• Preface
  • About PDV
  • Funding Acknowledgement
  • Datasets
  • Dedication
  • Contents
  • Contributors
  • 1 Overview
    • 1.1 Historical Perspective
    • 1.2 Infographics vs. Visualizations
    • 1.3 Principles of Analytical Design
      • 1.3.1 Comparisons
      • 1.3.2 Causality, Mechanism, Structure, Explanation
      • 1.3.3 Multivariate Analysis
      • 1.3.4 Integration of Evidence
      • 1.3.5 Documentation
      • 1.3.6 Content First and Foremost
    • 1.4 Dashboard Basics
      • 1.4.1 Dashboard Fundamentals
      • 1.4.2 Dashboard Structure
      • 1.4.3 Dashboard Design
      • 1.4.4 Examples
  • 2 Basics of Data Visualization
    • 2.1 Data Exploration
      • 2.1.1 Pre-Analysis Uses
      • 2.1.2 Data Exploration in Action: the Algae Bloom Dataset
    • 2.2 Workhorse Data Visualizations
      • 2.2.1 Rug Plots
      • 2.2.2 Bar Charts and Histograms
      • 2.2.3 Line Charts
      • 2.2.4 Scatterplots
    • 2.3 Representing Multivariate Observations
    • 2.4 Communicating Analysis Results
    • 2.5 Hall-of-Fame / Hall-of-Shame
      • 2.5.1 Misleading Charts
      • 2.5.2 Home Runs
      • 2.5.3 Strike-Outs
  • 3 Essentials of Visual Design
    • 3.1 The Mechanics of Visual Perception
      • 3.1.1 Background
      • 3.1.2 Gestalt Principles
      • 3.1.3 Data Viz and Gestalt Guidelines
      • 3.1.4 Examples
    • 3.2 Visual Design and Data Charts
      • 3.2.1 Visualization and Memory
      • 3.2.2 Colour, Sizing, and Layout Considerations
      • 3.2.3 Decluttering
      • 3.2.4 The Layered Grammar of Graphics
      • 3.2.5 Examples
    • 3.3 Accessibility Considerations
      • 3.3.1 Sonifications and Physicalizations
      • 3.3.2 Colourblindness
      • 3.3.3 General Resources
      • 3.3.4 Practical Suggestions
  • 4 Visualization and Storytelling
    • 4.1 Stories and Storytelling
      • 4.1.1 What Are Stories?
      • 4.1.2 Elements of Storytelling
      • 4.1.3 Form and Structure
      • 4.1.4 How to Tell a Story
    • 4.2 Effective Storytelling Visuals
      • 4.2.1 Stories and Illustrations
      • 4.2.2 Data and Stories
      • 4.2.3 Evolving a Storytelling Chart
      • 4.2.4 Anatomy of Storytelling Dashboards
  • 5 Practical Aspects of Data Visualization
    • 5.1 Visualization Toolkit
      • 5.1.1 Simple Text and Tables
      • 5.1.2 Scatterplots and Bubble Charts
      • 5.1.3 Linegraphs and Sparklines
      • 5.1.4 Bar Charts and Histograms
      • 5.1.5 Area Charts and Treemaps
      • 5.1.6 Boxplots
      • 5.1.7 Maps, Heat Maps, and Choropleths
      • 5.1.8 Parallel Coordinates
      • 5.1.9 Text Visualizations and Representations
      • 5.1.10 Trees, Dendrograms, and Network Diagrams
      • 5.1.11 Small Multiples
      • 5.1.12 Interactive and Animated Visualizations
      • 5.1.13 Miscellaneous Charts
    • 5.2 Visualization Software
      • 5.2.1 GUI Tools
      • 5.2.2 Command Line Tools
      • 5.2.3 Integrated Tools
    • 5.3 Getting Started With Power BI
      • 5.3.1 Power BI Set-Up
      • 5.3.2 Importing and Modeling Data
      • 5.3.3 Calculations and Charts
      • 5.3.4 Organizing Data
      • 5.3.5 Data Wrangling
    • 5.4 Power BI Beyond the Basics
      • 5.4.1 Importing Data
      • 5.4.2 Data Wrangling
      • 5.4.3 Data Modeling
      • 5.4.4 Best Practices
      • 5.4.5 Doing More With DAX
    • 5.5 Basic Visualizations in R
      • 5.5.1 Scatterplots
      • 5.5.2 Barplots
      • 5.5.3 Histograms
      • 5.5.4 Curves
      • 5.5.5 Boxplots
      • 5.5.6 Examples
    • 5.6 ggplot2 Visualizations in R
      • 5.6.1 Basics of ggplot2’s Grammar
      • 5.6.2 ggplot2 Miscellenea
      • 5.6.3 Examples
  • 6 Exercises
  • References
  • Published with bookdown

The Practice of Data Visualization

Contents

1. Overview
     1.1 Historical Perspective
     1.2 Infographics vs. Visualizations
     1.3 Principles of Analytical Design
     1.4 Dashboard Basics

2. Basics of Data Visualization
     2.1 Data Exploration
     2.2 Workhorse Data Visualizations
     2.3 Representing Multivariate Observations
     2.4 Communicating Analysis Results
     2.5 Hall-of-Fame / Hall-of-Shame

3. Essentials of Visual Design
     3.1 The Mechanics of Visual Perception
     3.2 Visual Design and Data Charts
     3.3 Accessibility Considerations

4. Visualization and Storytelling
     4.1 Stories and Storytelling
     4.2 Effective Storytelling Visuals

5. Practical Aspects of Data Visualization
     5.1 Visualization Toolkit
     5.2 Visualization Software
     5.3 Getting Started With Power BI
     5.4 Power BI Beyond the Basics
     5.5 Basic Visualizations in R
     5.6 ggplot2 Visualizations in R

6. Exercises