Module overview
This module will provide an introduction to basic statistical programming in R. It consists of lecturers and associated practical sessions for students to gain hands-on experience of statistical programming.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- enter, import and manipulate data.
- write functions, loops and code for conditional execution.
- produce basic graphics.
Syllabus
- R syntax
- Importing data
- Data manipulation
- Graphics
- Writing functions
- Loops and conditional execution
- R Markdown
Learning and Teaching
Teaching and learning methods
18 sessions, in combination of lectures and computer labs, (this may all be delivered online)
| Type | Hours |
|---|---|
| Independent Study | 57 |
| Teaching | 18 |
| Total study time | 75 |
Resources & Reading list
Textbooks
Garrett Grolemund and Hadley Wickham. R for Data Science. O’Reilly.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Class Exercise
- Assessment Type: Formative
- Feedback:
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat Information
Repeat type: Internal & External