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Exploratory Data Analysis with R

Roger D. Peng

This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization.

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About the Book

This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

If you are interested in a printed copy of this book, you can purchase one at Lulu.

Some of the topics we cover are

  • Making exploratory graphs
  • Principles of analytic graphics
  • Plotting systems and graphics devices in R
  • The base and ggplot2 plotting systems in R
  • Clustering methods
  • Dimension reduction techniques

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    This package contains the book and R code files corresponding to each of the chapters in the book. The package also contains the datasets used in all of the chapters so that the code can be fully executed.

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      This package includes the book, high definition lecture video files (720p) corresponding to each of the chapters, datasets and R code files for all chapters. The videos are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.

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    Author

    About the Author

    Roger D. Peng

    Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas, Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on the development of statistical methods for addressing environmental health problems and on developing tools for doing better data analysis. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng.

    Leanpub Podcast

    Episode 16

    An Interview with Roger D. Peng

    Contents

    Table of Contents

    1.Stay in Touch!

    2.Preface

    3.Getting Started with R

    1. 3.1Installation
    2. 3.2Getting started with the R interface

    4.Managing Data Frames with the dplyr package

    1. 4.1Data Frames
    2. 4.2The dplyr Package
    3. 4.3dplyr Grammar
    4. 4.4Installing the dplyr package
    5. 4.5select()
    6. 4.6filter()
    7. 4.7arrange()
    8. 4.8rename()
    9. 4.9mutate()
    10. 4.10group_by()
    11. 4.11%>%
    12. 4.12Summary

    5.Exploratory Data Analysis Checklist

    1. 5.1Formulate your question
    2. 5.2Read in your data
    3. 5.3Check the packaging
    4. 5.4Run str()
    5. 5.5Look at the top and the bottom of your data
    6. 5.6Check your “n”s
    7. 5.7Validate with at least one external data source
    8. 5.8Try the easy solution first
    9. 5.9Challenge your solution
    10. 5.10Follow up questions

    6.Principles of Analytic Graphics

    1. 6.1Show comparisons
    2. 6.2Show causality, mechanism, explanation, systematic structure
    3. 6.3Show multivariate data
    4. 6.4Integrate evidence
    5. 6.5Describe and document the evidence
    6. 6.6Content, Content, Content
    7. 6.7References

    7.Exploratory Graphs

    1. 7.1Characteristics of exploratory graphs
    2. 7.2Air Pollution in the United States
    3. 7.3Getting the Data
    4. 7.4Simple Summaries: One Dimension
    5. 7.5Five Number Summary
    6. 7.6Boxplot
    7. 7.7Histogram
    8. 7.8Overlaying Features
    9. 7.9Barplot
    10. 7.10Simple Summaries: Two Dimensions and Beyond
    11. 7.11Multiple Boxplots
    12. 7.12Multiple Histograms
    13. 7.13Scatterplots
    14. 7.14Scatterplot - Using Color
    15. 7.15Multiple Scatterplots
    16. 7.16Summary

    8.Plotting Systems

    1. 8.1The Base Plotting System
    2. 8.2The Lattice System
    3. 8.3The ggplot2 System
    4. 8.4References

    9.Graphics Devices

    1. 9.1The Process of Making a Plot
    2. 9.2How Does a Plot Get Created?
    3. 9.3Graphics File Devices
    4. 9.4Multiple Open Graphics Devices
    5. 9.5Copying Plots
    6. 9.6Summary

    10.The Base Plotting System

    1. 10.1Base Graphics
    2. 10.2Simple Base Graphics
    3. 10.3Some Important Base Graphics Parameters
    4. 10.4Base Plotting Functions
    5. 10.5Base Plot with Regression Line
    6. 10.6Multiple Base Plots
    7. 10.7Summary

    11.Plotting and Color in R

    1. 11.1Colors 1, 2, and 3
    2. 11.2Connecting colors with data
    3. 11.3Color Utilities in R
    4. 11.4colorRamp()
    5. 11.5colorRampPalette()
    6. 11.6RColorBrewer Package
    7. 11.7Using the RColorBrewer palettes
    8. 11.8The smoothScatter() function
    9. 11.9Adding transparency
    10. 11.10Summary

    12.Hierarchical Clustering

    1. 12.1Hierarchical clustering
    2. 12.2How do we define close?
    3. 12.3Example: Euclidean distance
    4. 12.4Example: Manhattan distance
    5. 12.5Example: Hierarchical clustering
    6. 12.6Prettier dendrograms
    7. 12.7Merging points: Complete
    8. 12.8Merging points: Average
    9. 12.9Using the heatmap() function
    10. 12.10Notes and further resources

    13.K-Means Clustering

    1. 13.1Illustrating the K-means algorithm
    2. 13.2Stopping the algorithm
    3. 13.3Using the kmeans() function
    4. 13.4Building heatmaps from K-means solutions
    5. 13.5Notes and further resources

    14.Dimension Reduction

    1. 14.1Matrix data
    2. 14.2Patterns in rows and columns
    3. 14.3Related problem
    4. 14.4SVD and PCA
    5. 14.5Unpacking the SVD: u and v
    6. 14.6SVD for data compression
    7. 14.7Components of the SVD - Variance explained
    8. 14.8Relationship to principal components
    9. 14.9What if we add a second pattern?
    10. 14.10Dealing with missing values
    11. 14.11Example: Face data
    12. 14.12Notes and further resources

    15.The ggplot2 Plotting System: Part 1

    1. 15.1The Basics: qplot()
    2. 15.2Before You Start: Label Your Data
    3. 15.3ggplot2 “Hello, world!”
    4. 15.4Modifying aesthetics
    5. 15.5Adding a geom
    6. 15.6Histograms
    7. 15.7Facets
    8. 15.8Case Study: MAACS Cohort
    9. 15.9Summary of qplot()

    16.The ggplot2 Plotting System: Part 2

    1. 16.1Basic Components of a ggplot2 Plot
    2. 16.2Example: BMI, PM2.5, Asthma
    3. 16.3Building Up in Layers
    4. 16.4First Plot with Point Layer
    5. 16.5Adding More Layers: Smooth
    6. 16.6Adding More Layers: Facets
    7. 16.7Modifying Geom Properties
    8. 16.8Modifying Labels
    9. 16.9Customizing the Smooth
    10. 16.10Changing the Theme
    11. 16.11More Complex Example
    12. 16.12A Quick Aside about Axis Limits
    13. 16.13Resources

    17.Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S.

    1. 17.1Synopsis
    2. 17.2Loading and Processing the Raw Data
    3. 17.3Results

    18.About the Author

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