How do property sales change according to different property types and age?Explain

Course Work:

Objective: implementing a visualization with R

– Pick a dataset of your interest

– Pose the initial questions (3 to 5) that you would like to answer

– Assess the fitness of the data

– Answer the questions by visualizing the dataset using R in an exploratory fashion

Written report:

maximum 10 pages (3000 words). In addition to the 10 pages main content, you can also add additional appendix (no limitations) if you want.

– Description of your data

– The description with the initial questions

– For each question, a description of your visualization strategies, including data cleaning, transformation, visual encoding, etc.

– An explanation of the exploratory process of generating new questions and visualizations.

– Critical discussion of your visualization design (e.g. why you pick these encodings or this visualization)

– A reflection on the development process

– You need to upload your R codes as well A Case Study: House Price Visualization

• Pick a dataset of your interest. The land registry Price Paid Data includes information on all property sales in England and Wales that are sold for full market value and are lodged with us for registration.

Format:

• Pose the initial questions (3 to 5) that you would like to answer.

RQ1: When do property sales generally happen? Which month is the best month to sell?

RQ2: How do property sales change according to different property types and age?

RQ3: How do property sale price compare across different counties?

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