Visualize This - Nathan Yau.pdf

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Table of Contents Cover Chapter 1: Telling Stories with Data More Than Numbers What to Look For Design Wrapping Up Chapter 2: Handling Data Gather Data Formatting Data Wrapping Up Chapter 3: Choosing Tools to Visualize Data Out-of-the-Box Visualization Programming

Illustration Mapping Survey Your Options Wrapping Up Chapter 4: Visualizing Patterns over Time What to Look for over Time Discrete Points in Time Continuous Data Wrapping Up Chapter 5: Visualizing Proportions What to Look for in Proportions Parts of a Whole Proportions over Time Wrapping Up Chapter 6: Visualizing Relationships What Relationships to Look For

Correlation Distribution Comparison Wrapping Up Chapter 7: Spotting Differences What to Look For Comparing across Multiple Variables Reducing Dimensions Searching for Outliers Wrapping Up Chapter 8: Visualizing Spatial Relationships What to Look For Specific Locations Regions Over Space and Time Wrapping Up

Chapter 9: Designing with a Purpose Prepare Yourself Prepare Your Readers Visual Cues Good Visualization Wrapping Up Introduction Learning Data

Chapter 1 Telling Stories with Data Think of all the popular data visualization works out there—the ones that you always hear in lectures or read about in blogs, and the ones that popped into your head as you were reading this sentence. What do they all have in common? They all tell an interesting story. Maybe the story was to convince you of something. Maybe it was to compel you to action, enlighten you with new information, or force you to question your own preconceived notions of reality. Whatever it is, the best data visualization, big or small, for art or a slide presentation, helps you see what the data have to say. More Than Numbers Face it. Data can be boring if you don’t know what you’re looking for or don’t know that there’s something to look for in the first place. It’s just a mix of numbers and words that mean nothing other than their raw values. The great thing about statistics and visualization

is that they help you look beyond that. Remember, data is a representation of real life. It’s not just a bucket of numbers. There are stories in that bucket. There’s meaning, truth, and beauty. And just like real life, sometimes the stories are simple and straightforward; and other times they’re complex and roundabout. Some stories belong in a textbook. Others come in novel form. It’s up to you, the statistician, programmer, designer, or data scientist to decide how to tell the story. This was one of the first things I learned as a statistics graduate student. I have to admit that before entering the program, I thought of statistics as pure analysis, and I thought of data as the output of a mechanical process. This is actually the case a lot of the time. I mean, I did major in electrical engineering, so it’s not all that surprising I saw data in that light. Don’t get me wrong. That’s not necessarily a bad thing, but what I’ve learned over the years is that data, while objective, often has a human dimension to it. For example, look at unemployment again. It’s easy to spout state averages, but as you’ve seen, it can vary a lot within the state. It can vary a lot by neighborhood. Probably someone you know lost a job over the past few years, and as the saying goes, they’re not just another statistic, represent individuals, so you should approach the data in that way. You don’t have to tell every individual’s story. However, there’s a subtle yet important difference right? The numbers

the unemployment between increasing by 5 percentage points and several hundred thousand people left jobless. The former reads as a number without much context, whereas the latter is more relatable. rate Journalism A graphics internship at The New York Times drove the point home for me. It was only for 3 months during the summer after my second year of graduate school, but it’s had a lasting impact on how I approach data. I didn’t just learn how to create graphics for the news. I learned how to report data as the news, and with that came a lot of design, organization, fact checking, sleuthing, and research. There was one day when my only goal was to verify three numbers in a dataset, because when The New York Times graphics desk creates a graphic, it makes sure what it reports is accurate. Only after we knew the data was reliable did we move on to the presentation. It’s this attention to detail that makes its graphics so good. Take a look at any New York Times graphic. It presents the data clearly, concisely, and ever so nicely. What does that mean though? When you look at a graphic, you get the chance to understand the data. Important points or areas are annotated; symbols and colors are carefully explained in a legend or with points;

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