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Plotting an aggregated time series with ggplot2

Aggregating data allows you to uncover general patterns and trends in your data, but can often lead to a loss of information and context. However, using methods from ggplot2 can give some context back to the aggregated data.

In this exercise, you'll practice plotting weekly aggregated temperature readings, weekly_avg, alongside the original, unaggregated time series, hourly_temperature, which represents temperature readings for an entire year, sampled every hour.

The time series hourly_temperature and weekly_avg, as well as the ggplot2 and zoo packages are available to you.

Diese Übung ist Teil des Kurses

<Kurs>Manipulating Time Series Data in R</Kurs>
Kurs ansehen

Übungsanweisungen

  • Using the ggplot() function, plot the hourly_temperature time series as a line plot.

  • Add the y-axis label "Degrees Celsius" and the title "Temperature Readings".

  • Complete the second call to geom_line() and aes() to overlay the weekly_avg time series on your plot.

  • Change the line color of the weekly aggregate to red and the size of the line to 2.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Create a plot of the hourly_temperature time series
ggplot(___, aes(___)) + 
  ___ + 
  scale_y_continuous() + 
  
  # Add axis label and title
  labs(___) + 

  # Add a line plot for the weekly aggregated time series
  geom_line(data = ___, aes(___),
            
  # Color the aggregated line in red, with a size of 2
            ___)
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