples to tell the full story in steps.
7. Highlighting Key Insights: Don’t Let Important Points Get Lost in the Fog
When presenting agricultural data—like crop yields or pest trends—highlighting key takeaways is essential. Emphasize crucial insights using color, labels, or icons, so they stand out and ensure your audience grasps the important information.
Color Accents: Use a contrasting color to emphasize a particular value or trend, such as a peak in crop yield. This draws the eye immediately to the key message.
Annotations: Small, clear annotations can provide extra context without cluttering the chart. Instead of using text-heavy labels for every data point, add annotations where needed to make important insights stand out.
Common Mistakes: Highlighting too much can turn your chart into a blur. Limit highlights to one or two areas, so they stand out against the rest of the chart.
8. Testing Your Chart: A Final Check to Banish Any Frights
Before finalizing any chart, test it. Show it to someone who hasn’t been involved in the analysis to see if they can interpret it easily. This "fresh eyes" test helps catch any potential issues or areas that may need refinement. For instance, a legend or a label that might seem clear to you could confuse someone else.
Ask for Feedback: Use quick feedback loops from colleagues, friends, or even team members who aren’t data specialists. If they can interpret the chart with ease, it’s a good sign your message is clear.
Check for Accessibility: Try viewing the chart in grayscale or with color blindness simulators to see if your visualizations are accessible to all viewers.
Halloween is the perfect time to remember that poorly designed charts can be as frightening as any monster—or as daunting as international commodity prices. But it doesn’t have to be that way. By following these best practices for design, chart selection, and color usage, your visualizations won’t just be clear and precise; they’ll also help people make better decisions without any confusion.
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