Small multiples are series of the same small graph repeated in one visual. He claims that most graphs can be shrunk way down without losing legibility or information. One way of achieving this he claims is through the Shrink Principle. He wants us to maximize data density and the size of the data matrix within reason. The data density of a graph is the proportion of the total size of the graph that is dedicated displaying data. This is according to Tufte possibly the worst graph ever: He calls out moiré vibration, heavy grids and self-promoting graphs that are used to demonstrate the graphic ability of the designer rather than display the data. Tufte has a whole chapter dedicated to what he calls Chartjunk – the excessive and unnecessary use of graphical effects in graphs. This graph would have a data-ink ratio of 1. The above is an electroencephalogram – a graph that records the electrical activity from the brain. Here is an example with a very high data-ink ratio. Tufte tests these principles on a whole range of examples to come up with a wide range of fresh designs that dramatically improve the legibility of the graphs. He puts forward the following 5 principles related to data ink : He put forward the data-ink ratio which is calculated by 1 minus the proportion of the graph that can be erased without loss of data-information. Tufte claims that good graphical representations maximize data-ink and erase as much non-data-ink as possible. A numerical change of 53% is represented by a graphical change (size of horizontal lines) of 783%.ĭata Ink is the ink on a graph that represents data. Here is one of Tufte’s examples of a graph with low graphical integrity.Īccording to Tufte the Lie Factor of this graph is 14.8. Graphics must not quote data out of context. The number of information carrying (variable) dimensions depicted should not exceed the number of dimensions in the data.In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units.Show data variation, not design variation.Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.Write out explanations of the data on the graph itself.The representation of numbers, as physically measured on the surface of the graph itself, should be directly proportional to the numerical quantities represented.Tufte goes on to list the following 6 principles of graphical integrity: If the Lie Factor is greater than 1 the graph overstates the effect. He does this by calculating a graph’s Lie Factor which can be calculated by dividing the size of the effect shown in the graphic by the size of the effect in the data. Tufte shows a whole range of graphs that either over or under represent the effects in the data. Visual representations of data must tell the truth. You have to admire the man’s determination! In this book Tufte laid out his key data visualization principles. He therefore decided to publish it himself, having to take a 2 nd mortgage to finance it. Tufte wanted its design to follow the principles it put forward. It is packed with examples of best and worst practices in the history of data visualization.Įven the book itself is designed beautifully. It made the top 100 non-fiction books of the 20 th century on. It is probably the most important book ever written on data visualization. His most famous work – The Visual Display of Quantitative Information was published in 1983. There is a very annoying graph that keeps popping up in Keynote presentations.Įdward Tufte invented the concept of Chart Junk.
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