Finding overlapping indices

Time series are said to overlap when there are observations in both time series with the same index, meaning they occur at the same point in time.

By creating a subset using the %in% operator, the overlapping points can be filtered out of one of the time series, allowing the two datasets to be combined.

In this exercise, you'll take two time series, coffee and coffee_overlap, and remove the elements that overlap.

The datasets coffee and coffee_overlap, as well as the zoo and lubridate packages, are available to you already.

This is a part of the course

“Manipulating Time Series Data in R”

View Course

Exercise instructions

Use the value matching operator %in% to determine the indices of coffee_overlap that overlap with the indices of coffee.
Use square brackets ([, ]) and the negation operator (!) to extract the values of coffee_overlap which are not part of overlapping_index.
Combine the coffee time series with coffee_subset and view the resulting autoplot.

Hands-on interactive exercise

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

# Determine the overlapping indexes
overlapping_index <-
  ___ %in% ___

# Create a subset of the elements which do not overlap
coffee_subset <- ___[___]

# Combine the coffee time series and the new subset
coffee_combined <- ___

autoplot(coffee_combined)

Edit and Run Code

Manipulating Time Series Data in R

IntermediateSkill Level

4.8+

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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!