This project provides visualizations tracking the changing status of COVID-19 cases. Visualizations will include current counts, relevent statistics on historic trends, and geovisualizations showing global and local hotspots.
Collaborators
ggsmith842
jaymie18
chriztopherton
Data References
2. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases Deprecated
3. https://www.kaggle.com/gpreda/coronavirus-2019ncov/data
We wanted to better understand the magnitude of the growing number of COVID-19 cases and convey our findings in an illustrative manner to spread awareness.
The COVID-19 Tracker dashboard visualizes the number of active cases using live data collected via API and historic time series data. The app complies this data in an easy to view format and calculated relative statistics as the situation changes (barplot,lineplot, heatmap).
Using Rstudio, we embedded R code into ShinyDashboard framework, implementing customizations and user functions with HTML/CSS.
Finding accurate, up-to-date data and presenting them properly proved difficult due to the nature rapidly changing situation surrounding COVID-19. Additionally, fact-checking interpretations at every step was tedious and time-consuming. But we knew incorrect analysis in this atmosphere could lead to worrisome findings, and we wanted our results to be as accurate as possible.
Strong collaboration, work-ethic, time management, application of classroom knowledge and skills. We also learned a lot about how Shiny Dashboard works and its applications in future projects.
The multitude of the numbers growing exponentially and dealing with live data that changes hourly is very different from working with static data that never changes.
With more data, we would like to forecast/project the number of new cases and then predict the peak infection number. This information will provide actionable insights for stakeholders on how to best prepare for the changing situation.