Introduction to R

[mitsumi]

Introduction to R
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 15 Hours | Lec: 103 | 1.35 GB
Genre: eLearning | Language: English

Learn the core fundamentals of the R language for interactive use as well as programming   

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 Descriptions. You will also learn about formatting various parts of a Description, covering a range of topics like Description 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.

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