Introduction to R

Learn the core fundamentals of the R language for interactive use as well as programming
Free tutorial
Rating: 4.3 out of 5 (1,358 ratings)
28,153 students
Introduction to R
Free tutorial
Rating: 4.3 out of 5 (1,358 ratings)
28,153 students
90 videos (15+ hours)
To educate you on the fundamentals of R
140+ exercise problems
To accelerate your learning of R through practice

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
Description

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 sections • 103 lectures • 14h 51m total length
  • Introduction to R
    13:40
  • Course Logistics
    03:51
  • Section 1: Material
    7 pages
  • Finding your way around R
    09:17
  • Exercise Answers - Finding your way around R
    02:44
  • Basic Commands
    08:07
  • Exercise Answers - Basic Commands
    02:40
  • Operators
    04:36
  • Exercise Answers - Operators
    02:08
  • Miscellaneous
    09:22
  • Exercise Answers - Miscellaneous
    02:07
  • Intro to R Studio
    03:31
  • Section 2: Material
    15 pages
  • Data Types
    12:05
  • Exercise Answers - Data Types
    03:41
  • Object Types
    15:44
  • Exercise Answers - Object Types
    01:30
  • Vectors
    11:19
  • Exercise Answers - Vectors
    01:43
  • Arrays and Matrices
    14:50
  • Exercise Answers - Arrays and Matrices
    02:58
  • Factors and Lists
    07:17
  • Exercise Answers - Factors and Lists
    06:34
  • Data Frames and Tables
    09:49
  • Exercise Answers - Data Frames and Tables
    05:14
  • Section 3: Material
    33 pages
  • Text Files
    12:21
  • Exercise Answers - Text Files
    01:31
  • Spreadsheets - Excel Files
    04:25
  • Exercise Answers - Excel Files
    02:27
  • Section 4: Material
    8 pages
  • Vector Operations
    14:26
  • Exercise Answers - Vector Operations
    03:12
  • Array Operations
    10:49
  • Exercise Answers - Array Operations
    03:14
  • Matrix Operations
    11:53
  • Exercise Answers - Matrix Operations
    03:30
  • Data Frame Operations
    14:05
  • Exercise Answers - Data Frame Operations
    03:49
  • Factor Operations
    11:12
  • Exercise Answers - Factor Operations
    03:32
  • Operations on Text
    11:47
  • Exercise Answers - Operations on Text
    02:42
  • Operations on Dates
    12:19
  • Exercise Answers - Operations on Dates
    03:23
  • Section 5: Material
    41 pages
  • Loops and Conditions
    07:35
  • Section 6: Material
    5 pages
  • Descriptive Statistics
    06:58
  • Exercise Answers - Descriptive Statistics
    03:28
  • Probability Distributions
    10:52
  • Exercise Answers - Probability Distributions
    01:26
  • Hypothesis Testing - One and Two Sample T-tests
    12:28
  • Exercise Answers - Hypothesis Testing - One and Two Sample T-tests
    03:21
  • Hypothesis Testing - KS-test and F-test
    06:11
  • Exercise Answers - Hypothesis Testing - KS-test and F-test
    01:37
  • Linear Modeling - Working with Formula Objects
    08:24
  • Exercise Answers - Linear Modeling - Working with Formula Objects
    01:52
  • Linear Modeling - Generating a Linear Model
    10:35
  • Exercise Answers - Linear Modeling - Generating a Linear Model
    04:19
  • Linear Modeling - Updating a Linear Model
    04:30
  • Exercise Answers - Linear Modeling - Updating a Linear Model
    01:36
  • Generalized Linear Models
    08:00
  • Non-Linear Regression
    08:09
  • Exercise Answers - Non Linear Regression
    02:22
  • Tree Models
    08:15
  • Exercise Answers - Tree Models
    04:10
  • Section 7: Material
    71 pages
  • Univariate Plots - I
    14:01
  • Exercise Answers - Univariate Plots - I
    03:52
  • Univariate Plots - II
    13:24
  • Exercise Answers - Univariate Plots - II
    02:30
  • Multivariate Plots - I
    14:32
  • Exercise Answers - Multivariate Plots - I
    04:25
  • Multivariate Plots - II
    11:41
  • Exercise Answers - Multivariate Plots - II
    03:57
  • Formatting a Plot - Points
    09:37
  • Exercise Answers - Formatting a Plot - Points
    03:56
  • Formatting a Plot - Lines
    09:02
  • Exercise Answers - Formatting a Plot - Lines
    02:38
  • Formatting a Plot - Regions and Layout
    12:57
  • Formatting a Plot - Axes
    10:40
  • Exercise Answers - Formatting a Plot - Axes
    01:29
  • Formatting a Plot - Text
    10:22
  • Exercise Answers - Formatting a Plot - Text
    02:06
  • Formatting a Plot - Color
    06:09
  • Exercise Answers - Formatting a Plot - Color
    02:06
  • Miscellaneous
    05:12
  • Exercise Answers - Miscellaneous
    01:03
  • Section 8: Material
    82 pages
  • Text files
    06:12
  • Exercise Answers - Text Files
    02:38
  • Graphics
    04:16
  • Exercise Answers - Graphics
    05:51
  • Section 9: Material
    9 pages
  • Creating Functions
    05:25
  • Exercise Answers - Creating Functions
    02:07
  • Arguments of a Function
    14:38
  • Exercise Answers - Arguments of a Function
    02:59
  • Others
    06:42
  • Exercise Answers - Others
    04:42
  • Section 10: Material
    17 pages

Instructor
Entrepreneur and Data Scientist
Jagannath Rajagopal
  • 4.3 Instructor Rating
  • 1,358 Reviews
  • 28,153 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.