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Rolling window functions

Rolling averages can be used to 'smooth out' noisy data, where you want to show how quickly or slowly the values change on average.

In this exercise, you'll take the time series us_temperatures, containing hourly temperature readings from three cities in the United States. Using rolling windows, you'll calculate rolling statistics – a mean, maximum, and median – with different window widths to see the effects of changing the window size!

Important to note is that rolling medians require an odd window width!

The us_temperatures time series and the zoo and ggplot2 packages are available to you.

This exercise is part of the course

Manipulating Time Series Data in R

View Course

Hands-on interactive exercise

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

# Create and plot the rolling average
___(___) %>%
  autoplot() + 
  theme_light() + 
  labs(y = "Degrees Celsius")
Edit and Run Code