Dot Plot Visualizations for Analyzing Comparative Data
The Dot Plot is an effective alternative to the Bar Graph data visualization in cases where the data contains groupings with similar values. Dot Plots do not use a zero baseline and are less cluttered than Bar Graphs, making them easier to interpret in many cases.
In essence, Dot Plots are statistical charts consisting of groups of data points plotted on a simple scale. Typical uses for Dot Plots include continuous, quantitative, univariate data like comparisons of market capitalization for a large number of equities grouped by country.
Dot Plots are one of the simplest statistical data visualizations you can use and are good choices for small to moderately sized data sets. They are particularly useful for highlighting clusters and gaps, as well as outliers. The other major advantage of Dot Plots over Bar Graph or other visualizations is the conservation of numerical information.
Compare measures of performance by category
Dot Plots are especially effective as a presentation tool to help people understand comparisons between grouped hierarchies of data. For example, sales reports that compare revenues by product line, store, region, department, and salesperson are a great place to use the Dot Plot. It's quite common to see such information displayed in tables. Unfortunately, this can make patterns in the data nearly impossible to see. Tables of data have their place, but in order to make good sense of them, you cannot simply read them as you can with a Dot Plot, you have to study them.
You can also use the Dot Plot information visualization to create Categorical Line Graphs. The X axis typically represents categorical periods like days or months rather than a specific time range. This form of the Dot Plot uses color to differentiate different categories of data and makes it easy to compare performance trends between categories.
Reduce visual clutter with Dot Plots
One of the best characteristics of Dot Plots is that they help you present and compare fairly large amounts of data without using a lot of "ink on the page", in the words of information visualization expert Edward Tufte. They are compact and yet can convey a lot of useful, understandable information.