MAS6024 Statistical Data Science in R

Semester 1, 2021/22 15 Credits
Lecturer: Dr Kevin Walters Timetable Reading List
Aims Outcomes Teaching Methods Assessment Full Syllabus

This module starts with some basic R, introduces further aspects of the R language, demonstrates how to use and write functions in R, introduces some programming tools and shows how R can be used to implement some Monte Carlo techniques. Alongside the programming aspects the module also illustrates basic exploratory data analysis in R and introduces the grammar of graphics employed in the ggplot2 graphics system. The R web application Shiny is also illustrated.

There are no prerequisites for this module.
No other modules have this module as a prerequisite.

Outline syllabus

  • Introduction to the R language.
  • Writing R functions.
  • Programming in R.
  • Monte Carlo Simulation in R.
  • Exploratory data analysis in R.
  • ggplot2


  • To enable students to write R code.
  • To give practice in writing functions in R.
  • To help students to write longer programs requiring more complex programming.
  • To illustrate how R can be used to implement a range of statistical methods via Monte Carlo simulation.
  • To give practice in applying Monte Carlo methods and interpreting results from them.
  • To give practice in creating reproducible work using R Markdown.
  • To give practice in implementing exploratory data analysis in R
  • To give practice in using ggplot2 and Shiny.

Learning outcomes

  • write simple functions and programs in R;
  • develop skills in writing longer, more complex R programs;
  • use Monte Carlo simulation in R to solve a range of problems;
  • understand how to perform effective exploratory data analysis and produce high quality plots in R
  • prepare technical documents with R Markdown.

Teaching methods

Computer practicals in the PC Laboratories; on-line discussion. Feedback on the assignment.

No lectures, no tutorials, 16 practicals


One piece of course work. No exam.

Full syllabus

`Computer labs' are 2hrs in length.

  • vectors, lists, matrices;
  • writing functions;
  • Monte Carlo simulation in R: Monte Carlo integration, importance sampling, Monte Carlo tests;
  • loops, branching;
  • the apply family;
  • exploratory data analysis in R
  • ggplot2;
  • Shiny;

Reading list

Type Author(s) Title Library Blackwells Amazon
B Grolemund, G. Hands-on Programming with R Blackwells Amazon
B Grolemund, G. and Wickham, H. R for Data Science Blackwells Amazon
B Xie, Y., Allaire, J.J. and Grolemund G. R Markdown: The definiitive Guide Blackwells Amazon

(A = essential, B = recommended, C = background.)

Most books on reading lists should also be available from the Blackwells shop at Jessop West.