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Introduction to R

Join our virtual R programming course. Learn data analysis with R, installation of R interpreter and RStudio, usage of R packages, and unique data science features of R. Step into the world of data science with confidence.

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  • Course Info
  • Instructor
  • System Requirements
  • What to know beforehand

Course Description

During this R programming virtual training course, you will learn how to use R for data analysis. We’ll start with a discussion of the R language and how to install the R language interpreter and RStudio, the popular R integrated development environment. We’ll learn how R can be extended with modules called packages and discuss some of the popular packages in use. We’ll delve into the unique features of R that make it so suitable for data science. The core R data structures; vectors, matrices, arrays, and data frames, will be discussed with examples. Before moving into predictive modeling, we’ll do a quick overview of key concepts in statistics. From there we will walk through the training, testing, and evaluation of a predictive model. Since many people will be using data from relational databases, we will explain how to use the RODBC package to read in data using a SQL Server table as an example. The course wraps up by discussing related topics such as scaling R programs, creating R based web applications, and creating dynamic presentations with R Markdown.

Course Outline ( Free Preview)

Introduction to R - What you need to get started

Module 01 - Getting Started with R 10 min.

Module 02 - An Introduction to RStudio

Module 03 - Extending R with Packages

Module 04A - Vector Fundamentals (Understanding Vectors) 20 min.

Module 04B - Vector Fundamentals (Leveraging Vectors) 22 min.

Module 05 - Matrices Fundamentals 24 min.

Module 06 - Array Fundamentals 21 min.

Module 07 - List Fundamentals 34 min.

Module 08 - Using Data Frames 33 min.

Module 09 - Statistics (What you need to know) 41 min.

Module 10 - Data Quality and Missing Data 23 min.

Module 11 - Evaluating Data Relationships 21 min.

Module 12 - Overview of Data Science 18 min.

Module 13 - Creating a Predictive Model 33 min.

Module 14 - Comparing Models 17 min.

Module 15 - Choosing a Predictive Model 14 min.

Module 16 - More On File Formats 41 min.

Module 17 - Reading Data from Relational Databases 29 min.

Module 18 - More Topics on R 42 min.

Module 19 - Wrapping Up 20 min.

This course includes:

  • 8+ hours of training
  • 21 Modules
  • * Access on mobile and browser
  • Certificate of Completion
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