R Shiny principles and software

Academic integrity declaration
By submitting work for assessment I hereby declare that I understand the University’s policy on academic
integrity and statement on the use of artificial intelligence software.
In accordance with these documents, I declare that the work submitted is original and solely my work, and that I
have not been assisted by another person (collusion) apart from where the submitted work is for a designated
collaborative task, in which case the individual contributions are indicated. I also declare that I have not used
any editing tools or sources without proper acknowledgment (plagiarism). Where the submitted work is a
computer program or code, I further declare that any copied code is declared in comments identifying the
source at the start of the program or in a header file, that comments inline identify the start and end of the
copied code, and that any modifications to code sources elsewhere are commented upon as to the nature of the
modification.
Unlimited Attempts Allowed
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 environment 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. Develop various types of visualisation platforms in order to analyse big data sets
Your task
This is an individual assignment.
You will develop one visually appealing and communicative, interactive data visualisation interface using R
based on a dataset selected by you.
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.
Assignment 2: Interactive Data Visualisation in R
2024/9/22
100 Points Possible
In Progress
NEXT UP: Submit Assignment
Attempt 1 Add Comment
2024/9/20 10:24 Canvas LMS
https://canvas.lms.unimelb.edu.au/courses/188035/assignments/468024 1/7You 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 defifined 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 can no longer call on Tableau to do some of the thinking
for you.
Ideas
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:
Makeover Monday (2018 to present) (https://makeovermonday.co.uk/) Tidy Tuesday (2018 to present)
(https://github.com/rfordatascience/tidytuesday)
Data is Plural (2016 to present) (https://www.data-is-plural.com/)
FiveThirtyEight open data (2014 to 2023) (https://data.fifivethirtyeight.com/)
BuzzFeed News (US-centric, 2014 to 2022) (https://github.com/BuzzFeedNews/everything)
Tableau has a blog post about free public data sets (https://www.tableau.com/learn/articles/free-publicdata-sets)
list of places to look for data (https://help.tableau.com/current/pro/desktop/enus/fifind_good_datasets.htm#places-to-look-for-data)
The
 data search engine Kaggle (https://www.kaggle.com/datasets?minUsabilityRating=9.00+or+higher)
IMPORTANT: The following topics are not allowed in 2024 because too many students selected them in
previous years:
Any topics relating to fifirearms or homicides in the United States, including gun violence, police
shootings, murders, and so on
Any topics relating to traffiffiffic accidents in the United States at a state or national scale (city scale is
allowed)
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.
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:
2024/9/20 10:24 Canvas LMS
https://canvas.lms.unimelb.edu.au/courses/188035/assignments/468024 2/7Lander, 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
Sievert, C. (2019). Interactive web-based data visualization with R, plotly, and shiny (https://plotly-r.com/)
Your R code should:
contain the library(...) commands necessary to load any packages required by your R code. You may use any
packages from CRAN (https://cran.r-project.org/web/packages/available_packages_by_name.html)
be written with the assumption that the directory containing your R script has been set as the working directory.
For example, read.csv("deaths-1900.csv")
contain a comment above every section of your script to describe what that section does.
Your R code should not:
contain install.packages(...) commands.
contain full fifile paths (e.g. "C:/Users/...") or fifile.choose() functions.
You will be penalised if the marker has to modify your code to get it working, and heavily penalised if the marker is
unable to get your code working with reasonable effffort.
Submission
This exercise is to be completed individually on your own time.
The assessment is worth 20% of your fifinal subject mark.
You must submit the following through Canvas:
1. A zipped fifile 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. There should be no
folders/directories inside the zip fifile unless absolutely unavoidable. 
2. A PDF design summary report, submitted simultaneously into Canvas (not inside your zipped fifile), containing
the following:
A one-page summary of your design;
An appendix (on a second page if required) that clearly describes all of the sources used in your design and
describes how the sources are used in your interface. [clarifification added 10 September]
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.
REMEMBER: Please read the important note above about topics that are not allowed. An interface based
on these topics will score zero marks.
Deadline
The submission deadline is Sunday 22 September 2024 at 23:59.
A late penalty may be applied on the basis of the lateness if no extension is approved prior to the deadline.
Students must apply for an extension directly to the Subject Admin, Peyman Jafary, via
jafary.p@unimelb.edu.au (mailto:jafary.p@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
2024/9/20 10:24 Canvas LMS
https://canvas.lms.unimelb.edu.au/courses/188035/assignments/468024 3/7received 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
The key assessment criteria are written below as part of the rubric.
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 challenges 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 challenges 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 challenges, substantial design
innovation, flflawless 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 fifind 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 your interface and the
design decisions involved.
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. 
2024/9/20 10:24 Canvas LMS
https://canvas.lms.unimelb.edu.au/courses/188035/assignments/468024 4/7Q&A
If you have any questions about Assessment 2, please post them on the Discussion Board
(https://canvas.lms.unimelb.edu.au/courses/188035/discussion_topics/1130374) 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 specifific questions, you can also email your tutor to