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R for Everyone: Advanced Analytics and Graphics

By: Material type: TextTextSeries: Addison-Wesley data and analytics seriesPublication details: Noida Pearson Education 2014Description: xxi,432p. : illustrations, maps ; 24 cmISBN:
  • 978933253242
Subject(s): DDC classification:
  • 005.13 LAN-R
Contents:
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.
Summary: 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.
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Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 005.13 LAN-R (Browse shelf(Opens below)) Checked out to ASHWINI JAYACHANDRAN (DCBSMC22004) 22/02/2023 DCB3314

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.

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.

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