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Case Study: Analyzing City Time Series Data in R

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

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4 Hours12 Videos50 Exercises11,803 Learners3950 XPQuantitative Analyst with R TrackTime Series with R Track

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Course Description

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.
  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.

    Play Chapter Now
    Review xts fundamentals
    50 xp
    Identify the time series
    50 xp
    Flight data
    100 xp
    Pick out the xts object
    50 xp
    Encoding your flight data
    100 xp
    Manipulating and visualizing your data
    50 xp
    Exploring your flight data
    100 xp
    Visualize flight data
    100 xp
    Calculate time series trends
    100 xp
    Saving and exporting xts objects
    50 xp
    Assessing flight trends
    50 xp
    Saving time - I
    100 xp
    Saving time - II
    100 xp
  2. 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!

    Play Chapter Now
    Merging time series data by row
    50 xp
    Exploring temperature data
    100 xp
    Next steps - I
    50 xp
    Merging using rbind()
    100 xp
    Visualizing Boston winters
    100 xp
    Merging time series data by column
    50 xp
    Subsetting and adjusting periodicity
    100 xp
    Generating a monthly average
    100 xp
    Using merge() and plotting over time
    100 xp
    Are flight delays related to temperature?
    50 xp
    Time series data workflow
    50 xp
    Next steps - II
    50 xp
    Expanding your data
    100 xp
    Are flight delays related to visibility or wind?
    50 xp
  3. 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.

    Play Chapter Now
    Handling missingness
    50 xp
    Exploring economic data
    100 xp
    Replace missing data - I
    100 xp
    Replace missing data - II
    100 xp
    Estimating missing GDP
    50 xp
    Lagging and differencing
    50 xp
    Exploring unemployment data
    100 xp
    Lagging unemployment
    100 xp
    Differencing unemployment
    100 xp
    Rolling functions
    50 xp
    Add a discrete rolling sum to GDP data
    100 xp
    Add a continuous rolling average to unemployment data
    100 xp
    Manipulating MA unemployment data
    100 xp
  4. 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.

    Play Chapter Now
    Advanced features of xts
    50 xp
    Encoding and plotting Red Sox data
    100 xp
    Calculate a closing average
    100 xp
    Calculate and plot a seasonal average
    100 xp
    Calculate and plot a rolling average
    100 xp
    Indexing commands in xts
    50 xp
    Extract weekend games
    100 xp
    Calculate a rolling average across all sports
    100 xp
    Viewing sports trends
    50 xp
    Congratulations
    50 xp

In the following tracks

Quantitative Analyst with RTime Series with R

Datasets

Flights arriving at Boston Logan airportBoston monthly average visibilityWind speeds in BostonBoston monthly temperature dataUS and Massachusetts unemploymentUS GDP dataBoston-area sports teams data

Prerequisites

Manipulating Time Series Data with xts and zoo in R
Lore Dirick Headshot

Lore Dirick

Director of Data Science Education 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 and is now Director of Data Science Education at Flatiron School, a coding school with branches in 8 cities and online programs.
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Matt Isaacs Headshot

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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