Extract weekend games

After calculating some useful indicators from your Red Sox data, it's time to step back and explore data from other Boston sports teams. Specifically, you've collected additional data on the New England Patriots (football), the Boston Bruins (hockey), and the Boston Celtics (basketball). Data for these teams, along with your redsox data, have been merged into a single xts object, sports, which now contains data on all games played by Boston-area sports teams from 2010 through 2015.

Before conducting further analysis, you want to refine your data into a few potentially useful subsets. In particular, it may be helpful to focus exclusively on weekend games involving Boston sports teams.

To identify games based on the day of the week, you should use the .indexwday() command, which tells you the day of the week of each observation in your xts object. These values range from 0-6, with Sunday equal to 0 and Saturday equal to 6.

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

Exercise instructions

Practice with the xts indexing commands by extracting the day of the week of each observation in your sports data using .indexwday(). Save these values to weekday and view the first few rows of this weekday object using head().
Create an index of weekend observations by combining the which() command with two calls to .indexwday(). The goal here is to extract only those dates falling on a Saturday or a Sunday. Save this index as weekend.
Generate a new xts object (weekend_games) containing only games falling on a weekend. Use head() to view the first few rows of your new weekend_games object.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Extract the day of the week of each observation
weekday <- .indexwday(___)
head(___)

# Generate an index of weekend dates
weekend <- which(.indexwday(___) == ___ | .indexwday(___) == ___)

# Subset only weekend games
weekend_games <- sports[___]
head(___)

Edit and Run Code

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

IntermediateSkill Level

4.9+

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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.

Exercise 1: Review xts fundamentals Exercise 2: Identify the time series Exercise 3: Flight data Exercise 4: Pick out the xts object Exercise 5: Encoding your flight data Exercise 6: Manipulating and visualizing your data Exercise 7: Exploring your flight data Exercise 8: Visualize flight data Exercise 9: Calculate time series trends Exercise 10: Saving and exporting xts objects Exercise 11: Assessing flight trends Exercise 12: Saving time - I Exercise 13: Saving time - II

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!

Exercise 1: Merging time series data by row Exercise 2: Exploring temperature data Exercise 3: Next steps - I Exercise 4: Merging using rbind()Exercise 5: Visualizing Boston winters Exercise 6: Merging time series data by column Exercise 7: Subsetting and adjusting periodicity Exercise 8: Generating a monthly average Exercise 9: Using merge() and plotting over time Exercise 10: Are flight delays related to temperature?Exercise 11: Time series data workflow Exercise 12: Next steps - II Exercise 13: Expanding your data Exercise 14: Are flight delays related to visibility or wind?

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.

Exercise 1: Handling missingness Exercise 2: Exploring economic data Exercise 3: Replace missing data - I Exercise 4: Replace missing data - II Exercise 5: Estimating missing GDP Exercise 6: Lagging and differencing Exercise 7: Exploring unemployment data Exercise 8: Lagging unemployment Exercise 9: Differencing unemployment Exercise 10: Rolling functions Exercise 11: Add a discrete rolling sum to GDP data Exercise 12: Add a continuous rolling average to unemployment data Exercise 13: Manipulating MA unemployment 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.

Exercise 1: Advanced features of xts Exercise 2: Encoding and plotting Red Sox data Exercise 3: Calculate a closing average Exercise 4: Calculate and plot a seasonal average Exercise 5: Calculate and plot a rolling average Exercise 6: Indexing commands in xts Exercise 7: Extract weekend games

Current Exercise

Exercise 8: Calculate a rolling average across all sports Exercise 9: Viewing sports trends Exercise 10: Congratulations