Exploratory data analysis with R

• 1. Stay in Touch! 
• 2. Preface 
• 3. Getting Started with R 
• 3.1 Installation 
• 3.2 Getting started with the R interface 
• 4. Managing Data Frames with the dplyr package 
• 4.1 Data Frames 
• 4.2 The dplyr Package 
• 4.3 dplyr Grammar 
• 4.4 Installing the dplyr package 
• 4.5 select() 
• 4.6 filter() 
• 4.7 arrange() 
• 4.8 rename() 
• 4.9 mutate() 
• 4.10 group_by() 
• 4.11 %>% 
• 4.12 Summary 
• 5. Exploratory Data Analysis Checklist 
• 5.1 Formulate your question 
• 5.2 Read in your data 
• 5.3 Check the packaging 
• 5.4 Run str() 
• 5.5 Look at the top and the bottom of your data 
• 5.6 Check your “n”s 
• 5.7 Validate with at least one external data source 
• 5.8 Try the easy solution first 
• 5.9 Challenge your solution 
• 5.10 Follow up questions 
• 6. Principles of Analytic Graphics 
• 6.1 Show comparisons
• 6.2 Show causality, mechanism, explanation, systematic structure 
• 6.3 Show multivariate data 
• 6.4 Integrate evidence 
• 6.5 Describe and document the evidence 
• 6.6 Content, Content, Content 
• 6.7 References 
• 7. Exploratory Graphs 
• 7.1 Characteristics of exploratory graphs 
• 7.2 Air Pollution in the United States 
• 7.3 Getting the Data 
• 7.4 Simple Summaries: One Dimension 
• 7.5 Five Number Summary 
• 7.6 Boxplot 
• 7.7 Histogram 
• 7.8 Overlaying Features 
• 7.9 Barplot 
• 7.10 Simple Summaries: Two Dimensions and Beyond 
• 7.11 Multiple Boxplots 
• 7.12 Multiple Histograms 
• 7.13 Scatterplots 
• 7.14 Scatterplot - Using Color 
• 7.15 Multiple Scatterplots 
• 7.16 Summary 
• 8. Plotting Systems 
• 8.1 The Base Plotting System 
• 8.2 The Lattice System 
• 8.3 The ggplot2 System 
• 8.4 References 
• 9. Graphics Devices 
• 9.1 The Process of Making a Plot 
• 9.2 How Does a Plot Get Created? 
• 9.3 Graphics File Devices 
• 9.4 Multiple Open Graphics Devices 
• 9.5 Copying Plots 
• 9.6 Summary 
• 10. The Base Plotting System 
• 10.1 Base Graphics 
• 10.2 Simple Base Graphics 
• 10.3 Some Important Base Graphics Parameters 
• 10.4 Base Plotting Functions 
• 10.5 Base Plot with Regression Line 
• 10.6 Multiple Base Plots 
• 10.7 Summary 
• 11. Plotting and Color in R 
• 11.1 Colors 1, 2, and 3 
• 11.2 Connecting colors with data 
• 11.3 Color Utilities in R 
• 11.4 colorRamp() 
• 11.5 colorRampPalette() 
• 11.6 RColorBrewer Package 
• 11.7 Using the RColorBrewer palettes 
• 11.8 The smoothScatter() function 
• 11.9 Adding transparency 
• 11.10 Summary 
• 12. Hierarchical Clustering 
• 12.1 Hierarchical clustering 
• 12.2 How do we define close? 
• 12.3 Example: Euclidean distance 
• 12.4 Example: Manhattan distance 
• 12.5 Example: Hierarchical clustering 
• 12.6 Prettier dendrograms 
• 12.7 Merging points: Complete 
• 12.8 Merging points: Average 
• 12.9 Using the heatmap() function 
• 12.10 Notes and further resources 
• 13. K-Means Clustering 
• 13.1 Illustrating the K-means algorithm 
• 13.2 Stopping the algorithm 
• 13.3 Using the kmeans() function 
• 13.4 Building heatmaps from K-means solutions 
• 13.5 Notes and further resources 
• 14. Dimension Reduction 
• 14.1 Matrix data 
• 14.2 Patterns in rows and columns 
• 14.3 Related problem 
• 14.4 SVD and PCA 
• 14.5 Unpacking the SVD: u and v 
• 14.6 SVD for data compression 
• 14.7 Components of the SVD - Variance explained 
• 14.8 Relationship to principal components 
• 14.9 What if we add a second pattern? 
• 14.10 Dealing with missing values 
• 14.11 Example: Face data 
• 14.12 Notes and further resources 
• 15. The ggplot2 Plotting System: Part 1 
• 15.1 The Basics: qplot() 
• 15.2 Before You Start: Label Your Data 
• 15.3 ggplot2 “Hello, world!” 
• 15.4 Modifying aesthetics 
• 15.5 Adding a geom 
• 15.6 Histograms 
• 15.7 Facets 
• 15.8 Case Study: MAACS Cohort 
• 15.9 Summary of qplot() 
• 16. The ggplot2 Plotting System: Part 2 
• 16.1 Basic Components of a ggplot2 Plot 
• 16.2 Example: BMI, PM2.5, Asthma 
• 16.3 Building Up in Layers 
• 16.4 First Plot with Point Layer 
• 16.5 Adding More Layers: Smooth 
• 16.6 Adding More Layers: Facets 
• 16.7 Modifying Geom Properties 
• 16.8 Modifying Labels 
• 16.9 Customizing the Smooth 
• 16.10 Changing the Theme 
• 16.11 More Complex Example 
• 16.12 A Quick Aside about Axis Limits 
• 16.13 Resources 
• 17. Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S. 
• 17.1 Synopsis 
• 17.2 Loading and Processing the Raw Data 
• 17.3 Results 
• 18. About the Author