This blog post is the second in a series of pieces around data visualisation that will be shared on the SAGE Campus blog throughout May. They have been created by Andy Kirk, a UK-based data visualisation specialist, design consultant, training provider, lecturer, author, speaker, researcher, editor of an award-winning website, and course instructor on Introduction to Data Visualisation.
Every week in May we will be sharing Andy’s observations from his 'little’ of visualisation design blog series. This week’s post covers labelling. Stay tuned throughout May for further posts on annotations, axis, and photo-imagery.
This is part of a series of posts about the 'little of visualisation design', respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I'm going to focus on just one small matter - a singular good or bad design choice - as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I'm just commenting on one.
The 'little' of this design concerns the creative placement of chart labels across a panel of small multiples. The piece in question comes from Zachary Labe looking at the last 70-years of autumn temperature anomalies in the Arctic.
The 'little' of this next design concerns the sensible positioning of categorical labels. This Daily Chart, by the Economist's data team, offers a view of the identified political persuasion of people living in selected swing states of America. The chart is a variation of the connected dot plot, with a separate row for each state.
The specific example here comes from a chart published by the BBC in this article about the slowing of the UK housing market. The line chart shows the upwards and downwards trends of two house price trackers changing over time, simple enough stuff until you glance at the x-axis labelling at which point it is quite easy to becoming confused about judging the 'when'.
SAGE Campus Bytsize courses are a series of short courses that teach core data science skills to people who are eager to learn, but short on time.
In Bytesize: Data Visualization you will learn effective presentation methods for various data types and variables, and how to create your own visualizations in Jupyter notebooks in Python or R. Find out more.