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Data Visualization Report in R

The length of the assignment report is limited to 1500 words.

Learning Outcomes Assessed

The learning outcomes assessed in this assignment are:

• Critically analyse data visualisation approaches with respect to human sensory modalities

• Create appropriate visualisations for temporal, dynamic, and high dimensionality data

• Devise methodologies for data interaction to facilitate exploratory data analysis

Introduction

In this coursework you will select a big data set and will produce two visualisations of this data set along with a final report. We are primarily interested in the design processes you take to achieve your final visualisations. All the different design stages must be documented (and can include draft plots) to justify the final visualisations. You are free to select a data set but it must satisfy the following criteria:

• It should be a big data set with more than 1000 data records items. It can be a data set that comes from your work or data that you will collect. it should be from an authentic and credible source. The data set should be in a format that can be read in R and converted into one or more data frames.

• The data frame(s) should contain at least 10 different columns with discrete and continuous values You must follow the visualisation process taught in the lectures and document it in a report.

Part 1 Design

During consolidation 1 (week 5) you can receive formative feedback on your dataset selection and brief formulation. This includes the following:

• A description of the data set that you have selected, where they are coming from, their format.

• Formulating your brief: The purpose of your project, audience, context and circumstances, its vision and its position in the purpose map

Part 2 Development of the Visualisation

You have to develop at least two different graphs of your data and present them either as a pdf document in poster format or a single webpage if you wish to include interactive visualisations. If you create a webpage you must host the page online and provide a link to a publicly accessible URL along with a screenshot. You do not have to create any interactive visualisations, two static visualisations developed and presented in a single pdf document will still meet the marking criteria. The visualisations must follow the design choices documented in Part 1 and must also align with the original purpose proposed when you formulated your brief. You must also submit the R implementation of the visualisations.

You are required to submit a three minute video explaining your visualisations.

Report Instructions

You should produce a report (max 1500 words) detailing :

• The dataset selected: Description of the data set and justification of its selection

• Formulating your brief: Purpose of the visualisation, context, type of questions that want to be answered, purposed map.

• Working with data:

• Transformation needed in the data: filtering missing data, adding columns and elimination of outliers with the corresponding code in R

• Examination of the data

• Exploratory data analysis with the corresponding code in R

• Prototype of the visualisation

• Description of the chart types and the main features in terms of colours, composition

• Code of the final charts

• Conclusions and reflections

This is a summative assessment, so both marks and feedback will be given. For this coursework you must submit:

• Report in pdf format (you can use the headings in the Marking Scheme to structure your report)

• R code as an R Script

• A 3-minute video-recorded presentation where you explain your visualisations

• Your visualisations as either a pdf file (if a poster) or screenshot and a publicly accessible weblink if a webpage.

Marking Scheme

Report and Code Design: (40 marks)

• Description of the data set and justification of its selection (5 marks)

• Definition of the context of the visualisation following the design process (3 marks)

• Project vision and purpose map (3 marks)

• Data examination and transformations required to make it suitable for examination using R to produce this transformation (3 marks)

• Exploratory Data Analysis. Rapid visualisation of the data to understand the data and identify the angle that will be used in the visualisation (10 marks)

• Result of the exploration (2 marks)

• Final decisions on the design of the visualisation and prototype and justification of data charts (4 marks)

• Correct design of R code (10 marks)

Presentation – Demo (20 marks)

• Correct execution of R code and displaying of the two charts (15 marks)

• Visual appealing of the product that matches the design (5 marks)

TOTAL 60 Marks