LUOHAN GONG. Shaolin Internal Training Set.

Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the mapping of geographic data. Both static and interactive graphics are described and the use of color, shape, shading, grouping, annotation, and animations are covered in detail. The book moves from a default look and feel for graphs, to graphs with customized colors, fonts, legends, annotations, and organizational themes. Features • Contains a wide breadth of graph types including newer and less well-known approaches • Connects each graph type to the characteristics of the data and the goals of the analysis • Moves the reader from simple graphs describing one variable to building visualizations that describe complex relationships among many variables • Provides newer approaches to creating interactive web graphics via JavaScript libraries • Details how to customize each graph type to meet users’ needs and those of their audiences • Gives methods for creating visualizations that are publication ready for print (in color or black and white) and the web • Suggests best practices • Offers examples from a wide variety of fields The book is written for those new to data analysis as well as the seasoned data scientist. It can be used for both teaching and research, and will particularly appeal to anyone who needs to describe data visually and wants to find and emulate the most appropriate method quickly. The reader should have some basic coding experience, but expertise in R is not required. Some of the later chapters (e.g., visualizing statistical models) assume exposure to statistical inference at the level of analysis of variance and regression. Cover Half Title Series Page Title Page Copyright Page Contents Preface 0.1. Why This Book? 0.2. Acknowledgments 0.3. Supporting Website 1. Introduction 1.1. How to Use This Book 1.2. Pre-requisites 1.3. Setup 2. Data Preparation 2.1. Importing Data 2.1.1. Text Files 2.1.2. Excel Spreadsheets 2.1.3. Statistical Packages 2.1.4. Databases 2.2. Cleaning Data 2.2.1. Selecting Variables 2.2.2. Selecting Observations 2.2.3. Creating/Recoding Variables 2.2.4. Summarizing Data 2.2.5. Using Pipes 2.2.6. Processing Dates 2.2.7. Reshaping Data 2.2.8. Missing Data 3. Introduction to ggplot2 3.1. A Worked Example 3.1.1. ggplot 3.1.2. geoms 3.1.3. grouping 3.1.4. scales 3.1.5. facets 3.1.6. labels 3.1.7. themes 3.2. Placing the data and mapping Options 3.3. Graphs as Objects 4. Univariate Graphs 4.1. Categorical 4.1.1. Bar Chart 4.1.2. Pie Chart 4.1.3. Tree Map 4.1.4. Waffle Chart 4.2. Quantitative 4.2.1. Histogram 4.2.2. Kernel Density Plot 4.2.3. Dot Chart 5. Bivariate Graphs 5.1. Categorical vs. Categorical 5.1.1. Stacked Bar Chart 5.1.2. Grouped Bar Chart 5.1.3. Segmented Bar Chart 5.1.4. Improving the Color and Labeling 5.1.5. Other Plots 5.2. Quantitative vs. Quantitative 5.2.1. Scatterplot 5.2.2. Line Plot 5.3. Categorical vs. Quantitative 5.3.1. Bar Chart (on Summary Statistics) 5.3.2. Grouped Kernel Density Plots 5.3.3. Box Plots 5.3.4. Violin Plots 5.3.5. Ridgeline Plots 5.3.6. Mean/SEM Plots 5.3.7. Strip Plots 5.3.8. Cleveland Dot Charts 6. Multivariate Graphs 6.1. Grouping 6.2. Faceting 7. Maps 7.1. Geocoding 7.2. Dot Density Maps 7.2.1. Interactive Maps with Mapview 7.2.2. Static Maps with ggmap 7.3. Choropleth Maps 7.3.1. Data by Country 7.3.2. Data by US State 7.3.3. Data by US County 7.3.4. Building a Choropleth Map Using the sf and ggplot2 Packages and a Shapefile 7.4. Going Further 8. Time-Dependent Graphs 8.1. Time Series 8.2. Dumbbell Charts 8.3. Slope Graphs 8.4. Area Charts 8.5. Stream Graphs 9. Statistical Models 9.1. Correlation Plots 9.2. Linear Regression 9.3. Logistic Regression 9.4. Survival Plots 9.5. Mosaic Plots 10. Other Graphs 10.1. 3-D Scatterplot 10.2. Bubble Charts 10.3. Biplots 10.4. Alluvial Diagrams 10.5. Heatmaps 10.6. Radar Charts 10.7. Scatterplot Matrix 10.8. Waterfall Charts 10.9. Word Clouds 11. Customizing Graphs 11.1. Axes 11.1.1. Quantitative Axes 11.1.2. Categorical Axes 11.1.3. Date Axes 11.2. Colors 11.2.1. Specifying Colors Manually 11.2.2. Color Palettes 11.3. Points and Lines 11.3.1. Points 11.3.2. Lines 11.4. Fonts 11.5. Legends 11.5.1. Legend Location 11.5.2. Legend Title 11.6. Labels 11.7. Annotations 11.7.1. Adding Text 11.7.2. Adding Lines 11.7.3. Highlighting a Single Group 11.8. Themes 11.8.1. Altering Theme Elements 11.8.2. Pre-Packaged Themes 11.9. Combining Graphs 12. Saving Graphs 12.1. Via Menus 12.2. Via Code 12.3. File Formats 12.4. External Editing 13. Interactive Graphs 13.1. plotly 13.2. ggiraph 13.3. Other Approaches 13.3.1. rbokeh 13.3.2. rCharts 13.3.3. highcharter 14. Advice Best Practices 14.1. Labeling 14.2. Signal-to-Noise-Ratio 14.3. Color Choice 14.4. y-Axis Scaling 14.5. Attribution 14.6. Going Further A. Datasets A.1. Academic Salaries A.2. Star Wars A.3. Mammal Sleep A.4. Medical Insurance Costs A.5. Marriage Records A.6. Fuel Economy Data A.7. Literacy Rates A.8. Gapminder Data A.9. Current Population Survey (1985) A.10. Houston Crime Data A.11. Hispanic and Latino Populations A.12. US Economic Timeseries A.13. US Population by Age and Year A.14. Saratoga Housing Data A.15. NCCTG Lung Cancer Data A.16. Titanic Data A.17. JFK Cuban Missle Speech B. About the Author C. About the QAC Bibliography Index Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the mapping of geographic data. Both static and interactive graphics are described and the use of color, shape, shading, grouping, annotation, and animations are covered in detail. The book moves from a default look and feel for graphs, to graphs with customized colors, fonts, legends, annotations, and organizational themes. • Contains a wide breadth of graph types including newer and less well-known approaches• Connects each graph type to the characteristics of the data and the goals of the analysis• Moves the reader from simple graphs, describing one variable, to building visualizations that describe complex relationships among many variables• Newer approaches to creating interactive web graphics via JavaScript libraries• Details how to customize each graph type to meet users’ needs and those of their audiences• Gives methods for creating visualizations that are publication ready for print (in color or black and white) and the web• Suggests best practices • Offers examples from a wide variety of fieldsIt is written for those new to data analysis as well as the seasoned data scientist. It can be used for both teaching and research. It will particularly appeal to anyone who needs to describe data visually and wants to find and emulate the most appropriate method quickly. The reader should have some basic coding experience, but expertise in R is not required. Some of the later chapters (e.g., visualizing statistical models) assume exposure to statistical inference at the level of analysis of variance and regression.