Calculate and plot a rolling average

The final baseball indicator you'd like to generate is the L10, or the moving win/loss average from the previous ten games. While the cumulative win/loss average tells you how the team is doing overall, the L10 indicator provides a more specific picture of the team's recent performance. Beyond the world of sports, this measure is comparable to a financial indicator focused on recent portfolio performance.

To generate a rolling win/loss average, return to the rollapply() command used in the previous chapter. In this case, you'll want to apply the mean function to the last 10 games played by the Red Sox at any given time during the 2013 season.

The redsox_xts object, including the win_loss column, is available in your workspace.

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

Exercise instructions

Generate a new xts object containing only the 2013 season. Call this object redsox_2013.
Use rollapply() to calculate your lastten_2013 indicator based on the win_loss column in redsox_2013. Set the width equal to 10 to include the last ten games played by the Red Sox and set the FUN argument to mean to generate an average of the win_loss column.
Use plot.xts() to view your new indicator during the 2013 season. Leave the ylim argument as is in the prewritten code.

Hands-on interactive exercise

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

# Select only the 2013 season
redsox_2013 <- ___["___"]

# Use rollapply to generate the last ten average
lastten_2013 <- rollapply(___$___, width = ___, FUN = ___)

# Plot the last ten average during the 2013 season
plot.xts(___, ylim = c(0, 1))

Edit and Run Code

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

IntermediateSkill Level

4.9+

Start Course for 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.

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

Current Exercise

Exercise 6: Indexing commands in xts Exercise 7: Extract weekend games Exercise 8: Calculate a rolling average across all sports Exercise 9: Viewing sports trends Exercise 10: Congratulations