Using merge() and plotting over time

Now that you have temperature data covering the same time period (2010-2015) at the same frequency (monthly) as your flights data, you are ready to merge.

To merge xts objects by column, you can use merge(). When two xts objects share the same periodicity, merge() is generally able to combine information into appropriate rows. Even when xts objects do not share the same periodicity, merge() will preserve the correct time ordering of those objects across disparate periods.

In this exercise, you'll merge your two xts objects by column and generate new plots exploring how flight delays relate to temperature. temps_monthly and flights_xts are available in your workspace.

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

Exercise instructions

Use merge() to combine flights_xts and temps_monthly. Because these xts objects share periodicity, your merge command should place temperature data in the appropriate row in your flights_xts object. Note that the order in which you list the objects to be merged determines where the columns will appear in the merged object. To keep things consistent, insert flights_xts first and temps_monthly second.
Examine the first few rows of your merged xts object (flights_temps) to confirm that the merge was successful. You should see the temperature data lined up with the flights data.
Use plot.zoo() to generate a single plot containing both the pct_delay and temps_monthly columns from flights_temps. Be sure to subset the relevant columns and specify plot.type as "single". Leave the lty argument as is.

Hands-on interactive exercise

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

# Use merge to combine your flights and temperature objects
flights_temps <- merge(___, ___)

# Examine the first few rows of your combined xts object


# Use plot.zoo to plot these two columns in a single panel
plot.zoo(___[,c("___", "___")], plot.type = "___", lty = lty)
legend("topright", lty = lty, legend = labels, bg = "white")

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

Current Exercise

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