Overview

This unit introduces R, one of the popular open source statistical programming languages commonly used in applied statistics and data science disciplines. Students will learn key programming principles of R and develop competence in programming in R, which ia essential for a statistician or data scientist to perform different types of statistical analyses.

Requisites

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Study Period 3
Location
Hawthorn
Start and end dates
01-September-2025
30-November-2025
Last self-enrolment date
14-September-2025
Census date
22-September-2025
Last withdraw without fail date
17-October-2025
Results released date
23-December-2025

Unit learning outcomes

Students who successfully complete this unit will be able to:
 

  1. Clean and prepare unorganised data for analysis using R
  2. Visualise data graphically using R
  3. Perform basic programming and user-defined function using R within the RStudio environment
  4. Write R programs to perform statistical analysis conduct and interpret hypothesis tests
  5. Simulate data from different probability distributions
  6. Perform and interpret linear regression using R
  7. Analyse and interpret categorical data using R

Teaching methods

All Applicable Locations

Activity Type Activity Total Hours Number of Weeks Hours Per Week Venue Type and Activity Detail
Online Learning activities 12 12 weeks 1 Collaboration
Online Directed Online Learning and Independent Learning 138 12 weeks 11.5 Discussion boards, Watching Lecture recording, Readings, Quizzes & assessments, Independent study, Assignment preparation, Revision
Total Hours: 150 Total Hours (per week): 12.5  

Assessment

Type Task Weighting ULO's
AssignmentIndividual 40% 1,2,3,4,5,6 
ExaminationIndividual 50% 3,4,6,7 
Online QuizzesIndividual 10% 1,2,3,4,5,6 

Content

  • Introduction to R and RStudio.
  • R data types and basic syntax.
  • Basic Principles of programming-functions, algorithms, loops.
  • Data summaries and graphics.
  • Hypothesis testing and comparison of means.
  • Probability distributions and simulation in R
  • Linear regression.
  • Categorical data analysis.
  • Introduction to time series

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.