Introduction to R

BAN420 Introduction to R

Autumn 2018

  • 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.

  • 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 in the same semester.

  • Grading Scale

    Pass/fail

  • Computer tools

    R (https://cran.r-project.org/) and RStudio (https://www.rstudio.com/) and contributed packages 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. Offered autumn 2018.

First week of autumn semester

Course responsible

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

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