Book Image

Visualize This

By : Nathan Yau‚ÄØ
Book Image

Visualize This

By: Nathan Yau‚ÄØ

Overview of this book

Visualize This is a guide on how to visualize and tell stories with data, providing practical design tips complemented with step-by-step tutorials. It begins with a description of the huge growth of data and visualization in industry, news, and gov't and opportunities for those who tell stories with data. Logically it moves on to actual stories in data-statistical ones with trends and human stories. the technical part comes up quickly with how to gather, parse and format data with Python, R, Excel, Google docs, and so on, and details tools to visualize data-native graphics for the Web like ActionScript, Flash libraries, PHP, JavaScript, CSS, HTML. Every chapter provides an example as well. Patterns over time and kinds of data charts are followed by proportions, chart types and examples. Next, examples and descriptions of outliers and how to show them, different kinds of maps, how to guide your readers and explain the data "in the visualization". The book ends with a value-add appendix on graphical perception.
Table of Contents (12 chapters)

Wrapping Up

Looking for relationships in your data can be challenging at times and requires more critical thinking than blindly graphing numbers, but it can also be the most rewarding and informative. It’s how your data, or rather, how the things that your data represents relate and interact with each other that’s interesting—that’s what makes for the best stories.

This chapter covered how to look for correlations between multiple variables, but explained relationships in a more general sense, too. Look at how everything relates to each other as a whole through distributions. Look within the distributions for outliers or patterns, and then think about the context of what you see. Then if you find something interesting, ask why. Think about the context of the data and possible explanations.

This is the best part about playing with data because you get to explore what the data is about and maybe dig up something interesting. Then when you dig enough, you can...