zoo and ggplot2

zoo objects, much like data frames, can be visualized with functions from the ggplot2 package. While this course doesn't go into too much detail regarding ggplot2 plots and functions, it's nonetheless helpful to review the syntax for more advanced plotting techniques than using autoplot() alone.

In this exercise, you'll explore using zoo and ggplot2 together. You'll take the card_prices time series from the previous exercise and generate a ggplot, complete with proper labels, a title, and a theme!

zoo, ggplot2, and card_prices are available to use.

This exercise is part of the course

Manipulating Time Series Data in R

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Exercise instructions

Within the call to aes(), enter the appropriate mappings to the x and y axes.
Plot the time series as a line, with the color in red.
Use the light theme from ggplot2
Include the axis labels and title: "Index" for the x-axis, "Price (EUR)" for the y-axis, and "Daily Card Prices for Trading Card Game" for the title.

Hands-on interactive exercise

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

# Enter the x and y axis mapping aesthetics
ggplot(card_prices, aes(x = ___, y = ___)) + 
  scale_y_continuous() + 
  # Plot the data with a red-colored line
  ___ + 

  # Use the light theme
  ___ + 

  # Enter the appropriate axis labels and title
  labs(
    ___
  )

Edit and Run Code

Manipulating Time Series Data in R

IntermediateSkill Level

4.9+

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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!