Introduction to R
About This Course
Course Description
With “Introduction to R“, you will gain a solid grounding of the fundamentals of the R language!
This course has about 90 videos and 140+ exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session.
From there, you will review topics in increasing order of difficulty, starting with Data/Object Types andOperations, Importing into R, and Loops and Conditions.
Next, you will be introduced to the use of R in Analytics, where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations.
After that, you will learn the use of R in Statistics, where you will see about using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, NonLinear Regression, and Trees.
Following that, the next topic will be Graphics, where you will learn to create 2dimensional Univariate and Multivariate plots. You will also learn about formatting various parts of a plot, covering a range of topics like Plot Layout, Region, Points, Lines, Axes, Text, Color and so on.
At that point, the course finishes off with two topics: Exporting out of R, and Creating Functions.
Each chapter is designed to teach you several concepts, and these have been grouped into subsections. A subsection usually has the following:
 A Concept Video
 An Exercise Sheet
 An Exercise Video (with answers)
Why take a course to learn R?
When I look to advancing my R knowledge today, I still face the same sort of situation as when I originally started to use R. Back when I was learning R, my approach was learn by doing. There was a lot of free material out there (and I refer to that early in the course) that gave me a framework, but the wording was highly technical in nature. Even with the R help and the free material, it took me up to a couple of months of experimentation to gain a certain level of proficiency. What I would have liked at that time was a way to learn the fundamentals quicker. I have designed this course with exactly that in mind.
Why my course?
For those of you that are new to R, this course will cover enough breadth/depth in R to give you a solid grounding. I use simple language to explain the concepts. Also, I give you 140+ exercise questions many of which are based on real world data for practice to get you up and running quickly, all in a single package. This course is designed to get you functional with R in little over a week.
For those beginners with some experience that have learnt R through experimentation, this course is designed to complement what you know, and round out your understanding of the same.
What are the requirements?
 Windows/Mac/Linux
 Basic proficiency in math – vectors, matrices, algebra
 Basic proficiency in statistics – probability distributions, linear modeling, etc
 A high speed internet connection
What am I going to get from this course?
 90 videos (15+ hours)
 To educate you on the fundamentals of R
 140+ exercise problems
 To accelerate your learning of R through practice
What is the target audience?
 Enterprise Data Analysts
 Students
 Anyone interested in Data Mining, Statistics, Data Visualization
Curriculum
Section 1: Getting Started  

Lecture 1 
Introduction to R

13:40  
Lecture 2 
Course Logistics

03:51  
Lecture 3 
Section 1: Material

7 pages  
Section 2: Your first R Session  
Lecture 4 
Finding your way around R

09:17  
Lecture 5 
Exercise Answers – Finding your way around R

02:44  
Lecture 6 
Basic Commands

08:07  
Lecture 7 
Exercise Answers – Basic Commands

02:40  
Lecture 8 
Operators

04:36  
Lecture 9 
Exercise Answers – Operators

02:08  
Lecture 10 
Miscellaneous

09:22  
Lecture 11 
Exercise Answers – Miscellaneous

02:07  
Lecture 12 
Intro to R Studio

03:31  
Lecture 13 
Section 2: Material

15 pages  
Section 3: Basics – Objects and Data Types  
Lecture 14 
Data Types

12:05  
Lecture 15 
Exercise Answers – Data Types

03:41  
Lecture 16 
Object Types

15:44  
Lecture 17 
Exercise Answers – Object Types

01:30  
Lecture 18 
Vectors

11:19  
Lecture 19 
Exercise Answers – Vectors

01:43  
Lecture 20 
Arrays and Matrices

14:50  
Lecture 21 
Exercise Answers – Arrays and Matrices

02:58  
Lecture 22 
Factors and Lists

07:17  
Lecture 23 
Exercise Answers – Factors and Lists

06:34  
Lecture 24 
Data Frames and Tables

09:49  
Lecture 25 
Exercise Answers – Data Frames and Tables

05:14  
Lecture 26 
Section 3: Material

33 pages  
Section 4: Importing Data into R  
Lecture 27 
Text Files

12:21  
Lecture 28 
Exercise Answers – Text Files

01:31  
Lecture 29 
Spreadsheets – Excel Files

04:25  
Lecture 30 
Exercise Answers – Excel Files

02:27  
Lecture 31 
Section 4: Material

8 pages  
Section 5: Data Mining/Manipulation  
Lecture 32 
Vector Operations

14:26  
Lecture 33 
Exercise Answers – Vector Operations

03:12  
Lecture 34 
Array Operations

10:49  
Lecture 35 
Exercise Answers – Array Operations

03:14  
Lecture 36 
Matrix Operations

11:53  
Lecture 37 
Exercise Answers – Matrix Operations

03:30  
Lecture 38 
Data Frame Operations

14:05  
Lecture 39 
Exercise Answers – Data Frame Operations

03:49  
Lecture 40 
Factor Operations

11:12  
Lecture 41 
Exercise Answers – Factor Operations

03:32  
Lecture 42 
Operations on Text

11:47  
Lecture 43 
Exercise Answers – Operations on Text

02:42  
Lecture 44 
Operations on Dates

12:19  
Lecture 45 
Exercise Answers – Operations on Dates

03:23  
Lecture 46 
Section 5: Material

41 pages  
Section 6: Loops and Conditions  
Lecture 47 
Loops and Conditions

07:35  
Lecture 48 
Section 6: Material

5 pages  
Section 7: Statistics  
Lecture 49 
Descriptive Statistics

06:58  
Lecture 50 
Exercise Answers – Descriptive Statistics

03:28  
Lecture 51 
Probability Distributions

10:52  
Lecture 52 
Exercise Answers – Probability Distributions

01:26  
Lecture 53 
Hypothesis Testing – One and Two Sample Ttests

12:28  
Lecture 54 
Exercise Answers – Hypothesis Testing – One and Two Sample Ttests

03:21  
Lecture 55 
Hypothesis Testing – KStest and Ftest

06:11  
Lecture 56 
Exercise Answers – Hypothesis Testing – KStest and Ftest

01:37  
Lecture 57 
Linear Modeling – Working with Formula Objects

08:24  
Lecture 58 
Exercise Answers – Linear Modeling – Working with Formula Objects

01:52  
Lecture 59 
Linear Modeling – Generating a Linear Model

10:35  
Lecture 60 
Exercise Answers – Linear Modeling – Generating a Linear Model

04:19  
Lecture 61 
Linear Modeling – Updating a Linear Model

04:30  
Lecture 62 
Exercise Answers – Linear Modeling – Updating a Linear Model

01:36  
Lecture 63 
Generalized Linear Models

08:00  
Lecture 64 
NonLinear Regression

08:09  
Lecture 65 
Exercise Answers – Non Linear Regression

02:22  
Lecture 66 
Tree Models

08:15  
Lecture 67 
Exercise Answers – Tree Models

04:10  
Lecture 68 
Section 7: Material

71 pages  
Section 8: Graphics  
Lecture 69 
Univariate Plots – I

14:01  
Lecture 70 
Exercise Answers – Univariate Plots – I

03:52  
Lecture 71 
Univariate Plots – II

13:24  
Lecture 72 
Exercise Answers – Univariate Plots – II

02:30  
Lecture 73 
Multivariate Plots – I

14:32  
Lecture 74 
Exercise Answers – Multivariate Plots – I

04:25  
Lecture 75 
Multivariate Plots – II

11:41  
Lecture 76 
Exercise Answers – Multivariate Plots – II

03:57  
Lecture 77 
Formatting a Plot – Points

09:37  
Lecture 78 
Exercise Answers – Formatting a Plot – Points

03:56  
Lecture 79 
Formatting a Plot – Lines

09:02  
Lecture 80 
Exercise Answers – Formatting a Plot – Lines

02:38  
Lecture 81 
Formatting a Plot – Regions and Layout

12:57  
Lecture 82 
Formatting a Plot – Axes

10:40  
Lecture 83 
Exercise Answers – Formatting a Plot – Axes

01:29  
Lecture 84 
Formatting a Plot – Text

10:22  
Lecture 85 
Exercise Answers – Formatting a Plot – Text

02:06  
Lecture 86 
Formatting a Plot – Color

06:09  
Lecture 87 
Exercise Answers – Formatting a Plot – Color

02:06  
Lecture 88 
Miscellaneous

05:12  
Lecture 89 
Exercise Answers – Miscellaneous

01:03  
Lecture 90 
Section 8: Material

82 pages  
Section 9: Exporting Data out of R  
Lecture 91 
Text files

06:12  
Lecture 92 
Exercise Answers – Text Files

02:38  
Lecture 93 
Graphics

04:16  
Lecture 94 
Exercise Answers – Graphics

05:51  
Lecture 95 
Section 9: Material

9 pages  
Section 10: Working with Functions  
Lecture 96 
Creating Functions

05:25  
Lecture 97 
Exercise Answers – Creating Functions

02:07  
Lecture 98 
Arguments of a Function

14:38  
Lecture 99 
Exercise Answers – Arguments of a Function

02:59  
Lecture 100 
Others

06:42  
Lecture 101 
Exercise Answers – Others

04:42  
Lecture 102 
Section 10: Material

17 pages  
Section 11: Conclusion  
Lecture 103 
Some final comments

01:04 
Instructor Biography
Jagannath Rajagopal, Entrepreneur and Data Scientist
Hi! You can call me Jag. I have spent most of the past 10 years implementing Statistical Forecasting Systems at major companies in North America and Asia. I graduated from Georgia Tech [Atlanta, GA, USA] with a Masters in Industrial Engineering and so have a statistics background.
As part of my prior job, I have had to work with data extensively – mining, analyzing and summarizing. I have developed routines to cleanse historical sales data for input to Statistical Forecasting algorithms. I have also had to teach Statistical Forecasting and the use of said techniques and algorithms to every client I have been at.
These days, I am an entrepreneur and am based in Mississauga, ON, Canada. I am focussed on a couple of areas, one of which is online education.
If you want to connect with me, you can find me on LinkedIn – just mention that you found me on Udemy. Also check out my Deep Learning YouTube channel, Facebook page and Twitter page.
Course Features
 Lectures 0
 Quizzes 0
 Duration 50 hours
 Skill level All level
 Language English
 Students 3385
 Assessments Self