Figure 1.3 How many 9s are there?
How did you do? It's easy to answer the question – you just look at all the values and count the 9s – but it takes a long time. We can make one change to the grid and make it very easy for you. Have a look at Figure 1.4.
Figure 1.4 Now it's easy to count the 9s.
Now the task is easy. Why? Because we changed the color: 9s are red, and all the other numbers are light gray.
Color differences pop out. It's as easy to find one red 9 on a table of hundreds of digits as it is on a 10-by-10 grid. Think about that for a moment: Your brain registers the red 9s before you consciously addressed the grid to count them. Check out the grid of 2,500 numbers in Figure 1.5. Can you see the 9?
Figure 1.5 There is a single 9 in this grid of 2,500 numbers. We wager you saw it before you started reading any other numbers on this page.
It's easy to spot the 9. Our eyes are amazing at spotting things like this.
Color (in this case, hue) is one of several preattentive attributes. When we look at a scene in front of us, or a chart, we process these attributes in under 250 milliseconds. Let's try out a couple more preattentive features with our table of 9s. In Figure 1.6, we've made the 9s a different size from the rest of the figures.
Figure 1.6 Differences in size are easy to see too.
Size and hue: Aren't they amazing? That's all very well when counting the 9s. What if our task is to count the frequency of each digit? That's a slightly more realistic task, but we can't just use a different color or size for each digit. That would defeat the preattentive nature of the single color. Look at the mess that is Figure 1.7.
Figure 1.7 Coloring every digit is nearly as bad as having no color.
It's not a complete disaster: If you're looking for the 6s, you just need to work out that they are red and then scan quickly for those. Using one color on a visualization is highly effective to make one category stand out. Using a few colors, as we did in Figure 1.2 to distinguish a small number of categories, is fine too. Once you're up to around eight to ten categories, however, there are too many colors to easily distinguish one from another.
To count each digit, we need to aggregate. Visualization is, at its core, about encoding aggregations, such as frequency, in order to gain insight. We need to move away from the table entirely and encode the frequency of each digit. The most effective way is to use length, which we can do in a bar chart. Figure 1.8 shows the frequency of each digit. We've also colored the bar showing the number 9.
Figure 1.8 There are 13 9s.
Since the task is to count the 9s in the data source, the bar chart is one of the best ways to see the results. This is because length and position are best for quantitative comparisons. If we extend the example one final time and consider which numbers are most common, we could sort the bars, as shown in Figure 1.9.
Figure 1.9 Sorted bar chart using color and length to show how many 9s are in our table.
This series of examples with the 9s reemphasizes the importance of visualizing data. As with Anscombe's Quartet, we went from a difficult-to-read table of numbers to an easy-to-read bar chart. In the sorted bar chart, not only can we count the 9s (the original task), but we also know that 9 was the third most common digit in the table. We can also see the frequency of every other digit.
The series of examples we just presented used color, size, and length to highlight the 9s. These are three of many preattentive attributes. Figure 1.10 shows 12 that are commonly used in data visualization.
Figure 1.10 Preattentive features.
Some of them will be familiar to you from charts you have already seen. Anscombe's Quartet (see Figure 1.1) used position and spatial grouping. The x- and y-coordinates are for position, while spatial grouping allows us to see the outliers and the patterns.
Preattentive attributes provide us with ways to encode our data in charts. We'll look into that in more detail in a moment, but not before we've talked about data.
To recap, we've seen how powerful the visual system is and looked at some visual features we can use to display data effectively. Now we need to look at the different types of data, in order to choose the best visual encoding for each type.
Types of Data
There are three types of data: categorical, ordinal, and quantitative. Let's use a photo to help us define each type.
Categorical Data
Categorical (or nominal) data represents things. These things are mutually exclusive labels without any numerical value. What nominal data can we use to describe the gentleman with me in the Figure 1.11?
● His name is Brent Spiner.
● By profession he is an actor.
● He played the character Data in the TV show Star Trek: The Next Generation.
Figure 1.11 One of your authors (Andy, on the right) with a celebrity.
Source: Author's photograph
Name, profession, character, and TV show are all categorical data types. Other examples include gender, product category, city, and customer segment.
Ordinal Data
Ordinal data is similar to categorical data, except it has a clear order. Referring to Brent Spiner:
● Brent Spiner's date of birth is Wednesday, February 2, 1949.
● He appeared in all seven seasons of Star Trek: The Next Generation.
● Data's rank was lieutenant commander.
● Data was the fifth of six androids made by Dr. Noonien Soong.
Other types of ordinal data include education experience, satisfaction level, and salary bands in an organization. Although ordinal values often have numbers associated with them, the interval between those values is arbitrary.