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R in Action, 2ed

Robert L. Kabacoff

ISBN: 9789351198079

608 pages

INR 849

Description

R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining and dynamic report writing.

 

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 Batch processing

    1.6 Using output as input: reusing results

    1.7 Working with large datasets

    1.8 Working through an example

    1.9 Summary

 

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

    2.6 Summary

 

3 Getting started with graphs

    3.1 Working with graphs

    3.2 A simple example

    3.3 Graphical parameters

    3.4 Adding text, customized axes and legends

    3.5 Combining graphs

    3.6 Summary

 

4 Basic data management

    4.1 A working example

    4.2 Creating new variables

    4.3 Recoding variables

    4.4 Renaming variables

    4.5 Missing values

    4.6 Date values

    4.7 Type conversions

    4.8 Sorting data

    4.9 Merging datasets

    4.10 Subsetting datasets

    4.11 Using SQL statements to manipulate data frames

    4.12 Summary

 

5 Advanced data management

    5.1 A data-management challenge

    5.2 Numerical and character functions

    5.3 A solution for the data-management challenge

    5.4 Control flow

    5.5 User-written functions

    5.6 Aggregation and reshaping

    5.7 Summary

 

Part 2 Basic methods

 

6 Basic graphs

    6.1 Bar plots

    6.2 Pie charts

    6.3 Histograms

    6.4 Kernel density plots

    6.5 Box plots

    6.6 Dot plots

    6.7 Summary

 

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

    7.6 Visualizing group differences

    7.7 Summary

 

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

    8.8 Summary

 

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

    9.9 Summary

 

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

    10.5 Summary

 

11 Intermediate graphs

    11.1 Scatter plots

    11.2 Line charts

    11.3 Corrgrams

    11.4 Mosaic plots

    11.5 Summary

 

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

    12.7 Summary

 

Part 4 Advanced methods

 

13 Generalized linear models

    13.1 Generalized linear models and the glm() function

    13.2 Logistic regression

    13.4 Poisson regression

    13.5 Summary

 

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

    14.5 Summary

 

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

    15.6 Summary

 

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 Summary

 

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 Using the rattle package for data mining

    17.8 Summary

 

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 Complete-case analysis (listwise deletion)

    18.7 Multiple imputation

    18.8 Other approaches to missing data

    18.9 Summary

 

 

Part 5 Expanding your skills

 

19 Advanced graphics with ggplot2

    19.1 The four graphics systems in R

    19.2 An introduction to the ggplot2 package

    19.3 Specifying the plot type with geoms

    19.4 Grouping

    19.5 Faceting

    19.6 Adding smoothed lines

    19.7 Modifying the appearance of ggplot2 graphs

    19.8 Saving graphs

    19.9 Summary

 

20 Advanced programming

    20.1 A review of the language

    20.2 Working with environments

    20.3 Object-oriented programming

    20.4 Writing efficient code

    20.5 Debugging

    20.6 Going further

    20.7 Summary

 

21 Creating a package

    21.1 Nonparametric analysis and the npar package

    21.2 Developing the package

    21.3 Creating the package documentation

    21.4 Building the package

    21.5 Going further

    21.6 Summary

 

22 Creating dynamic reports

    22.1 A template approach to reports

    22.2 Creating dynamic reports with R and Markdown

    22.3 Creating dynamic reports with R and LaTeX

    22.4 Creating dynamic reports with R and Open Document

    22.5 Creating dynamic reports with R and Microsoft Word

    22.6 Summary