FIT5147 Narrative 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 outcome
● 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 do
not need to use everything you have found, be selective. The visualisations should reflect the
answers to your questions.
2. Clearly define your intended audience. The audience might be your classmates, the general
public, politicians or whoever you like. The visualisations 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 design sheets (one sheet per slide). More
information about the presentation will be provided later on Moodle.
5. Implement your visualisation using R (Shiny) or JavaScript (D3). The use of other
tools/visualisation library/visualisation software is subject to approval by your tutor.
Specifically for R, you are not allowed to use the R markdown.
6. Write a report and export it to PDF.
7. Submit the report and source codes.
Report structure
Write a 15-pages (excluding bibliography, table of content, cover page, appendix) report consists of
the following sections:
1. Project title
Title of the narrative visualisation. This can be included in the cover page.
2. Your identity.
Your full name, student ID, Lab number, and tutor name. This can be included in the cover
3. Introduction
A precise and succinct description of what messages you wanted your narrative visualisation
to convey and who the intended audience is.
4. Design
This section contains a description of the visualisation design process. This summarises the
five design sheets (i.e. details alternatives designs you considered and justifications of your
final design).
5. Implementation
This section contains a high-level description of the implementation, including libraries used
and reasons for the implementation decisions. You are not expected to explain the codes in
6. User guide
This section contains instructions for viewing and exploring your narrative visualisation.
7. Conclusion
Summarise your findings and what you have achieved. Reflect on what you have learnt in
this project, including what in hindsight you might have done differently to improve the
8. Bibliography
Appropriate references. Refer to this page to see appropriate referencing styles.
9. Appendix
Place your five design sheets in the appendix. Make sure you provide clear images.
Your report should contain high-quality images of the visualisation. You should also briefly explain
any reasons why your project was challenging (e.g. extensive data set, advance use of D3, etc.) in
your report.
Marking Criteria
1. Presentation of Designs [3%]
a. Quality of oral presentation (confidence, speed, voice) and quality of slides (legibility,
design, images) [1%].
b. Logical structure [1%].
c. Choice of content (completeness, appropriate level, discussion of design and
implementation alternatives) [1%].
2. Visualisation Design [15%]
a. Appropriate use of five design sheet methodology and evaluation of alternatives
b. Quality of final 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 final design in terms of the human perceptual system and human
communication assumptions [3%].
3. Visualisation Implementation [5%]
a. Correctness and robustness, speed, accessibility [3%].
b. Comments and code quality [2%].
4. Visualisation Difficulty [10%]
Degree of difficulty, e.g. the use of different sources of non-tabular data very well, dealing
with large dataset, advanced D3 programming/advanced R(shiny) programming,
sophisticated user interaction (e.g. animation, linked interaction).
5. Project Continuity [2%]
Degree to which the visualisation and report describes data insights related to the questions
proposed in your submitted 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.
6. Project Report [5%]
a. Quality of writing, referencing, images, logical structure, grammar/spelling [1.5%].
b. Completeness [3.5%].
Submission due dates
● Submit your presentation slides to Moodle by Sunday, 16 May, 11:55PM. Presentations will
then take place during Week 11 & 12 (during your lab). Attendance both weeks is mandatory.
● Submit a PDF report and a zip file containing your code to Moodle by Monday, 7 June,
NOTE: Times are expressed in Aust/Melbourne local time.
How to submit
Submit a PDF file with your presentation containing all five design sheets. Name the file
StudentName_StudentID_Presentation.pdf and submit via Moodle (i.e., Assessments/Presentation).
Report and Source Codes
Submit a PDF report (max 15 pages) and a zipped file containing your visualisation source code and
any data files that are needed to run your code. Please ensure you name the file correctly using the
following format:
1. StudentName_StudentID_Report.pdf
These two files (i.e., .pdf and .zip) must be submitted via Moodle (i.e., Assessments/
Visualisation Project Code). Do not zip these files into one zip archive, submit two
independent PDF file and a zip file. Note that only .zip file is acceptable, other extensions
such as .rar are not recommended.
● We cannot mark any work submitted via email or sharing via GDrive. Please ensure that
you submit correctly via Moodle since it is only in this process that you complete the
required student declaration without which work cannot be assessed.
● 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 in Moodle, that
you download the submission and double-check its contents.
● 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.
● You DO NOT need to publish your app on the web.
Check your code
Please be sure to check your code runs properly. Check on other computers and operating systems if
possible. If it requires some steps then be sure to make very clear readme notes for your grader. Your
code must run on your graders computer on first attempt for us to be able to mark your submission.
If your submission does not run correctly 5% (from implementation) will be instantly deducted
from your grade. If, after some troubleshooting, we still can not get the code running further
deductions will occur as we will not be able to fully grade your narrative visualisation work.
Please note that your code will be checked for compliance with the University’s academic integrity
policy, along with your report. Be sure to acknowledge sources that influence your code through
your comments and do not copy complete designs from other sources.
Late submissions and special consideration
● We encourage everyone to submit the presentation slide on time. All Presentation Slides
submitted late will receive zero marks.
Report and Source Codes
● 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.
● For information on eligibility for Special Consideration, please refer to the relevant section
on the Assessment page on Moodle.
If you are retaking this unit from a previous semester, discuss your circumstance with your tutor and
ensure you have chosen a completely new topic and dataset.