Reconfiguring keys in tsibbles (fpp3 package): A Guide to Alternative Approaches for Data Analysis

Reconfiguring keys in a tsibble (fpp3 package)

In this article, we will explore how to reconfigure the keys of a tsibble object stored using the fpp3 package in R after performing column selection operations.

Understanding tsibbles and their keys

A tsibble is a type of time series data structure in R that combines the flexibility of tidiers with the performance of data frames. It stores both time series data and auxiliary metadata as separate columns, allowing for easier data manipulation and analysis.

Each tsibble object has one or more keys defined based on certain columns. These keys are used to identify specific rows within a tsibble and can be manipulated just like any other column in R.

The issue with selecting key columns

When you select columns from a tsibble using the select() function, the resulting tsibble will still maintain its original keys even if those selected columns are no longer included. This can lead to unexpected behavior or errors when working with your data.

For example, consider the following code:

library(fpp3)
PBS <- key(PBS)

# Select only certain columns
PBS |> select(Month, ATC1, Cost) 

# Attempting to print the keys will result in an error
 PBS |> print()

As you can see, attempting to print the keys of the resulting tsibble object results in an error.

Calculating totals instead of removing key columns

Instead of trying to remove or modify the key columns directly, we need to consider alternative approaches for our analysis or visualization needs. One possible solution is to calculate the total cost for each ATC1 using the group_by() and summarise() functions.

Here’s an example code snippet that demonstrates how this can be done:

# Group by ATC1 and summarize the total cost
PBS |> group_by(ATC1) |> summarise(Cost = sum(Cost))

# This will produce a tsibble object with only the desired columns

This approach avoids modifying the key columns directly and instead focuses on calculating new values for each group.

Conclusion

Reconfiguring keys in a tsibble (fpp3 package) requires careful consideration of the data structure and any potential modifications. Instead of trying to remove or modify key columns, we should focus on alternative approaches that achieve our desired outcome.

In this article, we explored how to calculate totals instead of removing key columns using the group_by() and summarise() functions. By adopting this approach, you can ensure that your data analysis is accurate and efficient.

Example use case

Suppose we have a tsibble object called PBS containing monthly sales data for various ATC1 codes. We want to calculate the total revenue for each ATC1 code without modifying the key columns.

We can achieve this using the following code:

# Group by ATC1 and summarize the total cost
PBS |> group_by(ATC1) |> summarise(Cost = sum(Cost))

# This will produce a tsibble object with only the desired columns

By adopting this approach, we can ensure that our data analysis is accurate and efficient without modifying the key columns directly.

Additional tips and resources

For more information on working with tsibbles in R, check out the official fpp3 documentation: https://fpp.github.io/fpp3-book/
The reprex package can help you create reproducible examples for your data analysis: https://reprex.tidyverse.org/
If you’re new to R or tidiers, consider taking an online course or attending a workshop to learn more about working with time series data structures.

Last modified on 2024-03-12