Practicing ggplot visualizations while exploring recent COVID-19 data pulled on MARCH 17, 2020

Data Sources: https://github.com/RamiKrispin/coronavirus

The raw data pulled and arranged by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from the following resources:

World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http:://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http:://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases

Tech Used: R Programming Language Jupyter Notebook

Findings:

• The number of new COVID-19 Cases are increasing at an exponential rate.
• Whilst China was reaching its apex, the rest of the world was slowly increasing at an exponential rate.
• Politcal decisions and the way countries report its data play significant roles in the increase of new cases.
• At the time of the release of this data, not all countries are hit equally and should observe the rate it is affecting countries such as Italy, Iran, and South Korea.