Comprehensive Guide to Combining Multiple Plots in ggplot2: Techniques and Best Practices

Introduction

In data visualization practice, there is frequent need to integrate graphical elements from different data sources into a single plotting area for comparative analysis. R's ggplot2 package, as a powerful graphics system, provides flexible mechanisms for multi-plot combination. This article systematically analyzes the technical implementation of ggplot2 multi-plot integration based on high-scoring solutions from Stack Overflow Q&A data, supplemented by auxiliary functions from the ggpubr package.

Problem Context and Core Requirements

The original problem involves two independent data frames visual1 and visual2, both containing ISSUE_DATE and COUNTED variables. The user wishes to integrate two scatter plots with their smoothing curves into a single graphic, while maintaining one set of data points in black and adjusting the other to a different color for distinction.

The initial separate plotting codes are:

ggplot(visual1, aes(ISSUE_DATE,COUNTED)) + geom_point() + geom_smooth(fill="blue", colour="darkblue", size=1)

and

ggplot(visual2, aes(ISSUE_DATE,COUNTED)) + geom_point() + geom_smooth(fill="red", colour="red", size=1)

Core Solution: Direct Layer Superposition

The best answer provides the most direct implementation approach—sequentially adding geometric objects from different data sources within the same ggplot object. This method is suitable for scenarios where data structures are similar but require independent control.

The complete code implementation is as follows:

p <- ggplot() +
  # Blue plot elements
  geom_point(data=visual1, aes(x=ISSUE_DATE, y=COUNTED)) + 
  geom_smooth(data=visual1, aes(x=ISSUE_DATE, y=COUNTED), fill="blue",
    colour="darkblue", size=1) +
  # Red plot elements
  geom_point(data=visual2, aes(x=ISSUE_DATE, y=COUNTED)) + 
  geom_smooth(data=visual2, aes(x=ISSUE_DATE, y=COUNTED), fill="red",
    colour="red", size=1)

Advantages of this approach include:

• Preservation of original data structure integrity without data preprocessing
• Precise control over graphical properties for each data source
• Clear code logic that is easy to understand and modify

However, this method also has limitations: inability to automatically generate grouped legends, requiring manual color explanations.

Advanced Solution: Dataset Integration and Group Mapping

When data sources share identical variable structures, a more elegant solution involves first merging the datasets, then utilizing ggplot2's grouping mechanism for graphical integration.

Data preprocessing steps:

visual1$group <- 1
visual2$group <- 2
visual12 <- rbind(visual1, visual2)

Integrated plotting code:

p <- ggplot(visual12, aes(x=ISSUE_DATE, y=COUNTED, group=group, col=group, fill=group)) +
      geom_point() +
      geom_smooth(size=1)

Notable advantages of this method:

• Automatic generation of grouped legends, enhancing graphic readability
• More concise code that aligns with ggplot2's syntactic philosophy
• Facilitation of subsequent graphic refinement and theme adjustments

Color Customization and Graphic Enhancement

For the color customization specified in the original requirement, precise control can be achieved through scale_color_manual() and scale_fill_manual() functions:

p + scale_color_manual(values = c("black", "red")) +
    scale_fill_manual(values = c("blue", "red"))

This maintains one set of data points in black while ensuring consistency between smoothing curve fill colors and border colors.

Extended Applications with ggpubr Package

The reference article demonstrates the powerful multi-plot layout capabilities of the ggpubr package. While the original problem focuses on element superposition within the same plotting area, practical applications often require arranging multiple independent graphics on the same page.

Basic multi-plot arrangement example:

library(ggplot2)
library(ggpubr)

# Create multiple base graphics
bxp <- ggplot(ToothGrowth, aes(x = dose, y = len)) + geom_boxplot()
dp <- ggplot(ToothGrowth, aes(x = dose, y = len)) + geom_dotplot(binaxis='y', stackdir='center')

# Use ggarrange for graphic arrangement
figure <- ggarrange(bxp, dp, labels = c("A", "B"), ncol = 2)

The ggarrange() function supports complex layout configurations including:

• Multi-row and multi-column grid layouts
• Graphic labels and annotations
• Special layouts with row and column spanning
• Multi-page graphic output

Technical Details and Best Practices

When implementing multi-plot combinations, several key technical considerations are essential:

Data Consistency Verification: Ensure merged data shares identical variable types and value ranges to avoid graphic distortion due to data discrepancies.

Graphical Element Hierarchy: In direct superposition methods, later-added elements overlay earlier ones, requiring careful attention to drawing order.

Color System Coordination: Employ harmonious color schemes that provide visual distinction between data sources while maintaining overall coherence.

Legend Management: In dataset integration methods, fine control over legend display and content can be achieved through the guides() function.

Performance Optimization Considerations

For large-scale datasets, graphic rendering performance becomes a critical factor:

• Perform appropriate sampling or aggregation before data merging
• Adjust span parameter in geom_smooth() to balance smoothness with computational overhead
• For static graphics, consider using static image formats rather than interactive graphics

Application Scenario Extensions

The techniques introduced in this article extend beyond simple dual-plot combinations to more complex scenarios:

• Multi-variable time series comparisons
• Visual comparisons between experimental and control groups
• Side-by-side display of different model fitting effects
• Spatial distribution comparisons across multiple data sources

Conclusion

ggplot2 offers multiple flexible strategies for multi-plot combination, ranging from simple element superposition to integration methods based on data grouping. Selecting the appropriate technical approach depends on specific data structure requirements and visualization objectives. Through the in-depth analysis and code examples provided in this article, readers should gain comprehensive mastery of ggplot2 multi-plot integration core technologies and be able to select optimal implementation schemes according to practical scenarios.

In practical applications, the dataset integration method is recommended as the primary approach, as it better aligns with ggplot2's design philosophy, automatically handles auxiliary elements like legends, and produces more professional and readable final graphics. For special layout requirements, extension packages like ggpubr provide powerful supplementary functionalities.