Visualize flight data

Now that you have a grip on your data, the next step is to visualize trends in your data over time. In this exercise, you'll plot the flights_xts data over time using a few different methods for plotting time series data.

Often the simplest way to plot xts objects is to use plot.xts(), which requires only a single argument for the y-axis in the plot. The x-axis is supplied by the time index in your xts object.

For more complicated plots, you may want to use plot.zoo(), which allows you to include multiple columns of data. In particular, the plot.type argument allows you to specify whether you'd like your data to appear in a single panel ("single") or multiple panels ("multiple"). This can be useful when comparing multiple columns of data over time.

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

Exercise instructions

Use plot.xts() to view the total monthly flights into BOS (total_flights) over time. This command only requires you to specify the data for the y-axis, although you do need to be specific about which column of data you want to plot.
Use another call to plot.xts() to produce a plot of monthly delayed flights into BOS over time.
Generate a plot of all four time series columns in flights_xts using plot.zoo(). Set the plot.type argument to "multiple" to produce a plot with four different panels. Leave the ylab argument as is.
Put all four plots on a single panel using another call to plot.zoo(). Leave the lty argument and the legend function as they are.

Hands-on interactive exercise

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

# Use plot.xts() to view total monthly flights into BOS over time
plot.xts(___)

# Use plot.xts() to view monthly delayed flights into BOS over time


# Use plot.zoo() to view all four columns of data in their own panels
plot.zoo(___, plot.type = "___", ylab = labels)

# Use plot.zoo() to view all four columns of data in one panel
plot.zoo(___, plot.type = "___", lty = lty)
legend("right", lty = lty, legend = labels)

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

Current Exercise

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 Exercise 9: Viewing sports trends Exercise 10: Congratulations