This course introduces the principles of data representation, summarisation and presentation with particular emphasis on the use of graphics. The course will use the R Language in a modern computing environment. Topics to be discussed include:

• Data representation; examples of good and bad graphics; principles of graphic construction; some pitfalls to be avoided; presentation graphics.
• Graphics environments; interactive graphics; windows; linked windows; graphics objects.
• Statistical graphics; stem and leaf plots, box plots, histograms; smoothing histograms; quantile-quantile plots; representing multivariate data; scatterplots; clustering; stars and faces; dynamic graphics including data rotation and brushing.
• Relationships between variables; smoothing scatterplots; simple regression; modelling and diagnostic plots; exploring surfaces; contour plots and perspective plots; multiple regression; relationships in time and space; time series modelling and diagnostic plots.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Demonstrate detailed knowledge of the R statistical computing language, particularly graphical capabilities.
2. Explain in detail and be able to apply the principles of good data representation.
3. Explain in detail and be able to use various graphics environments, interactive graphics and graphics objects.
4. Construct graphical representations of one dimensional data.
5. Construct graphical representations for multivariate data including scatterplots, and dynamic graphics.
6. Use diagnostic plots when conducting statistical modelling to explore and refine statistical models for data, including detailed explanations of such use.
7. Construct and interpret graphical displays for dependent data.

Research-Led Teaching

Statistics is a discipline that informs many other disciplines - basically, any discipline that generates or uses data (of almost any kind) can benefit from Statistics. This makes Statistics very naturally a companion to research-led teaching, and data visualisation is a key element of the way in which Statistics can allow researchers to see structure in high-dimensional data. In this course, we will look at a number of real data sets that address real research problems, and we will see (literally) how statistics can lead to a deeper understanding of the structures underlying the data. 

Examination Material or equipment

There is no final exam for this course.

Required Resources

Lecture notes, tutorials, computer code, assignments, datasets and other relevant materials (available on Wattle)

Class materials, including lecture notes, tutorials, computer code, datasets, assignments and other relevant materials, will be made available on the class web page hosted on Wattle at http://wattle.anu.edu.au. To log on to Wattle, you need to have an ANU ID (your student number) and a password (the same as for obtaining your e-mail). In order to access the class web page within Wattle, you will need to be formally enrolled in the course or you will need to have arranged access with me (e.g. if you are an Honours student). The class web page will be updated with new information on a regular basis, and will also contain links to other places of interest (such as an R workshop initially). It is essential that you visit the class web page regularly.

R Software (free to download)

The course makes extensive use of the free R software and its RStudio integrated development environment for statistical computing. PC/Mac labs are located at many places around campus: for an exhaustive list, visit https://services.anu.edu.au/information-technology/software-systems

Students should be able to use their student cards to access these computing laboratories and should have a computer account automatically set up for them upon registration for this course. If you have not registered for the course, your card will not allow you access to the lab.

A number of data sets will be analysed during lectures, live using R as much as possible. To assist you in understanding the data analyses, the R code used to produce displays discussed in class will be made available to you on the class web page. You are free to use and modify this code in conducting your own analyses. 

Learning R can be a daunting task, but there are numerous easy-to-read guides to R on the web. Also, you can use own-paced, interactive lessons within R using the swirl package (http://www.swirlstats.com). 

Recommended Resources

There are a variety of online platforms that can be used to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

1. Students will be given feedback in the following forms in this course:

• Individual feedback on assignments will be posted to Wattle within the Gradebook.
• Only you will be able to see feedback on your assignment.
• Summary feedback reflecting patterns in data analyses will be posted to Wattle. This
• feedback will not identify individuals, but will rather be broad in scope and describe general patterns of response to the analyses of the data. 

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Introduction and getting to know R	Chapter 1, R Workshop, Tutorial 1
2	R, Graphics in R	Chapter 1
3	Representing and comparing distributions	Chapter 2, Tutorial 2
4	Representing and comparing distributions	Chapter 2, Tutorial 3
5	Relationships between 2 variables	Chapter 3, ASSESSMENT: Assignment 1 due
6	Relationships between 2 variables	Chapter 3, Tutorial 4
7	Relationships between 3 and more variables	Chapter 4
8	Relationships between 3 and more variables	Chapter 4, Tutorial 5
9	Relationships between 3 and more variables	Chapter 4
10	Relationships between 3 and more variables/Time dependent data	Chapter 4, Chapter 5, Tutorial 6
11	Time Series and Dependent Data	Chapter 5, Tutorial 7, ASSESSMENT: Assignment 2 due
12	Graphical Construction	Chapter 6, Tutorial 8, ASSESSMENT: Project due during the exam period

Tutorial Registration

There are no separate tutorials. Some tutorial / computer lab demonstrations will be run during the lecture time.

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Assignment 1	20 %	24/03/2025	31/03/2025	1, 2, 3, 4
Assignment 2	20 %	19/05/2025	26/05/2025	1, 2, 3, 6, 7
Assignment 3 Project	60 %	02/06/2025	30/06/2025	1, 2, 3, 4, 5, 6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Participation

In-person attendance is encouraged. In-person classes (lectures, including tutorials/demonstrations using R) will be held as timetabled and recorded. All course materials, including recordings, will be posted on the main Wattle page and Echo360.

Examination(s)

There is no final exam for this course.

Assessment Task 1

Assignment 1

Assignment 1: Analysis and Presentation of Data Using R

Due Date: Monday 24 March 2025

Objective: Analyse a provided data set and present your findings in a concise, well-structured report. Analysis is to be conducted using R software. This assignment focuses on representing and comparing distributions.

Requirements:

• Format: Submit a concise, professional report that integrates both text and graphics.
• Graphics: Only include relevant graphics that directly support your analysis. Irrelevant or excessive visuals will lead to mark deductions. Ensure that all graphics are clearly presented with appropriate titles, axis labels, legends, and captions.
• Report Style: The text portion must provide relevant explanations and insights—simply annotating your graphics is insufficient. The report should be well-organised, clear, and focused.

Key Points to Consider:

• Clarity and Relevance: Your report should be concise and directly address the assignment objectives. Lengthy or overly detailed submissions may be penalised.
• Depth of Analysis: A strong analysis goes beyond surface-level observations, uncovering meaningful patterns, and insights within the data. Aim to interpret the results critically, highlighting key findings and their implications rather than simply describing the visuals.
• Length Limit: The maximum length is 4 single-sided pages. Any content exceeding this limit will not be assessed.
• Appendices: Include the R code used to generate the report's graphics in an appendix. Ensure the code is well-organised, commented, and clearly corresponds to the visuals presented in the report.

Submission Details: Via Turnitin

Additional Materials: The full task sheet and further guidelines will be posted on Wattle.

Assessment Task 2

Assignment 2

Assignment 2: Time Series and Dependent Data Analysis Using R

Due Date: Monday 19 May 2025

Objective: Analyse a provided data set and present your findings in a concise, well-structured report. Analysis is to be conducted using R software. This assignment focuses on time series and dependent data.

Requirements:

• Format: Submit a concise, professional report that integrates both text and graphics.
• Graphics: Only include relevant graphics that directly support your analysis. Irrelevant or excessive visuals will lead to mark deductions. Ensure that all graphics are clearly presented with appropriate titles, axis labels, legends, and captions.
• Report Style: The text portion must provide relevant explanations and insights—simply annotating your graphics is insufficient. The report should be well-organised, clear, and focused.

Key Points to Consider:

• Clarity and Relevance: Your report should be concise and directly address the assignment objectives. Lengthy or overly detailed submissions may be penalised.
• Depth of Analysis: A strong analysis goes beyond surface-level observations, uncovering meaningful patterns, trends, and insights within the data. Aim to interpret the results critically, highlighting key findings and their implications rather than simply describing the visuals.
• Length Limit: The maximum length is 4 single-sided pages. Any content exceeding this limit will not be assessed.
• Appendices: Include the R code used to generate the report's graphics in an appendix. Ensure the code is well-organised, commented, and clearly corresponds to the visuals presented in the report.

Submission Details: Via Turnitin

Additional Materials: The full task sheet and further guidelines will be posted on Wattle.

Assessment Task 3

Assignment 3 Project

Assignment 3: Final Project on Graphical Data Analysis

Due Date: Monday 2 June 2025

Objective:

The project is a key component of the course, designed to:

• Enhance your critical awareness and examination of statistical graphics.
• Apply principles and methods of graphical data analysis to address a substantial problem.

This project is an independent piece of work, to be completed without external assistance. It consists of two compulsory parts:

Part A: 20% of your total grade

Graphic Awareness:

Task: Collect five statistical graphics from published sources throughout the semester. Provide written critique on each.

Rules: Graphics must be sourced externally from published materials. Credit will be awarded for carefully selected, insightful graphics that reflect a strong understanding of effective data communication and the principles behind conveying information meaningfully.

For each graphic, include:

1. A copy of the graphic.
2. Full citation details (article title, authors, source, page numbers, etc.).
3. A concise discussion on the purpose, strengths, weaknesses, and potential improvements of the graphic. Redrawing improved versions is encouraged but optional.

Commentary Style: Brief, relevant, and insightful—avoid unnecessary length.

Part B: 40% of your total grade

Graphical Analysis:

Task: Select one data set from multiple options that will be provided. Analyse the data set, and prepare a concise, well-organised report.

Report Content:

1. Begin with a clear problem statement and purpose of your analysis.
2. Explain your methodology, detailing the rationale behind each approach chosen to address the problem and achieve the project’s objectives.
3. Incorporate relevant graphics that directly support and illustrate each key insight, providing clear interpretation and analysis within the report.

Focus: The analysis should primarily (though not exclusively) be graphical.

Key Points to Consider:

• Clarity and Insight: Ensure your report and commentary are clear, focused, and demonstrate critical thinking. Overly verbose or unfocused submissions may be penalised.
• Depth of Analysis: Highlight meaningful patterns, trends, and insights rather than just describing the visuals. Your work should reflect careful consideration and thoughtful interpretation.
• Length Limit: The entire project (including Part A and Part B) must not exceed 8 single-sided pages. Part B should be between 4 to 5 single-sided pages. Submissions that exceed this limit will only have the first 8 single-sided pages assessed. Attempts to bypass the page limit with small fonts or unreadable formatting will not be accepted.
• Appendices: Include the R code used to generate graphics in Part B of the project as an appendix. Ensure the code is well-organised, commented, and clearly corresponds to the visuals presented in the report.

Submission Details: Via Turitin

Additional Materials: The full task sheet and further guidelines will be posted on Wattle.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

 

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

Materials will be submitted via Turnitin. You will be required to electronically sign a declaration when you submit your assessable work. Please retain a copy of your assignment for your records.

Hardcopy Submission

Online submission only.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

• Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Not applicable

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students