Introduction to R

BAN420 Introduction to R

  • Topics

    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

    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

    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

    Recommended prerequisites

    Basic statistical knowledge as provided by MET2

  • Requirements for course approval

    Requirements for course approval

    A group project (2-4 students in each group), with a presentation during the course (conditional on participant numbers)

  • Assessment

    Assessment

    Group term paper (2-4 students in each group), based on the group project (conditional on participant numbers)

  • Grading Scale

    Grading Scale

    Pass/fail

  • Computer tools

    Computer tools

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

  • Semester

    Semester

    Autumn. Offered autumn 2018.

    First week of autumn semester

  • Literature

    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

Course responsible

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

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