Remember, the key tools for clear presentation of quantitative data to your audience are:
This blog is based on a handout that I use for my introductory course on visualizing and reporting data effectively. For those not having taken the course or are otherwise new to this: The pre-attentive attributes of visual perception are such that we perceive certain things instantly, and these are therefore powerful tools in designing data visuals. These appear in the first image below. Length of line, two dimensional position and to a lesser extent, intensity of hue and size of objects (as below) are the best choices for communicating quantitative magnitudes. The data/ink ratio is simply the ratio of how much of your chart or table depends on the data and how much is simply structure, like axis lines. You need some structure, but in generally, try to maximize the ratio. The best reference of which I know on these concepts is Stephen Few’s “Show Me the Numbers”, Analytics Press.
Use a table if precision is key and it is important to be able to look up individual numbers.
Use graphs to communicate trends, changes and relative magnitudes quickly.
If you use a table, minimize the non-data ink, and help your audience by subtly shading items you think they should see.
When you need your audience to clearly understand the relative magnitudes of numbers: use data graphics where the length of a line (like a bar graph, either horizontal or vertical), or two-dimensional position (like a line graph or scatter plot) convey the quantitative information.
Use other shapes and forms for variety when the need for precision is less critical. Use the other pre-attentive attributes for emphasis.
Use a consistent colour format. Excel provides a range of template colour themes and related palettes of shades of colour. Pick one and stick with it throughout your document or presentation.
Pick a neutral, non-decorative font, like helvetica (for example). Use only a couple of font sizes at most in data graphics.
Use a consistent aspect ratio. It’s not set in stone, but 1:1 is good for scatter plots, 2:1 for line graphs, as a rule of thumb.
Only use as much non-data ink as you need to make the structure of the graph clear.
Example: use light grey, etc., not stark black, axis lines and do not use grid lines unless necessary, and them make them light in colour.
Label your graph’s bars, slices, etc. directly, rather than with a legend (unless impractical, in which case you probably have other problems with your graph too).
Remember that some people have degrees of colour blindness. Use a colour-blind neutral palette and/or check your graph’s colour choices with an online simulator like “vischeck’ (vischeck.com).
Don’t 3-d anything.
And don’t use legends if you can put your labels into the graph itself.
Don’t use primary and bright colours for anything (except sparing use in order to make a piece of a graph or table pop out).
Don’t use pie charts with more than 4 or 5 slices. Use a bar graph.
Don’t use circles or doughnuts to show the relative magnitude of numbers.
Don’t use gridlines unless they are really needed, and then only lightly.
Finally, don’t use graphs with long colour legends off to the side or bottom. Consider whether or not you need to label your bars, lines, etc at all, and if so, how much is really needed. If needed, put the labels right on the graph.
There you have it. Some tips and some do’s and don’ts. As someone who doesn’t like rules, I do not take suggesting rules lightly. Believe me though, if you follow these guidelines, as you learned in my course, or if you read the literature, then you will have a much happier time visualizing your data!