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Corona Impact on the Financial Market
Inspiration
What it does
How I built it
We built a dashboard using Dash by Plot.ly.
BTS Data
We scraped 20 years of air carrier statistics from the Bureau of Transportation Statistics. Once these datasets were downloaded, we cleaned each in R to select for only flights that originated in China or Hong Kong and arrived in the United States. These datasets were transformed into a more workable configuration in and flights with fewer than one passenger were dropped. We then ran a for loop that summed the total number of passengers coming in to the United States from Hong Kong and China per month for each year of data and added them to a new dataframe. These 20 dataframes were combined and a time series plot was created using plotly for Python.
Ports of Entry We focused on visualizing the year over year percent change of foreign arrivals by ports of entry. The top 10 most visited ports of entry in the United States were selected for the purpose of data visualization. The dataset was a subset of the ‘Final COR Port of Entry’ provided by Fidelity. It was converted into a dataframe using pandas in Python. Then, we used Plotly to turn the percent changes into bar chart. Finally, the codes were integrated into dash for the dashboard.
Final COR Port of Entry
Challenges I ran into
Very limited data on coronavirus COVID-19 since it has been a recent development that is being monitored closely. Many factors actually influence airline industry.
Accomplishments that I'm proud of
What I learned
Plot.ly, Dash, CSS, Python To stay calm and persevere even though it seemed like we were going in circle during the initial brainstorming phase.
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