Book Review: Visualizing with Text, by Richard Brath


Visualizing with Text (2020), by Richard Brath (Partner at Uncharted Software, Inc.) is an intriguing addition to the data visualization literature. While most data visualization books gloss over the topic of text, Brath’s fertile mind fully embraces the subject. His clever use of text can improve data visualizations and his use of visualization techniques can enhance traditional text. His cross-disciplinary approach offers a fresh perspective that expands the range of design possibilities.


The use of text across disciplines

Brath introduces the topic by showing the breadth of his vision with text examples in the areas of: cartography, typography, tables, science classification and notation, code editors, alphanumeric charts, art and poetry, graphic design and advertising, comics, postmodern text, and data visualization. It is simply not possible to view these examples without thinking of new ways to incorporate text in design.

Brath highlights the advantages of encoding data with text. These include fast decoding, reduced cognitive load, and easy recognition. As an example, use of a legend on a chart requires the reader to go back and forth between the graphic and the legend to understand the chart. When it is possible to directly incorporate the legend into the graphic, the cognitive load on the reader is reduced.


A framework for using text

Brath identifies the differences between text that is read and graphics. Reading text requires focus to understand a sequential string of words. Visualization leverages our pre-attentive capabilities, where we perceive patterns before consciously focusing. The author bridges the gap between the two modes with a unifying framework.

Like most symbols in data visualization, text can be displayed using the visual variables, such as position, size, shape, color, or brightness. Text greatly expands this range of options. It has the additional elements of typeface, weight, case, oblique angle (italic), underlines, width, x-height, serifs, stress, contrast, and angle. In addition, there are non-type visual attributes like background color, gradients, superimposition, outline, and drop shadows.

The concept of text scope further expands the design possibilities. Brath defines text scope as a spectrum from characters, to syllables, words, phrases, sentences, paragraphs, and documents. We could also add collections of documents to this list. He finishes his framework with a discussion of three text layouts: prose, tables, and lists. The combination of visual variables, text variables, text non-type visual attributes, scope, and text layout form the foundation for his ideas on visualizing text.


Text in Data Visualizations

Brath moves methodically through a series of examples showing how text can be incorporated with data visualizations. He starts with the introduction of coded and full text labels as point marks. The advantage of this approach is that the text is frequently identifiable without resorting to a legend or tooltips.

His example of a scatterplot of National Parks shows how park names can be included in a visualization that also shows the number of visitors, park size, age, and region. Each park name is easily identifiable and integrated with other information. When situations become more complicated, Brath identifies techniques to accommodate difficult labeling cases, like large numbers of labels or very long labels.


Scatterplot of U.S. National Parks showing multiple variables.
Scatterplot of U.S. National Parks showing multiple variables.

Brath introduces his discussion of distributions with stem and leaf plots, using examples from rail timetables, poverty by state, and stock market sectors. He modifies traditional stem and leaf plots by using text, rather than numbers, to expand the leaves.

In an example of U.S. population density versus gun murders, he incorporates two variables in the text using font weight and color, while using the two-letter state abbreviations to identify the states. He makes this possible by rounding the poverty rates to the nearest percent.


Stem and Leaf Plot of U.S. state poverty, population density and gun murders.
Stem and Leaf Plot of U.S. state poverty, population density and gun murders.

Another variation of the stem and leaf plot shows the adjectives associated with characters in the Grimms’ fairy tales. The stem lists the characters, while the leaves show adjectives describing the characters, with adjective frequency decreasing from left to right with decreasing text boldness.


Stem and leaf plot of characters and adjectives describing them.