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by Jared P. Lander
R for Everyone: Advanced Analytics and Graphics
Title Page
Copyright Page
Dedication Page
About This eBook
Contents
Foreword
Preface
Acknowledgments
About the Author
Chapter 1. Getting R
1.1. Downloading R
1.2. R Version
1.3. 32-bit versus 64-bit
1.4. Installing
1.5. Revolution R Community Edition
1.6. Conclusion
Chapter 2. The R Environment
2.1. Command Line Interface
2.2. RStudio
2.3. Revolution Analytics RPE
2.4. Conclusion
Chapter 3. R Packages
3.1. Installing Packages
3.2. Loading Packages
3.3. Building a Package
3.4. Conclusion
Chapter 4. Basics of R
4.1. Basic Math
4.2. Variables
4.3. Data Types
4.4. Vectors
4.5. Calling Functions
4.6. Function Documentation
4.7. Missing Data
4.8. Conclusion
Chapter 5. Advanced Data Structures
5.1. data.frames
5.2. Lists
5.3. Matrices
5.4. Arrays
5.5. Conclusion
Chapter 6. Reading Data into R
6.1. Reading CSVs
6.2. Excel Data
6.3. Reading from Databases
6.4. Data from Other Statistical Tools
6.5. R Binary Files
6.6. Data Included with R
6.7. Extract Data from Web Sites
6.8. Conclusion
Chapter 7. Statistical Graphics
7.1. Base Graphics
7.2. ggplot2
7.3. Conclusion
Chapter 8. Writing R functions
8.1. Hello, World!
8.2. Function Arguments
8.3. Return Values
8.4. do.call
8.5. Conclusion
Chapter 9. Control Statements
9.1. if and else
9.2. switch
9.3. ifelse
9.4. Compound Tests
9.5. Conclusion
Chapter 10. Loops, the Un-R Way to Iterate
10.1. for Loops
10.2. while Loops
10.3. Controlling Loops
10.4. Conclusion
Chapter 11. Group Manipulation
11.1. Apply Family
11.2. aggregate
11.3. plyr
11.4. data.table
11.5. Conclusion
Chapter 12. Data Reshaping
12.1. cbind and rbind
12.2. Joins
12.3. reshape2
12.4. Conclusion
Chapter 13. Manipulating Strings
13.1. paste
13.2. sprintf
13.3. Extracting Text
13.4. Regular Expressions
13.5. Conclusion
Chapter 14. Probability Distributions
14.1. Normal Distribution
14.2. Binomial Distribution
14.3. Poisson Distribution
14.4. Other Distributions
14.5. Conclusion
Chapter 15. Basic Statistics
15.1. Summary Statistics
15.2. Correlation and Covariance
15.3. T-Tests
15.4. ANOVA
15.5. Conclusion
Chapter 16. Linear Models
16.1. Simple Linear Regression
16.2. Multiple Regression
16.3. Conclusion
Chapter 17. Generalized Linear Models
17.1. Logistic Regression
17.2. Poisson Regression
17.3. Other Generalized Linear Models
17.4. Survival Analysis
17.5. Conclusion
Chapter 18. Model Diagnostics
18.1. Residuals
18.2. Comparing Models
18.3. Cross-Validation
18.4. Bootstrap
18.5. Stepwise Variable Selection
18.6. Conclusion
Chapter 19. Regularization and Shrinkage
19.1. Elastic Net
19.2. Bayesian Shrinkage
19.3. Conclusion
Chapter 20. Nonlinear Models
20.1. Nonlinear Least Squares
20.2. Splines
20.3. Generalized Additive Models
20.4. Decision Trees
20.5. Random Forests
20.6. Conclusion
Chapter 21. Time Series and Autocorrelation
21.1. Autoregressive Moving Average
21.2. VAR
21.3. GARCH
21.4. Conclusion
Chapter 22. Clustering
22.1. K-means
22.2. PAM
22.3. Hierarchical Clustering
22.4. Conclusion
Chapter 23. Reproducibility, Reports and Slide Shows with knitr
23.1. Installing a LATEX Program
23.2. LATEX Primer
23.3. Using knitr with LATEX
23.4. Markdown Tips
23.5. Using knitr and Markdown
23.6. pandoc
23.7. Conclusion
Chapter 24. Building R Packages
24.1. Folder Structure
24.2. Package Files
24.3. Package Documentation
24.4. Checking, Building and Installing
24.5. Submitting to CRAN
24.6. C++ Code
24.7. Conclusion
Appendix A. Real-Life Resources
A.1. Meetups
A.2. Stack Overflow
A.3. Twitter
A.4. Conferences
A.5. Web Sites
A.6. Documents
A.7. Books
A.8. Conclusion
Appendix B. Glossary
List of Figures
List of Tables
General Index
Index of Functions
Index of Packages
Index of People
Data Index
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