Beginning R The Statistical Programming Language

ISBN-10: 111816430X

ISBN-13: 9781118164303

Edition: 2012

Authors: Mark Gardener

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Book content includes:Getting started with RSimple Summary StatisticsSimple Hypothesis TestingSimple GraphsFormula Notation and Complex StatisticsManipulating Data and Extracting ComponentsRegression (linear modeling)More About GraphsWriting Your Own Scripts: Beginning to Program
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Book details

List price: $41.99
Copyright year: 2012
Publisher: John Wiley & Sons, Incorporated
Publication date: 6/5/2012
Binding: Paperback
Pages: 504
Size: 7.25" wide x 9.25" long x 1.00" tall
Weight: 1.870
Language: English

Mark Gardener ( is an ecologist, lecturer, and writer working in the UK. His primary area of research was in pollination ecology and he has worked in the UK and around the word (principally Australia and the United States). Since his doctorate he has worked in many areas of ecology, often as a teacher and supervisor. He believes that ecological data, especially community data, is the most complicated and ill-behaved and is consequently the most fun to work with. He was introduced to R by a like-minded pedant whilst working in Australia during his doctorate. Learning R was not only fun but opened up a new avenue, making the study of community ecology a whole lot easier. He is currently self-employed and runs courses in ecology, data analysis, and R for a variety of organizations. Mark lives in rural Devon with his wife Christine, a biochemist who consequently has little need of statistics.

Introducing R: What It Is and How to Get It
Getting the Hang of R
The R Website
Downloading and Installing R from CRAN
Installing R on Your Windows Computer
Installing R on Your Macintosh Computer
Installing R on Your Linux Computer
Running the R Program
Finding Your Way with R
Getting Help via the CRAN Website and the Internet
The Help Command in R
Help for Windows Users
Help for Macintosh Users
Help for Linux Users
Help For All Users
Anatomy of a Help Item in R
Command Packages
Standard Command Packages
What Extra Packages Can Do for You
How to Get Extra Packages of R Commands
How to Install Extra Packages for Windows Users
How to Install Extra Packages for Macintosh Users
How to Install Extra Packages for Linux Users
Running and Manipulating Packages
Loading Packages
Windows-Specific Package Commands
Macintosh-Specific Package Commands
Removing or Unloading Packages
Starting Out: Becoming Familiar with R
Some Simple Math
Use R Like a Calculator
Storing the Results of Calculations
Reading and Getting Data into R
Using the combine Command for Making Data
Entering Numerical Items as Data
Entering Text Items as Data
Using the scan Command for Making Data
Entering Text as Data
Using the Clipboard to Make Data
Reading a File of Data from a Disk
Reading Bigger Data Files
The read.csv() Command
Alternative Commands for Reading Data in R
Missing Values in Data Files
Viewing Named Objects
Viewing Previously Loaded Named-Objects
Viewing All Objects
Viewing Only Matching Names
Removing Objects from R
Types of Data Items
Number Data
Text Items
Converting Between Number and Text Data
The Structure of Data Items
Vector Items
Data Frames
Matrix Objects
List Objects
Examining Data Structure
Working with History Commands
Using History Files
Viewing the Previous Command History
Saving and Recalling Lists of Commands
Alternative History Commands in Macintosh OS
Editing History Files
Saving Your Work in R
Saving the Workspace on Exit
Saving Data Files to Disk
Save Named Objects
Save Everything
Reading Data Files from Disk
Saving Data to Disk as Text Files
Writing Vector Objects to Disk
Writing Matrix and Data Frame Objects to Disk
Writing List Objects to Disk
Converting List Objects to Data Frames
Starting Out: Working With Objects
Manipulating Objects
Manipulating Vectors
Selecting and Displaying Parts of a Vector
Sorting and Rearranging a Vector
Returning Logical Values from a Vector
Manipulating Matrix and Data Frames
Selecting and Displaying Parts of a Matrix or Data Frame
Sorting and Rearranging a Matrix or Data Frame
Manipulating Lists
Viewing Objects within Objects
Looking Inside Complicated Data Objects
Opening Complicated Data Objects
Quick Looks at Complicated Data Objects
Viewing and Setting Names
Rotating Data Tables
Constructing Data Objects
Making Lists
Making Data Frames
Making Matrix Objects
Re-ordering Data Frames and Matrix Objects
Forms of Data Objects: Testing and Converting
Testing to See What Type of Object You Have
Converting from One Object Form to Another
Convert a Matrix to a Data Frame
Convert a Data Frame into a Matrix
Convert a Data Frame into a List
Convert a Matrix into a List
Convert a List to Something Else
Data: Descriptive Statistics and Tabulation
Summary Commands
Summarizing Samples
Summary Statistics for Vectors
Summary Commands With Single Value Results
Summary Commands With Multiple Results
Cumulative Statistics
Simple Cumulative Commands
Complex Cumulative Commands
Summary Statistics for Data Frames
Generic Summary Commands for Data Frames
Special Row and Column Summary Commands
The apply() Command for Summaries on Rows or Columns
Summary Statistics for Matrix Objects
Summary Statistics for Lists
Summary Tables
Making Contingency Tables
Creating Contingency Tables from Vectors
Creating Contingency Tables from Complicated Data
Creating Custom Contingency Tables
Creating Contingency Tables from Matrix Objects
Selecting Parts of a Table Object
Converting an Object into a Table
Testing for Table Objects
Complex (Flat) Tables
Making �Flat� Contingency Tables
Making Selective �Flat� Contingency Tables
Testing �Flat� Table Objects
Summary Commands for Tables
Cross Tabulation
Testing Cross-Table (xtabs) Objects
A Better Class Test
Recreating Original Data from a Contingency Table
Switching Class
Data: Distrib ution
Looking at the Distribution of Data
Stem and Leaf Plot
Density Function
Using the Density Function to Draw a Graph
Adding Density Lines to Existing Graphs
Types of Data Distribution
The Normal Distribution
Other Distributions
Random Number Generation and Control
Random Numbers and Sampling
The Shapiro-Wilk Test for Normality
The Kolmogorov-Smirnov Test
Quantile-Quantile Plots
A Basic Normal Quantile-Quantile Plot
Adding a Straight Line to a QQ Plot
Plotting the Distribution of One Sample Against Another
Si mple Hypothesis Testing
Using the Student’s t-test
Two-Sample t-Test with Unequal Variance
Two-Sample t-Test with Equal Variance
One-Sample t-Testing
Using Directional Hypotheses
Formula Syntax and Subsetting Samples in the t-Test
The Wilcoxon U-Test (Mann-Whitney)
Two-Sample U-Test
One-Sample U-Test
Using Directional Hypotheses
Formula Syntax and Subsetting Samples in the U-test
Paired t- and U-Tests
Correlation and Covariance
Simple Correlation
Significance Testing in Correlation Tests
Formula Syntax
Tests for Association
Multiple Categories: Chi-Squared Tests
Monte Carlo Simulation
Yates’ Correction for 2 n 2 Tables
Single Category: Goodness of Fit Tests
Introduction to Graphical Analysis
Box-whisker Plots
Basic Boxplots
Customizing Boxplots
Horizontal Boxplots
Scatter Plots
Basic Scatter Plots
Adding Axis Labels
Plotting Symbols
Setting Axis Limits
Using Formula Syntax
Adding Lines of Best-Fit to Scatter Plots
Pairs Plots (Multiple Correlation Plots)
Line Charts
Line Charts Using Numeric Data
Line Charts Using Categorical Data
Pie Charts
Cleveland Dot Charts
Bar Charts
Single-Category Bar Charts
Multiple Category Bar Charts
Stacked Bar Charts
Grouped Bar Charts
Horizontal Bars
Bar Charts from Summary Data
Copy Graphics to Other Applications
Use Copy/Paste to Copy Graphs
Save a Graphic to Disk
Formula Notation and Complex Statistic s
Examples of Using Formula Syntax for Basic Tests
Formula Notation in Graphics
Analysis of Variance (ANOVA)
Stacking the Data before Running Analysis of Variance
Running aov() Commands
Simple Post-hoc Testing
Extracting Means from aov() Models
More about Post-hoc Testing
Graphical Summary of ANOVA
Graphical Summary of Post-hoc Testing
Extracting Means and Summary Statistics
Model Tables
Table Commands
Interaction Plots
More Complex ANOVA Models
Other Options for aov()
Replications and Balance
Manipulating Data and Extracting Components
Creating Data for Complex Analysis
Data Frames
Matrix Objects
Creating and Setting Factor Data
Making Replicate Treatment Factors
Adding Rows or Columns
Summarizing Data
Simple Column and Row Summaries
Complex Summary Functions
The rowsum() Command
The apply() Command
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