R Visualizations Derive Meaning from Data
This book shows how to do data visualization using the R data analysis language. The emphasis is on how to, with examples from many R packages across a wide range of visualizations. The book features both a review of the highly capable ggplot2 visualization package, as well as a rethinking of how to...
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| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Milton
CRC Press
2020
CRC Press LLC Chapman & Hall |
| Edition: | 1 |
| Subjects: | |
| ISBN: | 1138599638, 9781138599635, 9781032243276, 1032243279 |
| Online Access: | Get full text |
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Table of Contents:
- Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- 1. Visualize Data -- 1.1 Introduction -- 1.1.1 Visualization and Analytics -- 1.1.2 Open-Source Software for Data Visualization -- 1.2 Data -- 1.2.1 R Objects -- 1.2.2 Employee Data Example -- 1.2.3 Types of Variables -- 1.2.4 Read Data -- 1.2.5 Variable Labels -- 1.2.6 Categorical Variables as Factors -- 1.2.7 Save the Data Frame -- 2. Visualization Quick Start -- 2.1 Visualization Systems -- 2.1.1 Relative Advantages of ggplot2 and lessR -- 2.1.2 Grayscale -- 2.2 Distribution of a Categorical Variable -- 2.2.1 Bar Chart of a Single Variable -- 2.2.2 Bar Charts of Multiple Variables -- 2.3 Distribution of a Continuous Variable -- 2.3.1 Default Histogram -- 2.3.2 Beyond the Histogram -- 2.4 Relation between Two Variables -- 2.4.1 Basic Scatterplot -- 2.4.2 Enhanced Scatterplot -- 2.5 Distribution of Values over Time -- 2.5.1 Time Series -- 2.5.2 Multiple Time Series -- 3. Visualize a Categorical Variable -- 3.1 Bars, Dots, and Bubbles -- 3.1.1 Horizontal Bar Chart of Counts -- 3.1.2 Cleveland Dot Plot of Counts -- 3.1.3 Bubble Plot of Counts -- 3.1.4 Display Proportions -- 3.2 Multiple Plots on a Single Panel -- 3.3 Provide the Numerical Values -- 3.3.1 Bar Chart of Individual Data Values -- 3.3.2 Vertical Long Value Labels -- 3.3.3 Cleveland Dot Plot of Individual Data Values -- 3.3.4 Visualize Means across Categories -- 3.4 Communicate with Bar Fill Color -- 3.4.1 Bar Fill Color Bifurcated by Value of Mean Deviations -- 3.4.2 Bar Chart of an Ordinal Variable -- 3.4.3 Custom Color for Individual Bars -- 3.5 Create a Report from Saved Output -- 3.6 Part-Whole Visualizations -- 3.6.1 Doughnut and Pie Charts -- 3.6.2 The Waffle Plot -- 3.6.3 The Treemap -- 4. Visualize a Continuous Variable -- 4.1 Histogram -- 4.1.1 Binning Continuous Data -- 4.1.2 Histogram Artifacts
- 4.1.3 Cumulative Histogram -- 4.1.4 Frequency Polygon -- 4.2 Density Plot -- 4.2.1 Enhanced Density Plot -- 4.2.2 Overlapping Density Curves -- 4.2.3 Rug Plot -- 4.2.4 Violin Plot -- 4.3 Box Plot -- 4.3.1 Classic Box Plot -- 4.3.2 Box Plot Adjusted for Asymmetry -- 4.4 One-Variable Scatterplot -- 4.5 Integrated Violin/Box/Scatterplot -- 4.5.1 VBS Plot -- 4.5.2 VBS Plot of Likert Data -- 4.5.3 Trellis Plots or Facets -- 4.6 Pareto Chart -- 5. Visualize the Relation of Two Continuous Variables -- 5.1 Enhance the Scatterplot -- 5.1.1 The Ellipse -- 5.1.2 Line of Best Fit -- 5.1.3 Annotate -- 5.2 Consideration of a Third Variable -- 5.2.1 Map Data from a Grouping Variable to Aesthetics -- 5.2.2 Trellis (Facet) Scatterplots -- 5.2.3 Map a Third Continuous Variable into a Visual Aesthetic -- 5.2.4 Plot Multiple Variables on the Same Panel -- 5.3 Inter-Relations of a Set of Variables -- 5.3.1 Scatterplot Matrix -- 5.3.2 Heat Map of a Correlation Matrix -- 5.4 Scatterplots for Large Data Sets -- 5.4.1 Smoothed Scatterplots -- 5.4.2 Contoured and Hex-Binned Scatterplots -- 6. Visualize Multiple Categorical Variables -- 6.1 Two Categorical Variables -- 6.1.1 Stacked Two-Variable Bar Chart -- 6.1.2 Unstacked Two-Variable Bar Chart -- 6.1.3 Trellis Plots or Facets -- 6.2 Other Styles for the Two-Variable Bar Chart -- 6.2.1 Sorted Two-Variable Bar Chart -- 6.2.2 Horizontal Bar Chart -- 6.2.3 Bar Chart with Legend on the Top -- 6.2.4 100% Stacked Bar Chart -- 6.2.5 Bar Chart of Means across Two Categorical Variables -- 6.2.6 Two-Variable Cleveland Dot Plot -- 6.2.7 Paired t-test Visualization -- 6.3 Mosaic Plots and Association Plots -- 6.3.1 The Mosaic Plot -- 6.3.2 Independence and Pearson Residuals -- 6.3.3 The Association Plot -- 7. Visualize over Time -- 7.1 Run Chart and Control Chart -- 7.1.1 Run Chart -- 7.1.2 Control Chart -- 7.2 Time Series
- 7.2.1 Filled Area Time Series -- 7.2.2 Stacked Multiple Time Series -- 7.2.3 Formatted Multi-Panel Time Series -- 7.2.4 Data Preparation for Date Variables -- 7.3 Forecasts -- 7.3.1 Time-Series Object -- 7.3.2 Seasonal/Trend Decomposition -- 7.3.3 Generate a Forecast -- 8. Visualize Maps and Networks -- 8.1 Maps -- 8.1.1 Map the World -- 8.1.2 Raster Images -- 8.1.3 Online Geocode Databases -- 8.1.4 Create a Country Map with Cities -- 8.1.5 Choropleth Map -- 8.2 Network Visualizations -- 8.2.1 Network Data -- 8.2.2 Visualizations -- 8.2.3 Network Analysis -- 9. Interactive Visualizations -- 9.1 Interactive Visualizations with Shiny -- 9.1.1 Static vs. Interactive Visualizations -- 9.1.2 Shiny Overview -- 9.2 Running a Shiny App -- 9.2.1 Shiny within RStudio -- 9.2.2 Publish Shiny Apps on the Web -- 10. Customize Visualizations -- 10.1 Color References -- 10.1.1 Describe Colors -- 10.1.2 Parameters fill and color -- 10.2 Palettes -- 10.2.1 Qualitative Palettes -- 10.2.2 Sequential Palettes -- 10.2.3 Divergent Palettes -- 10.3 Themes -- 10.3.1 Persistent Theme -- 10.3.2 Theme Applied to Current Visualization -- 10.4 Customize Individual Characteristics -- 10.4.1 List of Individual Characteristics -- 10.4.2 Customize a Single Analysis -- 10.4.3 Update and Save a Persistent Theme -- 10.4.4 Custom Margins -- References -- Index