Syllabus for Introduction to R

Introduktion till R

  • 7.5 credits
  • Course code: 2ST127
  • 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: 2020-12-11
  • Established by:
  • Revised: 2022-02-17
  • Revised by: The Department Board
  • Applies from: week 35, 2022
  • Entry requirements: 180 credits.
  • Responsible department: Department of Statistics

Learning outcomes

Leaning Outcomes

After completing the course, a student is expected to:
* have obtained basic knowledge of the programming language R
* be able to load, process and describe data in R
* be familiar with visualization of data with the help of R
* be able to independently implement simple algorithms and simulations in R
* be able to write and interpret simpler program code
* have an understanding for the types of problems that data analysis and computers are suitable for solving

Content

Content

The course covers basic data management in R, such as importing data, data cleaning and handling of data in vectors, matrices and "data frames". The course also covers description, tables and visualization of data. The course introduces fundamental programming with functions, vectorization, loops, control structures (such as if-clauses, while- and for-loops) and Monte Carlo simulation.

Instruction

Instruction

Teaching is given in the form of lectures, computer exercises and seminars.

Assessment

Assessment

The examination takes place partly through a written examination at the end of the course and through presentation orally and in writing of compulsory home assignments. If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator.

Syllabus Revisions

Reading list

The reading list is missing. For further information, please contact the responsible department.

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