Replicating a Facet Chart from the Forecast Package as a ggplot2 Object in R

Introduction

The forecast package in R provides an easy-to-use interface for making forecasts using various models, including ARIMA and exponential smoothing. One of its useful features is the ability to generate faceted plots that allow for easy comparison of different components of the forecast model. However, when using the forecast package with ggplot2, it can be challenging to replicate these faceted charts as a standalone ggplot2 object.

In this article, we’ll explore how to create a replica of the facet chart generated by the forecast package as a ggplot2 object. We’ll break down the process into manageable steps and provide explanations for each part.

Prerequisites

To follow along with this tutorial, you should have the following R packages installed:

fabletools
forecast
ggplot2

If these packages are not already installed in your R environment, you can install them using the following command:

install.packages(c("fabletools", "forecast", "ggplot2"))

Step 1: Load Required Libraries

First, we need to load the required libraries that will be used throughout this tutorial.

library(fabletools)
library(forecast)
library(ggplot2)

Step 2: Generate Forecast Object

Next, we’ll generate a forecast object using the fdeaths dataset and the forecast function from the forecast package.

Test_plt <- fdeaths |> 
  forecast()

This will create a forecast object called Test_plt.

Step 3: Create Residuals Object

We need to extract the residuals from the forecast object using the residuals() function. The residuals are the differences between the actual and predicted values in the forecast model.

Test_plt_residuals <- Test_plt$residuals

Step 4: Create Autocorrelation Function Plot

We’ll create an autocorrelation function plot using the ggAcf() function from the ggplot2 package. This will give us a plot that shows how the residuals change over time.

acfplot <- ggAcf(Test_plt_residuals, lag.max = 24) +
  labs(title = NULL)

This plot shows the autocorrelation function of the residuals, which can be useful for identifying any patterns or structure in the data.

Step 5: Create Histogram Plot

We’ll create a histogram plot using the gghistogram() function from the ggplot2 package. This will give us a plot that shows the distribution of the residuals.

histplot <- gghistogram(Test_plt_residuals, add.normal = TRUE, add.rug = TRUE) + 
  labs(x = "residuals")

This plot shows the histogram of the residuals, which can be useful for identifying any outliers or skewness in the data.

Finally, we’ll create a facet chart using the patchwork package. This will give us a plot that combines the autocorrelation function plot and the histogram plot into a single faceted chart.

library(patchwork)

tsplot <- autoplot(Test_plt_residuals) +
  geom_point(size = 0.5) +
  labs(y = NULL)

facets <- tsplot / (acfplot + histplot)

This will create a facet chart that combines the autocorrelation function plot and the histogram plot into a single graph.

Conclusion

In this tutorial, we’ve demonstrated how to replicate a faceted chart generated by the forecast package as a ggplot2 object. We broke down the process into manageable steps and provided explanations for each part. By following along with these steps, you should be able to create your own faceted charts using the forecast package and ggplot2.

Additional Resources

fabletools documentation: https://github.com/mbreuninger/fabletools
forecast documentation: https://robinloznica.github.io/forecasting/
ggplot2 documentation: http://ggplot2.tidyverse.org/

Last modified on 2024-08-02

Replicating a Facet Chart from the Forecast Package as a ggplot2 Object