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
2.5.5.1. 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.
2.5.5.2. 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
2.5.5.3. Exercises¶
- Use matplotlib to create a static visualization
- Use illustrator to combine multiple static visualizations
- Use ipyvega to create an interactive visualization
2.5.5.4. Further information¶
- Data visualization course by Jeff Heer
- The Visual Display of Quantitative Information by Edward Tufte