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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Description du cours
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.
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Analyste quantitatif en R
Aller à la pisteSéries chronologiques en R
Aller à la piste- 1
Flight Data
GratuitYou'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.
Review xts fundamentals50 xpIdentify the time series50 xpFlight data100 xpPick out the xts object50 xpEncoding your flight data100 xpManipulating and visualizing your data50 xpExploring your flight data100 xpVisualize flight data100 xpCalculate time series trends100 xpSaving and exporting xts objects50 xpAssessing flight trends50 xpSaving time - I100 xpSaving time - II100 xp - 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!
Merging time series data by row50 xpExploring temperature data100 xpNext steps - I50 xpMerging using rbind()100 xpVisualizing Boston winters100 xpMerging time series data by column50 xpSubsetting and adjusting periodicity100 xpGenerating a monthly average100 xpUsing merge() and plotting over time100 xpAre flight delays related to temperature?50 xpTime series data workflow50 xpNext steps - II50 xpExpanding your data100 xpAre flight delays related to visibility or wind?50 xp - 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.
Handling missingness50 xpExploring economic data100 xpReplace missing data - I100 xpReplace missing data - II100 xpEstimating missing GDP50 xpLagging and differencing50 xpExploring unemployment data100 xpLagging unemployment100 xpDifferencing unemployment100 xpRolling functions50 xpAdd a discrete rolling sum to GDP data100 xpAdd a continuous rolling average to unemployment data100 xpManipulating MA unemployment data100 xp - 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.
Advanced features of xts50 xpEncoding and plotting Red Sox data100 xpCalculate a closing average100 xpCalculate and plot a seasonal average100 xpCalculate and plot a rolling average100 xpIndexing commands in xts50 xpExtract weekend games100 xpCalculate a rolling average across all sports100 xpViewing sports trends50 xpCongratulations50 xp
Formation de 2 personnes ou plus ?
Donnez à votre équipe l’accès à la plateforme DataCamp complète, y compris toutes les fonctionnalités.Dans les titres suivants
Analyste quantitatif en R
Aller à la pisteSéries chronologiques en R
Aller à la pisteensembles de données
Flights arriving at Boston Logan airportBoston monthly average visibilityWind speeds in BostonBoston monthly temperature dataUS and Massachusetts unemploymentUS GDP dataBoston-area sports teams dataprérequis
Manipulating Time Series Data in R
Matt IsaacsPolitical Science PhD interested in data science in defense, security, and international relations
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