Visualization in R

BUS464 Visualization in R

  • Topics


    -Visualization is one of the key strengths of R. However, visualization involves many choices, and R offers such a wide range of choices that guidance is desirable. There are two major underlying techologies, base graphics and grid graphics, and several toolboxes built on these (trellis graphics and grammar of graphics on grid graphics). In addition, much work has been done on the effective use of shapes and colours.

  • Learning outcome

    Learning outcome

    Knowledge: on successful completion, the student will be able to

    • understand the principles underlying base and grid graphics
    • understand how toolkits build on grid graphics are structured
    • understand the choices involved in the effective use of colours and glyphs
    • understand the motivations underlying the choices made in visualization in R programming
    • use this understanding in customising graphical outputs


    Skills: on successful completion, the student will be able to

    • assign correct descriptions to visualization techniques used in scripts and functions encountered in simple workflows
    • define new visualization templates for workflow output


    General competence: on successful completion, the student will be able to

    • handle the graphical output of R functions with greater confidence
    • customise the graphical output of R functions to meet specific needs

  • Teaching


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

  • Recommended prerequisites

    Recommended prerequisites

    Programming skills with R are helpful. However, if necessary, an additional introduction to programming with R will be offered.

  • Requirements for course approval

    Requirements for course approval

    A group project, with a presentation during the course (conditional on participant numbers)

  • Assessment


    Group term paper, based on the group project (conditional on participant numbers)

  • Grading Scale

    Grading Scale


  • Computer tools

    Computer tools

    R ( and RStudio ( and contributed packages packages as needed.

  • Semester



  • Literature


    Unwin, A. (2015) Graphical Data Analysis with R. Boca Raton, FL: CRC Press.

    Murrell, P. (2011) R Graphics. Boca Raton, FL: CRC Press.

    Sarkar, D. (2008) Lattice: Multivariate Data Visualization with R. New York: Springer.

    Wickham, H. (2016) ggplot2: Elegant Graphics for Data Analysis. New York: Springer.


ECTS Credits
Teaching language

Course responsible

Roger Bivand, Department of Economics