We publish R & Python tutorials from scientists at academic and scientific institutions with a goal to give everyone in the world access to a free knowledge. Our tutorials cover different topics including statistics, data manipulation and visualization!

Introduction

R is an open source programming language with a lot of facilities for problem solving through statistical computing. At the time of writing this, there are more than 6K packages available in CRAN repository.

R is a language and an environment for everything related to data analysis. That includes statistical computing, data mining, data analysis, machine learning, predictive modelling, quantitative analysis, optimization and operations research etc – all of which are somewhat inter-related terms. Data scientists, analysts, statisticians, quantitative analysts, forecasters, bio-statisticians, financial analysts, research scientists. These are some of the professions where R is commonly used. But, is R limited to these guys? NO, and not necessary!

But before you get to the machine learning part, you need to first nail the basic R language, which is what this whole tutorial is all about. Besides, R is the best platform to master this vast spectrum of knowledge. This tutorial below is the first part of the planned 3 part video tutorial (part 2 and part 3) series that explains the core concepts in the simplest terms. So, Lets begin.

Find below the video tutorial(s) for this post.

- 01. Installing R 02:01
- 02. R Interface 05:17
- 03. Basic Math 03:25
- 04. Variables and Datatypes 04:12
- 05. Introducing Vectors 03:29
- 06. Set up your work directory 02:57
- 07. Create vectors sequences – Part 1 03:06
- 08. Create vectors sequences – Part 2 02:30
- 09. Random Numbers 01:59
- 10. Find and Remove Missing Values 02:59
- 12. Get specific items from vector using which() 03:36
- 13. Convert one variable type to another 03:10