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29/08/2023, 17:13 Assignment 2: Interactive Data Visualisation in R
Assignment 2: Interactive Data Visualisation in R
24/09/2023
100 Possible Points
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Attempt 1 Add Comment
Details
Objective
1. To produce an interactive R Shiny interface to present a dataset of your choice;
2. To use the techniques, principles, and software learned during the subject and lab sessions, including applying your feedback
from Assignment 1;
3. To demonstrate your ability to challenge yourself and innovate in a code-based enrivonment to present data in an engaging,
novel manner.
Learning outcomes
ILO 1. Apply the cognitive and technical principles of information visualisation across various domains
ILO 3. Analyse big spatial data sets using data visualisation and geovisualisation techniques
Context
In this task, you are going to develop one visually appealing and communicative interactive data visualisation interface using R
based on your selected dataset.
Referring to the R programming and Shiny exercises from Labs 4 to 7, as well as other online resources, you will need to combine,
adapt and build upon these to design and create your own interactive interface containing at least one form of novel interaction.
You will need to get familiar with your chosen dataset and design your interface with reference to the principles of interaction design,
cartography and data graphics learned in the lectures. You will assess your interface according to these principles and iteratively
redesign your interface to improve it.
Your resulting interface must have one or more data visualisations with some form of interaction, although the data visualisations
themselves do not all need to be interactive. The interface must communicate a clear message about your data to a dened
audience, and you must present a one-page design summary explaining your design decisions that help to achieve this.
Please note, that the focus of this assignment is to create an interface to present the dataset in your chosen scope and/or
geographic area. You are not expected to perform any in-depth data modelling, although data selection will be an important part of
the design process to ensure that only relevant data is available to the user.
What distinguishes this assignment from Assignment 1 is the focus on interface and interactivity and the need to think more carefully
about basic design principles – you no longer have Tableau to do some of the thinking for you.
Technical requirements
You will create an interface in R. You can use any packages you wish; however, you must use Shiny to create an app (graphical user
interface).
In class, we covered ggplot2 (and ggiraph) and some packages for spatial visualisation. Students who are not familiar with R
programming may prefer to explore these libraries more deeply.
If you would like to learn more, there are a number of online books on R and Shiny, for example:
Lander, J. P. (2014). R for everyone: Advanced analytics and graphics (https://cat.lib.unimelb.edu.au:443/record=b7253346~S30)
, 2nd edition, Addison-Wesley - introduction to R, includes a chapter on Shiny
Resnizky, H. (2015). Learning Shiny (https://cat2.lib.unimelb.edu.au/record=b7243085~S30) , O'Reilly - simple intro to R and Shiny,
limited material on data graphics
Chang, W. (2018). R Graphics Cookbook (https://cat.lib.unimelb.edu.au:443/record=b7253353~S30) , 2nd edition, O'Reilly - focus
on ggplot2 graphics
Submit Assignment
29/08/2023, 17:13 Assignment 2: Interactive Data Visualisation in R
https://canvas.lms.unimelb.edu.au/courses/154446/assignments/387909 2/7
Sievert, C. (2019). Interactive web-based data visualization with R, plotly, and shiny (https://plotly-r.com/) , CRC Press - a
more advanced look at interactive data graphics in R
Assessment
This exercise is to be completed individually in your own time.
The assessment is worth 20% of your nal subject mark.
You must submit the following through Canvas:
1. A zipped file containing any data sets you used and working R code that generates the data graphic with a clear
acknowledgement of any code used or adapted from other sources. Please note you are required to provide comments on your
R code: for every piece of code (e.g. a loop or a non-trivial calculation), you need to add a comment before the code to describe
it.
2. A PDF design summary report, submitted simultaneously into Canvas (not inside your zipped le), containing the following:
A one-page summary of your design;
An appendix that clearly describes all of the sources used in your design and describes how the sources are used in your
interface.
In your one-page summary, you can also provide extra information about your design to highlight any background work or to assist
the user in understanding and/or using your interface.
Your R code should contain the commands necessary to install and use any packages necessary for your R code to run. You will be
penalised if the marker has to manually install any necessary packages, and heavily penalised if the marker is unable to get your
code working with reasonable eort.
IMPORTANT: The following topics are not allowed in 2023 because too many students selected them in 2022:
Any topics relating to rearms or homicides in the United States, including gun violence, police shootings, murders,
and so on
Submissions based on these topics will receive zero marks for Assignment 2. The only exception is if you wish to incorporate
a data graphic on these topics as a minor part of a broader interface. In this case, please get permission from your tutor.
Data
The focus of Assignment 2 is to create an interactive data visualisation interface, NOT analyse a big dataset. That's why we suggest
using pre-packaged data which has already been formatted ready for visualisation. You are free to choose your own data source or
use a dataset from one of the following suggested sources:
Tidy Tuesday data (wide assortment of data, 2018 to present) (https://github.com/rfordatascience/tidytuesday)
FiveThirtyEight open data (US-centric, 2014 to present) (https://data.vethirtyeight.com/)
Washington Post (US-centric) (https://github.com/washingtonpost)
BuzzFeed News (US-centric, 2014 to 2022) (https://github.com/BuzzFeedNews/everything)
Tableau has a list of free public data sets (https://www.tableau.com/learn/articles/free-public-data-sets)
The data search engine Kaggle (https://www.kaggle.com/datasets?minUsabilityRating=9.00+or+higher) - consider ltering by
high usability score
Before choosing a dataset, please read the important note above about topics that are not allowed.
Deadline
The submission deadline is Sunday 24 September 2023 at 23:59. A late penalty may be applied on the basis of the lateness if
there is no extension being approved prior to the deadline. Students must apply for an extension directly to the Subject Admin
(bsaeidian@student.unimelb.edu.au (mailto:bsaeidian@student.unimelb.edu.au) ).
Assessments submitted after the original due date without an extension, or after the new due date if an extension has been
granted by the Subject Coordinator, will be subject to a penalty of 10% in the mark received in this assessment for each
working day the assessment task is late. For example, if you are late by one day and your assessment reaches a standard of
80 out of 100, you will now receive 70 in this assessment only.
Assessment Criteria
Submit Assignment
29/08/2023, 17:13 Assignment 2: Interactive Data Visualisation in R
https://canvas.lms.unimelb.edu.au/courses/154446/assignments/387909 3/7
The key assessment criteria are (out of 100%): Basic design (30%), Technical challenge (30%), Design innovation (30%), and Design
summary (10%). See the rubric for more details.
As a guide to grade-related criteria:
<50%: Inadequate work that in one or more respects fails to meet basic technical standards or apply basic design principles
50-60%: Satisfactory work that is a correctly submitted basic interface to the data for presentation purposes using basic visual
variables
60-70%: Good work that involves marginal additional technical challenge such as increased interactivity (such as displaying
multiple data layers on a map), marginal design innovation and moderate levels of design quality
70-80%: Excellent work that involves clear additional technical challenge such as greater interactivity (such as tools allowing the
user to explore the data set) or design innovation, and high levels of design quality
>80%: Outstanding work that demonstrates substantial additional technical challenge, substantial design innovation, awless
design, and involves work that clearly goes beyond that normally expected in class.
Hints
You are free to design any type of data graphic. You do not need to design an interface that contains spatial data, although you
are most welcome to do so if you wish. High-quality visualisations containing spatial data will be rewarded in the grade
accordingly.
You should aim to design your own data graphic, not simply duplicate an existing one. Copying will be penalised under any
categories and is a form of plagiarism.
You are encouraged to conduct extensive research to nd interesting and engaging ways of constructing your data graphic. This
might be where most of your time is spent.
Think carefully about your use of visual variables. These have been key discussion points in many lectures.
Consider the principles of data integrity, aesthetics, correspondence, and density in your design based on what has been
discussed in the lecture.
Your summary and interface must be carefully designed. If your interface requires a page of dense text to explain, it is unlikely
that the interface itself is well-designed and intuitive to use. Thus, it is recommended that you keep the design summary as brief
as you can while providing a clear explanation of how to use your interface.
Note, that your design summary will be assessed based on its design. You should take care to ensure the design summary is
carefully presented with attention to detail. For example, you may prefer to have an annotated diagram as your design summary
instead of text.
Spelling and grammar are part of the assessment as well. Your design summary and interface should exhibit attention to detail
and be free of errors.
Plagiarism
In short: you must clearly acknowledge any material you have used in your assessment. Plagiarism is copying, and use of
another’s work without proper acknowledgment (can be both known and unknown). The university has a clear policy prohibiting any
form of plagiarism. Further information can be found at https://academicintegrity.unimelb.edu.au/
(https://academicintegrity.unimelb.edu.au/) .
Note that it is acceptable to reuse ideas and code you have found on the web as long as the source is acknowledged and that use is
permitted by any license restrictions. If properly acknowledged, using other people’s code and ideas can count as independent
background research (see grade-related criteria above). If not properly acknowledged, using other people’s code and ideas is
plagiarism and will result in a mark of zero for this assessment. In serious cases, plagiarism may also result in failure of the entire
subject and further University disciplinary action.
Coda
Originally created by Matt Duckham, revised by Katerina Pavkova, Alan Thomas, and Bahram Saeidian. Licensed under a Creative
Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/) .
Q&A
If you have any questions about Assessment 2, please post them on the Discussion Board
(https://canvas.lms.unimelb.edu.au/courses/154446/discussion_topics/930239) for this assessment. The tutors will attend to questions
there on a regular basis. If you know the answer to any questions, you are also welcome to post your answer. You can also ask
questions in the lab sessions. We are a learning community and interaction is always welcome. Of course, if you have any specic
questions, you can also email your tutor to seek help.
Submit Assignment
29/08/2023, 17:13 Assignment 2: Interactive Data Visualisation in R
https://canvas.lms.unimelb.edu.au/courses/154446/assignments/387909 4/7
View Rubric
Submit Assignment
29/08/2023, 17:13 Assignment 2: Interactive Data Visualisation in R
https://canvas.lms.unimelb.edu.au/courses/154446/assignments/387909 5/7
GEOM90007 Assignment 2 Rubric
Criteria Ratings Pts
Basic design
of the
interface
view longer
description
/ 30 pts
Technical
challenge
view longer
description
/ 30 pts
30 to >23.8 pts
Excellent
The interface is
submitted in the
correct format.
The interface is
neat and wellpresented with
valid data story
and coherent
and clear
theme/message
. It is consistent
and
aesthetically
appealing.
There is
consistency in
fonts and
colours and the
interface is free
of grammatical
mistakes.
23.8 to >22.4
pts
Correct
The interface is
submitted in the
correct format.
The interface is
neat and wellpresented with
valid data story
and coherent
and clear
theme/message
. It is mostly
consistent and
aesthetically
appealing, but
has a few minor
issues in fonts,
colours, and/or
grammar and
spelling.
22.4 to >20.8
pts
Mostly Correct
The interface is
submitted in the
correct format
The interface is
neat and wellpresented with
valid data story
and coherent
and clear
theme/message
. It is generally
consistent and
aesthetically
appealing, but
has some minor
issues. The
fonts and
colours used in
the interface are
mostly
consistent.
Some
grammatical
mistakes.
20.8 to >19.4
pts
Flawed
The submission
of the interface
is mostly
correct. The
interface is
almost neat and
well-presented.
However, the
theme/message
is not crystalclear. There are
a few major
issues and
several minor
issues with the
consistency
and aesthetics
of the basic
design,
including the
fonts and
colours used in
the interface.
Some
grammatical
mistakes.
19.4 to >14.8
pts
Many Major
Issues
The submission
of the interface
is mostly
correct.
Attempts to
adhere to
design
principles, but
there are many
aws. Key
elements may
be missing.
There is an
attempt to
create data
story, but there
are many aws
such as using
unrelated
visualisations.
There are
issues with the
consistency of
the fonts and
colours and
grammatical
mistakes. There
are several
major and
minor issues.
14.8 to >0 pts
Fail (0% - 49%)
The les are
submitted in a
piecemeal way.
R code does
not run without
signicant
editing by the
marker. The
interface fails to
follow basic
design
conventions or
has severe data
integrity aws.
There are
several critical
issues, e.g., it is
incomprehensib
le and without
any theme; or
only a few
random
visualisations
have been
created, or no
work has been
submitted.
30 to >23.8 pts
Strong
Challenge
The work
involves
advanced
technical
challenges. The
R code uses
new or dierent
programming
techniques.
There is clear
evidence of
advanced
thinking. The
libraries
(packages)
used are well
chosen and
used in a
sophisticated
way. There are
a wide range of
appropriate and
23.8 to >20.8
pts
Developing
Challenge
The work
involves good
technical
challenges. The
R code
eectively uses
libraries
(packages)
and/or own
programming
and adapts the
interface to the
needs of the
graphic. There
are some
decent eorts
to use new and
appropriate
visualisations,
and there are
some change
20.8 to >17.8
pts
Minor
Technical
Challenge
The work
involves good
technical
challenges. The
R code uses
some new
programming
techniques that
are adapted to
the needs of the
graphic. There
are some eorts
to use new and
appropriate
visualisations
and there are
some changes
and
improvements
17.8 to >14.8
pts
Limited
Challenge
The interface
uses some new
techniques and
visualisations at
a basic level,
with some
attempts to
change and
improve the
defaults.
14.8 to >11.8
pts
Very Limited
Challenge
Limited,
ineective use
of new
techniques and
visualisations,
with minimal
attempts to
change and
improve the
defaults.
11.8 to >0 pts
Fail (0% - 49%)
The work
involves no
technical
challenge.
Almost the
entire R script
consists of
code copied
from Labs 4-7
or from the
Internet, with no
insight into how
to properly
adapt the code
to their
interface/data.
Submit Assignment
29/08/2023, 17:13 Assignment 2: Interactive Data Visualisation in R
https://canvas.lms.unimelb.edu.au/courses/154446/assignments/387909 6/7
GEOM90007 Assignment 2 Rubric
Criteria Ratings Pts
Design
innovation
view longer
description
/ 30 pts
Design
summary
view longer
description
/ 10 pts
Total Points: 0
new
visualisations in
the interface.
and
improvements
on the basis of
the defaults.
on the basis of
the defaults.
30 to >23.8 pts
Strong
Innovation
Excellent work.
Design is fresh
and creative yet
highly effective;
interface
reveals
interesting or
meaningful
patterns; is
notably
aesthetically
pleasing or
striking;
demonstrates
independent
background
research.
23.8 to >20.8
pts
Developing
Innovation
The interface
presents data
quite creatively
and eectively,
and interesting
patterns are
apparent, but
the interface
may need more
work to be
totally eective
(for example,
removing nondata-ink).
20.8 to >17.8
pts
Minor Design
Innovation
The interface
uses
moderately
innovative
design
elements that
are beyond
standard
elements
covered in
class. There are
some patterns
in the data, but
the patterns
could be more
meaningful and
eective in data
presentation.
17.8 to >14.8
pts
Limited
Innovation
It uses a few
design
elements that
are beyond
standard
elements
covered in
class. There are
some attempts
to investigate
some simple
and obvious
patterns, but
the patterns
could be more
meaningful and
eective in data
presentation.
14.8 to >10.8
pts
Very Limited
Innovation
Limited,
ineective
attempts at
innovation are
seen in the
design. There is
no attempt to
reveal patterns
in data.
10.8 to >0 pts
No Design
Innovation
Default settings
are used (or so
it appears when
looking at the
interface).
10 to >7.9 pts
Excellent
The design
summary is
well-structured
and eectively
explains the
design choices.
It is neat and
well-designed,
and there is
aesthetic
consistency in
fonts and
colours. It is
free of
grammatical
errors.
7.9 to >7.4 pts
Very Good
The design
summary is
well-structured
and explains
most design
choices. It is
mostly neat and
well-designed,
and there is
aesthetic
consistency in
fonts and
colours, but has
some minor
grammatical
errors.
7.4 to >6.9 pts
Good
The design
summary
explains most
design choices
but a few
elements are
missing. It is
generally neat
and welldesigned, and
there is
aesthetic
consistency in
fonts and
colours, but has
some minor
grammatical
errors.
6.9 to >6.4 pts
Fair
The design
summary
explains some
design choices,
but a few
elements are
missing. Also, it
could be
structured
better. It is
generally neat
with a few
minor issues.
There is
aesthetic
consistency in
fonts and
colours, but has
some minor
grammatical
errors.
6.4 to >4.9 pts
Minimal
Although the
design
summary
explains some
design choices,
a few key
elements are
missing. There
are also a few
issues with the
structure, e.g.,
too brief or
restates the
obvious. There
are a few major
issues with the
design of the
summary, e.g.
in aesthetic
consistency
and/or
neatness.
Finally, there are
some
grammatical
errors.
4.9 to >0 pts
Inadequate
There are major
issues with the
structure and
design of the
design
summary, or
there is no work
being
submitted.
Submit Assignment
29/08/2023, 17:13 Assignment 2: Interactive Data Visualisation in R
https://canvas.lms.unimelb.edu.au/courses/154446/assignments/387909 7/7
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