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

Specific conditions

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/