R Cookbook Proven Recipes for Data Analysis Statistics and Graphics 2nd edition by JD Long, Paul Teetor ISBN B07TDVNC15 ‎ 978-1492040644

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ISBN 10: B07TDVNC15
ISBN 13: ‎ 978-1492040644
Author: JD Long, Paul Teetor

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.

R Cookbook Proven Recipes for Data Analysis Statistics and Graphics 2nd Table of contents:

1. Getting Started and Getting Help

1.1. Downloading and Installing R

1.2. Installing RStudio

1.3. Starting RStudio

1.4. Entering Commands

1.5. Exiting from RStudio

1.6. Interrupting R

1.7. Viewing the Supplied Documentation

1.8. Getting Help on a Function

1.9. Searching the Supplied Documentation

1.10. Getting Help on a Package

1.11. Searching the Web for Help

1.12. Finding Relevant Functions and Packages

1.13. Searching the Mailing Lists

1.14. Submitting Questions to Stack Overflow or Elsewhere in the Community

2. Some Basics

2.1. Printing Something to the Screen

2.2. Setting Variables

2.3. Listing Variables

2.4. Deleting Variables

2.5. Creating a Vector

2.6. Computing Basic Statistics

2.7. Creating Sequences

2.8. Comparing Vectors

2.9. Selecting Vector Elements

2.10. Performing Vector Arithmetic

2.11. Getting Operator Precedence Right

2.12. Typing Less and Accomplishing More

2.13. Creating a Pipeline of Function Calls

2.14. Avoiding Some Common Mistakes

3. Navigating the Software

3.1. Getting and Setting the Working Directory

3.2. Creating a New RStudio Project

3.3. Saving Your Workspace

3.4. Viewing Your Command History

3.5. Saving the Result of the Previous Command

3.6. Displaying Loaded Packages via the Search Path

3.7. Viewing the List of Installed Packages

3.8. Accessing the Functions in a Package

3.9. Accessing Built-in Datasets

3.10. Installing Packages from CRAN

3.11. Installing a Package from GitHub

3.12. Setting or Changing a Default CRAN Mirror

3.13. Running a Script

3.14. Running a Batch Script

3.15. Locating the R Home Directory

3.16. Customizing R Startup

3.17. Using R and RStudio in the Cloud

4. Input and Output

4.1. Entering Data from the Keyboard

4.2. Printing Fewer Digits (or More Digits)

4.3. Redirecting Output to a File

4.4. Listing Files

4.5. Dealing with “Cannot Open File” in Windows

4.6. Reading Fixed-Width Records

4.7. Reading Tabular Data Files

4.8. Reading from CSV Files

4.9. Writing to CSV Files

4.10. Reading Tabular or CSV Data from the Web

4.11. Reading Data from Excel

4.12. Writing a Data Frame to Excel

4.13. Reading Data from a SAS File

4.14. Reading Data from HTML Tables

4.15. Reading Files with a Complex Structure

4.16. Reading from MySQL Databases

4.17. Accessing a Database with dbplyr

4.18. Saving and Transporting Objects

5. Data Structures

5.1. Appending Data to a Vector

5.2. Inserting Data into a Vector

5.3. Understanding the Recycling Rule

5.4. Creating a Factor (Categorical Variable)

5.5. Combining Multiple Vectors into One Vector and a Factor

5.6. Creating a List

5.7. Selecting List Elements by Position

5.8. Selecting List Elements by Name

5.9. Building a Name/Value Association List

5.10. Removing an Element from a List

5.11. Flattening a List into a Vector

5.12. Removing NULL Elements from a List

5.13. Removing List Elements Using a Condition

5.14. Initializing a Matrix

5.15. Performing Matrix Operations

5.16. Giving Descriptive Names to the Rows and Columns of a Matrix

5.17. Selecting One Row or Column from a Matrix

5.18. Initializing a Data Frame from Column Data

5.19. Initializing a Data Frame from Row Data

5.20. Appending Rows to a Data Frame

5.21. Selecting Data Frame Columns by Position

5.22. Selecting Data Frame Columns by Name

5.23. Changing the Names of Data Frame Columns

5.24. Removing NAs from a Data Frame

5.25. Excluding Columns by Name

5.26. Combining Two Data Frames

5.27. Merging Data Frames by Common Column

5.28. Converting One Atomic Value into Another

5.29. Converting One Structured Data Type into Another

6. Data Transformations

6.1. Applying a Function to Each List Element

6.2. Applying a Function to Every Row of a Data Frame

6.3. Applying a Function to Every Row of a Matrix

6.4. Applying a Function to Every Column

6.5. Applying a Function to Parallel Vectors or Lists

6.6. Applying a Function to Groups of Data

6.7. Creating a New Column Based on Some Condition

7. Strings and Dates

7.1. Getting the Length of a String

7.2. Concatenating Strings

7.3. Extracting Substrings

7.4. Splitting a String According to a Delimiter

7.5. Replacing Substrings

7.6. Generating All Pairwise Combinations of Strings

7.7. Getting the Current Date

7.8. Converting a String into a Date

7.9. Converting a Date into a String

7.10. Converting Year, Month, and Day into a Date

7.11. Getting the Julian Date

7.12. Extracting the Parts of a Date

7.13. Creating a Sequence of Dates

8. Probability

8.1. Counting the Number of Combinations

8.2. Generating Combinations

8.3. Generating Random Numbers

8.4. Generating Reproducible Random Numbers

8.5. Generating a Random Sample

8.6. Generating Random Sequences

8.7. Randomly Permuting a Vector

8.8. Calculating Probabilities for Discrete Distributions

8.9. Calculating Probabilities for Continuous Distributions

8.10. Converting Probabilities to Quantiles

8.11. Plotting a Density Function

9. General Statistics

9.1. Summarizing Your Data

9.2. Calculating Relative Frequencies

9.3. Tabulating Factors and Creating Contingency Tables

9.4. Testing Categorical Variables for Independence

9.5. Calculating Quantiles (and Quartiles) of a Dataset

9.6. Inverting a Quantile

9.7. Converting Data to z-Scores

9.8. Testing the Mean of a Sample (t-Test)

9.9. Forming a Confidence Interval for a Mean

9.10. Forming a Confidence Interval for a Median

9.11. Testing a Sample Proportion

9.12. Forming a Confidence Interval for a Proportion

9.13. Testing for Normality

9.14. Testing for Runs

9.15. Comparing the Means of Two Samples

9.16. Comparing the Locations of Two Samples Nonparametrically

9.17. Testing a Correlation for Significance

9.18. Testing Groups for Equal Proportions

9.19. Performing Pairwise Comparisons Between Group Means

9.20. Testing Two Samples for the Same Distribution

10. Graphics

10.1. Creating a Scatter Plot

10.2. Adding a Title and Labels

10.3. Adding (or Removing) a Grid

10.4. Applying a Theme to a ggplot Figure

10.5. Creating a Scatter Plot of Multiple Groups

10.6. Adding (or Removing) a Legend

10.7. Plotting the Regression Line of a Scatter Plot

10.8. Plotting All Variables Against All Other Variables

10.9. Creating One Scatter Plot for Each Group

10.10. Creating a Bar Chart

10.11. Adding Confidence Intervals to a Bar Chart

10.12. Coloring a Bar Chart

10.13. Plotting a Line from x and y Points

10.14. Changing the Type, Width, or Color of a Line

10.15. Plotting Multiple Datasets

10.16. Adding Vertical or Horizontal Lines

10.17. Creating a Boxplot

10.18. Creating One Boxplot for Each Factor Level

10.19. Creating a Histogram

10.20. Adding a Density Estimate to a Histogram

10.21. Creating a Normal Quantile–Quantile Plot

10.22. Creating Other Quantile–Quantile Plots

10.23. Plotting a Variable in Multiple Colors

10.24. Graphing a Function

10.25. Displaying Several Figures on One Page

10.26. Writing Your Plot to a File

11. Linear Regression and ANOVA

11.1. Performing Simple Linear Regression

11.2. Performing Multiple Linear Regression

11.3. Getting Regression Statistics

11.4. Understanding the Regression Summary

11.5. Performing Linear Regression Without an Intercept

11.6. Regressing Only Variables That Highly Correlate with Your Dependent Variable

11.7. Performing Linear Regression with Interaction Terms

11.8. Selecting the Best Regression Variables

11.9. Regressing on a Subset of Your Data

11.10. Using an Expression Inside a Regression Formula

11.11. Regressing on a Polynomial

11.12. Regressing on Transformed Data

11.13. Finding the Best Power Transformation (Box–Cox Procedure)

11.14. Forming Confidence Intervals for Regression Coefficients

11.15. Plotting Regression Residuals

11.16. Diagnosing a Linear Regression

11.17. Identifying Influential Observations

11.18. Testing Residuals for Autocorrelation (Durbin–Watson Test)

11.19. Predicting New Values

11.20. Forming Prediction Intervals

11.21. Performing One-Way ANOVA

11.22. Creating an Interaction Plot

11.23. Finding Differences Between Means of Groups

11.24. Performing Robust ANOVA (Kruskal–Wallis Test)

11.25. Comparing Models by Using ANOVA

12. Useful Tricks

12.1. Peeking at Your Data

12.2. Printing the Result of an Assignment

12.3. Summing Rows and Columns

12.4. Printing Data in Columns

12.5. Binning Your Data

12.6. Finding the Position of a Particular Value

12.7. Selecting Every nth Element of a Vector

12.8. Finding Minimums or Maximums

12.9. Generating All Combinations of Several Variables

12.10. Flattening a Data Frame

12.11. Sorting a Data Frame

12.12. Stripping Attributes from a Variable

12.13. Revealing the Structure of an Object

12.14. Timing Your Code

12.15. Suppressing Warnings and Error Messages

12.16. Taking Function Arguments from a List

12.17. Defining Your Own Binary Operators

12.18. Suppressing the Startup Message

12.19. Getting and Setting Environment Variables

12.20. Use Code Sections

12.21. Executing R in Parallel Locally

12.22. Executing R in Parallel Remotely

13. Beyond Basic Numerics and Statistics

13.1. Minimizing or Maximizing a Single-Parameter Function

13.2. Minimizing or Maximizing a Multiparameter Function

13.3. Calculating Eigenvalues and Eigenvectors

13.4. Performing Principal Component Analysis

13.5. Performing Simple Orthogonal Regression

13.6. Finding Clusters in Your Data

13.7. Predicting a Binary-Valued Variable (Logistic Regression)

13.8. Bootstrapping a Statistic

13.9. Factor Analysis

14. Time Series Analysis

14.1. Representing Time Series Data

14.2. Plotting Time Series Data

14.3. Extracting the Oldest or Newest Observations

14.4. Subsetting a Time Series

14.5. Merging Several Time Series

14.6. Filling or Padding a Time Series

14.7. Lagging a Time Series

14.8. Computing Successive Differences

14.9. Performing Calculations on Time Series

14.10. Computing a Moving Average

14.11. Applying a Function by Calendar Period

14.12. Applying a Rolling Function

14.13. Plotting the Autocorrelation Function

14.14. Testing a Time Series for Autocorrelation

14.15. Plotting the Partial Autocorrelation Function

14.16. Finding Lagged Correlations Between Two Time Series

14.17. Detrending a Time Series

14.18. Fitting an ARIMA Model

14.19. Removing Insignificant ARIMA Coefficients

14.20. Running Diagnostics on an ARIMA Model

14.21. Making Forecasts from an ARIMA Model

14.22. Plotting a Forecast

14.23. Testing for Mean Reversion

14.24. Smoothing a Time Series

15. Simple Programming

15.1. Choosing Between Two Alternatives: if/else

15.2. Iterating with a Loop

15.3. Defining a Function

15.4. Creating a Local Variable

15.5. Choosing Between Multiple Alternatives: switch

15.6. Defining Defaults for Function Parameters

15.7. Signaling Errors

15.8. Protecting Against Errors

15.9. Creating an Anonymous Function

15.10. Creating a Collection of Reusable Functions

15.11. Automatically Reindenting Code

16. R Markdown and Publishing

16.1. Creating a New Document

16.2. Adding a Title, Author, or Date

16.3. Formatting Document Text

16.4. Inserting Document Headings

16.5. Inserting a List

16.6. Showing Output from R Code

16.7. Controlling Which Code and Results Are Shown

16.8. Inserting a Plot

16.9. Inserting a Table

16.10. Inserting a Table of Data

16.11. Inserting Math Equations

16.12. Generating HTML Output

16.13. Generating PDF Output

16.14. Generating Microsoft Word Output

16.15. Generating Presentation Output

16.16. Creating a Parameterized Report

16.17. Organizing Your R Markdown Workflow

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Tags: JD Long, Paul Teetor, R Cookbook, Data Analysis