Radar graphs, also known as spider or web charts, are designed to plot 1 or more series of values over multiple quantitative categories by providing an axis for each category, arranged radially as equi-angular spokes around a central point. Values increase from the centre outwards, and each axis can have its own quantitative measure. The values for each category are plotted as points. Often, all the category values in a single series are connected by lines, forming an irregular polygon:

These graphs are unfamiliar to most readers and make it difficult to compare parts of the data. Readers may infer correlations or make comparisons across adjacent categories that are invalid.

Instead, plot the data as bar graphs with clustered columns, or a trellis graph for multiple data series.

Be restrained with use of colour. It can be more powerful and effective to use a single splash of a strong colour to highlight a key point than to colour the entire graph.

Adjust colours to suit the design concept, provided that this will not interfere with the integrity of the data (such as a map key, or colours relating directly to data or existing schemes). List colours and patterns in the legend in the same order in which they appear in the figure. In a series of figures, use the same colours for the same categories. Ensure that there is enough contrast between colours – steps of 20% tint for monochrome shading – or use patterns or patterned lines (eg dotted, dashed). Avoid zebra striping and rainbow colours: 

Look for opportunities to use colour meaningfully, and be careful not to imply meaning where there is none. For example, you could use 2 shades of a single colour to indicate different years of data, rather than red and green, unless you want to imply a judgment about the data series. The graph below uses 2 shades of blue to distinguish between 2 related data series (samples 1 and 2), where there is no need to colour each of the categories (laboratories B to I) differently:

Also keep in mind an individual’s ability to perceive particular colours – a common form of colourblindness makes red and green both appear brown to affected people. The graphs below show how someone with red–green colourblindness might perceive a red and green figure. For many people, colours with insufficient contrast are also difficult to perceive. This is a component of accessibility for figures: 

Discontinuous or exponential scales are sometimes used on axes when the range of data values is very wide – that is, very small data values need to be compared with very large data values. A discontinuous axis skips a number of intervals at some point along the axis before continuing. An exponential axis has unevenly spaced intervals, becoming smaller with distance away from the origin. These axis scales are not readily understood by most readers and may give a distorted visual impression or, worse, misrepresent the message of the data.

Find another way to depict the values so that their full scale and the contrast between them is clear – perhaps as 2 different graphs, or a combination of an overall graph and a zoomed-in graph of the critical portion that you want the reader to notice.

Axes that do not start at zero can also distort a reader’s perception of the information; however, they can be useful when the variation between data points occupies only a small range at large values. Make sure the axis labels are clear and, if comparing multiple graphs of similar datasets, ensure that the scale and divisions are consistent, to allow accurate comparison.

The most common reason for presenting dual-scaled axes is to show the reader similarities in the pattern of values for both measures. However, dual-scaled axes require readers to override their natural inclination to compare data values across the 2 measures – a comparison that is invalid. Consequently, it is almost always better to plot 2 separate graphs (Few 2008b). Use the same design for these graphs to draw attention to similarities in the patterns of data values across the 2 measures, and place the graphs close together on the page. Consider using labels on only one of the x axes, to further link the graphs in the reader’s mind. That is, remove labels from the top or bottom graph (but keep the axis line and tick marks).

Graphs can contain negative values (below zero). For negative values, the axis extends down (vertical y axis) or left (horizontal x axis) from zero. Zero should be clearly marked on the axis, often with a heavier or darker tick line. If both axes are quantitative and span zero, they should intersect at zero. If only 1 axis is quantitative and spans zero, it is customary to place the category axis at the end of the quantitative axis so that the axis labels do not obscure the data. An exception is graphs displaying deviation data, where the goal of the graph is to highlight differences between recorded measurements and some meaningful baseline. This baseline is often represented by the x axis, which intersects the y axis at a zero point that sits approximately halfway up the y axis.