When you visualize data, you represent it with a combination of visual cues that are scaled, colored, and positioned according to values. Dark-colored shapes mean something different from light-colored shapes, or dots in the top right of a two-dimensional space mean something different than dots in the bottom left.
Visualization is what happens when you make the jump from raw data to bar graphs, line charts, and dot plots. It’s the process that takes you from the grid of photos in Chapter 1, “Understanding Data,” to a bar graph over time, as shown in Figure 3-1.
It’s easy to think that this process is instant because software enables you to plug data in, and you get something back instantly, but there are steps and choices in between. What shape should you choose to encode your data? What color is most appropriate for the purpose and message? You can let the computer choose everything for you (it can save time), but there are advantages when you choose. At the least, if you know the elements of visualization and how they can be combined and modified, you know what to tell the computer to do rather than let the computer dictate everything you make.
In many ways, visualization is like cooking. You are the chef, and datasets, geometry, and color are your ingredients. A skilled chef, who knows the process of how to prepare ...