The book of R : a first course in programming and statistics

The book of R a first course in programming and statistics

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...

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Detalles Bibliográficos
Otros Autores: Davies, Tilman M., author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: San Francisco, California : No Starch Press 2016.
Edición:1st edition
Materias:
R (Computer program language)
Computer programming.
Statistics > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631596706719
Tabla de Contenidos:
  • 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.
  • 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?.
  • 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.
  • 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.
  • 20.2.3 Fitting Linear Models with lm.

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