Radar charts are ideal for when you are looking to compare many variables in a chart. By taking multivariate data, one is able to interpret the data graphically in a two-dimensional chat, typically with three or more quantitative variables. While the names of radar charts vary, from spider charts to star plots, from cobweb charts to polar charts, they are pretty much the same. Think of these as parallel coordinates plot (link) in polar coordinates.
Data visualizers will often caution about the use of these charts as thy can be hard for parsing out specific values. However, they are great tool for interpreting information when the data at hand is overwhelming. So let’s take a closer look at how radar charts work.
What are they good for?
Radars are good for visualizing multivariate data. If your dataset has a limited number of observations (up to 100) and your column variables are in the same scale.
How do they work?
Radar charts are composed by a sequence of equi-angular spokes, called radii, with each spoke representing one of the variables. The data length of a spoke is proportional to the maximum magnitude of the variable across all data points. A line is drawn connecting the data values for each spoke. This gives the plot a start-like appearance and the origin of one of the popular names for this plot.
Creating Radar Charts using Quadrigram
You can create a Radar Chart by simply selecting the chart from the corresponding icon in the “charts” toolbar menu. In terms of data, you need to select one or more than continuous variable and a collection of observations. The more continuous variables your dataset has, the better this chart performs.
In the following image you can see that the dataset we will be using. It contains several magnitudes of the 73 neighborhoods in Barcelona (Spain). Each magnitude is located in a column, corresponding to each neighborhood.
The graph is visualized by dragging the block that contains the dataset.
To refine the chart, the Fill Color and Opacity are adjusted in the settings panel.
You can also normalize the scales of your magnitudes. Check “Yes” in the “Uniform axes setting.
Change line opacity to improve readability.
You can also hide axes to delete unnecessary ink.