BUS463 Data structures in R

• Topics

  Topics

  -Understanding data structures is the key to empowering users of R, both in understanding what is going on, in reading the scripts of others, in customising results, and in writing scripts. Data structures have developed in R over time, so that both older and newer forms are found together in most workflows. Working from the ground upwards, we will see how they may be understood, and how this understanding gives insight into the language and its use.

• Learning outcome

  Learning outcome

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

  • understand basic data structures used in R programming
  • understand the role of data frames as a fundamental data container in R, and how other structures are needed for graphs
  • understand the representation of time and character encoding used in R
  • understand the development of class definition systems in R, including method dispatch
  • understand the motivations underlying the choices made among data structures in R programming
  • use this understanding in customising output of functions

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

  • assign correct descriptions to data structures used in scripts and functions encountered in simple workflows
  • define new composite data structures for workflow output, and appropriate standard methods

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

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

• Teaching

  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

  Assessment

  Group term paper, 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

  Spring

• Literature

  Literature

  Main book:

  • Gillespie C. & Lovelace, R. (2017) Efficient R Programming. Sebastopol, CA: O'Reilly.

            website: https://csgillespie.github.io/efficientR/

            publisher: http://shop.oreilly.com/product/0636920047995.do

  First strand:

  • Chambers, J. M. (1998) Programming with Data: A Guide to the S Language. New York: Springer. (chapter 1)
  • Chambers, J. M. (2008) Software for Data Analysis: Programming with R. New York: Springer. (chapters 1, 3, 5, 6)
  • Chambers, J. M. (2016) Extending R. Boca Raton, FL: CRC Press. (chapters 1-6, 9)

  Second strand:

  • Grolemund, G. (2014) Hands-On Programming with R. Sebastopol, CA: O'Reilly. (chapters 1, 2, 8)
  • Wickham, H. (2015) Advanced R. Boca Raton, FL: CRC Press. (chapters 1, 2, 7)
  • Wickham, H. & Grolemund, G. (2017) R for Data Science. Boca Raton, FL: CRC Press. (3, 7, 9, 10, 16)