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.

Show/hide contentOpenClose All

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




Computer Aided Lab




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 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 Nial Friel Lecturer / Co-Lecturer
Professor Brendan Murphy Lecturer / Co-Lecturer
Dr John O'Sullivan Lecturer / Co-Lecturer
Manisha Ganguly Tutor
Thais Menezes Tutor
Ms Claire Mullen Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Lecture Offering 1 Week(s) - Autumn: All Weeks Thurs 10:00 - 10:50
Laboratory Offering 1 Week(s) - Autumn: Weeks 2-12 Thurs 09:00 - 09:50
Laboratory Offering 2 Week(s) - Autumn: Weeks 2-12 Thurs 11:00 - 11:50
Laboratory Offering 3 Week(s) - 2 Tues 16:00 - 16:50
Laboratory Offering 3 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Tues 16:00 - 16:50
Laboratory Offering 4 Week(s) - Autumn: Weeks 2-12 Mon 09:00 - 09:50