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Data Visualization#

Charts can be more memorable, shareable, and quickly understood than written explanations. They help explore data, explain concepts, and share information effectively. Clear visuals strengthen [[Communication]] and [[Dashboards]].

Why Visualize Data#

  • Visualizing data helps spot patterns, trends, and unusual data points that are hard to see in averages or summaries alone. A chart can reveal what an aggregate hides (e.g: Anscombe's quartet).
  • Diagrams can explain concepts faster than text. A few-second visual can replace a long, confused explanation.
  • A good chart communicates faster than 1,000 words, but that power comes with responsibility. Misleading charts spread just as easily as accurate ones.
  • Plotting data helps spot potential errors and artefacts before publishing.

Chart Type Selection#

  • Pick the chart type and [[Metrics|metric]] that answers the exact question; rates, counts, and shares reveal different truths, so show multiple small views when one cut feels incomplete.
  • Use familiar or practical units (minutes, not standard deviations) when possible. They're easier to interpret and sense-check.

Clarity#

  • Keep labels horizontal and close to the data. Direct labels beat legends.
  • Don't include a legend, instead, label data series directly in the plot area (usually to the right of the most recent data point). Exception: many categories referring to many elements (e.g., maps).
  • Use small multiples when too many lines overlap. Splitting into panels makes individual trends easier to follow, though it trades off direct comparison between entities.
  • Sort categories logically (inherent order) or alphabetically (easier to skim).
  • Data looks better naked.
    • Reduce non-data-ink as much as possible without losing communicative power.
  • Don't include more precision than needed.
  • Format axis labels to match the figures being measured (e.g., currency for dollars).
  • Look at axis label spacing and increase intervals if crowded.

Color#

  • Match colors to concepts (plants → green, bad → red) so readers aren't forced into a Stroop test.
  • Use color-blind friendly palettes. About 4-5% of the population has some form of color blindness.
  • Direct labeling also helps color-blind readers distinguish categories.

Axes#

  • Start your y-axis at zero (assuming no negative values).
  • Avoid deceptive scale tricks.
    • Leave breathing room on axes instead of extreme zoom.
    • The lowest point shouldn't appear to be the lowest possible value.
  • Pair relative effects with absolute numbers (or prediction intervals instead of confidence intervals) to show real-world risk.

Context#

  • Include explanations for anomalous events directly on the graph.
  • For unfamiliar chart types, guide readers with annotations. Add a mini-tutorial if needed.
  • Include targets as asymptotes to help audiences see if you're on track.
  • Make the chart standalone. Add purpose, units, timeframe, and source so it can travel without losing meaning and slot into [[Dashboards]] or memos without extra explanation.
  • Titles for graphs should be the conclusion or key takeaway.
  • Always note the data source below the graph.

Reproducibility#

  • Publish provenance with the chart (data source, assumptions, and ideally a link to code) so others can verify or reuse it and keep [[Data Practices]] consistent.
  • A chart with no source isn't much better than claiming a trend was revealed in a dream.

Common Pitfalls#

  • Skip arrows or other glyphs that imply trends you can't support.
  • Don't use 3D charts. They distract and make values harder to read.
  • Avoid confidence intervals when showing variability. They're often misinterpreted as ranges. Consider prediction intervals or underlying percentages instead.
  • Try not to have too many data series; 5-8 is the usual limit depending on clustering.

Guiding Questions#

Ask yourself when creating a visualization:

  1. Is my chart type meaningful for the question?
  2. Can I make it clearer?
  3. If complicated, can I guide the viewer through it?
  4. Does the chart work as a standalone?
  5. Is my chart's presentation justifiable?
  6. Is my chart reproducible?

Tools#

Resources#