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.

This exercise is part of the course

Manipulating Time Series Data in R

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Exercise instructions

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.

Hands-on interactive exercise

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

# 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
            ___)

Edit and Run Code

Manipulating Time Series Data in R

IntermediateSkill Level

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You’ll learn to create “rolling” windows of a time series that move, or "roll" along with data, making it possible to summarize trends in the data across time, such as the average over success months of observations or the sum over several weeks of sales. Overall summary statistics, like mean, median, sum, maximum, and so on, do not always provide insight into how data changes over time, and rolling windows will allow you to compute statistics dynamically. In addition to rolling windows, you will also learn how to create expanding windows, which show how these summary statistics approach their final value.

Exercise 1: What is a rolling window?Exercise 2: Rolling window functions Exercise 3: Rolling windows versus aggregation Exercise 4: Applying functions to rolling windows Exercise 5: Rolling minimum Exercise 6: Rolling apply with a custom function Exercise 7: Expanding windows Exercise 8: Rolling versus expanding windows Exercise 9: Expanding sum Exercise 10: Expanding mean Exercise 11: Congratulations!