Manipulating Time Series Data in R: Case Studies
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
- 4 hours
- 12 Videos
- 50 Exercises
- 6,309 Participants
- 4,000 XP
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Course Description
This follow-up course on manipulating time series data in R does not cover new data manipulation concepts. Instead, you will strengthen your knowledge of the topics covered in Manipulating Time Series Data in R with xts & zoo using new exercises and interesting datasets.
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1
Flight Data
FreeYou'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.
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Economic Data
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.
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Weather Data
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!
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Sports Data
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.
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1
Flight Data
FreeYou'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.
-
Weather Data
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!
-
Economic Data
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.
-
Sports Data
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.
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Lore Dirick
Manager of Data Science Curriculum at Flatiron School
Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is now the Manager of Data Science Curriculum at Flatiron School, a coding bootcamp in NYC.
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Matt Isaacs
Political Science PhD interested in data science in defense, security, and international relations
Matt Isaacs is a former Course Development Intern at DataCamp . Matt holds a PhD in Political Science from Brandeis University and has extensive experience in applied data science across the public sector with a focus on analytics in defense, security, and international relations.
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