Enriching visualizations with color categorization

Color is a strong feature in Spotfire and an important visualization tool, which is often overlooked by report creators. It can be seen as merely a nice-to-have customization. However, color can be the difference between creating a stimulating and intuitive data visualization and an uninspiring and even confusing corporate report. Take some pride and care in the visual aesthetics of your analytics creations!

Let's take a look at the color properties of the table visualization. These are only available in the Analyst clients (however, you can still choose a pre-configured color scheme, or choose colors from the legend of a visualization if you're working in web-based clients):

1. Open the table visualization properties and select Colors, and then Add the Total population (Gapminder) column:

1. Now, you can add points and color rules, or adjust the color gradient. To adjust the existing color gradient, just click on the box next to the Max point and pick a new color, and do the same with the Min point:

1. To add a point with a color indicating the average, click Add Point. Spotfire will add a point with a specific value, so we need to change it to show the average. Click the drop-down to the right of the point and choose Average, then select a color that you'd like use to indicate the average within the gradient. This book is in grayscale, so it's difficult to represent the colors accurately, but I recommend starting off at a deep color for higher values, leading to a lighter shade of the same color for the average. Then, choose a totally different color for the minimum value, but one that does not clash with the gradient from high to average values:

You can also use some built-in color schemes in Spotfire—just click the icon that looks like several sheets of paper to expand the menu:

Out of interest, just look at the difference between the spread of the average and the max and the min and the average. I suspect that China and India really skew the population figures. In fact, we can check this out:

1. Close the dialog and return to the main visualization.
2. Select all the data in the scatter plot.
3. Sort the Total population column by descending. Now, look at the data:

You can see from the values that China and India are indeed responsible for skewing the data—their populations significantly outnumber those of the next two countries—USA and Russia. Note that I was still looking at data for 1974 at this point. Your results will be different if you are looking at a different year.