Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
"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
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
-
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
Product information
- Title: R in Action, 2nd Ed, video edition
- Author(s):
- Release date: May 2015
- Publisher(s): Manning Publications
- ISBN: None
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