Getting started with R, 2 credits
R för nybörjare
Course information
Language of instruction: English
Course period: January 16-17 and January 20, 2025
Campus teaching or online teaching: To be decided via questionnaire to participants to choose between fully in person or hybrid, most likely hybrid format (first session in person, remainder online)
Recommended prerequisites
MSc in natural sciences or related field, experience/education in basic statistics
Learning outcomes
This course introduces the command-based statistical software R, one of the most widely used, and highly versatile, statistical programs in natural sciences and related fields.
Learning outcomes: After this course, students
- are able to conduct basic statistical analyses in R, including deciding which analysis to use and how to interpret the results
- can visualise data with base graphics and ggplot2
- can write the associated R code and perform basic programming steps, and
- know how to approach learning more advanced statistical methods in R.
This course also enables students to use R independently in Göran Arnqvist´s course "Modern statistics in natural sciences". "Getting started with R" can be taken by itself or just before "Modern Statistics..." begins (course times are coordinated).
Learning outcomes for doctoral degree
Teaching the use of R for independent statistical analyses as well as the selection and critical evaluation of statistical methods is associated with the following three PhD examination goals (According to Higher Education Ordinance, Appendix 2. (1993:100):
• “…demonstrate broad knowledge and systematic understanding of the research field as well as advanced and up-to-date specialised knowledge in a limited area of this field" (Knowledge and Understanding)
• "...demonstrate the capacity for scholarly analysis and synthesis as well as to review and assess new and complex phenomena, issues and situations autonomously and critically” (Skills and Abilities)
• "...demonstrate the ability to identify and formulate issues with scholarly precision critically, autonomously and creatively, and to plan and use appropriate methods to undertake research and other qualified tasks within predetermined time frames and to review and evaluate such work (Skills and Abilities)
Course contents
The course covers: Data loading and manipulation, R command structure, common basic statistics (for example, correlation, ANOVA and regression), graphs (barplots and scatterplots), and a brief introduction to programming.
Instruction
In this course, students are first introduced to a topic and then spend most of the course time on independent solving of exercises. Exercises are designed to be challenging and ask students not only to write R code but also to make their own analysis decisions and to critically evaluate methods. Coding can be difficult but is best learnt by practice. We encourage learning and understanding by asking students to work at their own pace (continue earlier exercises later if needed) and by providing extensive individual help during the exercises with one assistant per 5-6 students. Once students are done with an exercise, we have full solutions available allowing students to self-correct their results and to get more coding input. These measures encourage students to work freely until they understand what they are doing as they do not have to worry that they would get completely stuck with coding or miss something if they need more time. The target teaching atmosphere in the exercises is one of high concentration among the students with some consulting with teachers in quiet voices (or digitally). During the sessions, students can sometimes be heard bursting out into "yes! now it works!".
Structure: The course consists of six half-day sessions, each composed of a lecture/demonstration (1h) and associated exercises (2h). Exercises focus on realistic analysis steps and involve real datasets. Students are encouraged to work at their own pace throughout the sessions. During the practice time students have access to individual help from myself and 2-3 assistants, depending on student number. Exercises are designed to encourage independent thinking (rather than repetition).
Assessment
Attendance in 5 out of 6 sessions and submission of an exercise.
Course examiner
Gabriela Montejo-Kovacevich, gabriela.montejo-kovacevich@scilifelab.uu.se
Department with main responsibility
Department of Ecology and Genetics
Contact person
Gabriela Montejo-Kovacevich, gabriela.montejo-kovacevich@scilifelab.uu.se
Application
Submit the application for admission to: Application Getting started with R
Submit the application not later than: 2024-12-16