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.
- Introduction to the R language.
- Writing R functions.
- Programming in R.
- Monte Carlo Simulation in R.
- Exploratory data analysis in R.
- 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.
- 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.
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.
`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
|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.