ECON30520 R for Economists

Academic Year 2022/2023

The purpose of the course is to introduce you to data analysis using the powerful and increasingly popular R language. The hope is that by the end you will have learned enough to carry out basic statistical projects. The course will fall roughly into three parts. The first will be how to enter and manipulate data in R. The second is how to present data and results graphically. The third part is In effect an applied econometrics module where we will run through a variety of useful techniques, showing how they can be computed in R. Alongside traditional regression techniques we will devote some time to machine learning. The emphasis throughout the course will be on taking real world data and figuring out how to analyse it in a sensible way and how to present the results of our analysis in an easily understandable way.

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Curricular information is subject to change

Learning Outcomes:

By the end of the course you should have enough grasp of R to complete a simple statistical project: tidying your data, graphing it and analysing it econometrically.

Indicative Module Content:

1. Tidying data.
2. Graphing data.
3. Ordinary least squares.
4. Time series.
5. Other regression techniques.
6. Basic machine learning.

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning






Approaches to Teaching and Learning:
The emphasis is on completing practical problem sets, analysing data in the ways that we discuss as we go along 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
STAT40620 - Data Programming with R

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: The student will be required to submit a short project based on the material covered in the course. Coursework (End of Trimester) n/a Graded Yes


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

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