![]() ![]() Head length and skull size tend to be correlated, but there are some birds with unusually long or short bills given their skull size. Head-length measurements include the length of the bill while skull-size measurements do not. The birds’ sex is indicated by color, and the birds’ skull size by symbol size. The bird with the longest head falls close to the maximum body mass observed, and the bird with the shortest head falls close to the minimum body mass observed.įigure 12.3: Head length versus body mass for 123 blue jays. (Note the terminology: We say that we plot the variable shown along the y axis against the variable shown along the x axis.) The dots form a dispersed cloud (hence the term scatter plot), yet undoubtedly there is a trend for birds with higher body mass to have longer heads. In this plot, head length is shown along the y axis, body mass along the x axis, and each bird is represented by one dot. ![]() To explore these relationships, I begin with a plot of head length against body mass (Figure 12.1). For example, birds with longer bills would be expected to have larger skull sizes, and birds with higher body mass should have larger bills and skulls than birds with lower body mass. We expect that there are relationships between these variables. ![]() The dataset contains information such as the head length (measured from the tip of the bill to the back of the head), the skull size (head length minus bill length), and the body mass of each bird. I will demonstrate the basic scatter plot and several variations thereof using a dataset of measurements performed on 123 blue jay birds. 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.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.8.1 Empirical cumulative distribution functions.8 Visualizing distributions: Empirical cumulative distribution functions and q-q plots.7.2 Visualizing multiple distributions at the same time.7 Visualizing distributions: Histograms and density plots.3.3 Coordinate systems with curved axes.2.2 Scales map data values onto aesthetics.2 Visualizing data: Mapping data onto aesthetics.Thoughts on graphing software and figure-preparation pipelines. ![]()
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