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In episode 3 of this mini-series, I discuss the impact of simplified data visualization and how to achieve it. Specifically, I address these factors that affect your visualization:

• Clutter

• Mis-alignment
  • Too many visual lines
    • Example misalinged header, legends,
  • Long categories below vertical bar charts not fitting well under the bars (avoid diagonal)
  • How many horizontal and vertical lines (start and stop of visual elements) do you have?
  • Can you avoid a legend?
  • Do you need these gridlines?
  • Do you need tickmarks?
• Too many elements like
  • Colour
  • Fonts
  • Bold, italic
  • Annotations
  • Header, subheader, sub-subheader
• “Unnecessary” annotations
  • File names
  • Source data
  • Program name
  • Validation status
• Understand gestalt principles
  • Proximity - put the description for treatment next to the lines - not in a legend
  • Similarity - re-use color for the lines and the font color of the treatment
  • Enclosure- box around the most important area (e.g. randomized period vs open-label follow up)
  • Continuity - bars being organized below each other have a same starting point - take out the line showing baseline
  • Connection - connect what belongs together - line charts over time
  • Closure - take away the box around your overall vis

Learn more on how to create simple but effective data visualization by listening to this podcast and share this with others who might learn from it!