Why swirl?

[This article was first published on Jon Calder's R Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Learn R, in R. –

swirl logo

swirl is a software package for the R programming language that turns the R
console into an interactive learning environment. Users receive immediate
feedback as they are guided through self-paced lessons in data science and R

I think I first came across swirl in mid-2014, while
working through the early stages of the
Data Science Specialization
on Coursera, put together by Johns Hopkins University professors
Roger Peng, Brian Caffo
and Jeff Leek.

The courses in this specialization make extensive use of swirl to introduce
and explore various topics in the form of interactive programming
assignments in R (e.g. R programming).
This is probably largely due to the fact that these three professors were
involved in the early development of swirl with it’s creator
Nick Carchedi who is a graduate of Johns Hopkins
Biostatistics. For those who may be new to R or data science – these Coursera
courses are an excellent resource which I can highly recommend.

Of course there are many, many great resources for getting started (and later
improving) with R programming: try the online learning suggestions from RStudio, or take a look through some
of these Quora answers
and you will find a wealth of R resources: online courses, books, tutorials and
more. If that’s not enough for you – Google is your friend. There is so much out

I’d like to focus on swirl in this post however, since I feel it doesn’t really
get as much attention as it should. There are three key ways in which I think
swirl has got it right as a learning platform:

  • it directly facilitates learning by doing
  • it runs from within R
  • it is free and open source

Learn by Doing

First is the “learn by doing” component, which is almost as crucial to
learning to program as it is to learning to ride a bike, or to drive a car.

code cat

Sure, programming is mostly cognitive, but in my experience you only begin to
internalize and understand concepts, syntax etc properly when you start to apply
them. The primary learning you do when learning to ride a bike or drive a car
happens when getting onto a bike or into a car for the first time and (usually)
having someone walk you through the interface, controls etc as you experience
them for yourself.

The need to learn by doing might seem obvious, but if you stop and think about
it for a moment, you might find that given the wealth of great R programming
books, videos, courses etc its entirely plausible that you could begin your
learning path there and after being pulled in different directions end up
spending very little (or no) time in R. A few days ago I was chatting to one of
my cousins who has been learning R on and off for a while now and when I asked
him how it was going he said he thinks his learning process so far has lacked
sufficient “hands on” practice with R.

swirl is great because it forces you to learn by doing, whether that means
copying code/syntax from an instruction, interrogating R’s help documentation in
order to work out how to make use of a particular function, or experimenting
with different options by trial and error.

Learn R, in R

Second is the “learn R, in R” component, which is what makes swirl pretty
unique. There are a few other learning platforms which embed or reproduce the R
console within their own web environments to produce a similar outcome, but at
the end of the day, in some or other (possibly quite small) respects none can be
equivalent to the reality of working in a local R environment on your own
computer, whether that be RStudio (recommended!), some other IDE of your
choosing, or even just running R from the terminal.

swirl in action

Since swirl runs within R itself, when you learn with swirl, you can work with
the exact same local R environment you will later use to program in R. By way of
a poor analogy, this is somewhat like the difference between taking your driving
lessons in someone elses car and taking them in the car that you plan to
use once you have obtained your license. If you have the option of the latter,
there are certainly benefits to doing so.

At the same time I do acknowledge that there can also be some downsides to this
when considered from certain perspectives (e.g. the swirl approach of embedding
the learning environment within R is not as flexible as the reverse and does
constrain the learning environment to some extent). However as mentioned above
I think what swirl has to offer is unique in this respect and it is worth
highlighting that the advantages are significant.

I am certainly not averse to other R learning platforms and realize that some
have other advantages over swirl. For example, it is worth noting that the
creator of swirl is now working at DataCamp, which
is an excellent learning platform that offers a host of courses in both Python
and R, some of which in fact still make use of swirl.

Free and Open Source

Third is the fact that swirl is completely free and open source. This means
that students, instructors and other R developers can dig into both swirl
itself and swirl lesson content, learn from it, and then suggest and/or
contribute towards corrections and improvements over time. Being able to look
behind the curtain opens up a world of learning opportunities.

For example, my first ever pull request on GitHub,
was in the swirl courses repository. It was a only simple spelling correction
for lesson content from one of the aforementioned Coursera swirl courses, but
you’ve got to start somewhere. It’s open source projects like swirl that
offer these opportunities for “getting started”.

In a little over two years since that first pull request, and I have been able
to contribute in a number of different ways to various swirl projects and
courses, learning as I go. A few months ago, I also decided to get started on
two of my own swirl courses, which I’m continuing to work on iteratively as and
when I can make time. I’ve still got a long way to go, but I’ve already learnt
so much through the process, and will definitely discuss these further in later

My recommendation is that you checkout swirl if you haven’t already done so,
because it is worth using. The easiest starting point is the
students page on the swirl website, which
provides all the steps you’ll need (especially if you are completely new to R).

And if you’re a more advanced R user, why not consider contributing a course?
The documentation for swirl is excellent, and it is very easy to get started
creating a course. All the information you’ll need to do so is provided on the
instructors page on the swirl website.
Also note that the developers of swirl have created a swirl course network
(SCN) in an effort to “list and organize all publicly available swirl courses”.
If you have created a swirl course or are considering doing so, please
take a look at this to find out how to share your course.

If you need help or would like to collaborate on a swirl course please get in
touch with me
. I’ll be happy to assist
where and when I am able to do so.

To leave a comment for the author, please follow the link and comment on their blog: Jon Calder's R Blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)