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 1
Location
Hawthorn
Start and end dates
26-February-2024
26-May-2024
Last self-enrolment date
10-March-2024
Census date
18-March-2024
Last withdraw without fail date
12-April-2024
Results released date
18-June-2024
Study Period 1
Location
Hawthorn
Start and end dates
26-February-2024
26-May-2024
Last self-enrolment date
10-March-2024
Census date
18-March-2024
Last withdraw without fail date
12-April-2024
Results released date
18-June-2024
Study Period 3
Location
Hawthorn
Start and end dates
26-August-2024
24-November-2024
Last self-enrolment date
08-September-2024
Census date
16-September-2024
Last withdraw without fail date
11-October-2024
Results released date
17-December-2024
Study Period 3
Location
Hawthorn
Start and end dates
26-August-2024
24-November-2024
Last self-enrolment date
08-September-2024
Census date
16-September-2024
Last withdraw without fail date
11-October-2024
Results released date
17-December-2024

Learning outcomes

Students who successfully complete this unit will be able to:

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

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Online
Learning activities		1.00 12 weeks 12
Online
Directed Online Learning and Independent Learning		11.50 12 weeks 138
TOTAL150

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On-campus
Class		2.00 12 weeks 24
Online
Learning activities		1.00 12 weeks 12
Online
Learning activities		2.00 12 weeks 24
Unspecified Activities
Various		7.50 12 weeks 90
TOTAL150

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.