Calculate a rolling average across all sports

Now that you've mastered subsetting your data to include only weekend games, your client would like you to take a different approach. Perhaps Boston's tourism industry receives a boost when local sports teams win more games at home.

Instead of focusing on weekend games, you are tasked with generating a rolling win/loss average focused on games played in Boston. To produce this indicator, you'll return to the rollapply() command used above, this time applying your calculation to all Boston-area sports teams but subsetting to include only games played at home.

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Case Study: Analyzing City Time Series Data in R

Anleitung zur Übung

Subset your sports data to include only data from games played in Boston (homegame = 1) using the data[column == x] format. Save this new object as homegames.
Use rollapply() to calculate the win/loss average of the last 20 homegames by Boston sports teams. You'll need to specify the win_loss column of your homegames data, set the width to 20, and set the FUN argument to mean. Save this indicator to your homegames object as win_loss_20.
Use a similar call to rollapply() to calculate a 100 game moving win/loss average. Save this indicator to your homegames object as win_loss_100.
Use plot.zoo() to visualize both indicators. Be sure to select the win_loss_20 and win_loss_100 columns and set the plot.type argument to "single" to view both in the same panel. Leave the lty and lwd arguments as they are.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Generate a subset of sports data with only homegames
homegames <- sports[sports$___ == ___]

# Calculate the win/loss average of the last 20 home games
homegames$win_loss_20 <- rollapply(___$___, width = ___, FUN = ___)

# Calculate the win/loss average of the last 100 home games
homegames$win_loss_100 <- 

# Use plot.xts to generate
plot.zoo(___[, c("___", "___")], plot.type = "___", lty = lty, lwd = lwd)

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Case Study: Analyzing City Time Series Data in R

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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 Exercise 8: Calculate a rolling average across all sports

Aktuelle Übung

Exercise 9: Viewing sports trends Exercise 10: Congratulations