The beginning textbook for any UX designer or anyone passionate about how information and patterns can be structured. ( )

A wonderful classic; by now rendered mostly redundant if you have read recent literature or work on visualizing information (granted, it was inspired by this). It was useful in providing me with several axioms to consider when attempting to visualize data... not because I necessarily agree with a specific approach. ( )

A fascinating, elegant, and beautiful book. ( )

Picture books for grownups! I'm reading another one of his books right away, this one was so fun. Well, it's fun if you enjoy charts, and learning about things like who invented the bar graph, and looking askance at poorly-made graphics. ( )

This is Classic in the field of Visualization. I highly recommend anyone into UX/UI to go through this book. It's simple read and won't take much time.

Outline:

Part I — Graphical Practice:

1 Graphical Excellence
2 Graphical Integrity
3 Sources of Graphical Integrity and Sophistication

Part II — Theory of Data Graphics:

1 - Data Ink and Graphical Redesign
2 – Chart junk: Vibrations, Grids and Ducks
3 - Data Ink Maximization and Graphical Design
4 - Multifunctioning Graphical Elements
5 - Data Density and Small Multiples
6 - Aesthetics and Technique in Data Graphical Design

Part III — Design for Display of Information
1 - Epilogue



Part I — 1) Graphical Practice: Graphical Excellence:

Tufte’s summarizes by saying, it is a matter of substance, statistics, and of design. It gives the viewer the greatest number of ideas in the shortest time with least amount of ink space. It consists of communicating complex ideas communicated with clarify, precision and efficiency.

- It is about clarity in communicating with precision, efficiency that shows data, induce user to think about substance of visualization, avoid distortion, present many numbers in small space, make large datasets coherent, encourage eye to compare various pieces of data, reveal data at several layers of detail, serve clear purpose, be closely integrated with statistical and verbal description of data.

Graphics reveal about data.

-Every Graph or Visualization should let the user to think about the data, not the methodology or technique
-Time Series Data

Two Greatest Scientist of Modern Graphical Design are J.H Lambert, Swiss German Mathematician and William Playfair, Scottish political economist.
Playfair preferred Graphics.

-Descriptive Chronology is not casual expression.

Charles Minard, the French Engineer who explained Napoleon’s army — combination of data-map and time series.

Most Modern Graphics are relational — x, y that encourages to find out casual relationship.

Part 1 — 2) Graphical Practice: Graphical Integrity:

For many of us, we constantly think of lies when we look at a graphic or statistic.
Around 1960, John Turkey made Graphical Practice respectable.

What is distortion in Data Graphic?

Lie Factor = size of effect shown in graphic/ size of effect in data
If it’s greater than 1.05 and less than .95, then it’s substantial

Show data variation not design variation.

Context is important for Graphical Integrity — compared to what?

Lying Graphic cheapen graphical art everywhere.


The Six principles of Graphical Integrity:

- Representation of numbers present in the graphic should be directly proportional to numerical quantities reported
- Clear, detailed and thorough labeling should be used to defeat graphical distortions and ambiguity
- Show data variations, not design variations
- In time series display, use standardized monetary units
- The number of information carrying dimensions should not exceed number of dimensions in data
- Graphics must not quote data out of context



Part 1 —3) Graphical Practice: Sources of Graphical Integrity and Sophistication

Why do they lie?
-Lack of Quantitive skills, the doctrine that statistical data is boring

Many believe that graphics are there to entertain unsophisticated readers. Japan has the highest use of statistical graphics in their newspaper.

Part II — 1) Theory of Data Graphics: Data Ink and Graphical Redesign:

Data Graphics should draw viewers attention to substance of data. It should form quantitive contents.

Fundamental principle is, “Above all else, show the data.” This is the principle for a theory of data graphics.

Data Ink ratio is data ink/total ink used in graphic. Remember to maximize the data ink ratio devoted to the data. Other side of data ink ratio is to erase non-data-ink, within reason.



There’s five principles in theory of data graphics produce substantial changes in graphical design.
-Above all else show the data
-Maximize the data-ink ratio
-Erase non-data-ink
-Erase redundant data-ink
-Revise and edit


Part II — 1) Theory of Data Graphics: Chartjunk, Vibrations, Grids and Duck

Interior decoration of a graphic produces a lot of raw ink that does not tell the viewer anything new. The Grid might include a lot of chart-junk.

When Graphics are taken over design or styles rather than quantitative data, it is called as Big Duck.

Part II — 2) Theory of Data Graphics: Data Ink Maximization and Graphical Design.

Reducing ink ratio in some of the charts might induce changes.

Part II — 3) Theory of Data Graphics: Multifunctioning Graphical Elements

The Same Ink should be used for more than one graphical purpose, it carries data information and performs a design function usually left to non-data-ink. Data based grid is shrewd graphical devise.

Sometimes, the puzzle and hierarchy of multifunctioning graphical elements can data graphics into visual puzzles, crypto graphical mysteries. Colors sometimes generate graphical puzzles. The shades of gray gives us more easier comprehension. This is the key.

Part II — 4) Theory of Data Graphics: Data Density and Small Multiples

How many statistical graphics take advantage of ability to detect large amounts of information in small space? Let’s begin with empirical measure of graphical performance and data density.

Data density of graphic = number of entries in data matrix / area of data graphic

More information is better than less information. Maximize data density and size of data matrix within reason. High volume data must be designed with care. The cost of chart junk, non-data-ink, and redundant data-ink is even more costly in data rich design. We apply shrink principle, which means graphics can be shrunk way down. Bertin’s crisp and elegant line displays small scale graphics in a single page.

Small Multiples, resemble frame of movies, series of graphs, series combination of variables.



A Well designed Small Multiple will contain:

- inevitability comparative
- deftly multivariate
- shrunken high density graphics
- based on large matrix
- drawn exclusively with data ink
- efficient in interpretation
- often narrative in content.



Part III — Theory of Data Graphics: 1) Aesthetic and Technique in Data Graphical Design:

-Graphical Elegance is often found in simplicity of design and complexity of data

Visually attractive graphics gather power from content. Basic structure for showing data are sentence, table and graphic. Often two or three of this should be combined. Make Complexity accessible.

Graphics should prefer towards horizontal, greater in length than height.
Lines in Data Graphic should be thin. Graphical elements look better when their proportions are in balance.

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