دانلود کتاب R in Action

فهرست مطالب :

R in Action, Third Edition MEAP V07
Copyright
Welcome letter
Brief contents
Chapter 1: Introduction to R
1.1 Why use R?
1.2 Obtaining and installing R
1.3 Working with R
1.3.1 Getting started
1.3.2 Using RStudio
1.3.3 Getting help
1.3.4 The workspace
1.3.5 Projects
1.4 Packages
1.4.1 What are packages?
1.4.2 Installing a package
1.4.3 Loading a package
1.4.4 Learning about a package
1.5 Using output as input: reusing results
1.6 Working with large datasets
1.7 Working through an example
1.8 Summary
Chapter 2: Creating a dataset
2.1 Understanding datasets
2.2 Data structures
2.2.1 Vectors
2.2.2 Matrices
2.2.3 Arrays
2.2.4 Data frames
2.2.5 Factors
2.2.6 Lists
2.2.7 Tibbles
2.3 Data input
2.3.1 Entering data from the keyboard
2.3.2 Importing data from a delimited text file
2.3.3 Importing data from Excel
2.3.4 Importing data from XML
2.3.5 Importing data from the Web
2.3.6 Importing data from SPSS
2.3.7 Importing data from SAS
2.3.8 Importing data from Stata
2.3.9 Accessing database management systems (DBMSs)
2.3.10 Importing data via Stat/Transfer
2.4 Annotating datasets
2.4.1 Variable labels
2.4.2 Value labels
2.5 Useful functions for working with data objects
2.6 Summary
Chapter 3: Basic data management
3.1 A working example
3.2 Creating new variables
3.3 Recoding variables
3.4 Renaming variables
3.5 Missing values
3.5.1 Recoding values to missing
3.5.2 Excluding missing values from analyses
3.6 Date values
3.6.1 Converting dates to character variables
3.6.2 Going further
3.7 Type conversions
3.8 Sorting data
3.9 Merging datasets
3.9.1 Adding columns to a data frame
3.9.2 Adding rows to a data frame
3.10 Subsetting datasets
3.10.1 Selecting variables
3.10.2 Dropping variables
3.10.3 Selecting observations
3.10.4 The subset() function
3.10.5 Random samples
3.11 Using dplyr to manipulate data frames
3.11.1 Basic dplyr functions
3.11.2 Using pipe operators to chain statements
3.12 Using SQL statements to manipulate data frames
3.13 Summary
Chapter 4: Getting started with graphs
4.1 Creating a graph with ggplot2
4.1.1 ggplot
4.1.2 Geoms
4.1.3 Grouping
4.1.4 Scales
4.1.5 Facets
4.1.6 Labels
4.1.7 Themes
4.2 ggplot2 details
4.2.1 Placing the data and mapping options
4.2.2 Graphs as objects
4.2.3 Exporting graphs
4.2.4 Common mistakes
4.3 Summary
Chapter 5: Advanced data management
5.1 A data-management challenge
5.2 Numerical and character functions
5.2.1 Mathematical functions
5.2.2 Statistical functions
5.2.3 Probability functions
5.2.4 Character functions
5.2.5 Other useful functions
5.2.6 Applying functions to matrices and data frames
5.3 A solution for the data-management challenge
5.4 Control flow
5.4.1 Repetition and looping
5.4.2 Conditional execution
5.5 User-written functions
5.6 Reshaping data
5.6.1 Transpose
5.6.2 Converting between wide to long dataset formats
5.7 Aggregating data
5.8 Summary
Chapter 6: Basic graphs
6.1 Bar charts
6.1.1 Simple bar charts
6.1.2 Stacked, grouped and filled bar charts
6.1.3 Mean bar charts
6.1.4 Tweaking bar charts
6.2 Pie charts
6.3 Tree maps
6.4 Histograms
6.5 Kernel density plots
6.6 Box plots
6.6.1 Using parallel box plots to compare groups
6.6.2 Violin plots
6.7 Dot plots
6.8 Summary
Chapter 7: Basic statistics
7.1 Descriptive statistics
7.1.1 A menagerie of methods
7.1.2 Even more methods
7.1.3 Descriptive statistics by group
7.1.4 Summarizing data interactively with dplyr
7.1.5 Visualizing results
7.2 Frequency and contingency tables
7.2.1 Generating frequency tables
7.2.2 Tests of independence
7.2.3 Measures of association
7.2.4 Visualizing results
7.3 Correlations
7.3.1 Types of correlations
7.3.2 Testing correlations for significance
7.3.3 Visualizing correlations
7.4 T-tests
7.4.1 Independent t-test
7.4.2 Dependent t-test
7.4.3 When there are more than two groups
7.5 Nonparametric tests of group differences
7.5.1 Comparing two groups
7.5.2 Comparing more than two groups
7.6 Visualizing group differences
7.7 Summary
Chapter 8: Regression
8.1 The many faces of regression
8.1.1 Scenarios for using OLS regression
8.1.2 What you need to know
8.2 OLS regression
8.2.1 Fitting regression models with lm()
8.2.2 Simple linear regression
8.2.3 Polynomial regression
8.2.4 Multiple linear regression
8.2.5 Multiple linear regression with interactions
8.3 Regression diagnostics
8.3.1 A typical approach
8.3.2 An enhanced approach
8.3.3 Multicollinearity
8.4 Unusual observations
8.4.1 Outliers
8.4.2 High-leverage points
8.4.3 Influential observations
8.5 Corrective measures
8.5.1 Deleting observations
8.5.2 Transforming variables
8.5.3 Adding or deleting variables
8.5.4 Trying a different approach
8.6 Selecting the “best” regression model
8.6.1 Comparing models
8.6.2 Variable selection
8.7 Taking the analysis further
8.7.1 Cross-validation
8.7.2 Relative importance
8.8 Summary
Chapter 9: Analysis of variance
9.1 A crash course on terminology
9.2 Fitting ANOVA models
9.2.1 The aov() function
9.2.2 The order of formula terms
9.3 One-way ANOVA
9.3.1 Multiple comparisons
9.3.2 Assessing test assumptions
9.4 One-way ANCOVA
9.4.1 Assessing test assumptions
9.4.2 Visualizing the results
9.5 Two-way factorial ANOVA
9.6 Repeated measures ANOVA
9.7 Multivariate analysis of variance (MANOVA)
9.7.1 Assessing test assumptions
9.7.2 Robust MANOVA
9.8 ANOVA as regression
9.9 Summary
Chapter 10: Power analysis
10.1 A quick review of hypothesis testing
10.2 Implementing power analysis with the pwr package
10.2.1 t-tests
10.2.2 ANOVA
10.2.3 Correlations
10.2.4 Linear models
10.2.5 Tests of proportions
10.2.6 Chi-square tests
10.2.7 Choosing an appropriate effect size in novel situations
10.3 Creating power analysis plots
10.4 Other packages
10.5 Summary
Chapter 11: Intermediate graphs
11.1 Scatter plots
11.1.1 Scatter-plot matrices
11.1.2 High-density scatter plots
11.1.3 3D scatter plots
11.1.4 Spinning 3D scatter plots
11.1.5 Bubble plots
11.2 Line charts
11.3 Corrgrams
11.4 Mosaic plots
11.5 Summary
Chapter 12: Resampling statistics and bootstrapping
12.1 Permutation tests
12.2 Permutation tests with the coin package
12.2.1 Independent two-sample and k-sample tests
12.2.2 Independence in contingency tables
12.2.3 Independence between numeric variables
12.2.4 Dependent two-sample and k-sample tests
12.2.5 Going further
12.3 Permutation tests with the lmPerm package
12.3.1 Simple and polynomial regression
12.3.2 Multiple regression
12.3.3 One-way ANOVA and ANCOVA
12.3.4 Two-way ANOVA
12.4 Additional comments on permutation tests
12.5 Bootstrapping
12.6 Bootstrapping with the boot package
12.6.1 Bootstrapping a single statistic
12.6.2 Bootstrapping several statistics
12.7 Summary
Chapter 13: Generalized linear models
13.1 Generalized linear models and the glm() function
13.1.1 The glm() function
13.1.2 Supporting functions
13.1.3 Model fit and regression diagnostics
13.2 Logistic regression
13.2.1 Interpreting the model parameters
13.2.2 Assessing the impact of predictors on the probability of an outcome
13.2.3 Overdispersion
13.2.4 Extensions
13.3 Poisson regression
13.3.1 Interpreting the model parameters
13.3.2 Overdispersion
13.3.3 Extensions
13.4 Summary
Chapter 14: Principal components and factor analysis
14.1 Principal components and factor analysis in R
14.2 Principal componentsxe \"PCA (principal components analysis)\"
14.2.1 Selecting the number of components to extract
14.2.2 Extracting principal componentsxe \"principal components:extracting\"
14.2.3 Rotating principal components
14.2.4 Obtaining principal components scores
14.3 Exploratory factor analysis
14.3.1 Deciding how many common factors to extract
14.3.2 Extracting common factors
14.3.3 Rotating factors
14.3.4 Factor scores
14.3.5 Other EFA-related packages
14.4 Other latent variable models
14.5 Summary
Chapter 15: Time series
15.1 Creating a time-series object in R
15.2 Smoothing and seasonal decomposition
15.2.1 Smoothing with simple moving averages
15.2.2 Seasonal decomposition
15.3 Exponential forecasting models
15.3.1 Simple exponential smoothing
15.3.2 Holt and Holt-Winters exponential smoothing
15.3.3 The ets() function and automated forecasting
15.4 ARIMA forecasting models
15.4.1 Prerequisite concepts
15.4.2 ARMA and ARIMA models
15.4.3 Automated ARIMA forecasting
15.5 Going further
15.6 Summary