paid course

Manipulating Time Series Data in R: Case Studies

  • 4 hours
  • 12 Videos
  • 50 Exercises
  • 2,002 Participants
  • 4000 XP

This course is part of these tracks:

Lore Dirick
Lore Dirick

Data Scientist at DataCamp

Lore is a data scientist at DataCamp. 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. At DataCamp, she is in charge of building out the Applied Finance curriculum.

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Matt Isaacs
Matt Isaacs

Senior Analytics Fellow, McKinsey & Company

Matthew Isaacs is a course development intern at DataCamp and a PhD candidate in Political Science at Brandeis University. His research uses time series methods to understand political violence and civil war.

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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.

  1. 1

    Flight Data

    Free

    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.

  2. 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!

  3. 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.

  4. 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.