SKL402 Introduction to R (E)

• Topics

  This intensive seminar is an introduction to R. The seminar is delivered digitally via short recorded lessons and practice problems. The aim is to establish a working level of programming skills so students can use R confidently as a tool in later courses.

  The content is organized as follows:

  • Getting started and working effectively
    • Installing R and a suitable working environment for coding on your own computer
    • Understanding files, folders, and paths
    • Writing scripts and running code
    • Installing and loading packages
    • Variables and basic data types
  • Programming fundamentals
    • Subsetting and simple transformations
    • Control flow:
      • Conditionals
      • Iterations
    • Writing functions
  • Data wrangling
    • Importing data and understanding common data formats
    • Cleaning and transforming data
    • Joining data sets
    • Creating summaries using grouping and aggregation
    • Handling missing values and data quality issues
  • Graphics
    • Reshaping data
    • Visualizing data
  • Analysis, reporting, and becoming self-sufficient
    • Estimating and handling basic regression models
    • Exporting tables and figures for use in reports and presentations
    • Brief demonstration of reproducible reporting
    • Debugging, reading documentation, getting help efficiently, and appropriately using generative AI support.

• Learning outcome

  Skills

  On successful completion of the course, the student can

  • set up and use R in an appropriate coding environment for structured work
  • handle files and folders efficiently
  • write, run, and modify R code using core language features and basic programming structures
  • import, clean, reshape, merge, and summarize data
  • create clear descriptive summaries, tables, and figures
  • estimate basic regression models and handle the resulting model objects

  General competence

  On successful completion of the course, the student can

  • complete a small end-to-end data task in R from raw data to results suitable for communication
  • work in a structured, reproducible way that makes analyses easy to rerun and check
  • produce readable scripts and output that others can understand and reuse

• Teaching

  The lessons are given as self-paced video lectures. Practice exercises of various levels will be provided.

• Recommended prerequisites

  Basic statistical competence equivalent to MET2.

• Credit reduction due to overlap

  Full point reduction against BAN420, BAN400, BAN401 and STR467.

• Compulsory Activity

  One individual mandatory assignment, early in the teaching semester.

  Approved compulsory activity in SKL402 is required for students to be eligible to sit the assessment in BAN430, BUS444N, and FIE401.

• Assessment

  90-minute multiple-choice home exam.

• Grading Scale

  Pass-Fail.

• Computer tools

  R and an IDE such as RStudion or Positron

• Literature

  The documentation of R and the packages that will be introduced in the course.