While working through the data examples in the last chapter, we used the Spotfire Table visualization, but now we're going to take a closer look. People will nearly always want to see the "underlying data", the details behind any visualization you create. The Table visualization meets this need.

The data used here is BaseballPlayerData.xls

Open the baseball analysis file from Chapter 1, Show Me the Data. If you didn't save it, just open a new analysis file and import the baseball player data.

I wrote at the very beginning of Chapter 1, Show Me the Data, that there is always more than one way to do the same thing in Spotfire...

Color is a strong feature in Spotfire and an important visualization tool, often underestimated by report creators. It can be seen as merely a nice-to-have customization, but paying attention to color can be the difference between creating a stimulating and intuitive data visualization rather than 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.

We saw how the Table visualization is perfect for showing and ordering detailed information. It's quite similar to a spreadsheet. The Bar Chart visualization is very good for visualizing categorical information, that is, where you have categories with supporting hard numbers—sales by region, for example. The region is the category, whereas the sales is the hard number or fact.

Bar charts are typically used to show a distribution. Depending on your data or your analytic requirement, the bars can be ordered by value, placed side by side, stacked on top of each other, or arranged vertically or horizontally.

There is a special case of the category and value combination and that is where you want to plot the frequencies of a set of numerical values. This type of bar chart is referred to as a histogram, and although it is number against number, it is still, in essence, a distribution plot. It is very common in fact to transform the continuous...

Trellising, whereby you divide a series of visualizations into individual panels, is a useful technique when you want to subdivide your analysis. In the example we've been working with, we might, for instance, want to split the visualization by league.

It's time to introduce one of the most important Spotfire concepts, called marking, which is central to the interactivity that makes Spotfire such a powerful analysis tool. Marking refers to the action of selecting data in a visualization. Every element you see is selectable, or markable, that is, a single row or multiple rows in a table, a single bar or multiple bars in a bar chart.

You need to understand two aspects to marking. First, there is the visual effect, or color(s) you see, when you mark (select) visualization elements. Second, there is the behavior that follows marking: what happens to data and the display of data when you mark something.

The Line Chart visualization is typically used to show trends, usually trends over time, but any functional relationship will work; infant mortality rate versus GDP per capita, for example. In many ways, the line chart is just what we would have called a graph in science class, plotting y against x and connecting the dots.

We need some new data to create a classic temporal line chart. I pulled some market interest rate data for the Czech Republic, Sweden, and the United Kingdom from the European Commission's Eurostat website. The plots illustrate the banking crisis that hit Sweden in 1992 and the Czech currency crisis of May 1997.

The Pie Chart visualization is useful for showing simple proportions for a limited number of variables. If you have four values to compare, then the familiar pie slices can be a powerful visual aid. Increase to ten values of near-equal proportion, and you lose the audience. The slices must also be mutually exclusive and add up to a meaningful whole, such as 100 percent. The popular media are guilty of presenting opinion poll pie charts that add up to more than 100 percent.

Also, because the proportions are determined by angles and triangular section areas, people have difficulty accurately estimating the proportions, especially when they are close. Therefore, pie charts are almost always labeled with numbers. There is an excellent article at http://eagereyes.org/techniques/pie-charts on the advantages and limitations of the pie chart.

Let's look at the pie chart in Spotfire.

The data we'll use here is BaseballPlayerData.xls, which you can download from...