R in Action, 2nd Ed, video edition

Video description

"Essential to anyone doing data analysis with R, whether in industry or academia."
Cristofer Weber, NeoGrid

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines.

Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide.
Inside:

  • Complete R language tutorial
  • Using R to manage, analyze, and visualize data
  • Techniques for debugging programs and creating packages
  • OOP in R
  • Over 160 graphs
This book/course is designed for readers who need to solve practical data analysis problems using the R language and tools. Some background in mathematics and statistics is helpful, but no prior experience with R or computer programming is required.

Dr. Rob Kabacoff is a seasoned researcher who specializes in data analysis. He has taught graduate courses in statistical programming and manages the Quick-R website at statmethods.net.

A go-to reference for general R and many statistics questions.
George Gaines, KYOS Systems Inc.

Accessible language, realistic examples, and clear code.
Samuel D. McQuillin, University of Houston

Offers a gentle learning curve to those starting out with R for the first time.
Indrajit Sen Gupta, Mu Sigma Business Solutions

NARRATED BY DALE OGDEN AND ROB KABACOFF

Table of contents

  1. PART 1. Getting started
    1. Chapter 1. Introduction to R
    2. Chapter 1. Obtaining and installing R
    3. Chapter 1. The workspace
    4. Chapter 1. Packages
    5. Chapter 1. Using output as input: reusing results
    6. Chapter 2. Creating a dataset
    7. Chapter 2. Data structures
    8. Chapter 2. Data frames
    9. Chapter 2. Factors
    10. Chapter 2. Data input
    11. Chapter 2. Importing data from Excel
    12. Chapter 2. Importing data from Stata
    13. Chapter 2. Annotating datasets
    14. Chapter 3. Getting started with graphs
    15. Chapter 3. A simple example
    16. Chapter 3. Text characteristics
    17. Chapter 3. Adding text, customized axes, and legends
    18. Chapter 3. Combining graphs
    19. Chapter 4. Basic data management
    20. Chapter 4. Recoding variables
    21. Chapter 4. Date values
    22. Chapter 4. Subsetting datasets
    23. Chapter 5. Advanced data management
    24. Chapter 5. Probability functions
    25. Chapter 5. A solution for the data-management challenge
    26. Chapter 5. User-written functions
    27. Chapter 5. Transpose
  2. PART 2. Basic methods
    1. Chapter 6. Basic graphs
    2. Chapter 6. Pie charts
    3. Chapter 6. Box plots
    4. Chapter 7. Basic statistics
    5. Chapter 7. Descriptive statistics by group
    6. Chapter 7. Frequency and contingency tables
    7. Chapter 7. Tests of independence
    8. Chapter 7. Correlations
    9. Chapter 7. T-tests
    10. Chapter 7. Nonparametric tests of group differences
  3. PART 3. Intermediate methods
    1. Chapter 8. Regression
    2. Chapter 8. OLS regression
    3. Chapter 8. Polynomial regression
    4. Chapter 8. Regression diagnostics
    5. Chapter 8. An enhanced approach
    6. Chapter 8. Unusual observations
    7. Chapter 8. Corrective measures
    8. Chapter 8. Selecting the “best” regression model
    9. Chapter 8. Taking the analysis further
    10. Chapter 9. Analysis of variance
    11. Chapter 9. Fitting ANOVA models
    12. Chapter 9. One-way ANOVA
    13. Chapter 9. One-way ANCOVA
    14. Chapter 9. Two-way factorial ANOVA
    15. Chapter 9. Multivariate analysis of variance (MANOVA)
    16. Chapter 10. Power analysis
    17. Chapter 10. Implementing power analysis with the pwr package
    18. Chapter 10. Linear models
    19. Chapter 10. Creating power analysis plots
    20. Chapter 11. Intermediate graphs
    21. Chapter 11. Scatter-plot matrices
    22. Chapter 11. Line charts
    23. Chapter 11. Mosaic plots
    24. Chapter 12. Resampling statistics and bootstrapping
    25. Chapter 12. Permutation tests with the coin package
    26. Chapter 12. Permutation tests with the lmPerm package
    27. Chapter 12. Additional comments on permutation tests
    28. Chapter 12. Bootstrapping with the boot package
  4. PART 4. Advanced methods
    1. Chapter 13. Generalized linear models
    2. Chapter 13. Logistic regression
    3. Chapter 13. Poisson regression
    4. Chapter 13. Extensions
    5. Chapter 14. Principal components and factor analysis
    6. Chapter 14. Principal components
    7. Chapter 14. Rotating principal components
    8. Chapter 14. Exploratory factor analysis
    9. Chapter 14. Rotating factors
    10. Chapter 14. Other latent variable models
    11. Chapter 15. Time series
    12. Chapter 15. Smoothing and seasonal decomposition
    13. Chapter 15. Exponential forecasting models
    14. Chapter 15. Holt and Holt-Winters exponential smoothing
    15. Chapter 15. ARIMA forecasting models
    16. Chapter 15. ARMA and ARIMA models
    17. Chapter 16. Cluster analysis
    18. Chapter 16. Calculating distances
    19. Chapter 16. Partitioning cluster analysis
    20. Chapter 16. Avoiding nonexistent clusters
    21. Chapter 17. Classification
    22. Chapter 17. Decision trees
    23. Chapter 17. Random forests
    24. Chapter 17. Support vector machines
    25. Chapter 17. Choosing a best predictive solution
    26. Chapter 17. Using the rattle package for data mining
    27. Chapter 18. Advanced methods for missing data
    28. Chapter 18. Exploring missing-values patterns
    29. Chapter 18. Understanding the sources and impact of missing data
    30. Chapter 18. Complete-case analysis (listwise deletion)
    31. Chapter 18. Other approaches to missing data
  5. PART 5. Expanding your skills
    1. Chapter 19. Advanced graphics with ggplot2
    2. Chapter 19. An introduction to the ggplot2 package
    3. Chapter 19. Grouping
    4. Chapter 19. Modifying the appearance of ggplot2 graphs
    5. Chapter 19. Saving graphs
    6. Chapter 20. Advanced programming
    7. Chapter 20. Control structures
    8. Chapter 20. Working with environments
    9. Chapter 20. Writing efficient code
    10. Chapter 20. Debugging
    11. Chapter 21. Creating a package
    12. Chapter 21. Developing the package
    13. Chapter 21. Printing the results
    14. Chapter 21. Creating the package documentation
    15. Chapter 21. Building the package
    16. Chapter 22. Creating dynamic reports
    17. Chapter 22. Creating dynamic reports with R and Markdown
    18. Chapter 22. Creating dynamic reports with R and LaTeX
    19. Chapter 22. Creating dynamic reports with R and Microsoft Word

Product information

  • Title: R in Action, 2nd Ed, video edition
  • Author(s): Robert I. Kabacoff
  • Release date: May 2015
  • Publisher(s): Manning Publications
  • ISBN: None