How you use this website to support your learning and understanding of R is of course up to you. Having said that here are few pointers that have worked for people in the past. The first thing you will need to do is download and install both R and RStudio on your computer. Take a look at the Setup link on the navbar at the top of this page for further instructions on how to set up your computer and download the required datasets. At the heart of this course is our ‘Introduction to R book’ which you can find by clicking on the R Book link in the navbar. The book is split into 9 Chapters which cover different aspects of using R and RStudio, from general orientation, basic R operations, importing and manipulating data, plotting data, programming in R, R markdown and using version control. You can test your understanding of each of these components by completing the associated exercises which you can find by clicking on Exercises. You will also find the solutions to the exercises here but we suggest that you don’t peek at the solutions too quickly and only use them to confirm your answers or if you get stuck and feel like throwing your keyboard out of the window! There are also some additional resources such as lecture slides we use during our in person courses, short how-to videos walking you through some important topics which you can find in the Learn R link. We’ve also created some stand alone tutorials covering topics such as RStudio Projects, R markdown and version control using Git and GitHub which can be found under the Tutorials link. We suggest that you take a look at the tutorials once you’ve finished the core part of this course.
This course assumes no previous experience or knowledge of using either R or RStudio. It also doesn’t assume any knowledge of programming or using a command-line interface, but if you have some experience, the content won’t come as so much of a shock. But don’t panic. Command-line interfaces and programming languages like R are incredibly powerful and will be utterly transformative to your research. There’s a learning curve, and it’s pretty steep in the beginning, but it’s surmountable and the payoff is worth it!
See the course syllabus for a general outline of the course content and timing. The course will start at 09:30 each morning and finish at 17:00. We will break for a one hour lunch break around 12:30. However, everyone learns R in their own way and at their own pace so this syllabus should be treated as indicative rather than absolute. There will also be plenty of opportunities to take short breaks during the course and we will also have a couple of 30 minute sessions on robust and reproducible research practices.
This course will be run entirely online this year. All interactive live sessions will be run using Blackboard Collaborate via MyAberdeen. During these live sessions you will all be working in the main meeting room as you work through the associated course exercises. If you have a question, or get stuck you’ll be able to get 1:1 support from one of the course instructors in a smaller breakout group. More information about this setup will be provided during our first session together.
Yes. As we’ll be running the course online you’ll need your own computer with internet access. Please take a look at the Setup link on the navbar at the top for further instructions on how to set up your computer prior to the course starting. Don’t worry, you don’t need a particularly powerful computer to install R and RStudio so anything from the last 5 years or so should be fine. What you will need is a reasonably stable internet connection to participate in the live Blackboard Collaborate sessions. If you think that your internet connection may not be up to it then please contact us to discuss alternatives.
We have thought long and hard about whether to teach this course using the ‘tidyverse’ collection of packages and approaches (if you’ve never heard of ‘tidyverse’ before then don’t worry about it!). In a perfect world we would have sufficient time to teach you both base R and ‘tidyverse’, however we only have 3 days together and this is not really enough time to cover both topics in sufficient detail. In the end we decided that it was more important that by the end of the course you have a good fundamental grasp of base R which will provide you with the foundation to go on and learn ‘tidyverse’ approaches in your own time (if you want to). Having said that, if you have previous experience using ‘tidyverse’ then feel free to complete the course exercises using this approach.
Not a problem. Feel free to use the IDE or script editor of your choice. One of the great things about R is that YOU decide how you want to use it. See Section 1.4 of our Introduction to R book for some suggestions for alternatives to RStudio.
It’s inevitable that you will get stuck at some point either during the course or beyond. Whilst attending the course please feel free to ask plenty of questions (remember, we’ve all been there!). After the course, take a look at the Resources section for further information on where to get help and how to help yourself. There is also an active Aberdeen Study Group which you can join for free and you don’t need to be based in Aberdeen to participate. The group has regular online R co-working sessions and their website hosts a bunch of useful and informative tutorials.
The only way to get more comfortable using R is to use R! I strongly suggest that having completed this course you start using R to summarise, manipulate, plot and analyse your own data as soon as possible. If you don’t have any data yet, then ask your supervisor, friends or family for some (I’m sure they will be delighted!) or follow one of the many excellent tutorials available online (see the resources page for more information). My suggestion to you, is that whilst you are getting to grips with R, uninstall any other statistics software you have on your computer and only use R. This may seem a little extreme but will hopefully remove the temptation to ‘just do it quickly’ in a more familiar environment and consequently slow down your learning of R. Believe me, anything you can do in your existing statistics software package you can do in R (often better and more efficiently).