Advanced Programming in R, 6 credits

Avancerad programmering i R, 6 hp


Main field of study

Computer Science

Course level

Second cycle

Course type

Single subject and programme course


Krzysztof Bartoszek

Course coordinator

Krzysztof Bartoszek

Director of studies or equivalent

Ann-Charlotte Hallberg

Available for exchange students



ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Timetable module Language Campus ECV
Single subject course (Half-time, Day-time) Autumn 2019 201935-201944 1+3+4 English Linköping, Valla
Single subject course (Half-time, Day-time) Autumn 2019 201935-201944 1+3+4 English Linköping, Valla
F7MSL Statistics and Machine Learning, Master´s Programme 1 (Autumn 2019) 201935-201944 1+3+4 English Linköping, Valla E

Main field of study

Computer Science

Course level

Second cycle

Advancement level


Course offered for

  • Masters Programme in Statistics and Machine Learning

Entry requirements

A bachelor’s degree in one of the following subjects: statistics, mathematics, applied mathematics, computer science, engineering, or equivalent. Completed courses in calculus, linear algebra, statistics and programming are required.

English corresponding to the level of English in Swedish upper secondary education (English 6/B).
Exemption from Swedish 3/B

Intended learning outcomes

After completion of the course the student should on an advanced level be able to:
- write R programs based on programming techniques such as reading data from file or Internet, assignment and manipulation of data structures, defining own functions, iterations, conditional (if-then-else) statements and debugging,
- speed up R programs by using parallel programming and performance enhancement tools,
- organize the own code in the form of an R package.

Course content

The course introduces general programming techniques and their practical implementation in the R language. More specifically, the course includes:
- reading data from file, from the internet, and printing to output,
- data structures, functions and objects,
- iteration and conditional statements,
- numerical linear algebra in R,
- debugging,
- object-oriented programming,
- performance enhancement,
- parallel programming,
- literate programming,
- development of R packages.

Teaching and working methods

The teaching comprises lectures and computer exercises. The lectures are devoted to presentations of concepts and methods. The computer exercises provide practical experience of programming in R.Homework and independent study are a necessary complement to the course.
Language of instruction: English.


Written reports on computer exercises. One final written or oral examination. Detailed information about the examination can be found in the course’s study guide.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.



Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.


Institutionen för datavetenskap
Code Name Scope Grading scale
LAB1 Laboratory work 3 credits EC
DAT2 Computer Exam 3 credits EC
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