Above All Else Show the Data

Best Practices in Data Visualization

University of Houston


What is the point of data visualization?

What pattern do you see?

“Anscombe Data” “Anscombe Data”

Example from Anscombe (1973).

Data graphics visually display measured quantities by means of combined use of points, line, a coordinate system, numbers, symbols, words, shading and color (Tufte 2015).

But not all graphics are created equal


  • Principles of Graphical Excellence
  • Data distortions
  • Data-ink
  • Identify graphic crimes
  • The importance of color
  • Socially responsible visualizations
  • Resources

Principles of Graphical Excellence

Excellence in statistical graphics consist of complex ideas communicated with clarity, precision, and efficiency (Tufte 2015, pg 14)

Graphics should…

  • show the data
  • induce viewer to think about the substance and not methodology, design, or technology
  • avoid distorting the data
  • present many numbers in a small space
  • make large data sets coherent
  • compare different pieces of the data
  • reveal the data at several levels of detail
  • display a clear purpose

Data Maps: Death Rates from Cancer, Female

Data Maps: Death Rates from Cancer, Male

Graphical Integrity: Proportionality

  • The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities (pg.56)

  • Different people see the same areas differently and perceptions change with experience and context

  • Tables sometimes outperform graphics in clarity, but only for small data sets of 20 numbers or less

Fuel Economy Standards

Fuel Economy Standards

\[ \frac{27.5-18.0}{18.0} \times 100=53\% \]

\[ \frac{5.3-0.6}{0.6} \times 100=783\% \]

Graphical Integrity: Labeling

  • Clear, detailed, and thorough labeling should be used to defeat distortion and ambiguity

  • Write out explanations of the data and label important events

  • Includes labeling the coordinate system and providing an informative title

Fuel Economy Clarified


  • A large share of ink on a graphic should present data-information

  • Data ink is the non-erasable core of a graphic arranged in response to variation in the numbers represented

  • Every bit of ink on a graphic requires a reason - and that reason is to present new information

Erase non-data-ink

What elements of this chart are not data-ink?

Erase non-data-ink

The grid lines!

Erase non-data-ink

Erase non-data-ink

Erase non-data-ink

Pie Charts

The Pie Proof1

What is the pattern here?

Can you see the pattern now?

Pies are for eating, not charting

What’s wrong with this visualization?

What’s wrong with this visualization?

What’s wrong with this visualization?


Colors are an important aesthetic

  • All data visualizations use color, depending on how you define black and white

  • Three ways to use colors

    • Qualitative colors show category

    • Sequential colors show order, rank, or numeric values

    • Diverging colors show values that move around a zero mark




Color Brewer



Improving graphics with/out color1

Move color mapping to bar height

Improving graphics with/out color

Move color mapping to bar height and position

Color consistency

Color to highlight relationships in context

Color ramps must match psychology

Accessible colors

Equity Awareness

Equity awareness in data visualization1

  • Data analysts should think intentionally about how we can learn from and speak to audiences that reflect the diversity of the people and communities we focus

  • Systemic discrimination is and can be generated by how we use and misuse data

    If I were one of the data points on this visualization, would I feel offended? - Kim Bui

Demonstrating empathy

  • Put people first: data shown reflect the lives and experiences of real people

  • Use personal connections to help readers and users better connect with the material: pair charts with personal stories

  • Create a platform for engagement: interactive graphics allow users to find themselves in the data

  • Consider how framing an issue can create a biased emotional response

  • If the underlying data is biased, graphics can amplify bias and the harm that bias generates

Put people first

Put people first

Language and racial equity awareness

Ordering data purposefully

Colors and equity awareness

Colors and equity awareness


  • Remember the Principles of Graphical Excellence: clarity, precision, and efficiency

  • Avoid data distortion through Graphical Integrity

  • Maximize data-ink ratio

  • Have a clear purpose for your graphics: what do you want to communicate?

  • Do no harm: be aware how your data graphics perpetuate bias or systemic discrimination

Above All Else Show the Data!

Additional Resources

Data-to-Viz: Find the Right Viz


WTF Viz: Examples of what not to do


Atkinson Hyperlegible Font

Free downloadable font for low-vision readers by the Braille Institute used throughout this presentation.


Jorge Martinez

University of Houston

Director of Research and Reporting

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