Using the data contained in the file covid_au_state.csv, create a visualisation that: • Shows the total number of new confirmed cases daily throughout Australia since the first cases recorded on 25 January 2020. Highlight the three days with the highest number of new cases. • Compares the daily growth factor across all the eight states in Australia from 17 March 2020 to 16 August 2020


Using the data contained in the file covid_au_state.csv, create a visualisation that: • Shows the total number of new confirmed cases daily throughout Australia since the first cases recorded on 25 January 2020. Highlight the three days with the highest number of new cases. • Compares the daily growth factor across all the eight states in Australia from 17 March 2020 to 16 August 2020. (The growth factor is calculated by dividing the new cases on the current day with the new cases of the previous day.) [Data source: covid19data.com.au.] TASK 2 [25 marks] Using the data contained in the file grand_slam_data.csv, create a visualisation that: • Compares the performance all the winners for each of the four Grand Slam tournaments (Australian Open, French Open, Wimbledon, U.S. Open); and highlights the top winner for each tournament. • Shows and highlights how many times the winners and the runner-ups met each other on each of the Grand Slam tournament finals for the 40 most recent tournaments (from 2008 to 2017). TASK 3 [25 marks] Using the data contained in the file big_stock_data.csv, create a Shiny interactive data visualisation that allows the user to: • View the change over time of a chosen share based on either the closing price or the share volume. • Compare the shares performance between two or more companies. [Note: You only need to describe one screenshot for this task – as we will run your R code to check the interactivity of your visualisation.] TASK 4 [25 marks] Using the data contained in the file crime_data.csv and the ggmap R package, create a Shiny interactive data visualisation that allows the user to: • View the spatial density of one or more offense types in the Houston area. • View the spatial density of all the crimes happened for a time period (e.g. from 01/03/2010 to 30/04/2010). [Note: You may want exclude the offense type of Theft when creating the visualisation. You only need to describe one screenshot for this task – as we will run your R code to check for the interactivity of your visualisation.]



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