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R cookbook : proven recipes for data analysis, statistics, and graphics / J.D. Long & Paul Teetor
Bibliographic Record Display
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Title:R cookbook : proven recipes for data analysis, statistics, and graphics / J.D. Long & Paul Teetor
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Author/Creator:Long, J. D., author.
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Other Contributors/Collections:Teetor, Paul, 1954- author.
Teetor, Paul, 1954- R cookbook.
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Published/Created:Sebastopol, CA : O'Reilly, 2019.
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Holdings
Holdings Record Display
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Location:DAVID LAM LIBRARY stacksWhere is this?
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Call Number: QA276.45.R3 L66 2019
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Number of Items:1
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Status:Available
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Location:DAVID LAM LIBRARY stacksWhere is this?
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Library of Congress Subjects:R (Computer program language)
Mathematical statistics--Data processing.
Statistics--Data processing.
Multiple comparisons (Statistics)
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Edition:Second edition
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Description:xvii, 579 pages : illustrations ; 24 cm
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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.
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Notes:Previous edition: published as by Paul Teetor. 2011.
Includes bibliographical references and index.
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ISBN:9781492040682 paperback
1492040681 paperback
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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.