STAT40730 Data Programming with R (Online)

Academic Year 2024/2025

This module introduces students with no previous programming experience to the open-source statistical programming language R. Topics include: manipulating vectors, matrices, arrays and lists; basic programming constructs and programme flow; graphical methods; dealing with large data sets; simple statistical methods.

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Curricular information is subject to change

Learning Outcomes:

At the end of the course students should be able to use R to:
- Load in and manipulate data sets of any size and structure
- Find help and use functions which they have not met before
- Create professional quality graphical summaries of data
- Perform simple statistical analyses

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning


Online Learning




Approaches to Teaching and Learning:
Weekly video lecture and screencast material, non-assessed lab sheets. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have had previous experience of using computers, including web searching and creating spreadsheets.

Learning Recommendations:

Some familiarity with Microsoft Office (or equivalent), programming concepts such as loops and functions.

Module Requisites and Incompatibles
STAT30340 - Data Programming with R, STAT40180 - Data Programming with R, STAT40620 - Data Programming with R

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Quizzes/Short Exercises: There will be 4 small tests, each worth 2.5%, which will be available on Brightspace. Week 4, Week 6, Week 9, Week 12 Alternative linear conversion grade scale 40% No


Assignment(Including Essay): There will be one small assignment worth 2% due in week 3 and two main 2 assignments, each worth 19% due in week 6 and 10. Week 3, Week 6, Week 10 Alternative linear conversion grade scale 40% No


Assignment(Including Essay): The project will involve using the R programming tools covered in the course. Week 15 Alternative linear conversion grade scale 40% No



Carry forward of passed components
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Professor Brendan Murphy Lecturer / Co-Lecturer
John O'Sullivan Lecturer / Co-Lecturer