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  3. Case Study: Analyzing City Time Series Data in R

Exercise

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.

Instructions

100 XP
  • 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.