Course - Learning R for research and reporting: From basics to advanced applications - PSY6016
PSY6016 - Learning R for research and reporting: From basics to advanced applications
About
Lessons are not given in the academic year 2024/2025
Examination arrangement
Examination arrangement: Home examination
Grade: Passed / Not Passed
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Home examination | 100/100 | 21 days |
Course content
Our program offers a thorough and hands-on exploration of R programming, covering the most important modern packages. Participants are thus provided with a strong foundation in the language and equipped with the skills necessary for advanced applications in research and reporting. Based on a combination of online-lectures, assignments, and live-sessions, you will be provided with the tools to explore and exploit the R universe. As a special feature, we offer individual consultations with the course holders - expert statisticians and authors of two books on the R software - as part of the course.
Learning outcome
Following this course, you will become a proficient user of R, capable of effectively communicating complex data insights through well-informed reports.
Knowledge:
- The student will learn and thoroughly understand how the R software can be used to solve a large variety of research-related questions and matters including data management, data analysis and automated/reproducible reporting.
Skills:
- The student will be able to program scripts in the R-language to solve analytical problems. The students will be able to use both traditional base R functions as well as the modern "tidyverse" packages/functions depending on their needs (tidying data, writing functions etc.).
General competence:
- The student will have a solid understanding of the R software’s capabilities and will understand how they can be deployed flexibly to support high-standard research activities ranging from design to reporting.
Learning methods and activities
- Live lectures
- Individual assignment work
- Recorded assignment review
- Live individual consultancy
Further on evaluation
The course will be evaluated with a three week home exam.
Specific conditions
Admission to a programme of study is required:
Continuing Education at Department of Psychology (IPSEVU)
Required previous knowledge
Mastergrad
Course materials
Recommended reading list:
Andrews, M. (2021). Doing Data Science in R. Sage.
Baruffa, O. (accessed, 2023). Big Book of R. https://www.bigbookofr.com/
Mehmetoglu, M. & Mittner, M. (2022). Applied Statistics Using R: A Guide for the Social Sciences. Sage.
Wickham, H. & Grolemund, G. (accessed, 2023). R for Data Science. https://r4ds.had.co.nz/
No
Version: 1
Credits:
15.0 SP
Study level: Further education, higher degree level
No
Language of instruction: English
Location: Trondheim
- Statistics
Department with academic responsibility
Department of Psychology
Department with administrative responsibility
Section for quality in education and learning environment
Examination
Examination arrangement: Home examination
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD Home examination 100/100 INSPERA
-
Room Building Number of candidates - Spring ORD Home examination 100/100 INSPERA
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"