Introduction to R ADED11789E
- Academic Session: 2019-20
- School: Short Courses
- Credits: 10
- Level: Level 1 (SCQF level 7)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Available to Erasmus Students: No
This course will introduce students to R: a free, open-source, flexible statistical software for manipulating, analysing and visualising data, used in degree-level Psychology and other disciplines. Using the RStudio interface, students will learn how to wrangle data - to combine and extract information of interest, and to clean up data into the format required for further analysis. Students will learn how to perform descriptive analyses to extract summary information and to visualise data in academic-standard plots. The course aims to provide students with the starter skills and confidence to continue developing their R skills independently after the conclusion of the course.
Mondays 18:00 - 20:00
2 hours per week for 10 weeks
Requirements of Entry
None. No previous knowledge of statistics is assumed.
Equally weighted weekly homework exercises performing functions taught in class and submitted individually, although peer discussion will be encouraged. 8 exercises available for completion, with the highest 4 marks counting towards the course grade (25% each, total = 100%).
The course aims to:
■ Introduce the environment of R and RStudio, and to demonstrate the potential strength of these tools when dealing with data.
■ Support students who are new to coding to develop the skills to handle numerical data using R and RStudio.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ Demonstrate how R and RStudio work by transforming and cleaning different types of data into an appropriate format for analysis, and by creating graphical representations to visualise different types of data.
■ Calculate summary statistics using R.
■ Interpret common error messages and use in-built help facilities to debug their code.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.