R in Action 3rd Edition by Robert Kabacoff ISBN 9781638357018 1638357013

R in Action 3rd Edition by Robert Kabacoff – Ebook PDF Instant Download/Delivery: 9781638357018, 1638357013

Instant download Full Chapter of R in Action 3rd Edition after payment

 

Product details:

ISBN 10: 1638357013
ISBN 13: 9781638357018 
Author: Robert I. Kabacoff 

Table of contents:

Part 1. Getting started

1 Introduction to R

• 1.1 Why use R?
• 1.2 Obtaining and installing R
• 1.3 Working with R
• 1.4 Packages
• 1.5 Using output as input: Reusing results
• 1.6 Working with large datasets
• 1.7 Working through an example

2 Creating a dataset

• 2.1 Understanding datasets
• 2.2 Data structures
• 2.3 Data input
• 2.4 Annotating datasets
• 2.5 Useful functions for working with data objects

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
• 3.6 Date values
• 3.7 Type conversions
• 3.8 Sorting data
• 3.9 Merging datasets
• 3.10 Subsetting datasets
• 3.11 Using dplyr to manipulate data frames
• 3.12 Using SQL statements to manipulate data frames

4 Getting started with graphs

• 4.1 Creating a graph with ggplot2
• 4.2 ggplot2 details

5 Advanced data management

• 5.1 A data management challenge
• 5.2 Numerical and character functions
• 5.3 Control flow
• 5.4 User-written functions
• 5.5 Reshaping data
• 5.6 Aggregating data

Part 2. Basic methods

6 Basic graphs

• 6.1 Bar charts
• 6.2 Pie charts
• 6.3 Tree maps
• 6.4 Histograms
• 6.5 Kernel density plots
• 6.6 Box plots
• 6.7 Dot plots

7 Basic statistics

• 7.1 Descriptive statistics
• 7.2 Frequency and contingency tables
• 7.3 Correlations
• 7.4 T-tests
• 7.5 Nonparametric tests of group differences
• 7.5.1 Comparing two groups
• 7.6 Visualizing group differences

Part 3. Intermediate methods

8 Regression

• 8.1 The many faces of regression
• 8.2 OLS regression
• 8.3 Regression diagnostics
• 8.4 Unusual observations
• 8.5 Corrective measures
• 8.6 Selecting the “best” regression model
• 8.7 Taking the analysis further

9 Analysis of variance

• 9.1 A crash course on terminology
• 9.2 Fitting ANOVA models
• 9.3 One-way ANOVA
• 9.4 One-way ANCOVA
• 9.5 Two-way factorial ANOVA
• 9.6 Repeated measures ANOVA
• 9.7 Multivariate analysis of variance (MANOVA)
• 9.8 ANOVA as regression

10 Power analysis

• 10.1 A quick review of hypothesis testing
• 10.2 Implementing power analysis with the pwr package
• 10.3 Creating power analysis plots
• 10.4 Other packages

11 Intermediate graphs

• 11.1 Scatter plots
• 11.2 Line charts
• 11.3 Corrgrams
• 11.4 Mosaic plots

12 Resampling statistics and bootstrapping

• 12.1 Permutation tests
• 12.2 Permutation tests with the coin package
• 12.3 Permutation tests with the lmPerm package
• 12.4 Additional comments on permutation tests
• 12.5 Bootstrapping
• 12.6 Bootstrapping with the boot package

Part 4. Advanced methods

13 Generalized linear models

• 13.1 Generalized linear models and the glm() function
• 13.2 Logistic regression
• 13.3 Poisson regression
• 14 Principal components and factor analysis
• 14.1 Principal components and factor analysis in R
• 14.2 Principal components
• 14.3 Exploratory factor analysis
• 14.4 Other latent variable models

15 Time series

• 15.1 Creating a time-series object in R
• 15.2 Smoothing and seasonal decomposition
• 15.3 Exponential forecasting models
• 15.4 ARIMA forecasting models
• 15.5 Going further

16 Cluster analysis

• 16.1 Common steps in cluster analysis
• 16.2 Calculating distances
• 16.3 Hierarchical cluster analysis
• 16.4 Partitioning-cluster analysis
• 16.5 Avoiding nonexistent clusters
• 16.6 Going further

17 Classification

• 17.1 Preparing the data
• 17.2 Logistic regression
• 17.3 Decision trees
• 17.4 Random forests
• 17.5 Support vector machines
• 17.6 Choosing a best predictive solution
• 17.7 Understanding black box predictions
• 17.8 Going further

18 Advanced methods for missing data

• 18.1 Steps in dealing with missing data
• 18.2 Identifying missing values
• 18.3 Exploring missing-values patterns
• 18.4 Understanding the sources and impact of missing data
• 18.5 Rational approaches for dealing with incomplete data
• 18.6 Deleting missing data
• 18.7 Single imputation
• 18.8 Multiple imputation
• 18.9 Other approaches to missing data

Part 5. Expanding your skills

19 Advanced graphs

• 19.1 Modifying scales
• 19.2 Modifying themes
• 19.3 Adding annotations
• 19.4 Combining graphs
• 19.5 Making graphs interactive

20 Advanced programming

• 20.1 A review of the language
• 20.2 Working with environments
• 20.3 Non-standard evaluation
• 20.4 Object-oriented programming
• 20.5 Writing efficient code
• 20.6 Debugging
• 20.7 Going further

21 Creating dynamic reports

• 21.1 A template approach to reports
• 21.2 Creating a report with R and R Markdown
• 21.3 Creating a report with R and LaTeX
• 21.4 Avoiding common R Markdown problems
• 21.5 Going further

22 Creating a package

• 22.1 The edatools package
• 22.2 Creating a package
• 22.3 Sharing your package
• 22.4 Going further

People also search:

r in action second edition
r in action (3rd edition github)
r in action 2ed
r in action second edition pdf
r in action amazon