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Case Study: Analyzing City Time Series Data in R

IntermediarNivel de competențe

Actualizat 01.2026

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

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RProbability & Statistics

4 h

12 videoclipuri

50 Exerciții

3,950 XP

14,090

Certificat de realizare

Îndrăgit de cursanți din mii de companii

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Descrierea cursului

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.

Cerințe prealabile

Manipulating Time Series Data in R

1

Flight Data

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.

Review xts fundamentals

Identify the time series

Flight data

Pick out the xts object

Encoding your flight data

Manipulating and visualizing your data

Exploring your flight data

Visualize flight data

Calculate time series trends

Saving and exporting xts objects

Assessing flight trends

Saving time - I

Saving time - II

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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 row

Exploring temperature data

Next steps - I

Merging using rbind()

Visualizing Boston winters

Merging time series data by column

Subsetting and adjusting periodicity

Generating a monthly average

Using merge() and plotting over time

Are flight delays related to temperature?

Time series data workflow

Next steps - II

Expanding your data

Are flight delays related to visibility or wind?

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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 missingness

Exploring economic data

Replace missing data - I

Replace missing data - II

Estimating missing GDP

Lagging and differencing

Exploring unemployment data

Lagging unemployment

Differencing unemployment

Rolling functions

Add a discrete rolling sum to GDP data

Add a continuous rolling average to unemployment data

Manipulating MA unemployment data

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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 xts

Encoding and plotting Red Sox data

Calculate a closing average

Calculate and plot a seasonal average

Calculate and plot a rolling average

Indexing commands in xts

Extract weekend games

Calculate a rolling average across all sports

Viewing sports trends

Congratulations

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Case Study: Analyzing City Time Series Data in R

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