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Data Visualisation Guide

Visualising with text: visual text properties

4 minutes read

Visualising text

Many of the examples of visualisations that use text do more than just arranging text to let patterns emerge from a data set. Many also use text properties like font size, font weight and colour to encode additional dimensions of the data.

Let’s return to the stem-and-leaf plot like visualisation that arranges the US states code according to poverty rate in each state.

A stem-and-leaf plot of US states poverty rates, with varying font colour and font weights

Source: Visualizing with Text, by Richard Brath

The legends below the plot explain how the font weight and colours are used to encode additional dimensions in the plot: a higher font weight means a higher population density, and a darker colour means a higher gun murder rate.

In his book Visualizing with Text, Richard Brath shows that text has a much wider palette of visual attributes than traditional visual elements like circles, bars and lines have. In the figure below, the top row contains the visual attributes that are shared between text and traditional visual elements. The bottom row shows the visual attributes that are exclusive to text.

10 properties of text that can be used to encode data: position, size, hue, orientation, brightness, weight, oblique, case, typeface and underline

Source: Visualizing with Text, by Richard Brath

The plot below uses text colour in a smart way. It shows the sex ratio of workers in the sectors of the American economy by colouring parts of the name of the sector in purple for the share of women active in the sector, and in green for the share of men.

A word cloud with the names of sectors coloured in two colours representing the proportion of men and women in each sector

Source: @geokaramanis

In the visualisation above, the font size is determined by the number of characters in each sector name (names with less characters have a larger font size to make them fit). An extended version of this chart could map the font size of the sectors to the share of each sector in the total economy.

Below is another example of a “text stem-and-leaf plot”, showing data about sectors in the Standard & Poor’s index. This time, additional data is encoded in the colours and the casing of the sector codes. Sectors with negative values have white text on a coloured background, while sectors with positive values have the colours applied to the text itself. Lower case text means low volatility, upper case means high volatility.

Stacked 4 letter codes with varying colour, casing and background colours

Source: Visualizing with Text, by Richard Brath

Related pages

Visualising with text: stem-and-leaf plots

Visualising with text: scattered labels

Visualising with text: stacked words

Visualisation of text: word clouds

Visualisation of text: page thumbnails

Data dense time series

Visualising text