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  3. Case Study: Analyzing City Time Series Data in R
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Next steps - II

Your temperature data revealed a few potential avenues for exploring the causes of flight delays and cancellations. However, your client is insisting that flight arrival patterns in Boston are influenced by visibility and wind, not temperature. Before moving forward, you'll need to collect more data.

After conducting extensive research, you've identified some relevant data on weekly average visibility and wind speed in the Boston area. Which of the following steps would you take before merging these data with your existing monthly xts object, flights_temps?

  1. Encode the data to an xts object with a time-based index.
  2. Convert the data to monthly periodicity using to.period() with the first observation per week.
  3. Make sure each data object has only a single column of information.
  4. Convert the data to monthly periodicity using split() and lapply() to generate monthly averages.
  5. Check the periodicity and duration of your xts objects before using merge().
  6. Remove the existing temperature information from flights_temps before using merge().

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