1  Why You Should Read This Book

Data science is a growth area in industry, public service and academia. Although not everybody needs to become a professional data scientist, we should all strive to become critical consumers of data that are presented to us by news media, governments and lobby groups. If you want to take the leap from being a consumer to being a mindful producer of data analysis and visualisation, I invite you to join me on a journey from basic programming to publication-ready infographics.

1.1 Finding programmatic solutions for data analysis and visualisation problems

Data analysis is the process of exploring, transforming and modelling data to discover useful information, summarise the discoveries and draw informed conclusions. Data visualisation is the graphical representation of information and data. Data visualisation is also the name of a field of research that aims to develop effective visual techniques for data analysis at various stages (exploration, interpretation and reporting).

The purpose of this book is to teach data analysis and visualisation in a hands-on manner. Often, data need to be transformed before they are ready to be presented in visual form. We will learn how to apply the necessary transformations with computer programs. If you have worked with spreadsheet software before (e.g. Microsoft Excel^® or Google Sheets^®), you already have a basic understanding of how to store and represent data on a computer. However, spreadsheet software has limitations when data management tasks need to be automated (e.g. for producing automated reports whenever a data set is updated). Spreadsheet software also has limited support for creating bespoke customised infographics. By the end of this book, you will have learned the programming language R, which offers a principled, customisable alternative to spreadsheet software.

1.2 Becoming a responsible producer of data visualisation

Data visualisation has a long history. Maps of the night sky are among the earliest attempts by humans to represent data (positions of stars and their brightness) in graphical form, dating back at least to 1534 BC (Spaeth, 2000). While early approaches to data visualisation were mostly ad hoc, Renaissance mathematicians began to systematically describe how to present data in graphical form. For example, René Descartes popularised one of the cornerstones of modern data visualisation in 1637: the two-dimensional coordinate system that we now refer to as the ‘Cartesian’ coordinate system in his honour (Hatfield, 2018). On the basis of Cartesian coordinates, the Scottish engineer William Playfair invented many types of diagrams that are still in common use today such as bar charts in 1786 and pie charts in 1801 (Friendly and Denis, 2001).

Thanks to advances in computer technology, we are currently experiencing a proliferation of infographics, both in quantity and variety. However, not every diagram produced by a computer is automatically well crafted. I now highlight some common problems with infographics encountered in the wild.

Friendly, M. and Denis, D.J. (2001) “Milestones in the history of thematic cartography, statistical graphics, and data visualization.” https://www.datavis.ca/milestones/.

Hatfield, G. (2018) “René Descartes,” in E.N. Zalta (ed.) The Stanford Encyclopedia of Philosophy. Summer 2018. Metaphysics Research Lab, Stanford University.

Spaeth, O.V. (2000) “Dating the Oldest Egyptian Star Map,” Centaurus, 42(3), pp. 159–179. Available at: https://doi.org/10.1034/j.1600-0498.2000.420301.x.