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