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    R cookbook : proven recipes for data analysis, statistics, and graphics / J.D. Long & Paul Teetor

    • Title:R cookbook : proven recipes for data analysis, statistics, and graphics / J.D. Long & Paul Teetor
    •    
    • Author/Creator:Long, J. D., author.
    • Other Contributors/Collections:Teetor, Paul, 1954- author.
      Teetor, Paul, 1954- R cookbook.
    • Published/Created:Sebastopol, CA : O'Reilly, 2019.
    • Holdings

       
    • Library of Congress Subjects:R (Computer program language)
      Mathematical statistics--Data processing.
      Statistics--Data processing.
      Multiple comparisons (Statistics)
    • Edition:Second edition
    • Description:xvii, 579 pages : illustrations ; 24 cm
    • Summary:"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." -- Provided by publisher.
    • Notes:Previous edition: published as by Paul Teetor. 2011.
      Includes bibliographical references and index.
    • ISBN:9781492040682 paperback
      1492040681 paperback
    • Contents:Machine generated contents note: 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 Statistic
      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
      Contents note continued: 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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