Hands-On Exploratory Data Analysis with R

Table of contents

1. Title Page
2. Copyright and Credits
   1. Hands-On Exploratory Data Analysis with R
3. Dedication
4. About Packt
   1. Why subscribe?
   2. Packt.com
5. Contributors
   1. About the authors
   2. About the reviewer
   3. Packt is searching for authors like you
6. Preface
   1. Who this book is for
   2. What this book covers
   3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
   4. Code in Action
      1. Conventions used
   5. Get in touch
      1. Reviews
7. Section 1: Setting Up Data Analysis Environment
8. Setting Up Our Data Analysis Environment
   1. Technical requirements
   2. The benefits of EDA across vertical markets
   3. Manipulating data
      1. Examining, cleaning, and filtering data
      2. Visualizing data
      3. Creating data reports
   4. Installing the required R packages and tools
      1. Installing R packages from the Terminal
      2. Installing R packages from inside RStudio
   5. Summary
9. Importing Diverse Datasets
   1. Technical requirements
   2. Converting rectangular data into R with the readr R package
      1. readr read functions
         1. read_tsv method
         2. read_delim method
         3. read_fwf method
         4. read_table method
         5. read_log method
   3. Reading in Excel data with the readxl R package
   4. Reading in JSON data with the jsonlite R package
      1. Loading the jsonlite package
   5. Getting data into R from web APIs using the httr R package
   6. Getting data into R by scraping the web using the rvest package
   7. Importing data into R from relational databases using the DBI R package
   8. Summary
10. Examining, Cleaning, and Filtering
    1. Technical requirements
    2. About the dataset
    3. Reshaping and tidying up erroneous data
       1. The gather() function
       2. The unite() function
       3. The separate() function
       4. The spread() function
    4. Manipulating and mutating data
       1. The mutate() function
       2. The group_by() function
       3. The summarize() function
       4. The arrange() function
       5. The glimpse() function
    5. Selecting and filtering data
       1. The select() function
       2. The filter() function
    6. Cleaning and manipulating time series data
    7. Summary
11. Visualizing Data Graphically with ggplot2
    1. Technical requirements
    2. Advanced graphics grammar of ggplot2
       1. Data
       2. Layers
       3. Scales
       4. The coordinate system
       5. Faceting
       6. Theme
    3. Installing ggplot2
    4. Scatter plots
    5. Histogram plots
    6. Density plots
    7. Probability plots
       1. dnorm()
       2. pnorm()
       3. rnorm()
    8. Box plots
    9. Residual plots
    10. Summary
12. Creating Aesthetically Pleasing Reports with knitr and R Markdown
    1. Technical requirements
    2. Installing R Markdown
       1. Working with R Markdown
    3. Reproducible data analysis reports with knitr
    4. Exporting and customizing reports
    5. Summary
13. Section 2: Univariate, Time Series, and Multivariate Data
14. Univariate and Control Datasets
    1. Technical requirements
    2. Reading the dataset
    3. Cleaning and tidying up the data
    4. Understanding the structure of the data
    5. Hypothesis tests
       1. Statistical hypothesis in R
          1. The t-test in R
          2. Directional hypothesis in R
          3. Correlation in R
    6. Tietjen-Moore test
    7. Parsimonious models
    8. Probability plots
    9. The Shapiro-Wilk test
    10. Summary
15. Time Series Datasets
    1. Technical requirements
    2. Introducing and reading the dataset
    3. Cleaning the dataset
    4. Mapping and understanding structure
    5. Hypothesis test
       1. t-test in R
       2. Directional hypothesis in R
    6. Grubbs' test and checking outliers
    7. Parsimonious models
    8. Bartlett's test
    9. Data visualization
       1. Autocorrelation plots
       2. Spectrum plots
       3. Phase plots
    10. Summary
16. Multivariate Datasets
    1. Technical requirements
    2. Introducing and reading a dataset
    3. Cleaning the data
    4. Mapping and understanding the structure
    5. Hypothesis test
       1. t-test in R
       2. Directional hypothesis in R
    6. Parsimonious model
    7. Levene's test
    8. Data visualization
       1. Principal Component Regression
       2. Partial Least Squares Regression
    9. Summary
17. Section 3: Multifactor, Optimization, and Regression Data Problems
18. Multi-Factor Datasets
    1. Technical requirements
    2. Introducing and reading the dataset
    3. Cleaning the dataset
    4. Mapping and understanding data structure
    5. Hypothesis test
       1. t-test in R
       2. Directional hypothesis in R
    6. Grubbs test and checking outliers
    7. Parsimonious model
    8. Multi-factor variance analysis
    9. Exploring graphically the dataset
    10. Summary
19. Handling Optimization and Regression Data Problems
    1. Technical requirements
    2. Introducing and reading a dataset
    3. Cleaning the dataset
    4. Mapping and understanding the data structure
    5. Hypothesis test
       1. t-test in R
       2. Directional hypothesis in R
    6. Grubbs' test and checking outliers
    7. Parsimonious model
    8. Exploration using graphics
    9. Summary
20. Section 4: Conclusions
21. Next Steps
    1. Technical requirements
    2. What to learn next
    3. Why R?
       1. Environmental setup
       2. R syntax
       3. R packages
       4. Understanding the help system
    4. The data analysis workflow
       1. Data import
       2. Manipulating data
       3. Visualizing data
       4. Reporting results
       5. Standout as R wizard
    5. Building a data science portfolio
    6. Datasets in R
    7. Getting help with exploratory data analysis
    8. Summary
22. Other Books You May Enjoy
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