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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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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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