Monash University
FIT5147 Data Exploration and Visualisation
Semester 1, 2022

FIT5147 Data Visualisation Project

In this project, you are asked to create an interactive narrative visualisation that communicates
some of your findings from the Data Exploration Project.
It is an individual assignment and worth 40% of your total mark for FIT5147.

Relevant Learning Outcomes
● Choose appropriate data visualisations.
● Implement interactive data visualisations using R (Shiny) or JavaScript (D3).

Overview of the Tasks
1. Identify which findings from the Data Exploration Project you wish to communicate. You can
be selective, and you do not need to share everything you have found. The visualisations and
accompanying narration should reflect the answers to the questions in your Data
Exploration Project.
2. Clearly define your intended audience. The audience might be your classmates, the general
public, politicians or whoever you like. The interactive narrative visualisation should be
designed for the intended audience.
3. Design an interactive narrative visualisation using the Five Design-Sheet methodology.
4. Prepare a short presentation based on your five resulting design sheets (one sheet per
slide).
5. Submit the slides for your presentation in Week 11.
6. Present your presentation to your tutorial in Week 11 or 12.
7. Implement your visualisation using R (Shiny) or JavaScript (D3). The use of other
visualisation libraries and packages is subject to approval by your tutor (see the section
“Notes on Implementation”). Note that you are not allowed to use R Markdown.
8. Write a report and export it to PDF.
9. Submit your report and your source code (see the section “How to Submit”) at the start of
the exam period.

Presentation Details
The presentation is an opportunity to gain feedback on your designs from your tutors and peers.
Prepare a three minute presentation based on your five design sheets. Your presentation should
consist of 6 slides covering:
1. An Introduction: Name, project title, aims and motivation (one slide)
2. Each of your five design sheets (i.e., one sheet per slide).
The design slides you present must match those submitted on Moodle. If you do not manage your
presentation and go overtime, your tutors may stop your presentation, which may restrict the
feedback you get on elements of your design.
Report Structure
Write a 15-page (excluding cover page, table of contents, bibliography, appendix) report that
consists of the following sections:
1. Project title
Title of your narrative visualisation. This can be included in the cover page.
2. Your identity
Your full name, student ID, tutorial number, and tutor name. This can be included in the
cover page.
3. Introduction
A short and succinct description of what findings and messages you wanted your narrative
visualisation to convey, and who the intended audience is.
4. Design
A description and justification of your narrative visualisation design process. This should
briefly refer to each of your five design sheets (you must provide the design sheets in the
Appendix), and justify your design choices based on the theoretical content of the unit
(throughout Weeks 1-12), for instance: describing consistency in design and interaction;
reasons for a particular colour palette; referring to aspects of the human visual system or
genres of narration style; etc. It is important that this section justifies the final design
choices you made, rather than simply listing the charts you have used.
5. Implementation
A high-level description of your implementation, including libraries used, references to
external code sources such as templates, and reasons for any differences between your final
design and implementation, if applicable. You are not required to explain the code in detail.
You should also briefly explain the reasons why your implementation was challenging (e.g.,
extensive wrangling, complex user interactions, advanced use of D3, etc. - see Marking
Criteria 4 for more information).
6. User guide
Instructions for how to view and use your narrative visualisation. This should emphasise any
parts of your visualisation that may be easily missed by a reader (e.g., obscure user
interaction, hidden information boxes).
7. Conclusion
Summarise your findings and what you have achieved with your narrative visualisation.
Reflect on what you have learnt in this project, including what in hindsight you might have
done differently to improve the result and any future work that you would like to do.
8. Bibliography
Appropriate references of all resources that have influenced your work in IEEE or APA style
(refer to the Monash University Library's guide). This should include any code templates,
design influences and sources on theory, as well as references which influence any data
insights.
9. Appendix
Include your five design sheets in the Appendix. Make sure you provide clear images and any
handwriting is understandable.
Your report should contain high-quality images of your narrative visualisation and five design sheets.
It is recommended that you export your PDF using a local word processor (e.g., Microsoft Word), as
exporting your document as a PDF directly from Google Docs will result in low-quality images. Make
sure you can read and understand the PDF document and its images at A4 size without requiring
further enlargement.

Notes on the Design
● The design must provide a narrative. Text and visualisation techniques should both be used
to tell a data story, which clearly presents and articulates the findings and insights you have
chosen to communicate about the topic of interest.
● Your design must follow the Five Design-Sheet process and provide all the required
information according to the Five Design-Sheet template. The designs for Sheets 2-4 must be
distinct from each other. The final design on Sheet 5 is expected to be a refinement of one of
those sheets.
● Sheets 2-4 should each communicate the entire narrative independently from each other.
In other words, Sheets 2-4 should be complete designs that can each stand on their own.
None of these sheets should respond to only a subset of the intended overall narrative, and
should consist of more than just a single visualisation technique (e.g., one graph).
Notes on the Implementation
● Your implemented narrative visualisation should be based on the result of your Five Design-
Sheet process. It does not need to follow it exactly, but it should resemble the final design in
Sheet 5. Small changes to your final design are allowed (e.g., layout position, visualisation
choices, navigation method, colour) but any such differences between your design and how
it was implemented must be explained and justified in the Implementation section of your
report. Likewise, any differences between the final design in your presentation and that in
your report in light of feedback to your presentation should be explained and justified.
● As a rule of thumb, all visualisation packages and libraries that are covered in this unit are
allowed for your implementation. This includes, but is not limited to:
○ For R Shiny: ggplot2, ggmap, ggraph, Leaflet, Plotly, igraph, wordcloud, etc.
RMarkdown cannot be used.
○ For D3: D3 itself, Leaflet, MapBox, etc. Libraries which act as high-level wrappers
for D3 are NOT allowed (e.g., C3.js, dimple).
If you are unsure if a particular visualisation package or library is allowed, please discuss it
with your tutor.
● Tools or packages used for data wrangling, data cleaning, Shiny theming, HTML5 templating,
CSS styling, etc., are not subject to these rules and can be used freely (i.e., anything other
than the visualisations themselves).
● For performance reasons, it is recommended that you pre-format all of your data files
before loading them into R Shiny or D3. In other words, all data wrangling and cleaning steps
(if any) should be performed outside of your narrative visualisation code. You are not
required to include the code for data wrangling and cleaning as part of your submission.
However, if you have done considerable work since your Data Exploration Project, then you
should describe these steps in your DVP report (see Marking Criteria 3).
Marking Criteria
Data Visualisation Project: Presentation [3%]
● Quality of oral presentation (confidence, speed, voice) and quality of slides (legibility, design,
images) [1%].
● Logical structure [1%].
● Choice of content (completeness, appropriate level, discussion of design and
implementation alternatives) [1%].

Data Visualisation Project: Report and Source Code [37%]
When grading your submission, all components (i.e., the quality of your narrative visualisation
design, technical implementation, and the written report) are taken into account:
1. Visualisation Design [15%]
a. Appropriate use of Five Design-Sheet methodology and evaluation of your
alternative designs [5%].
b. Quality of implemented narrative visualisation design: clear signposting of messages
and intended narrative, provision of appropriate context for the reader, clean and
appropriate layout, attention to detail, good use of colour, references to data
sources, and appropriateness for the intended audience [7%].
c. Justification of your final design in terms of the human perceptual system and
human communication assumptions [3%].
2. Visualisation Implementation [5%]
a. Correctness and robustness, performance and usability [3%].
b. Code comments and code quality [2%].
3. Project Continuity [2%]
The degree to which the visualisation and report describes data insights related to the
questions proposed in your submitted Project Proposal and explored during your Data
Exploration Project. Further exploration or improvements can be done, but need to be
described and justified within the report word limit along with the expected data
visualisation components.
4. Project Difficulty [10%]
The degree to which the visualisation project demonstrates sophistication and complexity in
terms of its technical, theoretical and design implementation. Marks for this section will be
allocated for the following:
a. Sophisticated use of different data sources, in particular non-tabular data [2%].
b. Dealing with very large datasets [2%].
c. Advanced implementation of D3 / R (Shiny) [3%]
d. Sophisticated user interaction (e.g., animation, linked interaction) [3%]
Note: Other technical, theoretical and/or design aspects will be considered for marks in this
difficulty section. It is therefore crucial to make the marker aware of the complexity of your
project by ensuring you mention and justify all elements in your written report.
5. Project Report [5%]
a. Quality of writing, images, logical structure, grammar/spelling, appropriate
academic referencing [1.5%].
b. Completeness (i.e., all the above sections should be submitted and complete)
[3.5%].
Check Your Code!
Please be sure to check that your code runs correctly. Check on other computers and operating
systems if possible. If you do not have access to another computer you can try checking via the
Monash MoVE platform.
If your code requires some steps for it to run, then be sure to make these very clear in readme notes
for your marker and describe this in the User Guide section of your report. Your code must run on
your marker’s computer on the first attempt for us to be able to mark your submission. If your
submission does not run correctly, 5% (from the implementation mark) will be instantly deducted
from your grade. If after some troubleshooting your grader is still unable to get the code to run,
further deductions will occur as we will not be able to fully grade your interactive narrative
visualisation.
Your code must also contain meaningful comments and be formatted and designed in such a way
that it is easily readable and understandable.
Originality
As this is academic work, it must be original and must clearly indicate what elements were your work
and what are based on someone-else’s work. If you are including facts, data, opinions or any other
written or graphical information from another source, you must cite the source and reference the
bibliographic details for the source, using the APA or IEEE style guide. This includes any third-party
programming code or software you use in your data exploration and analysis. If you directly quote or
replicate any material from a reference, you must do so in a manner appropriate to the APA or IEEE
style guide. Be sure to acknowledge sources that influence your code through your comments and
references in your bibliography. Do not copy complete designs from any external sources.
If you are retaking this unit from a previous semester, please ensure you choose a completely new
design and use different code for your implementation. The text, design and code cannot have been
used in any other unit. Likewise, you cannot reuse any code or written content that you have used in
any previous assessment tasks for any units. The only self-plagiarism that is allowed is the questions
you set in your Project Proposal this semester and reusing some R code from your Data Exploration
Project this semester (if you wish).
If your work is believed to not be original, due to potential instances of plagiarism, collusion with
other students or contract cheating, your academic integrity will be reviewed. If any breaches of the
academic integrity are confirmed, penalties may be applied to your assignment, the unit and/or even
your enrolment in your course.
Submission Due Dates
● Submit your presentation slides to Moodle, due Monday Week 11 (see Moodle for date and
time). Presentations will take place during Week 11 & 12 in your tutorial. Attendance for
both weeks is mandatory.
● Submit a PDF report and a zip file containing your code and the data to Moodle, due the first
Monday of exam period (see Moodle for date and time).

NOTE: All submission times are in Melbourne, Australia local time.

How to Submit
Presentation
1. Prepare a PDF file containing all five of your design sheets.
2. Name the file StudentName_StudentID_Presentation.pdf
3. Submit it via Moodle under Assessments/Presentation (3%).

Report and Source Code
1. Prepare a PDF report (max 15 pages) and a ZIP file containing the source code for your
narrative visualisation and any data files that are required to run it.
2. Name the files using the following format:
a. StudentName_StudentID_Report.pdf
b. StudentName_StudentID_Code.zip
3. Submit both files via Moodle under Assessments/Data Exploration Project Submission (33%).
These must be two separate files. Do not put your PDF inside of the ZIP archive. Note that
only .zip is recommended, and you should not use other extensions such as .rar or .7z.

Notes on submissions:
● We cannot mark any work submitted via email or stored on file hosts such as Google
Drive. Please ensure that you submit correctly via Moodle since it is only in this process that
you complete the required student declaration, without which your work cannot be
assessed.
● Your assignment MUST show a status of "Submitted for grading" before it will be marked.
If your submission shows a status of "Draft (not submitted)”, it will not be assessed and will
incur late penalties if submitted after the due date/time. Note that this applies even if your
file was uploaded to Moodle as draft prior to the due date.
● It is your responsibility to ENSURE that the files you submit are the correct files. We strongly
recommend after uploading a submission, and prior to actually submitting on Moodle, that
you download the submission and double-check its contents.
● Turnitin is used to help staff review the academic integrity for all submissions. For this
reason, it will not be shared with students unless a student’s work is under review.
● There is a maximum file size of 500MB. This is rarely hit by students in the unit, but it can
cause an issue if your data files are very large. If you believe the limit affects you, check your
zipped folder size and look to reduce the size of your data (e.g., by removing columns you
are not using). If this is not possible, then only then can you consider storing your data
remotely, e.g., via Google Drive, but be sure to test your code and provide access. Be sure to
note this restriction in your code comments and any instructions, if needed. If access and
instructions are not provided, your mark will be penalised.
● You do not need to publish your app on the web.

Late Submissions and Special Consideration
Presentation
● We encourage everyone to submit their presentation slides on time.
All Presentation Slides submitted late will receive zero marks.

Report and Source Code
● Assessments received after the submission deadline, or after the extended submission date
for those with special consideration, will be penalised 10% of the available total marks per
day up to a maximum of seven days. Submissions seven days after the due date will receive
a mark of zero, and may not receive feedback.
● For information on eligibility for Extensions and Special Consideration, please refer to the
relevant section on the Assessment page on Moodle.