Selecting a window from a time series

Real-life time series datasets can span decades, as in the case of long-term climate and meteorological studies, financial records, or stock market data. By creating a window from a time series, you can hone in on a specific region of important data or exclude parts of the data that may not be necessary to consider.

In this exercise, you'll select a window from the dow_jones time series, containing real-world stocks information from the Dow Jones Industrial Average – the stock prices from 30 major companies based in the United States.

The dow_jones dataset and the ggplot2, zoo, and lubridate packages are available to you.

This exercise is part of the course

Manipulating Time Series Data in R

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Exercise instructions

Create a window from the dow_jones dataset which starts on January 1, 2019, and ends on January 1, 2021; assign this to dow_jones_window.
Generate an autoplot for the original dow_jones dataset with the y-axis label "Price (USD)".
Generate an autoplot for the dow_jones_window dataset with the y-axis label "Price (USD)".

Hands-on interactive exercise

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

# Create a window from dow_jones
___ <- ___

# Create an autoplot from the original dow_jones
autoplot(___) + 
  labs(___)

# Create an autoplot from dow_jones_window
autoplot(___) + 
  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!