In this course, you will strengthen your knowledge of time series topics through interactive exercises and interesting datasets. You’ll explore a variety of datasets about Boston, including data on flights, weather, economic trends, and local sports teams.
You've been hired to understand the travel needs of tourists visiting the Boston area. As your first assignment on the job, you'll practice the skills you've learned for time series data manipulation in R by exploring data on flights arriving at Boston's Logan International Airport (BOS) using xts & zoo.
In this chapter, you'll expand your time series data library to include weather data in the Boston area. Before you can conduct any analysis, you'll need to do some data manipulation, including merging multiple xts objects and isolating certain periods of the data. It's a great opportunity for more practice!
Now it's time to go further afield. In addition to flight delays, your client is interested in how Boston's tourism industry is affected by economic trends. You'll need to manipulate some time series data on economic indicators, including GDP per capita and unemployment in the United States in general and Massachusetts (MA) in particular.
Having exhausted other options, your client now believes Boston's tourism industry must be related to the success of local sports teams. In your final task on this project, your supervisor has asked you to assemble some time series data on Boston's sports teams over the past few years.
DatasetsFlights arriving at Boston Logan airportBoston monthly average visibilityWind speeds in BostonBoston monthly temperature dataUS and Massachusetts unemploymentUS GDP dataBoston-area sports teams data
PrerequisitesManipulating Time Series Data with xts and zoo in R
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