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