Reference no: EM133762998

Data Visualisation in R

Assessment - Analytical Report

Your Task

This assessment is to be completed individually. In this assessment, you assess your ability to investigate and analyse data visualisation techniques using the R language.

Assessment Description

For this assessment, you are required to prepare an investigative report that explores and analyses data visualisation techniques using the R language.

Write an investigative report that focuses on the use of data visualisation techniques for advanced data analysis. The report should cover the following aspects:
Introduction:
Provide an introduction to the importance and significance of data visualisation in advanced data analysis.
Explain the role of data visualisation in gaining insights, identifying patterns, and making informed decisions.
Theoretical Foundations:
Discuss the theoretical foundations and principles of data visualisation, including perception, cognition, and visual encoding.
Explore the latest research and advancements in data visualisation theory and their implications for practical applications.
Data Visualisation Techniques in R:
Investigate and analyse various advanced data visualisation techniques available in R, such as but not limited to, interactive visualisations, geospatial visualisations, network visualisations, and time series visualisations.
Evaluate the strengths, limitations, and considerations for each technique and their applicability to different data analysis scenarios.
Design Principles and Best Practices:
Explore the design principles and best practices for creating effective and engaging data visualisations.
Discuss the role of colour, layout, typography, and interactivity in enhancing the visual representation and user experience.
Analyse real-world examples of well-designed data visualisations and extract insights from their design choices.
Data Preparation and Wrangling for Visualisation:
Investigate advanced data preparation and wrangling techniques in R to optimise data for visualisation purposes.
Explore strategies for handling missing data, outliers, and data transformations to improve the effectiveness and accuracy of visual representations.
Interactive Visualisations and Dashboards:
Investigate the creation of interactive visualisations and dashboards using R tools and libraries, such as but not limited to, Shiny and Plotly.
Analyse the benefits and challenges of interactive visualisations in data exploration, analysis, and communication.
Case Study Examples:
Include at least two in-depth case study examples that demonstrate the application of advanced data visualisation techniques using R in complex data analysis scenarios.
Describe the datasets, the visualisation techniques used, the analytical process, and the insights gained from each case study.
Conclusion:
Summarise the key findings and insights from the investigation of data visualisation techniques using the R language.
Reflect on the challenges, opportunities, and future directions in the field of data visualisation.

Learning outcome 1: Investigate data visualisation techniques using R language.

Assessment Instructions
Students must conduct research externally and included references in order to produce a well referenced assessment. You should use at least ten (10) sources of information and reference these in accordance with the Kaplan Harvard Referencing Style. These may include websites, social media sites, industry reports, census data, journal articles, and newspaper articles. These references should be presented as in-text citations and a referencing list at the end of your assessment (not included in the word limit). Wikipedia and other ‘popular' sites are not to be used.

Report: You must submit your report in Word document or in PDF format.
R Code: Include the complete R code you used to perform the analysis. Ensure that the code is documented with comments.
a) You must submit your R code in .r format extension. Any other formats will not be accepted.