Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Public Health Research

Postgraduate Course: Introduction to R for Public Health (PUHR11114)

Course Outline
SchoolDeanery of Molecular, Genetic and Population Health Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe course introduces the basic principles of using the R programming environment for statistical data analysis: it focuses on epidemiological applications in a wide variety of health sciences contexts, and assumes students have already undertaken some introductory learning in statistical methodology.
Course description This course is designed to equip students who have already undertaken some learning in the principles of statistical methodology with a grounding in the practical skills of data analysis using the open source R statistical programming language. The course will introduce good practice in data management and key skills in exploratory and inferential statistical analysis. Extensive use will be made of tidyverse packages such as readr, tidyr, diplyr and ggplot2. Statistical topics to be covered include numerical and graphical descriptive methods, simple one and two sample comparisons of categorical and continuous data using both confidence intervals and hypothesis tests, contingency tables and risk ratios, direct and indirect standardisation, measures for diagnostic testing, correlation and simple linear regression. The course (delivered online) will be based around 5 weekly case studies/ projects that will require students to undertake software-based analyses after using a mix of short recorded lectures and readings designed to illustrate each set of principles, with each week's activity building on the last. Each week, students will be encouraged to discuss their analysis plans, share useful code 'shortcuts' and discuss their results

on the discussion boards. The case studies will be based on real data sets and/ or common problems in epidemiology.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesTraining in introductory-level statistical methods (coverage: 1 and 2-sample problems, hypothesis testing, confidence intervals, simple linear regression, graphical methods).
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  60
Course Start Block 3 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0 %, Coursework 100 %, Practical Exam 0 % The assessment will be a project, comprising a report on a guided analysis of a data set.
Feedback Students will receive continuous feedback via Discussion Boards, and undertake a short formative practical exercise around week 4 of the course, which will be in the style of the final assessment.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Apply a range of statistical methods to data analysis problems in epidemiology.
  2. Critically evaluate common problems in epidemiology in order to select appropriate statistical approaches.
  3. Use the R statistical programming language with confidence and interpret and communicate output
  4. Show autonomy in the design and execution of basic statistical analysis in epidemiological problems
Reading List
R for Data Science (2017). Wickham H. and Grolemund, G.O┬┐Reilly Media: California
Additional Information
Graduate Attributes and Skills The skills developed by this course are key for most types of epidemiological enquiry, and thus fall broadly under the overarching Enquiry and Lifelong Learning attribute. In particular, the core tasks of analysis and project work involve problem solving, critical thinking and evaluation, which map closely to the Research and Enquiry cluster. However, this will also foster Personal and Intellectual Autonomy, contributing to the student's ability to conceive, design, execute and interpret epidemiological research
Course organiserDr Niall Anderson
Tel: (0131 6)50 3212
Course secretaryMr Sam Chase
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information