Introduction to R

BAN420 Introduction to R

Autumn 2020

  • Topics

    R is among the most powerful and widely used programming languages for data analysis in both science and businesses. R is a free open source tool, and new packages and functionalities are continuously being added.

    The course is intended for students without prior experience with R or other programming languages. In the course, you will:

    • set up your own R programming environment on your personal computer using Rstudio.
    • learn how to write, execute and modify R code and R scripts.
    • load data sets into R, create effective numerical and graphical summary statistics, and see how to use R to perform some common statistical analyses.
    • use programming techniques such as loops, conditionals and functions, to effectively solve practical and analytical issues that we encounter when working with data.

    After successfully completing the course, you will be comfortably able to use R as your tool for data analysis. Please note that BAN420 overlaps with the first part of BAN400. It is required to pass BAN420 in order to continue with BAN400.

  • Learning outcome

    Knowledge: On successful completion, the student can

    • understand the importance and usefulness of R as a tool in data analysis
    • understand the importance of reproducibility in data analysis.
    • understand the importance of documentation when creating scripts.

    Skills: On successful completion, the student can

    • use functions, loops, assignments, subsetting and conditionals in an R-script.
    • read and understand documentation of packages and functions.
    • use basic data structures (lists, arrays, matrices, vectors and data frames) as appropriate.
    • combine, merge and reshape data sets in R.
    • create and export convincing tables and figures for use in reports and presentations.
    • independently resolve warnings, errors, and other basic programming issues

    General competence: On successful completion, the student can

    • work efficiently in R and RStudio.
    • conduct reproducible data analysis with R.

  • Teaching

    This course combines lectures and programming tutorials. Lectures focus on methodological issues. In programming tutorials, the student will implement the methodologies using R.

  • Recommended prerequisites

    Basic statistical knowledge as provided by MET2

  • Requirements for course approval

    Compulsory attendance and participation in 80% of class activities.

    Approved homework assignments.

    Approved term paper project plan.  

  • Assessment

    Group term paper (2-4 students in each group) and presentation of the term paper. Both elements must be taken and passed in the same semester in order to pass the course.

  • Grading Scale

    Pass/fail

  • Computer tools

    R (https://cran.r-project.org/) and RStudio (https://www.rstudio.com/) and contributed packages as needed.

  • Literature

    Venables, Smith and the R Core Team (2017): An Introduction to R.

    Freely available from: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf

Overview

ECTS Credits
2.5
Teaching language
English
Semester

Autumn. First week of Autumn semester. Offered Autumn 2020.

Please note: Due to the present corona situation, please expect parts of this course description to be changed before the autumn semester starts. Particularly, but not exclusively, this relates to teaching methods, mandatory requirements and assessment.

Course responsible

Associate Professor Håkon Otneim, Department of Business and Management Science.

Adjunct Associate Professor Ole Petter Moe-Hansen, Department of Business and Management Science.