Part I - Choose your data visualisation

Choose a data visualisation on the web such that:

• There are three distinct ways in which you believe the data visualisation can be improved.Data visualisations that require challenging improvement to implement will be highly regarded. Here are some questions to ask when thinking about ways that a visualisation can be improved:
  • Does the visualisation answer a practical question, or could a better question be posed? Could additional data or variables be added to improve insight?
  • Are there any potential issues with data integrity (unreliable or unknown source, inappropriate data, insufficient data)
  • Are there any potential ethical issues (real or perceived)?
  • Are there any major perceptual, colour or accessibility issues that need to be fixed?
  • Are the methods used potentially deceptive?
• The visualisation must be a single plot, non-interactive, data visualisation. Faceted data visualisations are acceptable.
• You are able to openly source the original or similar data.
• The data visualisation is based on real data and includes variables that are sufficient to answer a practical question using data visualisation.
• To the best of your knowledge (or the knowledge of Google), the visualisation has not been previously critiqued by data visualisation experts or discussed in the teaching materials of this course.
• You will work independently on the assignment and do not collude with other students using the same visualisation.

Part II - Deconstruct

• Identify the objective of the data visualisation and the target audience.
• Choose the three most significant improvements that you will make to the original. You must propose three. No more, no less.
• Using the dot points in the template, very briefly explain each of the improvements that are needed.

Part III - Reconstruct

• Source the original data or data as close as possible to the original. Data sourced from reputable sources is highly regarded. Avoid taking data or statistics directly from the original data visualisation if the original is not the source. You can also add additional data sources if required to address an issue identified in the deconstruction.
• Use ggplot2 or any other R-based data visualisation package (base graphical functions are not permitted) to reconstruct a data visualisation that implements the three previously explained improvements
• Reference your data source/s.

Give me interesting 5 to6 data visualisation related to this with it's link for the datasets too . Or I need a data visualisation where in reconstruct we can add additional data related to it to improve the visualisation.