Small multiples are a powerful tool to visualize very large amounts of data at once. Further, and among the women who died nearly all were traveling in 3rd class. We see clearly that most men died and most women survived. This visualization provides an intuitive and highly interpretable visualization of the fate of the Titanic passengers. The columns and rows are labeled, so it is immediately clear which of the six plots corresponds to which combination of survival status and class.įigure 21.1: Breakdown of passengers on the Titanic by gender, survival, and class in which they traveled (1st, 2nd, or 3rd). The result is six bar plots, which we arrange in two columns (one for passengers who died and one for those who survived) of three rows (one for each class) (Figure 21.1). Within each of these six slices of data, there are both male and female passengers, and we can visualize their numbers using bars. We can subdivide this dataset by the class in which each passenger travelled and by whether a passenger survived or not. More recently, this technique is also sometimes referred to as “faceting”, named after the methods that create such plots in the widely used ggplot2 plot library (e.g., facet_grid(), see Wickham ( 2016)).Īs a first example, we will apply this technique to the dataset of Titanic passengers. Columns, rows, or individual panels in the grid are labeled by the values of the data dimensions that define the data slices. Regardless of terminology, the key idea is to slice the data into parts according to one or more data dimensions, visualize each data slice separately, and then arrange the individual visualizations into a grid. Cleveland 1993 Becker, Cleveland, and Shyu 1996). An alternative term, “trellis plot”, was popularized around the same time by Cleveland, Becker, and colleagues at Bell Labs (W. The term “small multiple” was popularized by Tufte ( 1990). However, when preparing such figures, there are a few issues we need to pay attention to, such as appropriate axis scaling, alignment, and consistency between separate panels. In general, these figures are intuitive and straightforward to interpret. We have encountered both types of multi-panel figures in many places throughout this book. Compound figures consist of separate figure panels assembled in an arbitrary arrangement (which may or may not be grid based) and showing entirely different visualizations, or possibly even different datasets. Each panel shows a different subset of the data but all panels use the same type of visualization. Small multiples are plots consisting of multiple panels arranged in a regular grid. There are two distinct categories of such figures: 1. These are figures that consist of multiple figure panels where each one shows some subset of the data. To visualize such datasets, it can be helpful to create multi-panel figures. When datasets become large and complex, they often contain much more information than can reasonably be shown in a single figure panel. 30.1 Thinking about data and visualization.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.
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