BAN422 Visualization in R
-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.
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
This course combines lectures and programming tutorials. Lectures focus on methodological issues. In programming tutorials, the student will implement the learned methodologies using R.
Programming skills with R are helpful. However, if necessary, an additional introduction to programming with R will be offered.
Credit reduction due to overlap
Course identical to BUS464.
Requirements for course approval
A group project, with a presentation during the course (conditional on participant numbers).
Course approval from BUS464 is valid for BAN422.
Group term paper, based on the group project (conditional on participant numbers).
This course is a continuation of BUS464 and the total number of attempts applies to the course (not the course code).
R (https://cran.r-project.org/) and RStudio (https://www.rstudio.com/) and contributed packages packages as needed.
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
Spring. First week of spring semester. Offered spring 2019.
Professor Roger Bivand, Department of Economics