- Content Every graph should stand on its own
- It should tell its story without a need for detailed explanatory text or supporting documents.
- It should be clear, effective and informative for the intended audience.
- Communication Tailor each graph to its primary communication purpose
- What insight is the graph intended to convey? Is it intuitive?
- Avoid packing too much information into a single display and distracting from the main message.
- Information Maximize the data-to-ink ratio
- Each spot of ink should be necessary for imparting the main message
- Do not clutter a graph with what you don't need. Less is more.
- Annotation Provide legible text and information
- Position annotation (including legends) so that it aids interpretation and does not distract from the message.
- Use legible font that can be read without eye strain or a great deal of effort. Consider the format (presentation or document)
- Axes Design axes to aid interpretation of a graph
- Scale axes to show the interesting features of the data; for example, for longitudinal data, use time (on a continuous scale) instead of visit number (on an ordinal scale).
- Give careful consideration to inclusion of the zero of each axis; if excluded, ensure its absence is clearly sign-posted.
- Avoid crowded axes.
- Use the same axis scales on graphs that need to be compared.
- Choose the appropriate style of axes. For example, select between a box, X and Y axes, X only, Y only; consider grid lines; ensure intelligent placing of tick marks.
- If the nature of the data suggests the shape of the graphics, follow that suggestion; otherwise, use horizontal graphics about 50% wider than tall.
- Styles Make symbols and plot lines distinct and readable
- Choose plot symbols with simple, familiar shapes and intuitive interpretation (eg ‘A' for active and ‘P' for placebo)
- If a graph is to be displayed by projection onto a screen, or in a poster, use thick lines, large symbols and large fonts to achieve legible display.
- Where possible and appropriate, data representations (such as styles of symbols, lines and bars) should have the same meaning across all similar graphs within a package; for example, if one line graph uses a solid blue line to represent Placebo, all graphs in the package should use a solid blue line for Placebo.
- Colors Make use of color if appropriate for the medium of communication
- Use color only when it decodes information. When color is used, choose contrasting and clearly visible colors; avoid yellow, and contrasts with red, green or brown which are difficult for people with color-deficient vision.
- If a graph may be viewed in black and white, ensure that all distinctions made by color are also made by other features such as symbols and line-styles.
- For black-and-white media, make use of line-styles (dashing and gray levels) that are easy to distinguish.
- Design backgrounds to set off the graph, not compete with it.
- Choose area fills that are distinct but compatible.
- Make secondary plot lines lighter in weight, color or style.
- Keep reference lines and grids distinct from other data lines.
- Color Brewer is an excellent reference for choice of colors.
- Techniques Use established techniques to clarify the message
- Show causality: when a causal relationship exists between variables make sure it is easily discernable from the graph.
- Make comparisons from a common baseline.
- Sort categories according to relevant features of the data.
- Do not introduce spurious dimensions to a graph, as they reduce clarity.
- Combine multiple images into a single display when information needs to be presented together.
- When a graph summarizes data at an aggregate level, always plot estimates of variability in the data.
- Types of plots Use the simplest plot that is appropriate for the information to be displayed (see Select the Right Graph for My Question)
- To show a distribution of values, use whichever form is most appropriate: rugplot, strip plot, dotplot, boxplot, histogram, CDF plot, or more specialized display.
- Use scatter and line plots to show association between a pair of variables, thinking carefully about the representation of variability of actual data.
- Use trellis displays to show changes in association between a pair of variables with respect to a third variable.
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