代写program、代做R程序语言

日期：2024-09-22 09:50

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 seek help.

View Rubric

GEOM90007 Assignment 2 Rubric

Criteria Ratings Pts

Data

correspondence

25 to >19.8 pts

Outstanding

The interface is

neat, totally

consistent and

crystal-clear to

understand.

There is a

strong, coherent

theme/message

that reveals

interesting and

insightful

patterns in the

data. Highly

tailored to the

audience’s

needs. Overall,

the interface is

virtually

flflawless.

19.8 to >17.3

pts

Very Good

The interface is

neat, consistent

and easy to

understand.

There is a

logical

theme/message

that reveals

meaningful

patterns in the

data. Tailored to

the audience’s

needs. Overall,

there are a few

minor issues.

17.3 to >14.8

pts

Good

The interface is

mostly neat,

somewhat

consistent and

mostly easy to

understand.

Some parts

could be clearer.

There is a

theme/message

that reveals

some patterns

in the data.

Overall, there

are several

minor issues or

a few major

issues.

14.8 to >11.1

pts

Flawed

The interface is

not neat and

quite

inconsistent. It

is mostly

confusing and

diffiffifficult to

understand. The

theme/message

may be weak or

unclear, with a

limited number

of relevant

patterns

apparent.

Overall, the

interface has

several major

and minor

issues.

11.1 to >0 pts

Inadequate

The interface is

incomprehensible.

It does not

reveal any

relevant

patterns in the

data. There is

no theme or

visual hierarchy

at all; a few

random

visualisations

have been

created.

/ 25 pts

Data integrity

15 to >11.8 pts

Outstanding

No integrity

issues at all.

Visualisation

types are

always

appropriate and

implemented

with care and

accuracy.

11.8 to >10.3

pts

Very Good

No serious

integrity

concerns or

flflaws.

Visualisation

types are

appropriate and

implemented

correctly. Some

minor issues.

10.3 to >8.8 pts

Good

Minor data

integrity flflaws

exist.

Visualisation

types are

generally

appropriate but

may be

implemented

with some gaps.

8.8 to >6.5 pts

Flawed

Serious data

integrity flflaws

exist.

Inappropriate

visualisation

types may be

used. Key

elements may

be frequently

misleading or

absent.

6.5 to >0 pts

Inadequate

There is no

respect for data

integrity. A

critical data

integrity flflaw

may exist which

undermines a

major part of

the interface's

message.

/ 15 pts

Data density and

aesthetics

25 to >19.8 pts

Outstanding

The interface’s

look is fresh,

creative,

innovative and

striking, yet

highly effffective

and not overly

complex. Well19.8

to >17.3

pts

Very Good

The dashboard

is aesthetically

pleasing and

demonstrates

developing

creativity and

innovation. Data

17.3 to >14.8

pts

Good

The

dashboard’s

look is effffective

with basic

creativity and

innovation. Data

density issues

14.8 to >11.1

pts

Flawed

The dashboard

has aesthetic

flflaws. Limited or

ineffffective

attempts at

creative and

innovative

11.1 to >0 pts

Inadequate

The

dashboard’s

look is

inconsistent and

violates

essential design

principles,

creating a

/ 25 pts

2024/9/20 10:24 Canvas LMS

https://canvas.lms.unimelb.edu.au/courses/188035/assignments/468024 5/7GEOM90007 Assignment 2 Rubric

Criteria Ratings Pts

balanced layout;

data density is

neither too high

nor too low.

Attention to

detail is

flflawless. Overall

the dashboard

is nearprofessional

quality.

density

does

not interfere

with the

interface’s

purpose. Some

minor issues

need

improvement

(e.g. the

balance of the

dashboard’s

layout). Minor

issues with

attention to

detail (e.g.

spelling).

occasionally

interfere with

the interface's

purpose. Design

principles are

addressed with

several minor

issues or a few

major issues.

There are gaps

in the attention

to detail.

presentation.

Data density is

too low or too

high; the

available screen

space could be

used more

effffectively.

Several major

and minor

issues in design

principles. Low

attention to

detail.

negative

experience for

the audience.

Data density

may be so low

or high that

large parts of

the interface

cannot be

understood.

Technical challenge

and interaction

design

25 to >19.8 pts

Strong

Challenge

Advanced Shiny

features are

used

masterfully,

demonstrating

independent

background

research. The

interaction

design is fully

intuitive and

nearprofessional

quality.

The

dashboard is an

outstanding

example of

what is possible

with Shiny.

19.8 to >17.3

pts

Developing

Challenge

Advanced Shiny

features are

used effffectively.

They adapt to

the needs of the

graphic. The

interaction

design is

appropriate,

intuitive and

high quality.

17.3 to >14.8

pts

Minor

Challenge

Advanced Shiny

features add

some value, but

more effffort is

needed to

maximise their

effffectiveness.

The interaction

design is often

intuitive but

sometimes

confusing.

14.8 to >11.1

pts

Limited

Challenge

Limited use of

advanced Shiny

features. A few

efffforts were

made, but these

efffforts are of

limited value to

the dashboard’s

audience. The

interface often

violates the

norms of

interaction

design; major

parts are not

intuitive or

confusing to

use.

11.1 to >0 pts

Poor or No

Challenge

Ineffffective or no

use of

advanced Shiny

features.

Interacting with

the interface is a

very frustrating

or impossible

experience.

There may be

major bugs.

/ 25 pts

Design summary 10 to >7.8 pts

Outstanding

The design

summary is

well-structured

and strongly

justififies all

design choices.

The student

clearly

understands

concepts from

lectures and is

able to put

these ideas into

practice

effffectively. The

layout is

7.8 to >6.8 pts

Very Good

The design

summary is

well-structured

and justififies the

design choices.

The student

shows a good

understanding

of some

concepts from

lectures and

links these to

their design

choices. The

layout is

effffective.

6.8 to >5.8 pts

Good

The design

summary

explains some

design choices,

but a few

elements are

missing. Some

basic links are

made between

the lecture

material and the

interface

design. The

structure is

adequate. The

layout is basic

5.8 to >4.3 pts

Flawed

The design

summary has

major flflaws. For

instance, it may

be highly

descriptive or

restates the

obvious; it may

belong to the

wrong genre of

writing (e.g.

user tutorial); it

may be too

brief. The

structure needs

improvement.

4.3 to >0 pts

Inadequate

EITHER: The

design

summary

contains little or

no relevant

content at all.

OR: It exceeds

the one-page

limit. OR: No

summary was

submitted at all

(zero marks).

/ 10 pts

2024/9/20 10:24 Canvas LMS

https://canvas.lms.unimelb.edu.au/courses/188035/assignments/468024 6/7Choose a submission type.

GEOM90007 Assignment 2 Rubric

Criteria Ratings Pts

creative. The

writing is

crystal-clear.

and mostly

effffective.

The layout is

crude or

ineffffective.

Total Points: 0

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