R Visualizations Derive Meaning from Data.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Gerbing, David
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Milton : CRC Press LLC, 2020.
Temas:
Information visualization.
R (Computer program language)
Visualisation de l'information.
R (Langage de programmation)
Information visualization
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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