Four-Week Course

Visualization in R

Start from beginner and move up to advanced by the end of four weeks.

Visualization in R

Welcome to the four-week course on visualization in R. The purpose of this course is to acquaint you with the basics of the statistical computing language and to get you visualizing your data as quickly as possible. Sections focus on the how with practical tutorials and tips.

Who this is for

This is intended for beginner to intermediate users of R and anyone who wants to learn how to visualize his or her data. While it’s helpful to have programming experience, you do not need to have any, as we walk through the basics to get you up and running.

What you need

All you need is a computer with R installed or one that you can install R on.

How to get the most out of this

The course is structured for roughly 5 to 10 hours per week, depending on how much time you want to spend with each section. Go through section-by-section to start from basics and work towards more advanced visualization.

When you work through a tutorial, download the source first and follow along rather than entering every snippet in R. For simple examples, it’s easy to copy and paste code, but when you get into more complex examples it’s easy to enter typos or get the code structure mixed up. Here’s what code will look like through the course, which is typically a cue to enter something in R:

# This is code.
paste("hello", "world")

At the end of each week is an extra credit section. This provides practice exercises and additional resources to learn more about abstract concepts. You’ll improve much quicker if you work through these sections.


Here’s what you cover each week.

Week 1

Get setup, learn the basics, and make charts in R with a few lines of code.

Week 2

Extend R by installing and working with packages and create custom charts to fit your needs.

Week 3

Spatial data. Map various data types and formats, and make geographic maps that look good.

Week 4

Refine what you learned the first three weeks with multi-faceted views, reusable code, and tools that are not R.