Introduction to R programming

Introduction to R programming

A general understanding of how R programming works and you’ll find it easy and in fact exciting to work in R!

Finding R difficult? It doesn’t need to be. This course will walk you through the basics in a way that is easy to follow. Dr. Martin teaches with examples that use datasets that are already built into R Studio, so you can replicate the code at home.

The trick with R programming is to start with the basics and have a clear analysis plan. It should look something like this: 1) import and inspect your data, 2) clean and shape your data, including selecting variables, filtering rows, and dealing with missing values, 3) describe your data, including tables that summarise key parameters, 4) visualize your data, and 5) undertake appropriate statistical and modeling analysis.

This course will give you a general understanding of how R programming works. You’ll find it easy and in fact exciting to work in R once you’ve overcome the initial fear of programming. It makes a lot of sense to do data analysis using R because you’ll have a clean record of how you drew your conclusions. Working with R also facilitates collaboration with others (so you can get help from experts very easily).

If this is your first step into the world of R programming – welcome aboard. You’ll love it. So don’t delay, sign up now and start the adventure!

What you’ll learn?
  • Install R and R studio
  • Work comfortably within R studio
  • Download packages
  • Install data
  • Do basic manipulation of data
  • Understand how R works with different types of data
  • Be able to perform some basic statistical analysis
Who is this course for?
  • Beginners.
  • No experience is needed. You will need everything you need to know.
Course content
1 – Why use R?
1. Why use R
2 – How to download and install R and R studio
1. How to download and install R and R studio
3 – Import data and install packages
1. Important data and installing packages
2. Import data from excel
4 – Data manipulation
1. Types of data in R
5. Advanced filtering and subsetting your data
6. Dealing with missing data
5 – Statistical analysis (an example)
1. A data analysis example