2.5.5. Visualizing data

  • Leverage the human visual system’s abilities to process visual information

  • Use easily understandable data encodings

    • Utilize position and length

    • Use a small number of easily discriminable colors

    • Avoid 3D and animation

  • Display data in its context

    • Use appropriate scales

    • Compare

    • Lay data out on familiar maps such as geographical and pathway maps

  • Use small multiples to visualize additional dimensions

  • Avoid red/green color palettes to accommodate colorblindness

  • Avoid distracting viewers with unnecessary data and other unnecessary visual marks Interactive exploratory data visualization

Static visualizations are helpful to depicting data. However, static visualizations are generally limited to a few dimensions. Consequently, static visualizations can generally only depict small fraction of large data sets. Alternatively, interactive data visualizations can enable exploration of larger and higher dimensional datasets. See d3js.org for inspiring examples of interactive data visualizations, The major disadvantages of interactive data visualization are that are more complex and take more time to create. Software tools

Below are several recommend tools for creating data visualizations:

  • Exploring data

    • Tableau

  • Creating specific plots

    • Python and matplotlib: useful for plotting data

  • Combining plots into figures

    • Illustrator

    • Inkscape

  • Creating interactive data visualizations

    • JavaScript and D3.js

    • ipyvega Exercises

  • Use matplotlib to create a static visualization

  • Use illustrator to combine multiple static visualizations

  • Use ipyvega to create an interactive visualization Further information