Introduction to R

Introduction to R, available at Free, has an average rating of 4.5, with 103 lectures, based on 1808 reviews, and has 44048 subscribers.

You will learn about 90 videos (15+ hours) To educate you on the fundamentals of R 140+ exercise problems To accelerate your learning of R through practice This course is ideal for individuals who are Enterprise Data Analysts or Students or Anyone interested in Data Mining, Statistics, Data Visualization It is particularly useful for Enterprise Data Analysts or Students or Anyone interested in Data Mining, Statistics, Data Visualization.

Enroll now: Introduction to R

Summary

Title: Introduction to R

Price: Free

Average Rating: 4.5

Number of Lectures: 103

Number of Published Lectures: 103

Number of Curriculum Items: 103

Number of Published Curriculum Objects: 103

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

90 videos (15+ hours)
To educate you on the fundamentals of R
140+ exercise problems
To accelerate your learning of R through practice

Who Should Attend

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

Target Audiences

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

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 Rin 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)

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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.�

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Introduction to R

Lecture 2: Course Logistics

Lecture 3: Section 1: Material

Chapter 2: Your first R Session

Lecture 1: Finding your way around R

Lecture 2: Exercise Answers – Finding your way around R

Lecture 3: Basic Commands

Lecture 4: Exercise Answers – Basic Commands

Lecture 5: Operators

Lecture 6: Exercise Answers – Operators

Lecture 7: Miscellaneous

Lecture 8: Exercise Answers – Miscellaneous

Lecture 9: Intro to R Studio

Lecture 10: Section 2: Material

Chapter 3: Basics – Objects and Data Types

Lecture 1: Data Types

Lecture 2: Exercise Answers – Data Types

Lecture 3: Object Types

Lecture 4: Exercise Answers – Object Types

Lecture 5: Vectors

Lecture 6: Exercise Answers – Vectors

Lecture 7: Arrays and Matrices

Lecture 8: Exercise Answers – Arrays and Matrices

Lecture 9: Factors and Lists

Lecture 10: Exercise Answers – Factors and Lists

Lecture 11: Data Frames and Tables

Lecture 12: Exercise Answers – Data Frames and Tables

Lecture 13: Section 3: Material

Chapter 4: Importing Data into R

Lecture 1: Text Files

Lecture 2: Exercise Answers – Text Files

Lecture 3: Spreadsheets – Excel Files

Lecture 4: Exercise Answers – Excel Files

Lecture 5: Section 4: Material

Chapter 5: Data Mining/Manipulation

Lecture 1: Vector Operations

Lecture 2: Exercise Answers – Vector Operations

Lecture 3: Array Operations

Lecture 4: Exercise Answers – Array Operations

Lecture 5: Matrix Operations

Lecture 6: Exercise Answers – Matrix Operations

Lecture 7: Data Frame Operations

Lecture 8: Exercise Answers – Data Frame Operations

Lecture 9: Factor Operations

Lecture 10: Exercise Answers – Factor Operations

Lecture 11: Operations on Text

Lecture 12: Exercise Answers – Operations on Text

Lecture 13: Operations on Dates

Lecture 14: Exercise Answers – Operations on Dates

Lecture 15: Section 5: Material

Chapter 6: Loops and Conditions

Lecture 1: Loops and Conditions

Lecture 2: Section 6: Material

Chapter 7: Statistics

Lecture 1: Descriptive Statistics

Lecture 2: Exercise Answers – Descriptive Statistics

Lecture 3: Probability Distributions

Lecture 4: Exercise Answers – Probability Distributions

Lecture 5: Hypothesis Testing – One and Two Sample T-tests

Lecture 6: Exercise Answers – Hypothesis Testing – One and Two Sample T-tests

Lecture 7: Hypothesis Testing – KS-test and F-test

Lecture 8: Exercise Answers – Hypothesis Testing – KS-test and F-test

Lecture 9: Linear Modeling – Working with Formula Objects

Lecture 10: Exercise Answers – Linear Modeling – Working with Formula Objects

Lecture 11: Linear Modeling – Generating a Linear Model

Lecture 12: Exercise Answers – Linear Modeling – Generating a Linear Model

Lecture 13: Linear Modeling – Updating a Linear Model

Lecture 14: Exercise Answers – Linear Modeling – Updating a Linear Model

Lecture 15: Generalized Linear Models

Lecture 16: Non-Linear Regression

Lecture 17: Exercise Answers – Non Linear Regression

Lecture 18: Tree Models

Lecture 19: Exercise Answers – Tree Models

Lecture 20: Section 7: Material

Chapter 8: Graphics

Lecture 1: Univariate Plots – I

Lecture 2: Exercise Answers – Univariate Plots – I

Lecture 3: Univariate Plots – II

Lecture 4: Exercise Answers – Univariate Plots – II

Lecture 5: Multivariate Plots – I

Lecture 6: Exercise Answers – Multivariate Plots – I

Lecture 7: Multivariate Plots – II

Lecture 8: Exercise Answers – Multivariate Plots – II

Lecture 9: Formatting a Plot – Points

Lecture 10: Exercise Answers – Formatting a Plot – Points

Lecture 11: Formatting a Plot – Lines

Lecture 12: Exercise Answers – Formatting a Plot – Lines

Lecture 13: Formatting a Plot – Regions and Layout

Lecture 14: Formatting a Plot – Axes

Lecture 15: Exercise Answers – Formatting a Plot – Axes

Lecture 16: Formatting a Plot – Text

Lecture 17: Exercise Answers – Formatting a Plot – Text

Lecture 18: Formatting a Plot – Color

Lecture 19: Exercise Answers – Formatting a Plot – Color

Lecture 20: Miscellaneous

Lecture 21: Exercise Answers – Miscellaneous

Lecture 22: Section 8: Material

Chapter 9: Exporting Data out of R

Lecture 1: Text files

Instructors

Jagannath Rajagopal
Entrepreneur and Data Scientist

Rating Distribution

1 stars: 41 votes
2 stars: 47 votes
3 stars: 285 votes
4 stars: 732 votes
5 stars: 703 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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