Revised by: Programme council for Master Programmes in Public Health, 2023-09-13
Contents
The course will focus on the use of R for the analysis of data. In addition to familiarization with standard R functions as well as functions for graphics and statistical analysis the students will learn how to program by creating their own functions. The use of script to facilitate reproducible results is central. Students will use large language models (AI) and other AI tools to increase efficiency in their workflow but also critically over these tools and their use.
Expected learning outcomes
Knowledge and understanding: After completing the course, students shall be able to:
Comprehend and be familiar with standard functions in R.
Comprehend and be familiar with functions for graphical presentation in R
Have a basic understanding on how to formulate and undertake statistical computations in R
Have a basic knowledge on how to access documentation and find help for R.
Understand how AI tools can be used to generate R code.
Skills and abilities: After completing the course, students shall be able to:
Use R to clean and describe data.
Use R to visualize data.
Perform statistical analysis with R such as significance tests, ANOVA, generalized linear models and survival analysis.
Undertake basic programming in R.
Create reproducible (R Markdown) reports that integrate results, R code and text.
Use AI to create R-code.
Judgement and approach: After completing the course, students shall be able to:
Critically discuss and evaluate the application of R and AI to improve code.
Create reproducible analysis where R code and explanatory texts are used by using for example R Markdown, Sweave or Pandoc.
Required Knowledge
For non-programme students applying as single-course students, the requirements are 120 ECTS, and Proficiency in English equivalent to Swedish upper secondary course English B/6.
Form of instruction
The course is given daytime as a distance course at 25%. Teaching is performed through plenary lectures, web-lectures, group exercises and practical computer exercises. Teaching is given in English. Internet connection with access to Canvas and video conferencing software together with a functional computer for installation of R and RStudio is a requirement. The course will provide both demonstrations and practical exercises. One mandatory group task is given during the course.
To be able to understand and assimilate the statistical methods a basic knowledge in statistics is recommended.
Earlier experience of R or programming is not a requirement
Examination modes
The following grades are used: Fail (U), Pass (G) or Pass with Distinction (VG). The examination encompasses a home exam followed by an individual oral examination of a (by the teachers chosen) task from the home exam and a group work oral presentation.
The written exam is graded as Fail, Pass or Pass with Distinction. The oral examination and contribution to the group work are graded as Fail or Pass.
To pass the course, the student must pass all three parts of the examination. The overall grade is a summary of the parts and is only awarded when the student has passed all three parts. Pass with Distinction as the overall grade requires Pass with Distinction on the written exam, Pass on the oral examination and Pass on the group work. Pass as the overall grade requires Pass on the written exam, Pass on the oral exam and Pass on the group work.
Students who do not pass the regular examination have the right to a new examination (see Umeå university's rules for course examinations).
The examiner can grant the student the right to an alternative form of examination in special circumstances. A student who receives a Pass grade is not allowed to re-sit in an attempt to receive a higher grade. A student who has received a Fail grade twice is entitled to request to have another examiner appointed, unless there are specific reasons against it. A written request should be sent to the Director of Studies.
Examiners may decide to deviate from the modes of assessment in the course syllabus. Individual adaptation of modes of assessment must give due consideration to the student's needs. The adaptation of modes of assessment must remain within the framework of the intended learning outcomes in the course syllabus. Students who require an adapted examination - and have received a decision on the right to support from the coordinator at the Student Services Office for students with disabilities - must submit a request to the department holding the course no later than 10 days before the examination. The examiner decides on the adaptation of the examination, after which the student will be notified.
