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 (1,234 ratings)
25,906 students
Introduction to R
Free tutorial
Rating: 4.2 out of 5 (1,234 ratings)
25,906 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
Curriculum
11 sections103 lectures14h 51m total length
  • Introduction to R
  • Course Logistics
  • Section 1: Material
  • Finding your way around R
  • Exercise Answers - Finding your way around R
  • Basic Commands
  • Exercise Answers - Basic Commands
  • Operators
  • Exercise Answers - Operators
  • Miscellaneous
  • Exercise Answers - Miscellaneous
  • Intro to R Studio
  • Section 2: Material
  • Data Types
  • Exercise Answers - Data Types
  • Object Types
  • Exercise Answers - Object Types
  • Vectors
  • Exercise Answers - Vectors
  • Arrays and Matrices
  • Exercise Answers - Arrays and Matrices
  • Factors and Lists
  • Exercise Answers - Factors and Lists
  • Data Frames and Tables
  • Exercise Answers - Data Frames and Tables
  • Section 3: Material
  • Text Files
  • Exercise Answers - Text Files
  • Spreadsheets - Excel Files
  • Exercise Answers - Excel Files
  • Section 4: Material
  • Vector Operations
  • Exercise Answers - Vector Operations
  • Array Operations
  • Exercise Answers - Array Operations
  • Matrix Operations
  • Exercise Answers - Matrix Operations
  • Data Frame Operations
  • Exercise Answers - Data Frame Operations
  • Factor Operations
  • Exercise Answers - Factor Operations
  • Operations on Text
  • Exercise Answers - Operations on Text
  • Operations on Dates
  • Exercise Answers - Operations on Dates
  • Section 5: Material
  • Loops and Conditions
  • Section 6: Material
  • Descriptive Statistics
  • Exercise Answers - Descriptive Statistics
  • Probability Distributions
  • Exercise Answers - Probability Distributions
  • Hypothesis Testing - One and Two Sample T-tests
  • Exercise Answers - Hypothesis Testing - One and Two Sample T-tests
  • Hypothesis Testing - KS-test and F-test
  • Exercise Answers - Hypothesis Testing - KS-test and F-test
  • Linear Modeling - Working with Formula Objects
  • Exercise Answers - Linear Modeling - Working with Formula Objects
  • Linear Modeling - Generating a Linear Model
  • Exercise Answers - Linear Modeling - Generating a Linear Model
  • Linear Modeling - Updating a Linear Model
  • Exercise Answers - Linear Modeling - Updating a Linear Model
  • Generalized Linear Models
  • Non-Linear Regression
  • Exercise Answers - Non Linear Regression
  • Tree Models
  • Exercise Answers - Tree Models
  • Section 7: Material
  • Univariate Plots - I
  • Exercise Answers - Univariate Plots - I
  • Univariate Plots - II
  • Exercise Answers - Univariate Plots - II
  • Multivariate Plots - I
  • Exercise Answers - Multivariate Plots - I
  • Multivariate Plots - II
  • Exercise Answers - Multivariate Plots - II
  • Formatting a Plot - Points
  • Exercise Answers - Formatting a Plot - Points
  • Formatting a Plot - Lines
  • Exercise Answers - Formatting a Plot - Lines
  • Formatting a Plot - Regions and Layout
  • Formatting a Plot - Axes
  • Exercise Answers - Formatting a Plot - Axes
  • Formatting a Plot - Text
  • Exercise Answers - Formatting a Plot - Text
  • Formatting a Plot - Color
  • Exercise Answers - Formatting a Plot - Color
  • Miscellaneous
  • Exercise Answers - Miscellaneous
  • Section 8: Material
  • Text files
  • Exercise Answers - Text Files
  • Graphics
  • Exercise Answers - Graphics
  • Section 9: Material
  • Creating Functions
  • Exercise Answers - Creating Functions
  • Arguments of a Function
  • Exercise Answers - Arguments of a Function
  • Others
  • Exercise Answers - Others
  • Section 10: Material

Instructor
Entrepreneur and Data Scientist
Jagannath Rajagopal
  • 4.2 Instructor Rating
  • 1,234 Reviews
  • 25,906 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.