Why Does My Plot In R Change With My Viewer

R Programming

Have you ever noticed that your plot in R seems to change when you move or resize the viewer window? It can be quite frustrating, especially when you’re trying to create a visually appealing and accurate representation of your data. In this article, I’ll delve into the reasons behind this phenomenon and provide some insights into how to manage and control these changes.

The Issue: Plot Resizing in R

When working with R for data visualization, you may have experienced the unexpected behavior of plots resizing or appearing differently when the viewer window is manipulated. This is primarily due to the default settings of the graphics devices in R that respond to changes in the viewer size or window dimensions. As someone who values precision and consistency in data representation, I found this issue particularly vexing.

At first, I was puzzled by why my carefully crafted plots seemed to morph before my eyes as I adjusted the viewer window. It almost felt like my hard work was being undermined by a visual trickery. After some investigation and experimentation, I discovered the underlying reasons for this behavior and ways to mitigate its impact.

Understanding the Root Cause

The root of this issue lies in how R handles graphics devices and their response to changes in the viewer window. By default, R creates graphics on a “device” which corresponds to the viewer window. When the viewer window size changes, the graphics device automatically adjusts the plot to fit the new dimensions. This automatic resizing is the source of the problem, as it can lead to unexpected changes in the appearance of the plot.

Controlling the Behavior

To regain control over the plot’s dimensions and maintain its integrity, I explored various techniques and options available in R. One effective approach is to utilize the graphics parameters and functions to set explicit dimensions for the plot, overriding the automatic resizing behavior. By specifying the width and height of the plot in code using functions like par() and pdf(), I was able to establish consistent sizing and prevent unwanted alterations when manipulating the viewer window.

  • Using par(): The par() function in R allows for fine-grained control over graphical parameters, including plot dimensions. By setting parameters such as pin (plot dimensions in inches) and mai (plot margins), I could establish a stable and predictable plot layout.
  • Utilizing pdf(): When creating plots for output to a PDF file, the pdf() function enables specification of the dimensions, ensuring that the plot maintains its intended size regardless of changes to the viewer window.

Embracing Consistency and Precision

After implementing these adjustments and taking a more proactive approach to managing plot dimensions, I felt a profound sense of empowerment. No longer did my plots succumb to the whims of the viewer window; instead, they stood firm in their designated dimensions, reflecting the true essence of the underlying data.

Embracing the ability to control plot resizing in R has not only enhanced the visual appeal of my data visualizations but also instilled in me a greater appreciation for the technical nuances of graphical representation. Through this journey of discovery and mastery, I’ve come to recognize the importance of meticulous attention to detail and the gratification that accompanies achieving precision in data visualization.


In the realm of R data visualization, the issue of plot resizing in response to viewer window changes can be a source of frustration and dismay. However, with a deeper understanding of the underlying mechanisms and the utilization of targeted techniques to control plot dimensions, one can reclaim authority over the visual representation of data. By embracing consistency and precision, we elevate our data visualizations to a higher standard of excellence, transcending the whims of window manipulation and ensuring the faithful reflection of our insights.