Introduction

Shortly before the French Revolution and deeply embedded in the eighteenth century’s fascination with numbers, statistics and tables, the English writer, engineer, and somewhat disreputable figure William Playfair published 1786 his Commercial and Political Atlas, a book in which statistical data were, for the first time, represented in graphical form. The Atlas marks the birth of the line, bar, and pie charts, and with them, the beginning of data visualization as we know it today.

Nevertheless, it went largely unnoticed and had little influence on how quantitative information was presentend in the 19th century. It would take more than 150 years for data visualisation to become part of the informational landscape in books and newspapers.

Although Playfair’s graphs have often been discussed in the history of statistics, design, and data visualization, a historical study of his sources, intellectual aims and the possible reasons why his innovative method of conveying quantitative information was long overlooked has yet to be written. ^[3]

In an effort to understand the historical reasons for Playfair’s limited impact on his aimed audience, this Jupyter Book examines his works, reconstructs his graphs, and explores his historical sources, interpreting the numerical data that underpinned his visualizations. The motivation behind it is to demonstrate how data visualisation techniques can enhance our understanding of quantitative sources, helping historians to answer their question.

If you are a historian and less a digital humanist, don’t worry this Jupyter Book is designed especially for you. The code is explained step by step and serves purely as a tool to explore the subject. If you simply wish to see the results and visualisations, you can press the play button to run the examples. The primary focus is on studying Playfair’s graphs, not on learning to code ;)

If you are a designer or data analyst interested in a more historically informed approach to Playfair, you are equally welcome. Please feel free to reach out with any insights or ideas about analyzing and visualising the data.