This book covers some of the basics of visualizing data in R and summarizing highdimensional data with statistical multivariate analysis techniques. There is less of an emphasis on formal statistical inference methods, as inference is typically not the focus of EDA. Rather, the goal is to show the data, summarize the evidence and identify interesting patterns while eliminating ideas that likely won’t pan out. Throughout the book, we will focus on the R statistical programming language. We will cover the various plotting systems in R and how to use them effectively. We will also discuss how to implement dimension reduction techniques like clustering and the singular value decomposition. All of these techniques will help you to visualize your data and to help you make key decisions in any data analysis.
Author(s): Roger D. Peng
Publisher: Leanpub
Year: 2016
Language: English
Commentary: This version was published on 2016-07-20
Pages: 0
• 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
