TY  - BOOK
AU  - Jared P Lander 
TI  - R for Everyone: Advanced Analytics and Graphics
T2  -  	Addison-Wesley data and analytics series
SN  - 978933253242
U1  - 005.13 LAN-R 
PY  - 2014///
CY  - Noida
PB  - Pearson Education
KW  -  	      R (Computer program language)     Scripting languages (Computer science)     Statistics -- Data processing.
N1  -  	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 -- 2. The R Environment -- 2.1.Command Line Interface -- 2.2. RStudio -- 2.3. Revolution Analytics RPE -- 2.4. Conclusion -- 3.R Packages -- 3.1. Installing Packages -- 3.2. Loading Packages -- 3.3. Building a Package -- 3.4. Conclusion -- 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 -- 5. Advanced Data Structures -- 5.1.data.frames -- 5.2. Lists -- 5.3. Matrices -- 5.4. Arrays -- 5.5. Conclusion -- 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 -- 7. Statistical Graphics -- 7.1. Base Graphics -- 7.2.ggplot2 -- 7.3. Conclusion -- 8. Writing R Functions -- 8.1. Hello, World! -- 8.2. Function Arguments -- 8.3. Return Values -- 8.4.do. call -- 8.5. Conclusion -- 9. Control Statements -- 9.1. If and else -- 9.2.switch -- 9.3.ifelse -- 9.4.Compound Tests -- 9.5. Conclusion -- 10. Loops, the Un-R Way to Iterate -- 10.1. For Loops -- 10.2. While Loops -- 10.3. Controlling Loops -- 10.4. Conclusion -- 11. Group Manipulation -- 11.1. Apply Family -- 11.2.aggregate -- 11.3.plyr -- 11.4.data.table -- 11.5. Conclusion -- 12. Data Reshaping -- 12.1.cbind and rbind -- 12.2. Joins -- 12.3.reshape2 -- 12.4. Conclusion -- 13. Manipulating Strings -- 13.1.paste -- 13.2.sprintf -- 13.3. Extracting Text -- 13.4. Regular Expressions -- 13.5. Conclusion -- 14. Probability Distributions -- 14.1. Normal Distribution -- 14.2. Binomial Distribution -- 14.3. Poisson Distribution -- 14.4. Other Distributions -- 14.5. Conclusion -- 15. Basic Statistics -- 15.1. Summary Statistics -- 15.2. Correlation and Covariance -- 15.3.T-Tests -- 15.4. ANOVA -- 15.5. Conclusion -- 16. Linear Models -- 16.1. Simple Linear Regression -- 16.2. Multiple Regression -- 16.3. Conclusion -- 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 -- 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 -- 19. Regularization and Shrinkage -- 19.1. Elastic Net -- 19.2. Bayesian Shrinkage -- 19.3. Conclusion -- 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 -- 21. Time Series and Autocorrelation -- 21.1. Autoregressive Moving Average -- 21.2. VAR -- 21.3. GARCH -- 21.4. Conclusion -- 22. Clustering -- 22.1.K-means -- 22.2. PAM -- 22.3. Hierarchical Clustering -- 22.4. Conclusion -- 23. Reproducibility, Reports and Slide Shows with knitr -- 23.1. Installing 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 -- 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 -- A. Real-Life Resources -- A.1. Meetups -- A.2. Stackoverflow -- A.3. Twitter -- A.4. Conferences -- A.5. Web Sites -- A.6. Documents -- A.7. Books -- A.8. Conclusion -- B. Glossary
N2  - Using the free, open source R language, scientists, financial analysts, public policy professionals, and programmers can build powerful statistical models capable of answering many of their most challenging questions. But, for non-statisticians, R can be difficult to learn-and most books on the subject assume far too much knowledge to help the non-statistician. R for Everyone is the solution. Drawing on his extensive experience teaching new users through the New York City R User Group, professional statistician Jared Lander has written the perfect R tutorial for everyone who's new to statistical programming and modeling
ER  -