Computer labs
The goal of these labs is to introduce you to, and build up your proficiency with, R and RStudio. You’ll be using these throughout the course, both to learn the statistical concepts discussed in the lectures and also to analyze real data and come to informed conclusions. To straighten out which is which:
- R is the name of the programming language itself;
- RStudio is a convenient interface.
The R language is the standard statistical tool used by most statisticians at universities. One reason data scientists and statisticians like to use R is that all known statistical techniques are available in R. Whenever someone develops a new statistical technique, one of the first things they do is produce an R package so that the technique becomes available in R. The reason they do this for R rather than for one of the commercial alternatives is that R is open source and freely available to all, and of course that the previous methods on which the new method builds are already available in R.
Feeling comfortable using R is not only important for this module and any further statistics modules you may take at the Department of Mathematics of the University of York, it can also be an important factor for your future career (see the article “R skills attract the highest salaries”. Even though R is specially designed for statistics, it is consistently in the list of the top ten most important programming languages compiled by the IEEE spectrum magazine.
As the labs progress, you are encouraged to explore beyond what the labs dictate; a willingness to experiment will make you a much better programmer.
Assessment
The five main labs (imaginatively named “Lab 1” to “Lab 5”) count for credit: your best 4 out of 5 will marks will count for 20% of the module mark.
Each lab will have an accompanying Moodle quiz. As you work through each lab you will find places where you are asked to perform a calculation and then enter your mark in the appropriate quiz.
The online quizzes will give you immediate feedback and allow you to try again if you get an answer wrong. However there will be a 20% deduction for each wrong attempt at a part of a question.
The Intro lab does not count for credit, but you should attempt this in the first week of the semester to make sure that:
- you can successfully access R
- you know how to enter answers in the accompanying Moodle quiz.
Schedule
(Each link will only work once the relevant lab has been released.)
| Lab | Hand-out date | Quiz due date (10am) |
|---|---|---|
| Intro Lab (not for assessment) | Tuesday 26 Sep (Week 1) | — |
| Lab 1 | Thursday 5 Oct (Week 2) | Monday 9 Oct (Week 3) |
| Lab 2 | Thursday 19 Oct (Week 4) | Monday 23 Nov (Week 5) |
| Lab 3 | Thursday 9 Nov (Week 6) | Monday 13 Nov (Week 7) |
| Lab 4 | Thursday 23 Nov (Week 8) | Monday 27 Nov (Week 9) |
| Lab 5 | Thursday 7 Dec (Week 10) | Monday 11 Dec (Week 11) |