The Mystery of the Blank Graphs: Understanding ggplot and Plotting in R

Introduction

As a data scientist or researcher, creating visualizations to communicate complex insights is an essential skill. In this article, we’ll delve into the world of ggplot2, a popular R package for creating high-quality statistical graphics. We’ll explore why your graphs might be appearing blank when sourcing a script that includes plotting code.

Understanding ggplot2 and Plotting in R

ggplot2 is built on top of the grammar of graphics, a system introduced by Larry Edgeworth. This approach emphasizes the use of a consistent vocabulary to create visualizations. The ggplot() function serves as the foundation for creating plots, allowing you to specify various elements such as aesthetics (e.g., color, shape), geometry (e.g., points, lines), and layers.

In R, when you run code that includes plotting commands (like ggplot(), geom_bar(), etc.), the graphics device is automatically created, the plot is rendered, and the resulting image is stored in a temporary file. This process is often referred to as “printing” or “displaying” the plot.

However, when sourcing another script that includes plotting code, the situation changes. In this scenario, the sourced script doesn’t have access to the R environment’s graphics context. As a result, the plots aren’t created or displayed.

The Role of dev.off()

In your original script, you’re using pdf() and then closing the device with dev.off(). While this is typically used in isolation (e.g., when printing a single plot), it can also affect how your sourced script behaves. When dev.off() is called within a sourced script, it closes the graphics device, preventing any subsequent plots from being created.

However, here’s the catch: pdf() and dev.off() are not equivalent to calling print() explicitly. In other words, just because you open a PDF device doesn’t mean that the plot will be automatically printed or displayed when sourcing another script.

The Solution: Wrapping ggplot Code with print()

To resolve this issue, simply wrap your ggplot code within a call to print(ggplot()). This ensures that the graphics context is maintained, and the plot is created and displayed correctly.

Here’s an example:

ggplot(m1table, aes(y=GNIpc2005, fill=Level)) + 
  geom_bar(aes(x=Country1), data=m1table, stat="identity") +
  coord_flip() +
  ggtitle("GNI Per Capita, 2005") +
  xlab("Country") +
  ylab("GNI per capita, Atlas method (current US$)") +
print(ggplot(m1table, aes(y=GNIpc2005, fill=Level)) + 
      geom_bar(aes(x=Country1), data=m1table, stat="identity") +
      coord_flip() +
      ggtitle("GNI Per Capita, 2005") +
      xlab("Country") +
      ylab("GNI per capita, Atlas method (current US$)"))

By doing so, you’re effectively creating the graphics context and printing the plot when sourcing the script.

Additional Considerations

When working with sourced scripts, it’s essential to understand how R’s graphics environment works. This includes using print() and dev.off() judiciously, as well as considering factors like output device (e.g., PDF, PNG), resolution, and file naming conventions.

Additionally, be aware that different plotting libraries in R (e.g., png(), Scatterplot3D()) work differently than ggplot2. Familiarize yourself with these alternatives to ensure your visualizations are displayed correctly.

Troubleshooting Tips

If you’re still experiencing issues with blank graphs when sourcing a script, try the following:

• Verify that the sourced script is being executed within an R environment.
• Check that print() and dev.off() are not being called prematurely or unexpectedly.
• Inspect the R console for any error messages related to graphics rendering.

Conclusion

Creating high-quality statistical graphics in R requires a solid understanding of ggplot2, as well as awareness of R’s graphics environment. By wrapping your ggplot code within print(), you can ensure that plots are created and displayed correctly when sourcing scripts. Remember to consider factors like output device, resolution, and file naming conventions to produce professional-grade visualizations.

Whether you’re a seasoned data scientist or just starting out with R, mastering the art of plotting will help you communicate complex insights more effectively. With practice and patience, you’ll become proficient in using ggplot2 and other R libraries to create stunning statistical graphics.

Last modified on 2024-08-26