Introduction to R

Learn the core fundamentals of the R language for interactive use as well as programming
Free tutorial
Rating: 4.2 out of 5 (1,527 ratings)
32,853 students
English [Auto]
90 videos (15+ hours)
To educate you on the fundamentals of R
140+ exercise problems
To accelerate your learning of R through practice


  • Windows/Mac/Linux
  • Basic proficiency in math - vectors, matrices, algebra
  • Basic proficiency in statistics - probability distributions, linear modeling, etc
  • A high speed internet connection


UPDATE: As of Nov 22, 2018, this course is now free! Many thanks to all my existing students who made it possible for the wider audience to benefit from the course material :-)

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 and Operations, 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, Non-Linear Regression, and Trees. 

Following that, the next topic will be Graphics, where you will learn to create 2-dimensional Univariate and Multi-variate 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 sub-sections. A sub-section 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. 

Who this course is for:

  • Enterprise Data Analysts
  • Students
  • Anyone interested in Data Mining, Statistics, Data Visualization

Course content

11 sections103 lectures14h 51m total length
  • Introduction to R
  • Course Logistics
  • Section 1: Material
    7 pages


Entrepreneur and Data Scientist
Jagannath Rajagopal
  • 4.2 Instructor Rating
  • 1,527 Reviews
  • 32,853 Students
  • 1 Course

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.

Check out my Deep Learning YouTube channel, Facebook page and Twitter page.