This is a collection of interactive courses for use with the swirl R package. You'll find instructions for installing courses further down on this page. Some courses are still in development and we'd love to hear any suggestions you have as you work through them.

For more information regarding swirl, visit swirlstats.com or the swirl GitHub repository. If you'd like to write your own interactive content, please visit the Instructors page of our website.

Here are our current offerings, organized by level of difficulty:

• R Programming: The basics of programming in R
• R Programming E: Same as the original, but modified slightly for in-class use (see below ***)
• The R Programming Environment

*** R Programming E is identical to R Programming, except we've eliminated the prompts for Coursera credentials at the end of each lesson and instead give students the option to send an email to their instructor notifying them of completion. Admittedly, it's sort of a hack until we come up with a more robust solution for in-class use (i.e. an instructor "dashboard").

• Regression Models: The basics of regression modeling in R
• Getting and Cleaning Data: dplyr, tidyr, lubridate, oh my!

• Statistical Inference: This intermediate to advanced level course closely follows the Statistical Inference course of the Johns Hopkins Data Science Specialization on Coursera. It introduces the student to basic concepts of statistical inference including probability, hypothesis testing, confidence intervals and p-values. It concludes with an initiation to topics of particular relevance to big data, issues of multiple testing and resampling.
• Advanced R Programming

Since our users come from a variety backgrounds, it's very hard to label material as Beginner, Intermediate, or Advanced. If you find something that is labelled Beginner to be challenging, please don't be discouraged. The first step of learning anything is to acknowledge that you are capable of understanding it. True understanding will come with time and practice.

• Writing swirl Courses: An interactive guides and example for swirl course authors. The first group of lessons cover basics. The rest cover special topics useful primarily as samples--points of departure for one's own material. For more comprehensive documentation about writing your own swirl courses see http://swirlstats.com/swirlify/.

This is the preferred method of installing courses. It automates the process by allowing you to do everything right from the R console.

1. Make sure you have a recent version version of swirl:

install.packages("swirl")

2. Enter the following from the R console, substituting the name of the course that you wish to install:

library(swirl)
install_course("Course Name Here")
swirl()

For example, install_course("R Programming") will install the R Programming course. Please note that course names are case sensitive!

If that doesn't work for you...

If the automatic course installation method outlined above does not work for you, then there's a simple alternative.

1. Find the course you want to install on the Swirl Course network website.
2. Follow the manual installation instructions on the course page.

If that does not work for you, consider taking a look at the legacy manual install instructions.

If you'd like to remove a course at any time, you can use uninstall_course("Course Name Here").

Instructors around the world are using swirl in their classrooms. We think this is awesome. If you're an instructor, please feel free to do the same -- free of charge. While your students may be paying to take your course or attend your institution, we simply ask that you don't charge people directly for the use of our software or instructional content.

If you are not sure about a particular use case, don't hesitate to post a question to our Google Group.