Calculate and plot a seasonal average

In the previous exercise you used endpoints() and period.apply() to quickly calculate the win/loss average for the Boston Red Sox at the end of each season. But what if you need to know the cumulative average throughout each season? Statisticians and sports fans alike often rely on this average to compare a team with its rivals.

To calculate a cumulative average in each season, you'll need to return to the split-lapply-rbind formula practiced in Chapter Three. First, you'll split the data by season, then you'll apply a cumulative mean function to the win_loss column in each season, then you'll bind the values back into an xts object.

A custom cummean() function, which generates a cumulative sum and divides by the number of values included in the sum, has been generated for you. The redsox_xts data, 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

Use split() to break up the redsox_xts data into seasons (in this case, years). Assign this to redsox_seasons.
Use lapply() to calculate the cumulative mean for each season. For this exercise, a cummean() function has been designed which calculates the sum (using cumsum()) and divides by the number of entries in the sum (using seq_along()). Save this data to redsox_ytd.
Use do.call() with rbind to convert your list output to a single xts object (redsox_winloss) which contains the win/loss average throughout each season.
Use plot.xts() to view the cumulative win/loss average during the 2013 season. Leave the ylim argument as is in your prewritten code.

Hands-on interactive exercise

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

# Split redsox_xts win_loss data into years 
redsox_seasons <- split(___$___, f = "___")

# Use lapply to calculate the cumulative mean for each season
redsox_ytd <- lapply(___, cummean)

# Use do.call to rbind the results
redsox_winloss <- do.call(___, ___)

# Plot the win_loss average for 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+

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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

Current Exercise

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 Exercise 9: Viewing sports trends Exercise 10: Congratulations