Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)

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来自 University of Michigan 的课程

Applied Plotting, Charting & Data Representation in Python

2251 个评分

University of Michigan

Applied Plotting, Charting & Data Representation in Python

2251 个评分

课程 2（共 5 门，Specialization Applied Data Science with Python）

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

从本节课中

Module 1: Principles of Information Visualization

In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.

Introduction4:29

About the Professor: Christopher Brooks1:25

Tools for Thinking about Design (Alberto Cairo)8:36

Graphical heuristics: Data-ink ratio (Edward Tufte)4:44

Graphical heuristics: Chart junk (Edward Tufte)5:11

Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)3:47

The Truthful Art (Alberto Cairo)8:40

与讲师见面

Christopher Brooks

Edward Tufte's emphasis on simplicity and minimalism has grown a great following.

He's largely seen as one of the most influential writers on the topic of

visualizations for charts and graphs.

In his book, Beautiful Evidence,

he introduced the idea that took minimalism in charts to a new level.

Instead of making a chart a separate artifact to be studied on it's own,

he suggested it be reduced and embedded within the context of discussion.

He argued that a small graph, say a time series line graph, could convey so

much more information quickly that it would be invaluable to readers.

He called these items sparklines and referred to them as data words.

An intriguing way to bridge the gap between text and figures.

He suggested that sparklines could not only be represented directly in text, but

also could be embedded in tables along with the data they describe.

This thought became so natural that it's even been pick up by one of the most

common pieces of spreadsheet analysis software, Microsoft Excel.

Here is a picture of tabular data from the NASDAQ stock exchange,

showing the opening price of four technology stocks over one-month period.

Take a moment to consider this data.

Which stock has had a bad month with a downward trend?

And which has had a good month with an upward trend?

What if I added sparklines in the cells below?

Here you can see immediately that Apple has had a poor month with

decreased stock price.

And IBM has had good month with increasing stock prices.

With Amazon and Intel it's a little less clear, and we see some spikes throughout.

Now these sparklines don't help us to answer who has the highest stock price, or

who had the greatest wins or losses.

But they do give us a general feeling for the trend behind the data.

And actually, sparklines are used for data where trends or

distribution characteristics are important.

Here's another example of sparklines used in Google Finance's website.

Notice how you can actually quickly zoom in on pieces of the distribution with

these sparklines.

So they're not just a representation of the data but

a method of interacting with them.

There have been lots of different uses of sparklines.

And customized interfaces, such as video games, are a great example.

A modern take on sparklines, which I think of as kind of neat,

is called the sparktweet.

Where Unicode block characters are used to display a bar graph inside the 140

characters allotted by Twitter.

For instance,

here a user has tweeted her hours of sleep over the month of April as a bar graph.

The last Tufte principle that I want to share with you is one called lie factor.

Lie factor is the size of an effect shown in the graphic

divided by the size of the effect actually in the data.

It's often done unconsciously, to try and

help tell a narrative, however, it's misleading to the observers.

There are lots of different examples of lie factor.

This one from Time Magazine in 1979 provides a great example, I think.

In it, various barrels of oil are shown, showing the price of oil over six years.

But it's unclear to the viewer what the size of one barrel means in relation

to the others, in part because there's an element of perspective here.

Are the barrels different sizes, or do they just appear to be so

because some are in front of the others?

Is it the volume of the barrel that represents the growth of the cost, or

the height of the barrel?

Sparklines and lie factor are just two more tools that Edward Tufte has given us

to understand and communicate data.

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