Syllabus for Statistical Programming with R

Statistisk programmering med hjälp av R

Syllabus

  • 7.5 credits
  • Course code: 2ST105
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2008-05-29
  • Established by:
  • Revised: 2022-10-14
  • Revised by: The Department Board
  • Applies from: Autumn 2022
  • Entry requirements:

    120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science

  • Responsible department: Department of Statistics

Learning outcomes

After completing the course, the student is expected to

  • be able to use and program in the programming language R
  • be able to implement simple algorithms in R independently
  • have developed good habits in programming in R to ensure efficient and safe code in order to facilitate collaborations
  • be familiar with data visualization techniques in R
  • be able to use R to solve statistical problems, including data handling and data analysis
  • understand the foundations of and be able to design and describe simulation studies

Content

Concepts and basic definitions in programming, arrays, matrices and data frames, the usage and definitions of procedures, functions and packages, vectorization, loops, control structures (if, while, for), importing data, visualization of data, simulation studies, Latex.

Instruction

Teaching is given in the form of lectures, labs and/ or as seminars.

Assessment

The examination takes place through a written examination at the end of the course and/or through written and/or oral presentation of compulsory assignments.

Reading list

Reading list

Applies from: Autumn 2022

Some titles may be available electronically through the University library.

  • Matloff, Norman S. The art of R programming : tour of statistical software design

    San Francisco: No Starch Press, c2011.

    Find in the library

  • Grolemund, Garrett; Wickham, Hadley R for Data Science

    O'Reilly Media, 2016

    Find in the library