UV9918V6 - Introduction to R: a free software environment for statistical computing and graphics

Course description

Course content

R is a free software environment (https://www.r-project.org/) for statistical computing and graphics that has gained much popularity in the recent years and is widely used in both academia and industry.  The course introduces the essential concepts and syntax for getting started with R. Although some programming basics will be covered, problem-based scenarios including daily routine data management tasks will take center piece.

Learning outcome

After completing the course, you

  • know basic programming concepts and are familiar with the R operating system
  • can run, modify, and write R syntax to perform routine data management tasks

have the necessary knowledge and skills to start learning R beyond the introductory level

Admission

PhD candidates at the Faculty of Educational Sciences will be given priority, but it is also possible for others to apply for the course. Deadline for registration is February 15th.

Candidates admitted to a PhD-program at the Faculty of Educational Sciences (UV): Apply by Studentweb.

Other applicants: apply through Nettskjema.

Maximum number of participants is 25.

Prerequisites

Formal prerequisite knowledge

No prior knowledge is assumed, but do bring your laptop to class for the hands-on computer lab parts.

Teaching

Organizer: CEMO (Centre for Educational Measurement at University of Oslo)

Coordinator/Responsible: Johan Braeken, Stefan Schauber and Björn Andersson

Teaching: 4 sessions, 09:00-15:00, March 1st, 2nd, 12th and 16th

Location: schedule available here

The course will include lectures in combination with in-class computer exercises, and homework assignments in the free statistical software environment R.

 

Literature

Grolemund, G. & Wickham, H. (2017). R for Data Science: http://r4ds.had.co.nz/. (480 pages)

Optional further reading: Wickham, H. (2014). Advanced R: http://adv-r.had.co.nz/. (450 pages)

Examination

To obtain 1 credit, 80 % attendance in the course is required. To obtain 3 credits, 80 % attendance and a successfully completed assignment is required. A more specific description of the assignment will be given at the course.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Published Jan. 16, 2018 10:19 AM - Last modified Jan. 18, 2018 2:57 PM