Guidelines for effective data visualization
Encoding: translating the data into a visual element on a chart/map/etc.
1. Simplicity
Colour
Increment: Use increments like (0, 2, 4, 6,..) instead of , say (0, 3, 6, 9,...)
Scale: Don't plot two unrelated series with one scale on left and one on the right.
Style: Flat and simple. No 3D effects, shadows, distracting shadings.
Set the baseline to zero.
2. Type of encoding object and attribute used to create a plot
Encoding objects: points, lines, bars
Value-encoding attributes [to show different pieces of information]: point position, line length, color
everyone has different perceptions of visualizations but there are a few simple steps that everyone can follow.
Which visual encoding method is best for you?
Inappropriate encoding
Appropriate encoding
Select meaningful axis ranges
- When absolute magnitudes are important, the vertical axis should begin at zero.
Data transformation
- plotting on a logarithmic vertical axis can remove skewness in datasets with ranges that include very large and small values (Cleveland, 1994).
Graph aspect ratio
- ratio of a graph's height to width
Overlapping points
Plot overlapping points in a way that density differences become clear in scatter plots
Method 1
Overlapping points (cont.)
Method 2
Maintain similar vertical or horizontal axis ranges across subplots
Select an appropriate colour scheme
