PFG0060 Basic R programming, 4.0 Credits
Subjects Other Social Science
No Level Indicated
Pass / Failed
The requirements for attaining different grades are described in the course assessment criteria which are contained in a supplement to the course syllabus. Current information on assessment criteria shall be made available at the start of the course.
Admitted to a postgraduate program. The course is suitable for all graduate students. No programming experience is required, but students are recommended to possess knowledge in basic mathematical statistics.
The aim of the course is to provide basic knowledge of the R language and on the skills of writing R scripts for practical applications. The course will focus on the core of R programming language, and data manipulation with R.
Upon completion of the course the student should be able to:
- master the basic knowledge of R language,
- master functions commonly used for data manipulation,
- generate basic descriptive statistics, conduct a simple multiple linear regression analysis and specification test, and
- produce different types of data plot.
The course consists of lectures, computer exercises and self-study.
The course begins with an orientation connected to the following concepts:
- What is R and what can R do?
- An IDE (Integrated Development Environment) for R
Further, the course covers programming of R, specifically:
- Data types and data structures: vector, list, matrix, data frame, factor
- Import data and write out data
- Data manipulation
- Control flow
- Write simple function
- Regression analysis
In computer exercise, students will write R scripts to solve specific problems by using the knowledge from the lecturers. Exercise materials are provided by the lecturer.
The student is expected to bring his/her own laptop for computer exercises.
The Department reserves the right to cancel the course if there are not more than 5 students who have applied for the course. There is no tuition fee. The students should bring their own laptops for computer exercises. The student is responsible for any housing and travel costs. Students associated with Statistics-related programs and the ECOS research school at SLU have priority to the course.
Please note that the course will run from January 18 to February 4, 2021. The course will meet from January 18-22, attendance to which is obligatory. Please note that in view of the COVID-19-related restrictions, the obligatory meetings may be run either as virtual only (via e.g. zoom) or with a virtual option for participants who cannot attend physical meetings.
Department of Forest Economics