by Robert I. Kabacoff | Released May 2015 | ISBN: None

"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 Show and hide more

1. PART 1. Getting started
   • Chapter 1. Introduction to R 00:09:42
   • Chapter 1. Obtaining and installing R 00:06:42
   • Chapter 1. The workspace 00:06:24
   • Chapter 1. Packages 00:07:47
   • Chapter 1. Using output as input: reusing results 00:05:39
   • Chapter 2. Creating a dataset 00:04:59
   • Chapter 2. Data structures 00:07:14
   • Chapter 2. Data frames 00:06:02
   • Chapter 2. Factors 00:09:04
   • Chapter 2. Data input 00:08:05
   • Chapter 2. Importing data from Excel 00:08:35
   • Chapter 2. Importing data from Stata 00:07:21
   • Chapter 2. Annotating datasets 00:04:43
   • Chapter 3. Getting started with graphs 00:06:34
   • Chapter 3. A simple example 00:07:13
   • Chapter 3. Text characteristics 00:04:13
   • Chapter 3. Adding text, customized axes, and legends 00:09:02
   • Chapter 3. Combining graphs 00:06:22
   • Chapter 4. Basic data management 00:06:14
   • Chapter 4. Recoding variables 00:09:32
   • Chapter 4. Date values 00:08:31
   • Chapter 4. Subsetting datasets 00:09:14
   • Chapter 5. Advanced data management 00:06:56
   • Chapter 5. Probability functions 00:07:52
   • Chapter 5. A solution for the data-management challenge 00:07:03
   • Chapter 5. User-written functions 00:04:31
   • Chapter 5. Transpose 00:06:31
2. PART 2. Basic methods
   • Chapter 6. Basic graphs 00:09:02
   • Chapter 6. Pie charts 00:09:55
   • Chapter 6. Box plots 00:10:22
   • Chapter 7. Basic statistics 00:08:20
   • Chapter 7. Descriptive statistics by group 00:04:17
   • Chapter 7. Frequency and contingency tables 00:07:33
   • Chapter 7. Tests of independence 00:04:41
   • Chapter 7. Correlations 00:08:51
   • Chapter 7. T-tests 00:05:10
   • Chapter 7. Nonparametric tests of group differences 00:08:24
3. PART 3. Intermediate methods
   • Chapter 8. Regression 00:08:37
   • Chapter 8. OLS regression 00:06:17
   • Chapter 8. Polynomial regression 00:09:38
   • Chapter 8. Regression diagnostics 00:07:13
   • Chapter 8. An enhanced approach 00:10:56
   • Chapter 8. Unusual observations 00:06:46
   • Chapter 8. Corrective measures 00:07:18
   • Chapter 8. Selecting the “best” regression model 00:08:58
   • Chapter 8. Taking the analysis further 00:09:59
   • Chapter 9. Analysis of variance 00:06:57
   • Chapter 9. Fitting ANOVA models 00:04:46
   • Chapter 9. One-way ANOVA 00:05:58
   • Chapter 9. One-way ANCOVA 00:04:46
   • Chapter 9. Two-way factorial ANOVA 00:06:44
   • Chapter 9. Multivariate analysis of variance (MANOVA) 00:08:29
   • Chapter 10. Power analysis 00:08:36
   • Chapter 10. Implementing power analysis with the pwr package 00:07:21
   • Chapter 10. Linear models 00:08:37
   • Chapter 10. Creating power analysis plots 00:04:54
   • Chapter 11. Intermediate graphs 00:06:54
   • Chapter 11. Scatter-plot matrices 00:09:16
   • Chapter 11. Line charts 00:08:06
   • Chapter 11. Mosaic plots 00:04:38
   • Chapter 12. Resampling statistics and bootstrapping 00:07:18
   • Chapter 12. Permutation tests with the coin package 00:08:01
   • Chapter 12. Permutation tests with the lmPerm package 00:05:19
   • Chapter 12. Additional comments on permutation tests 00:04:25
   • Chapter 12. Bootstrapping with the boot package 00:08:29
4. PART 4. Advanced methods
   • Chapter 13. Generalized linear models 00:09:32
   • Chapter 13. Logistic regression 00:09:45
   • Chapter 13. Poisson regression 00:07:04
   • Chapter 13. Extensions 00:04:54
   • Chapter 14. Principal components and factor analysis 00:06:48
   • Chapter 14. Principal components 00:09:14
   • Chapter 14. Rotating principal components 00:05:19
   • Chapter 14. Exploratory factor analysis 00:05:26
   • Chapter 14. Rotating factors 00:05:38
   • Chapter 14. Other latent variable models 00:04:10
   • Chapter 15. Time series 00:07:33
   • Chapter 15. Smoothing and seasonal decomposition 00:10:02
   • Chapter 15. Exponential forecasting models 00:06:13
   • Chapter 15. Holt and Holt-Winters exponential smoothing 00:05:06
   • Chapter 15. ARIMA forecasting models 00:05:54
   • Chapter 15. ARMA and ARIMA models 00:08:50
   • Chapter 16. Cluster analysis 00:10:10
   • Chapter 16. Calculating distances 00:09:15
   • Chapter 16. Partitioning cluster analysis 00:08:44
   • Chapter 16. Avoiding nonexistent clusters 00:03:30
   • Chapter 17. Classification 00:09:07
   • Chapter 17. Decision trees 00:08:32
   • Chapter 17. Random forests 00:04:28
   • Chapter 17. Support vector machines 00:07:26
   • Chapter 17. Choosing a best predictive solution 00:05:08
   • Chapter 17. Using the rattle package for data mining 00:08:29
   • Chapter 18. Advanced methods for missing data 00:10:22
   • Chapter 18. Exploring missing-values patterns 00:08:16
   • Chapter 18. Understanding the sources and impact of missing data 00:06:14
   • Chapter 18. Complete-case analysis (listwise deletion) 00:10:09
   • Chapter 18. Other approaches to missing data 00:04:42
5. PART 5. Expanding your skills
   • Chapter 19. Advanced graphics with ggplot2 00:06:19
   • Chapter 19. An introduction to the ggplot2 package 00:08:20
   • Chapter 19. Grouping 00:09:08
   • Chapter 19. Modifying the appearance of ggplot2 graphs 00:08:45
   • Chapter 19. Saving graphs 00:02:28
   • Chapter 20. Advanced programming 00:11:26
   • Chapter 20. Control structures 00:06:46
   • Chapter 20. Working with environments 00:07:49
   • Chapter 20. Writing efficient code 00:07:45
   • Chapter 20. Debugging 00:10:18
   • Chapter 21. Creating a package 00:09:32
   • Chapter 21. Developing the package 00:05:19
   • Chapter 21. Printing the results 00:06:06
   • Chapter 21. Creating the package documentation 00:04:09
   • Chapter 21. Building the package 00:08:45
   • Chapter 22. Creating dynamic reports 00:07:00
   • Chapter 22. Creating dynamic reports with R and Markdown 00:07:09
   • Chapter 22. Creating dynamic reports with R and LaTeX 00:06:16
   • Chapter 22. Creating dynamic reports with R and Microsoft Word 00:07:35
Show and hide more