![]() However, making points partially transparent is not always sufficient to solve the issue of overplotting. Because points have been made partially transparent, points that lie on top of other points can now be identified by their darker shade. However, these black dots are fully occluded by yellow dots, so that it looks like there are no four-wheel drive cars with a 2.0 liter engine.įigure 18.2: City fuel economy versus engine displacement. Moreover, the dataset contains two four-wheel drive cars with 2.0 liter engines, which are represented by black dots. Therefore, in Figure 18.1 these 21 cars are represented by only four distinct points, so that 2.0 liter engines appear much less popular than they actually are. For example, there are 21 cars total with 2.0 liter engine displacement, and as a group they have only four different fuel economy values, 19, 20, 21, or 22 mpg. Due to this rounding, many car models have exactly identical values. Engine displacement is measured in liters and is rounded to the nearest deciliter. In this dataset, fuel economy is measured in miles per gallon (mpg) and is rounded to the nearest integer value. Our dataset contains fuel economy during city driving and engine displacement for 234 popular car models released between 19 (Figure 18.1). We first consider a scenario with only a moderate number of data points but with extensive rounding. 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. ![]()
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