STAT40620 Data Programming with R

Academic Year 2022/2023

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


Computer Aided Lab


Specified Learning Activities


Autonomous Student Learning




Approaches to Teaching and Learning:
Lectures and Lab practical sessions 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have had previous experience of using computers, including web searching and creating spreadsheets. Some familiarity with statistiics (mean and variance, correlation, linear regression) is expected.

Learning Recommendations:

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

Module Requisites and Incompatibles
ECON30520 - R for Economists, STAT40180 - Data Programming with R, STAT40730 - Data Prog with R (Online)

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Computer labs Throughout the Trimester n/a Standard conversion grade scale 40% No


Multiple Choice Questionnaire: Multiple choice exams during the semester Varies over the Trimester n/a Standard conversion grade scale 40% No


Project: Coursework project Coursework (End of Trimester) n/a Standard conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring Yes - 2 Hour
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 Nial Friel Lecturer / Co-Lecturer
Professor Brendan Murphy Lecturer / Co-Lecturer
Mr John O'Sullivan Lecturer / Co-Lecturer
Lukasz Kaczmarczyk Tutor
Mr Wenxuan Liu Tutor
Brian O'Sullivan Tutor
Thais Pacheco Menezes Tutor
Silvia Scarpa Tutor
Niyati Seth Tutor