## Book description

Conquer the complexities of this open source statistical language

R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming.

• R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex

• This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually used

• Covers getting started with R and using it for simple summary statistics, hypothesis testing, and graphs

• Shows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regression

• Provides beginning programming instruction for those who want to write their own scripts

Beginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence.

1. Cover
2. Contents
3. Chapter 1: Introducing R: What It Is and How to Get It
1. Getting the Hang of R
2. Running the R Program
3. Finding Your Way with R
4. Command Packages
5. Summary
4. Chapter 2: Starting Out: Becoming Familiar with R
1. Some Simple Math
2. Reading and Getting Data into R
3. Viewing Named Objects
4. Types of Data Items
5. The Structure of Data Items
6. Examining Data Structure
7. Working with History Commands
8. Saving Your Work in R
9. Summary
5. Chapter 3: Starting Out: Working With Objects
1. Manipulating Objects
2. Viewing Objects within Objects
3. Constructing Data Objects
4. Forms of Data Objects: Testing and Converting
5. Summary
6. Chapter 4: Data: Descriptive Statistics and Tabulation
1. Summary Commands
2. Summarizing Samples
3. Summary Tables
4. Summary
7. Chapter 5: Data: Distribution
1. Looking at the Distribution of Data
2. Summary
8. Chapter 6: Simple Hypothesis Testing
1. Using the Student’s t-test
2. The Wilcoxon U-Test (Mann-Whitney)
3. Paired t- and U-Tests
4. Correlation and Covariance
5. Tests for Association
6. Summary
9. Chapter 7: Introduction to Graphical Analysis
1. Box-whisker Plots
2. Scatter Plots
3. Pairs Plots (Multiple Correlation Plots)
4. Line Charts
5. Pie Charts
6. Cleveland Dot Charts
7. Bar Charts
8. Copy Graphics to Other Applications
9. Summary
10. Chapter 8: Formula Notation and Complex Statistics
1. Examples of Using Formula Syntax for Basic Tests
2. Formula Notation in Graphics
3. Analysis of Variance (ANOVA)
4. Summary
11. Chapter 9: Manipulating Data and Extracting Components
1. Creating Data for Complex Analysis
2. Summarizing Data
3. Summary
12. Chapter 10: Regression (Linear Modeling)
1. Simple Linear Regression
2. Multiple Regression
3. Curvilinear Regression
4. Plotting Linear Models and Curve Fitting
5. Summarizing Regression Models
6. Summary
13. Chapter 11: More About Graphs
1. Adding Elements to Existing Plots
2. Matrix Plots (Multiple Series on One Graph)
3. Multiple Plots in One Window
4. Exporting Graphs
5. Summary
14. Chapter 12: Writing Your Own Scripts: Beginning to Program
1. Copy and Paste Scripts
2. Creating Simple Functions
3. Making Source Code
4. Summary
15. Appendix: Answers to Exercises
1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10
11. Chapter 11
12. Chapter 12
16. Introduction