English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 14 Hours | 2.33 GB
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.
Table of Contents
1 Introduction to R
2 Obtaining and installing R
3 The workspace
4 Packages
5 Using output as input – reusing results
6 Creating a dataset
7 Data structures
8 Data frames
9 Factors
10 Data input
11 Importing data from Excel
12 Importing data from Stata
13 Annotating datasets
14 Getting started with graphs
15 A simple example
16 Text characteristics
17 Adding text, customized axes, and legends
18 Combining graphs
19 Basic data management
20 Recoding variables
21 Date values
22 Subsetting datasets
23 Advanced data management
24 Probability functions
25 A solution for the data-management challenge
26 User-written functions
27 Transpose
28 Basic graphs
29 Pie charts
30 Box plots
31 Basic statistics
32 Descriptive statistics by group
33 Frequency and contingency tables
34 Tests of independence
35 Correlations
36 T-tests
37 Nonparametric tests of group differences
38 Regression
39 OLS regression
40 Polynomial regression
41 Regression diagnostics
42 An enhanced approach
43 Unusual observations
44 Corrective measures
45 Selecting the “best” regression model
46 Taking the analysis further
47 Analysis of variance
48 Fitting ANOVA models
49 One-way ANOVA
50 One-way ANCOVA
51 Two-way factorial ANOVA
52 Multivariate analysis of variance (MANOVA)
53 Power analysis
54 Implementing power analysis with the pwr package
55 Linear models
56 Creating power analysis plots
57 Intermediate graphs
58 Scatter-plot matrices
59 Line charts
60 Mosaic plots
61 Resampling statistics and bootstrapping
62 Permutation tests with the coin package
63 Permutation tests with the lmPerm package
64 Additional comments on permutation tests
65 Bootstrapping with the boot package
66 Generalized linear models
67 Logistic regression
68 Poisson regression
69 Extensions
70 Principal components and factor analysis
71 Principal components
72 Rotating principal components
73 Exploratory factor analysis
74 Rotating factors
75 Other latent variable models
76 Time series
77 Smoothing and seasonal decomposition
78 Exponential forecasting models
79 Holt and Holt-Winters exponential smoothing
80 ARIMA forecasting models
81 ARMA and ARIMA models
82 Cluster analysis
83 Calculating distances
84 Partitioning cluster analysis
85 Avoiding nonexistent clusters
86 Classification
87 Decision trees
88 Random forests
89 Support vector machines
90 Choosing a best predictive solution
91 Using the rattle package for data mining
92 Advanced methods for missing data
93 Exploring missing-values patterns
94 Understanding the sources and impact of missing data
95 Complete-case analysis (listwise deletion)
96 Other approaches to missing data
97 Advanced graphics with ggplot2
98 An introduction to the ggplot2 package
99 Grouping
100 Modifying the appearance of ggplot2 graphs
101 Saving graphs
102 Advanced programming
103 Control structures
104 Working with environments
105 Writing efficient code
106 Debugging
107 Creating a package
108 Developing the package
109 Printing the results
110 Creating the package documentation
111 Building the package
112 Creating dynamic reports
113 Creating dynamic reports with R and Markdown
114 Creating dynamic reports with R and LaTeX
115 Creating dynamic reports with R and Microsoft Word
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