Index
A
- absolute number vs. percentages, A Case for Stacked Bars and Stacked Densities
- accent color scales, Color as a Tool to Highlight
- Action–Background–Development–Climax–Ending story format, What Is a Story?
- aesthetics, Visualizing Data: Mapping Data onto Aesthetics-Scales Map Data Values onto Aesthetics, Data Exploration Versus Data Presentation
- age pyramids, Visualizing Multiple Distributions at the Same Time
- Albers projection, Projections
- amounts, visualizing, Amounts, Visualizing Amounts-Dot Plots and Heatmaps
- anticorrelated variables, Correlograms
- associations, visualizing, Visualizing Associations Among Two or More Quantitative Variables-Paired Data
- automation, Thoughts on Graphing Software and Figure-Preparation Pipelines
- axes, Cartesian Coordinates-Coordinate Systems with Curved Axes
- axis labels, Axis and Legend Titles
B
- background grids, Providing the Appropriate Amount of Context, Background Grids-Background Grids
- along both axes, in scatterplots, Background Grids
- for figures about change in y axis values, Background Grids
- grid lines running perpendicular to key variable of interest, Background Grids
- horizontal reference lines, Background Grids
- in bar plots, Background Grids
- in ggplot2 software, Background Grids
- in scatterplots of paired data, using diagonal line with, Paired Data
- recommendations on use, Summary
- removing gray background and grid lines, Background Grids
- using diagonal line instead of in scatterplots of paired data, Paired Data
- using gray background and grid lines, Background Grids
- backgrounds
- bad figures, Ugly, Bad, and Wrong Figures
- balancing data and context, Balance the Data and the Context-Summary
- bandwidth, Visualizing a Single Distribution
- bar charts, Bar Plots
- bar plots
- background grid in, Background Grids
- flawed approach to visualizing nested proportions, Nested Proportions Gone Wrong
- limitations of stacked bars, Mosaic Plots and Treemaps
- line drawings in, Avoid Line Drawings
- omitting axes, Axis and Legend Titles
- on log scales, Visualizations Along Logarithmic Axes
- 3D bars, problems with, Avoid Gratuitous 3D, Avoid 3D Position Scales
- visualizing amounts, Visualizing Amounts-Bar Plots
- visualizing proportions, A Case for Pie Charts
- vs. pie charts, Direct Area Visualizations
- with error bars, Visualizing the Uncertainty of Point Estimates
- bars
- Bayesian statistics, Visualizing the Uncertainty of Point Estimates
- bibliography, Annotated Bibliography-Books on Broadly Related Topics
- binning data
- bitmap graphics, Bitmap and Vector Graphics-Bitmap and Vector Graphics
- blue–yellow color-vision deficiency, Not Designing for Color-Vision Deficiency, Designing Legends with Redundant Coding
- boxplots, Distributions
- bubble charts, x–y relationships
- visualizing associations among quantitative variables, Scatterplots
C
- captions, Figure Titles and Captions-Figure Titles and Captions
- Cartesian coordinates, Cartesian Coordinates-Cartesian Coordinates
- cartograms, Geospatial Data, Visualizing Geospatial Data, Cartograms-Cartograms
- categorical data, Aesthetics and Types of Data
- choropleths, Color to Represent Data Values
- color, Aesthetics and Types of Data
- as a tool to distinguish, Color as a Tool to Distinguish-Color as a Tool to Distinguish
- as a tool to highlight, Color as a Tool to Highlight-Color as a Tool to Highlight
- as representation of data values, Color to Represent Data Values-Color to Represent Data Values
- common pitfalls of use, Common Pitfalls of Color Use-Not Designing for Color-Vision Deficiency
- encoding categorical variable by bar color, Grouped and Stacked Bars
- fundamental uses in data visualizations, Color Scales
- in photographic images, Lossless and Lossy Compression of Bitmap Graphics
- line drawings and, Avoid Line Drawings
- manipulations, packages used, Technical Notes
- mapping data values onto colors in heatmaps, Dot Plots and Heatmaps
- redundant coding, Redundant Coding
- (see also redundant coding)
- using gray background for figures, Background Grids
- using in visualizations of geospatial data, Geospatial Data
- color scales, Scales Map Data Values onto Aesthetics, Color Scales-Color as a Tool to Highlight
- color-vision deficiency, Not Designing for Color-Vision Deficiency-Not Designing for Color-Vision Deficiency, Redundant Coding-Designing Figures Without Legends
- ColorBrewer color scales, Color as a Tool to Distinguish, Color to Represent Data Values
- compound figures, Multipanel Figures, Compound Figures-Compound Figures
- compression, lossless and lossy, of bitmap graphics, Lossless and Lossy Compression of Bitmap Graphics-Lossless and Lossy Compression of Bitmap Graphics
- confidence bands, Uncertainty, Visualizing Uncertainty, Visualizing the Uncertainty of Curve Fits
- confidence interval, Visualizing the Uncertainty of Point Estimates
- confidence levels, Visualizing the Uncertainty of Curve Fits
- confidence strips, Uncertainty
- conformal projection, Projections
- connected scatterplots, x–y relationships
- consistency, achieving without being repetitive, Be Consistent but Don’t Be Repetitive-Be Consistent but Don’t Be Repetitive
- content and design, separation of, Separation of Content and Design-Separation of Content and Design
- context in figures, Balance the Data and the Context
- (see also balancing data and context)
- continuous data, Aesthetics and Types of Data
- contour lines, x–y relationships, Contour Lines-Contour Lines
- coordinate systems and axes, Coordinate Systems and Axes-Coordinate Systems with Curved Axes
- coordinates, Aesthetics and Types of Data
- correlated or anticorrelated variables, Correlograms
- correlation coefficients
- correlograms, x–y relationships
- credible intervals, Visualizing the Uncertainty of Point Estimates
- cumulative densities, Distributions
- cumulative distribution, Empirical Cumulative Distribution Functions
- (see also empirical cumulative distribution functions)
- curve fits, visualizing uncertainty of, Visualizing the Uncertainty of Curve Fits-Hypothetical Outcome Plots
- CVD (see color-vision deficiency)
D
- data
- data exploration vs. data presentation, Data Exploration Versus Data Presentation-Separation of Content and Design
- data source statements, Figure Titles and Captions
- data transformations (for 3D visualizations), Avoid 3D Position Scales
- data values, using color to represent, Color to Represent Data Values-Color to Represent Data Values, Geospatial Data
- data visualization software
- data–ink ratio, Providing the Appropriate Amount of Context
- dates and time, Aesthetics and Types of Data
- decomposition of time series, Detrending and Time-Series Decomposition
- density plots
- design and content, separation of, Separation of Content and Design-Separation of Content and Design
- deterministic construal errors, Visualizing the Uncertainty of Point Estimates
- detrending and time-series decomposition, Detrending and Time-Series Decomposition-Detrending and Time-Series Decomposition
- deuteranomaly/deuteranopia, Not Designing for Color-Vision Deficiency, Designing Legends with Redundant Coding
- dimension reduction, Dimension Reduction-Paired Data
- direct area visualizations, Direct Area Visualizations-Direct Area Visualizations
- direct labeling
- directory of visualizations, Directory of Visualizations-Uncertainty
- discrete data, Aesthetics and Types of Data
- discrete outcome visualization, Framing Probabilities as Frequencies
- distinguishing discrete items or groups, using color, Color as a Tool to Distinguish-Color as a Tool to Distinguish
- distributions, visualizing, Distributions
- diverging color scales, Color to Represent Data Values, Not Designing for Color-Vision Deficiency
- dose-response curves, visualizing with multiple time series, Multiple Time Series and Dose–Response Curves-Time Series of Two or More Response Variables
- dot plots
- dviz.supp (R package), Technical Notes
E
- empirical cumulative distribution functions (ECDFs), Empirical Cumulative Distribution Functions-Empirical Cumulative Distribution Functions
- Environmental Systems Research Institute (ESRI), Projections
- equal-area projection, Projections
- equator, Projections
- error bars, Uncertainty, Visualizing Distributions Along the Vertical Axis, Visualizing Uncertainty
- estimates, Visualizing the Uncertainty of Point Estimates
- visualizing, Uncertainty
- (see also error bars; uncertainty, visualizing)
- European Petroleum Survey Group (EPSG), Projections
- exploration of data (see data exploration vs. data presentation)
- eye, Preface
- eye plots, Uncertainty, Visualizing the Uncertainty of Point Estimates
G
- Gaussian kernel, Visualizing a Single Distribution
- generalized additive models (GAMs), Smoothing
- geographic regions
- geospatial data, Visualizing Geospatial Data-Cartograms
- ggplot2, Preface
- Goode hemolosine projection, Projections
- graded confidence bands, Uncertainty, Visualizing the Uncertainty of Curve Fits
- graded error bars, Uncertainty, Visualizing the Uncertainty of Point Estimates
- grids, background (see background grids)
- grouped bar plots, Grouped and Stacked Bars
- grouping variables, Visualizing Many Distributions at Once
H
- half-eyes, Uncertainty, Visualizing the Uncertainty of Point Estimates
- hand-drawn or manually post-processed figures, Data Exploration Versus Data Presentation
- hatching, Avoid Line Drawings
- heatmaps, Amounts
- hex bins, x–y relationships
- highlighting specific elements in data, using color, Color as a Tool to Highlight-Color as a Tool to Highlight
- highly skewed distributions, Highly Skewed Distributions-Quantile-Quantile Plots
- histograms, Distributions, Framing Probabilities as Frequencies
- historical texts, Historical Texts
- HOPs (see hypothetical outcome plots)
- human perception
- hypothetical outcome plots, Hypothetical Outcome Plots-Hypothetical Outcome Plots
L
- labels
- legends
- levels (of a factor), Aesthetics and Types of Data, Scales Map Data Values onto Aesthetics
- line drawings, avoiding, Avoid Line Drawings-Avoid Line Drawings
- line graphs, x–y relationships
- linear axes, visualizations along, Visualizations Along Linear Axes-Visualizations Along Logarithmic Axes
- linear relationships between variables, Showing Trends with a Defined Functional Form
- linear scales, Nonlinear Axes
- linear-log plots, Showing Trends with a Defined Functional Form
- lines, width of, Aesthetics and Types of Data
- LOESS (locally estimated scatterplot smoothing), Smoothing
- log (logarithmic) scales, Nonlinear Axes, Showing Trends with a Defined Functional Form
- log-linear plots, Showing Trends with a Defined Functional Form
- log-log plots, Showing Trends with a Defined Functional Form
- log-normal distributions, Highly Skewed Distributions, Quantile-Quantile Plots
- logarithmic axes, visualizations along, Visualizations Along Logarithmic Axes-Visualizations Along Logarithmic Axes
- lossless compression, Lossless and Lossy Compression of Bitmap Graphics
- lossy compression, Lossless and Lossy Compression of Bitmap Graphics
P
- paired data, x–y relationships, Paired Data-Paired Data
- parallel sets, Proportions
- parameters, Visualizing the Uncertainty of Point Estimates
- partial densities, visualizing proportions, Visualizing Proportions Separately as Parts of the Total
- partial transparency, using for overlapping dots, Partial Transparency and Jittering
- PDF files
- percentages vs. absolute number, A Case for Stacked Bars and Stacked Densities
- phase portraits, Time Series of Two or More Response Variables
- (see also connected scatterplots)
- pie charts
- plotting software, Preface
- (see also data visualization software)
- focus of tutorials, Preface
- PNG files, Lossless and Lossy Compression of Bitmap Graphics
- point estimates, Visualizing the Uncertainty of Point Estimates-Visualizing the Uncertainty of Curve Fits
- points, Handling Overlapping Points
- polar coordinates, Coordinate Systems with Curved Axes
- poles, Projections
- population, Visualizing the Uncertainty of Point Estimates
- position, Aesthetics and Types of Data
- position scales, Scales Map Data Values onto Aesthetics
- post-processing draft figures, Data Exploration Versus Data Presentation
- posterior, Visualizing the Uncertainty of Point Estimates
- posterior distributions, Visualizing the Uncertainty of Point Estimates
- power-law distributions, Highly Skewed Distributions
- presentation of data (see data exploration vs. data presentation)
- principal component analysis (PCA), Dimension Reduction
- principal components (PCs), Dimension Reduction
- principle of proportional ink, The Principle of Proportional Ink
- (see also proportional ink, principle of)
- prior, Visualizing the Uncertainty of Point Estimates
- probability distributions, Framing Probabilities as Frequencies
- probability, framing probabilities as frequencies, Framing Probabilities as Frequencies-Framing Probabilities as Frequencies
- programmatically generated figures (see automation)
- programming books for data visualization, Programming Books
- projections (map), Projections-Projections
- proportional ink, principle of, The Principle of Proportional Ink-Direct Area Visualizations, Providing the Appropriate Amount of Context
- proportions, visualizing, Proportions, Visualizing Proportions-Visualizing Proportions Separately as Parts of the Total
- protanomaly/protanopia, Not Designing for Color-Vision Deficiency, Designing Legends with Redundant Coding
- protein data visualization, Appropriate Use of 3D Visualizations
R
- R language, Thoughts on Graphing Software and Figure-Preparation Pipelines
- R Markdown, Technical Notes
- rainbow scale, Using Nonmonotonic Color Scales to Encode Data Values
- raster graphics (see bitmap graphics)
- ratios
- redundant coding, Redundant Coding-Designing Figures Without Legends
- red–green color-vision deficiency, Not Designing for Color-Vision Deficiency, Designing Legends with Redundant Coding
- references, References-References
- related topics, books on, Books on Broadly Related Topics
- relationships (x–y), visualizing, x–y relationships
- repeatability, Reproducibility and Repeatability
- repetitiveness, avoiding while being consistent, Be Consistent but Don’t Be Repetitive-Be Consistent but Don’t Be Repetitive
- reproducibility, Reproducibility and Repeatability
- response variable, Visualizing Many Distributions at Once
- ridgeline plots, Distributions, Uncertainty
- rotated labels, Bar Plots
S
- sample, Visualizing the Uncertainty of Point Estimates
- sample size, Visualizing the Uncertainty of Point Estimates
- saturation of colors, Encoding Too Much or Irrelevant Information, Designing Legends with Redundant Coding
- scale-free distributions, Highly Skewed Distributions
- scales, Scales Map Data Values onto Aesthetics-Scales Map Data Values onto Aesthetics
- scatterplots
- background grids along both axes, Background Grids
- connected, x–y relationships, Time Series of Two or More Response Variables
- drawing linear trend lines on top of points, Showing Trends with a Defined Functional Form
- line drawings in, Avoid Line Drawings
- locally estimated scatterplot smoothing (LOESS), Smoothing
- of paired data, using diagonal line instead of background grid, Paired Data
- 3D, and 3D position scales, Avoid 3D Position Scales
- 3D, problems with, Avoid 3D Position Scales
- using direct labeling instead of legends, Designing Figures Without Legends
- visualizations of large numbers of points, x–y relationships
- visualizing associations among quantitative variables, Scatterplots-Correlograms
- visualizing paired data, Paired Data
- with error bars, Visualizing the Uncertainty of Point Estimates
- seasonal decomposition of time series by LOESS, Detrending and Time-Series Decomposition
- sequential color scales, Color to Represent Data Values, Using Nonmonotonic Color Scales to Encode Data Values
- shading
- shape, Aesthetics and Types of Data
- shape scales, Scales Map Data Values onto Aesthetics
- significant differences, Visualizing the Uncertainty of Point Estimates
- simplicity, disadvantage of, Make Your Figures Memorable
- sina plots, Distributions, Visualizing Distributions Along the Vertical Axis
- size, Aesthetics and Types of Data
- size scales, Scales Map Data Values onto Aesthetics
- slopegraphs, Paired Data
- small multiples, Small Multiples-Small Multiples
- smoothing, Smoothing-Smoothing
- source code for this book, Technical Notes
- splines, Smoothing
- square-root scales, Nonlinear Axes
- stacked bar plots, Grouped and Stacked Bars
- stacked densities
- stacked histograms, Distributions
- standard deviation, Visualizing the Uncertainty of Point Estimates
- standard error, Visualizing the Uncertainty of Point Estimates
- statistical sampling, Visualizing the Uncertainty of Point Estimates
- statistics texts, Statistics Texts
- stories
- story arc, What Is a Story?
- strip charts, Distributions
T
- tables, Tables-Tables
- caption location relative to display items, Tables
- example plots with, Tables
- formatting in figures, Tables
- horizontal or vertical lines, causing visual clutter, Tables
- technical notes, Technical Notes
- telling a story and making a point, Telling a Story and Making a Point-Be Consistent but Don’t Be Repetitive
- text, Aesthetics and Types of Data
- themes, Separation of Content and Design
- thinking about data and visualization, books on, Thinking About Data and Visualization
- 3D (see under Symbols)
- TIFF files, Lossless and Lossy Compression of Bitmap Graphics
- time series, Visualizing Time Series and Other Functions of an Independent Variable-Time Series of Two or More Response Variables, Visualizing Trends
- titles
- topography, showing in 3D, Appropriate Use of 3D Visualizations
- transparency
- transverse Mercator projection, Projections
- treemaps, Proportions
- trellis plots, Small Multiples
- (see also small multiples)
- trends, visualizing, Visualizing Trends-Detrending and Time-Series Decomposition
- tritanomaly/tritanopia, Not Designing for Color-Vision Deficiency, Designing Legends with Redundant Coding
- 2D (see under Symbols)
U
- ugly, bad, and wrong figures, Ugly, Bad, and Wrong Figures
- uncertainty, visualizing, Uncertainty, Visualizing Uncertainty-Hypothetical Outcome Plots
- unordered factors, Scales Map Data Values onto Aesthetics
- USA, Albers projection map of, Projections
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