Spotting Differences - Data Points: Visualization That Means Something - page 228

What to Look For
It's easy to compare across a single variable. One house has more square
feet than another house, or one cat weighs more than another cat. Across
two variables, it is a little more difficult, but it's still doable. The first
house has more square feet, but the second house has more bathrooms.
The first cat weighs more and has short hair, whereas the second cat
weighs less and has long hair.
What if you have one hundred houses or one hundred cats to classify?
What if you have more variables for each house, such as number of bed-
rooms, backyard size, and housing association fees? You end up with the
number of units times the number of variables. Okay, now it is more tricky,
and this is what we focus on.
Perhaps your data has a number of variables, but you want to classify or
group units (for example, people or places) into categories and find the
outliers or standouts. You want to look at each variable for differences,
but you also want to see differences across all variables. Two basketball
players could have completely different scoring averages, but they could
be almost identical in rebounds, steals, and minutes played per game. You
need to find differences but not forget the similarities and relationships,
just like, oh yes, the sports commentators.
Comparing across Multiple Variables
One of the main challenges when dealing with multiple variables is to
determine where to begin. You can look at so many variations and subsets
that it can be overwhelming if you don't stop to think about what data you
have. Sometimes, it's best to look at all the data at once, and interesting
points could point you in the next interesting direction.
Getting Warmer
One of the most straightforward ways to visualize a table of data is to show
it all at once. Instead of the numbers though, you can use colors to indicate
values, as shown in Figure 7-1.