732A94 · 6 credits · Advanced level (A1N) · Datavetenskap
Advanced Programming in R
Active · next start HT 2026
76%
passed the latest regular sitting
DAT4 · 20 Oct 2025 · 29 students
69%
passed the regular sittings, all sittings over the last 5 years
DAT4 · 177 students
72%
Harder than 72% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 64 students.
Other module
DAT4 Computer Exam
- A
- B
- C
- D
- E
- F
Main rounds
69% passed · 177 students
- 76%29
- 80%35
- 61%28
- 64%42
- 67%43
Later rounds
40% passed · 73 students
- 57%7
- 33%6
- 43%7
- 0%8
- 63%8
- 50%8
- 44%16
- 29%7
- 33%6
- Pass a later round: 40% (73 students), compared with 69% (177 students) on the ordinary exam
- Get B or A: 22% (250 students)
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 50%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB2 Laboratory work
- P
Main rounds
100% passed · 147 students
- 100%29
- 100%25
- 100%22
- 100%41
- 100%30
Later rounds
100% passed · 6 students
- 100%6
- Pass a later round: 100% (6 students), compared with 100% (147 students) on the ordinary exam
Show small sittings (9)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
- 100%1
- 100%2
- 100%2
- 100%1
- 100%2
- 100%3
- 100%4
Other module
DAT2 Computer Exam
- F
No sittings with at least 5 students.
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 0%1
- 0%1
Other module
DAT3 Computer Exam
- C
- E
No sittings with at least 5 students.
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
About the course
Aim
After completing the course, the student shall be able to:
- apply parallel programming and performance enhancement tools in order to improve R code,
- create basic and advanced functions in R using programming techniques such as reading data from file or Internet, assignment and manipulation of data structures, defining own functions, iterations and conditional (if-then-else) statements
- apply debugging techniques on R-code,
- create an R package and organize one’s code into it,
- plan and carry out course work within given time frames
Content
The course introduces general programming techniques and their practical implementation in the R language.
The course covers the following topics:
- 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
- computational complexity
- parallel programming
- literate programming
- development of R packages
- Examiner
- Johan Alenlöv
- Main field of study
- Datavetenskap