Assessing flight trends
Wow! You've already extracted quite a bit of information from your flights_xts
data. Visualizing time series data - and various values derived from these data - is a critical component of any time series analysis, whether you are interested in stock returns, user retention, or opinion polls.
On the right you can see a slightly cleaned version of the plot you generated in the previous exercise. Which of the following is a reasonable conclusion to draw from this plot?
Before drawing any conclusions, be sure to familiarize yourself with the different axis scales produced by plot.zoo()
. For example, diverted flights are generally on a much smaller scale (0 - 0.4%) than delayed flights (0 - 30%).
This exercise is part of the course
Case Study: Analyzing City Time Series Data in R
Hands-on interactive exercise
Turn theory into action with one of our interactive exercises
