PSY9510 – Introduction to Statistics with R
Schedule, syllabus and examination date
Course content
R is a free and open-source programming language, popular for data manipulation, statistical analyses, and graphics. This course provides a broad introduction to programming fundamentals with a focus on how R can be used for data management, descriptive statistics, graphics, statistical analysis and reproducible research.
Learning outcome
The course walks the participants through the basics of R programming and provides skills for organizing analysis projects, R code as well as skills for data management (RStudio Projects) and reproducible statistical analysis (Quarto). The course starts using base R and progresses into various functions from the tidyverse package. It also provides a thorough guide to the grammar of graphics for developing publication-ready figures via the ggplot2 package.
The learning objectives of the course are as follows:
- Become familiar with R and the RStudio environment.
- Understand datatypes and their characteristics.
- Manipulate different data structures in R.
- Perform data cleaning and data management using base R and the tidyverse package.
- Carry out data quality checks.
- Write and organize R syntax and implement reproducible analyses within RStudio.
- Produce descriptive statistics in R.
- Run basic statistical analyses.
- Produce publication-ready graphics with ggplot2 package.
Emphasis is placed on the practical mastery of the tools mostly used to manipulate behavioral data. Students can use the acquired skills while working on their thesis and other reproducible research projects. An important goal of this course is to foster students’ analytical independence in R so that they are capable of developing their skills in the future.
Admission to the course
This is an elective course in the PhD program in Psychology.?PhD candidates at the Department of Psychology need to sign up to the course in Studentweb. Please contact the administration if you have problems to sign up in Studentweb.?
Master and other PhD students can apply for the course, but admission priority is given to PhD students. Master students and candidates from PhD programs at other institutions can apply to the course through this online form, however candidates from UiO will be given first priority.?
The registration deadline is written in the online form and you will receive an email shortly after the deadline if you got admitted to the course.
All candidates need to be signed up in Studentweb before the first day of teaching.?
Recommended previous knowledge
Students are expected to have a basic understanding of statistics (e.g., linear regression), but no previous knowledge of programming or R language is required. Only the willingness to learn to write code is required.
Teaching
The course duration is equivalent of 24 seminar hours and is offered each semester. Seminars are composed of mini-lectures and practical computer exercises.
Recommended literature (optional):
Wickham, H., ?etinkaya-Rundel, M., & Grolemund, G. (2023).?R for data science. O'Reilly Media, Inc.. Available online at https://r4ds.hadley.nz/.
Examination
1 credit point is awarded for 80% participation and submitting a reproducible Quarto report. For the home assignment, you can use all course study materials, your notes, and any available online materials.
Language of examination
English.
Grading scale
Grades are awarded on a pass/fail scale. Read more about?the grading system.
More about examinations at UiO
- Use of sources and citations
- How to use AI as a student
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.