The book of R : a first course in programming and statistics /
by Tilman M. Davies.
Bok Engelsk 2016 · Electronic books.
| Omfang | 1 online resource (835 pages) : : illustrations, tables
|
|---|---|
| Utgave | 1st edition
|
| Opplysninger | Intro -- Title Page -- Copyright Page -- Brief Contents -- Contents in Detail -- Preface -- Acknowledgments -- Introduction -- A Brief History of R -- About This Book -- Part I: The Language -- Part II: Programming -- Part III: Statistics and Probability -- Part IV: Statistical Testing and Modeling -- Part V: Advanced Graphics -- For Students -- For Instructors -- Part I: The Language -- Chapter 1: Getting Started -- 1.1 Obtaining and Installing R from CRAN -- 1.2 Opening R for the First Time -- 1.2.1 Console and Editor Panes -- 1.2.2 Comments -- 1.2.3 Working Directory -- 1.2.4 Installing and Loading R Packages -- 1.2.5 Help Files and Function Documentation -- 1.2.6 Third-Party Editors -- 1.3 Saving Work and Exiting R -- 1.3.1 Workspaces -- 1.3.2 Scripts -- 1.4 Conventions -- 1.4.1 Coding -- 1.4.2 Math and Equation References -- 1.4.3 Exercises -- Exercise 1.1 -- Chapter 2: Numerics, Arithmetic, Assignment, and Vectors -- 2.1 R for Basic Math -- 2.1.1 Arithmetic -- 2.1.2 Logarithms and Exponentials -- 2.1.3 E-Notation -- Exercise 2.1 -- 2.2 Assigning Objects -- Exercise 2.2 -- 2.3 Vectors -- 2.3.1 Creating a Vector -- 2.3.2 Sequences, Repetition, Sorting, and Lengths -- Exercise 2.3 -- 2.3.3 Subsetting and Element Extraction -- Exercise 2.4 -- 2.3.4 Vector-Oriented Behavior -- Exercise 2.5 -- Chapter 3: Matrices and Arrays -- 3.1 Defining a Matrix -- 3.1.1 Filling Direction -- 3.1.2 Row and Column Bindings -- 3.1.3 Matrix Dimensions -- 3.2 Subsetting -- 3.2.1 Row, Column, and Diagonal Extractions -- 3.2.2 Omitting and Overwriting -- Exercise 3.1 -- 3.3 Matrix Operations and Algebra -- 3.3.1 Matrix Transpose -- 3.3.2 Identity Matrix -- 3.3.3 Scalar Multiple of a Matrix -- 3.3.4 Matrix Addition and Subtraction -- 3.3.5 Matrix Multiplication -- 3.3.6 Matrix Inversion -- Exercise 3.2 -- 3.4 Multidimensional Arrays -- 3.4.1 Definition.. - 12.2.2 Measuring Completion Time: How Long Did It Take? -- Exercise 12.2 -- 12.3 Masking -- 12.3.1 Function and Object Distinction -- 12.3.2 Data Frame Variable Distinction -- Part III: Statistics and Probability -- Chapter 13: Elementary Statistics -- 13.1 Describing Raw Data -- 13.1.1 Numeric Variables -- 13.1.2 Categorical Variables -- 13.1.3 Univariate and Multivariate Data -- 13.1.4 Parameter or Statistic? -- Exercise 13.1 -- 13.2 Summary Statistics -- 13.2.1 Centrality: Mean, Median, Mode -- 13.2.2 Counts, Percentages, and Proportions -- Exercise 13.2 -- 13.2.3 Quantiles, Percentiles, and the Five-Number Summary -- 13.2.4 Spread: Variance, Standard Deviation, and the Interquartile Range -- Exercise 13.3 -- 13.2.5 Covariance and Correlation -- 13.2.6 Outliers -- Exercise 13.4 -- Chapter 14: Basic Data Visualization -- 14.1 Barplots and Pie Charts -- 14.1.1 Building a Barplot -- 14.1.2 A Quick Pie Chart -- 14.2 Histograms -- 14.3 Box-and-Whisker Plots -- 14.3.1 Stand-Alone Boxplots -- 14.3.2 Side-by-Side Boxplots -- 14.4 Scatterplots -- 14.4.1 Single Plot -- 14.4.2 Matrix of Plots -- Exercise 14.1 -- Chapter 15: Probability -- 15.1 What Is a Probability? -- 15.1.1 Events and Probability -- 15.1.2 Conditional Probability -- 15.1.3 Intersection -- 15.1.4 Union -- 15.1.5 Complement -- Exercise 15.1 -- 15.2 Random Variables and Probability Distributions -- 15.2.1 Realizations -- 15.2.2 Discrete Random Variables -- 15.2.3 Continuous Random Variables -- 15.2.4 Shape, Skew, and Modality -- Exercise 15.2 -- Chapter 16: Common Probability Distributions -- 16.1 Common Probability Mass Functions -- 16.1.1 Bernoulli Distribution -- 16.1.2 Binomial Distribution -- Exercise 16.1 -- 16.1.3 Poisson Distribution -- Exercise 16.2 -- 16.1.4 Other Mass Functions -- 16.2 Common Probability Density Functions -- 16.2.1 Uniform -- Exercise 16.3 -- 16.2.2 Normal.. - 20.2.3 Fitting Linear Models with lm.. - 3.4.2 Subsets, Extractions, and Replacements -- Exercise 3.3 -- Chapter 4: Non-numeric Values -- 4.1 Logical Values -- 4.1.1 TRUE or FALSE? -- 4.1.2 A Logical Outcome: Relational Operators -- Exercise 4.1 -- 4.1.3 Multiple Comparisons: Logical Operators -- Exercise 4.2 -- 4.1.4 Logicals Are Numbers! -- 4.1.5 Logical Subsetting and Extraction -- Exercise 4.3 -- 4.2 Characters -- 4.2.1 Creating a String -- 4.2.2 Concatenation -- 4.2.3 Escape Sequences -- 4.2.4 Substrings and Matching -- Exercise 4.4 -- 4.3 Factors -- 4.3.1 Identifying Categories -- 4.3.2 Defining and Ordering Levels -- 4.3.3 Combining and Cutting -- Exercise 4.5 -- Chapter 5: Lists and Data Frames -- 5.1 Lists of Objects -- 5.1.1 Definition and Component Access -- 5.1.2 Naming -- 5.1.3 Nesting -- Exercise 5.1 -- 5.2 Data Frames -- 5.2.1 Construction -- 5.2.2 Adding Data Columns and Combining Data Frames -- 5.2.3 Logical Record Subsets -- Exercise 5.2 -- Chapter 6: Special Values, Classes, and Coercion -- 6.1 Some Special Values -- 6.1.1 Infinity -- 6.1.2 NaN -- Exercise 6.1 -- 6.1.3 NA -- 6.1.4 NULL -- Exercise 6.2 -- 6.2 Understanding Types, Classes, and Coercion -- 6.2.1 Attributes -- 6.2.2 Object Class -- 6.2.3 Is-Dot Object-Checking Functions -- 6.2.4 As-Dot Coercion Functions -- Exercise 6.3 -- Chapter 7: Basic Plotting -- 7.1 Using plot with Coordinate Vectors -- 7.2 Graphical Parameters -- 7.2.1 Automatic Plot Types -- 7.2.2 Title and Axis Labels -- 7.2.3 Color -- 7.2.4 Line and Point Appearances -- 7.2.5 Plotting Region Limits -- 7.3 Adding Points, Lines, and Text to an Existing Plot -- Exercise 7.1 -- 7.4 The ggplot2 Package -- 7.4.1 A Quick Plot with qplot -- 7.4.2 Setting Appearance Constants with Geoms -- 7.4.3 Aesthetic Mapping with Geoms -- Exercise 7.2 -- Chapter 8: Reading and Writing Files -- 8.1 R-Ready Data Sets -- 8.1.1 Built-in Data Sets.. - 8.1.2 Contributed Data Sets -- 8.2 Reading in External Data Files -- 8.2.1 The Table Format -- 8.2.2 Spreadsheet Workbooks -- 8.2.3 Web-Based Files -- 8.2.4 Other File Formats -- 8.3 Writing Out Data Files and Plots -- 8.3.1 Data Sets -- 8.3.2 Plots and Graphics Files -- 8.4 Ad Hoc Object Read/Write Operations -- Exercise 8.1 -- Part II: Programming -- Chapter 9: Calling Functions -- 9.1 Scoping -- 9.1.1 Environments -- 9.1.2 Search Path -- 9.1.3 Reserved and Protected Names -- Exercise 9.1 -- 9.2 Argument Matching -- 9.2.1 Exact -- 9.2.2 Partial -- 9.2.3 Positional -- 9.2.4 Mixed -- 9.2.5 Dot-Dot-Dot: Use of Ellipses -- Exercise 9.2 -- Chapter 10: Conditions and Loops -- 10.1 if Statements -- 10.1.1 Stand-Alone Statement -- 10.1.2 else Statements -- 10.1.3 Using ifelse for Element-wise Checks -- Exercise 10.1 -- 10.1.4 Nesting and Stacking Statements -- 10.1.5 The switch Function -- Exercise 10.2 -- 10.2 Coding Loops -- 10.2.1 for Loops -- Exercise 10.3 -- 10.2.2 while Loops -- Exercise 10.4 -- 10.2.3 Implicit Looping with apply -- Exercise 10.5 -- 10.3 Other Control Flow Mechanisms -- 10.3.1 Declaring break or next -- 10.3.2 The repeat Statement -- Exercise 10.6 -- Chapter 11: Writing Functions -- 11.1 The function Command -- 11.1.1 Function Creation -- 11.1.2 Using return -- Exercise 11.1 -- 11.2 Arguments -- 11.2.1 Lazy Evaluation -- 11.2.2 Setting Defaults -- 11.2.3 Checking for Missing Arguments -- 11.2.4 Dealing with Ellipses -- Exercise 11.2 -- 11.3 Specialized Functions -- 11.3.1 Helper Functions -- 11.3.2 Disposable Functions -- 11.3.3 Recursive Functions -- Exercise 11.3 -- Chapter 12: Exceptions, Timings, and Visibility -- 12.1 Exception Handling -- 12.1.1 Formal Notifications: Errors and Warnings -- 12.1.2 Catching Errors with try Statements -- Exercise 12.1 -- 12.2 Progress and Timing -- 12.2.1 Textual Progress Bars: Are We There Yet?.. - Exercise 16.4 -- 16.2.3 Student's t-distribution -- 16.2.4 Exponential -- Exercise 16.5 -- 16.2.5 Other Density Functions -- Part IV: Statistical Testing and Modeling -- Chapter 17: Sampling Distributions and Confidence -- 17.1 Sampling Distributions -- 17.1.1 Distribution for a Sample Mean -- 17.1.2 Distribution for a Sample Proportion -- Exercise 17.1 -- 17.1.3 Sampling Distributions for Other Statistics -- 17.2 Confidence Intervals -- 17.2.1 An Interval for a Mean -- 17.2.2 An Interval for a Proportion -- 17.2.3 Other Intervals -- 17.2.4 Comments on Interpretation of a CI -- Exercise 17.2 -- Chapter 18: Hypothesis Testing -- 18.1 Components of a Hypothesis Test -- 18.1.1 Hypotheses -- 18.1.2 Test Statistic -- 18.1.3 p-value -- 18.1.4 Significance Level -- 18.1.5 Criticisms of Hypothesis Testing -- 18.2 Testing Means -- 18.2.1 Single Mean -- Exercise 18.1 -- 18.2.2 Two Means -- Exercise 18.2 -- 18.3 Testing Proportions -- 18.3.1 Single Proportion -- 18.3.2 Two Proportions -- Exercise 18.3 -- 18.4 Testing Categorical Variables -- 18.4.1 Single Categorical Variable -- 18.4.2 Two Categorical Variables -- Exercise 18.4 -- 18.5 Errors and Power -- 18.5.1 Hypothesis Test Errors -- 18.5.2 Type I Errors -- 18.5.3 Type II Errors -- Exercise 18.5 -- 18.5.4 Statistical Power -- Exercise 18.6 -- Chapter 19: Analysis of Variance -- 19.1 One-Way ANOVA -- 19.1.1 Hypotheses and Diagnostic Checking -- 19.1.2 One-Way ANOVA Table Construction -- 19.1.3 Building ANOVA Tables with the aov Function -- Exercise 19.1 -- 19.2 Two-Way ANOVA -- 19.2.1 A Suite of Hypotheses -- 19.2.2 Main Effects and Interactions -- 19.3 Kruskal-Wallis Test -- Exercise 19.2 -- Chapter 20: Simple Linear Regression -- 20.1 An Example of a Linear Relationship -- 20.2 General Concepts -- 20.2.1 Definition of the Model -- 20.2.2 Estimating the Intercept and Slope Parameters.. - The Book of R is a comprehensive, beginner-friendly guide to R, the world's most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you'll find everything you need to begin using R effectively for statistical analysis. You'll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You'll even learn how to create impressive data visualizations with R's basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R How to access R's thousands of functions, libraries, and data sets How to draw valid and useful conclusions from your data How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R's functionality. Make The Book of R your doorway into the growing world of data analysis.
|
| Emner | |
| Sjanger | |
| Dewey | |
| ISBN | 1-4920-1748-5. - 1-59327-779-2
|
