Similarly, the legend titles show that point coloring indicates the birds’ sex and point size indicates the birds’ skull size in milimeters. In this plot, the axis titles clearly indicate that the x axis shows body mass in grams and the y axis shows head length in milimeters. To present an example of a plot where all axes and legends are appropriately labeled and titled, I have taken the blue jay dataset discussed at length in Chapter 12 and visualized it as a bubble plot (Figure 22.3). (Axis titles are often colloquially referred to as axis labels.) Axis and legend titles and labels explain what the displayed data values are and how they map to plot aesthetics. ![]() Just like every plot needs a title, axes and legends need titles as well. Figures with integrated titles, subtitles, and data source statements are appropriate, however, if they are meant to be used as stand-alone infographics or to be posted on social media or on a web page without accompanying caption text. For this reason, in the context of conventional book or article publishing, we do not normally integrate titles into figures. And, if a publication is laid out such that each figure has a regular caption block underneath the display item, then the title must be provided in that block of text. Either the title is integrated into the actual figure display or it is provided as the first element of the caption underneath the figure. The underlying principle is that a figure can have only one title. I do so because the two styles have different application areas, and figures with integrated titles are not appropriate for conventional book layouts. In a direct comparison, you may find Figure 22.2 more attractive than Figure 22.1, and you may wonder why I am choosing the latter style throughout this book. Data sources: Transparency International & UN Human Development ReportĪlternatively, I could incorporate the figure title-as well as other elements of the caption, such as the data source statement-into the main display (Figure 22.2). This figure was inspired by a posting in The Economist online ( 2011). 30.1 Thinking about data and visualizationįigure 22.1: Corruption and human development: The most developed countries experience the least corruption.29.5 Be consistent but don’t be repetitive.28.2 Data exploration versus data presentation.28 Choosing the right visualization software.27.2 Lossless and lossy compression of bitmap graphics.27 Understanding the most commonly used image file formats.26.3 Appropriate use of 3D visualizations.23.1 Providing the appropriate amount of context.20.1 Designing legends with redundant coding.19.3 Not designing for color-vision deficiency.19.2 Using non-monotonic color scales to encode data values.19.1 Encoding too much or irrelevant information.18.1 Partial transparency and jittering.17.2 Visualizations along logarithmic axes.16.3 Visualizing the uncertainty of curve fits.16.2 Visualizing the uncertainty of point estimates.16.1 Framing probabilities as frequencies.14.3 Detrending and time-series decomposition.14.2 Showing trends with a defined functional form.13.3 Time series of two or more response variables.13.2 Multiple time series and dose–response curves.13 Visualizing time series and other functions of an independent variable.12 Visualizing associations among two or more quantitative variables.10.4 Visualizing proportions separately as parts of the total.10.3 A case for stacked bars and stacked densities.9.2 Visualizing distributions along the horizontal axis. ![]() 9.1 Visualizing distributions along the vertical axis.9 Visualizing many distributions at once. ![]()
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