#### Section 1 : ntroduction and Overview of R

 Lecture 1 Introduction to Comprehensive R Programming Course 1:53 Lecture 2 Introduction and Getting Started 14:54 Lecture 3 Getting Started and First R Session Lecture 4 First R Session (part 2) 14:51 Lecture 5 First R Session (part 3) 15:8 Lecture 6 Matrices, Lists and Dataframes. 14:59 Lecture 7 Introduction to Functions 15:2 Lecture 8 Functions and Default Arguments 14:49 Lecture 9 More Examples of Functions (part 1) 14:49 Lecture 10 More Functions Examples (part 2) 12:0 Lecture 11 More Functions Examples (part 3) 11:12 Lecture 12 More Functions Examples (part 4) 12:26 Lecture 13 More Functions Examples (part 5) 10:20 Lecture 14 More Functions Examples (part 6) 8:32

#### Section 2 : What are Vector Data Structures in R

 Lecture 15 Homemade t-test Exercise Solution Lecture 16 Section 2 Exercise and Package Demonstrations 14:24 Lecture 17 Begin Discussion of Vectors 15:31 Lecture 18 More Examples of Vectors 14:36 Lecture 19 Common Vector Operations and More 14:18 Lecture 20 Findruns Example and Vectors Exercises 14:12

#### Section 3 : More Discussion of Vector Data Structures

 Lecture 21 Vector-Based Programming Exercise Solution (part 1 Lecture 22 Vector Exercise Solution (part 2) and Begin Genera 16:6 Lecture 23 Continue General Vector Discussion 16:3 Lecture 24 More General Vector Examples 12:57 Lecture 25 More on Vectors and Vector Equality 16:41 Lecture 26 Extended Vector Example and Exercise 13:8

#### Section 4 : Finish Vectors and Begin Matrices

 Lecture 27 Finish Vector Discussion Lecture 28 Vector-Maker Exercise Solutions 17:8 Lecture 29 Begin Discussion of Matrices and Arrays 14:57 Lecture 30 Filtering Matrices and More Examples 15:55 Lecture 31 Still More Matrices Examples 16:52

#### Section 5 : Finish Matrices and Begin Lists Discussion

 Lecture 32 Min-Merge Vector Exercise Solutions 15:15 Lecture 33 Game of Craps Exercise Solution 9:3 Lecture 34 Naming Matrix Rows and Columns 15:47 Lecture 35 Lists General List Operations 11:48 Lecture 36 Processing Text with Lists 14:47 Lecture 37 Applying Functions to Lists 17:32 Lecture 38 Vector and Matrix Exercise 4:53

#### Section 6 : Continue Lists Discussion

 Lecture 39 Review Programming Exercises Lecture 40 Finish Programming Exercise Review and Begin Discu 15:16 Lecture 41 List Data Structures General Discussion (part 2) 16:22 Lecture 42 List Data Structures General Discussion (part 3). 15:46 Lecture 43 Lists Data Structures General Discussion (part 4) 15:48

#### Section 7 : Details About Dataframe Data Structures

 Lecture 44 Dataframe-Maker Exercise 13:52 Lecture 45 List-Maker Exercise; Begin General Dataframe Discu 15:9 Lecture 46 Extracting Subdata Frames Lecture 47 A Salary Survey Extended Example 16:0 Lecture 48 Merging Dataframes 14:30 Lecture 49 End Dataframes Discussion; Matrix Exercise 14:10

#### Section 8 : More Matrix and List Examples

 Lecture 50 Covariance Matrix Exercise Solution 12:22 Lecture 51 List Example Tree Growth (part 1) 14:5 Lecture 52 List Example Tree Growth (part 2) 10:45 Lecture 53 Factor Data Types 14:33 Lecture 54 Factors tapply() and split() Functions 15:59 Lecture 55 Factor Levels versus Values 10:58 Lecture 56 Pascal's Triangle Exercise 2:38

#### Section 9 : Programming in R Environments

 Lecture 57 Pascal's Triangle Exercise Solution Lecture 58 Begin Programming Structures 15:32 Lecture 59 Environment and Scope Issues 14:16 Lecture 60 Nesting Multiple Environments Lecture 61 Referencing Variables in Other Frames 14:53 Lecture 62 Writing to Global Variables and Recursion 14:5 Lecture 63 Replacement and Anonymous Functions 14:5 Lecture 64 Sorting Programs Exercise 7:9

#### Section 10 : Performing Math and Simulations

 Lecture 65 Sorting Programs Exercise Solution (part 1) Lecture 66 Sorting Programs Exercise Solution (part 2) 13:57 Lecture 67 Calculating a Probability Lecture 68 Linear Algebra Operations 17:8 Lecture 69 Set Operations and Simulation 15:23 Lecture 70 Combinatorial Simulations (part 1) 10:40 Lecture 71 Combinatorial Simulations (part 2) 15:28 Lecture 72 Winning at Roulette Exercise 7:39

#### Section 11 : Object Oriented Programming (OOP) and S3 and S4 Classes

 Lecture 73 Winning at Roulette Exercise solution 13:18 Lecture 74 Introduction to OOP in R 11:17 Lecture 75 OOP Example lm() Function 10:32 Lecture 76 Writing S3 Classes 9:34 Lecture 77 Using Inheritance 7:23 Lecture 78 Compressing Matrices Example (part 1) 14:37 Lecture 79 Compressing Matrices Example (part 2) 2:36 Lecture 80 Writing S3 Classes Exercise 2:36 Lecture 81 Writing S4 Classe 14:11 Lecture 82 Implementing S4 Generic Functions 16:32 Lecture 83 Writing S4 Classes Exercise 3:41 Lecture 84 Live S3 and S4 Class Development 7:36 Lecture 85 Continue S3 Class Development 13:19 Lecture 86 Developing a Corresponding S4 Class 10:1

#### Section 12 : Input and Output

 Lecture 87 Writing S3 Classes Exercise Solution 9:9 Lecture 88 Writing S4 Classes Exercise Solution 8:3 Lecture 89 Using the scan() Function for Input 15:41 Lecture 90 Using the readline(), cat() and print() Functions 12:14 Lecture 91 Using readLines() Function; Text Data 12:45 Lecture 92 Example R Program powers.R 15:10 Lecture 93 Example R Program quad2b.R 8:55 Lecture 94 Reading and Writing Files (part 1) 5:49 Lecture 95 Reading and Writing Files (part 2) 13:43

#### Section 13 : String Processing and Manipulation

 Lecture 96 Character and String Manipulation 8:39 Lecture 97 Displaying and Concatenating Strings (part 1) 10:16 Lecture 98 Displaying and Concatenating Strings (part 2) 14:0 Lecture 99 Manipulating Parts of a String 10:0 Lecture 100 Breaking Apart Character Values 14:13 Lecture 101 Regular Expressions (slides) 10:27 Lecture 102 Regular Expression Examples (R scripts, part 1) 13:32 Lecture 103 Regular Expression Examples (R scripts, part 2) 11:36 Lecture 104 The Regexpr() and Gregexpr() Functions (part 1) 12:41 Lecture 105 The Regexpr() and Gregexpr() Functions (part 2) 9:49 Lecture 106 Testing a Filename for a Suffix 8:41 Lecture 107 Forming Filenames Example 8:46 Lecture 108 Substitutions and Tagging 14:54 Lecture 109 Reverse String Exercise 1:45

#### Section 14 : Enhancing Program Execution Performance

 Lecture 110 Introduction to Profiling Lecture 111 Enhancing Performance 14:56 Lecture 112 Speeding Up Monte Carlo Simulations 5:56 Lecture 113 Drawing Balls From an Urn Example 13:50 Lecture 114 Generating a Powers Matrix Example 13:38 Lecture 115 Resource Management 10:54 Lecture 116 Program Efficiencies and Scoping Rules 12:1 Lecture 117 More Scoping Rules 4:47 Lecture 118 Numerical Accuracy and Program Efficiency 7:47 Lecture 119 More on Numerical Accuracy 10:56