Creating Cohesive Plots with ggplot2

In the world of data visualization, ggplot2 in R has established itself as a powerful tool for creating flexible and customizable graphics. This blog post dives into the aspects of creating cohesive plots using ggplot2, a part of the tidyverse collection. We’ll cover everything from prerequisites like required packages and demo datasets, to advanced techniques such as using a common legend for combined plots, aligning plot panels, and inserting textual or graphical elements within plots. By the end of this guide, you’ll have a comprehensive understanding of how to organize and present your data visually in a manner that resonates clearly with audiences. Whether you’re a beginner or someone looking to polish your ggplot2 skills, this post is your roadmap to proficient data visualization.

Prerequisites

Required R package

Before we dive into creating plots, ensure that you have the necessary R packages. The primary package is ggplot2, part of the tidyverse collection. You can install it by executing

install.packages("ggplot2")

. Apart from ggplot2, several other packages enhance its functionality, such as dplyr for data manipulation, and ggpubr, cowplot, and gridExtra for advanced plot arranging.

To work efficiently, we recommend installing the whole tidyverse suite, which includes ggplot2 and other complementary packages. This can be done with

install.packages("tidyverse")

. Also, updating your R environment to the latest version is always a good practice to leverage new features and performance improvements.

Demo data sets

To illustrate different plotting techniques, we’ll use demo datasets available within R and ggplot2. The

mpg

dataset, accessible via ggplot2, will serve for many our examples. It contains data on fuel economy from various car models, suitable for a broad range of plot types including scatter, line, and bar plots.

Another useful dataset is

diamonds

, also within ggplot2, which provides comprehensive data on diamond prices and attributes. Both datasets offer various opportunities to demonstrate data visualization techniques while staying grounded in real-world data scenarios.

Create some plots

Creating plots with ggplot2 involves understanding the grammar of graphics. Each plot starts with a call to

ggplot()

, where you specify the dataset and aesthetic mappings. Adding geometric objects (geoms) such as points, lines, or bars further defines the plot type.

For instance, to create a scatter plot using the

mpg

dataset, you can use a syntax like:

ggplot(mpg, aes(x = displ, y = hwy)) + geom_point()

. This basic command will create a scatter plot of displacement vs. highway mileage. Other layers, like themes and scales, can be added to customize plots further.

Use common legend for combined ggplots

Use ggpubr R package

The ggpubr package simplifies combining plots with a shared legend. It provides intuitive functions like

ggarrange()

which allow multiple plots to be arranged with ease. Set the legend parameter to “right” or “bottom” to share legends across plots.

Using ggpubr, you can maintain consistency across graphs, making your visualization more cohesive. This approach also conserves real estate on the page by avoiding repetitive legends, which can often confuse readers with redundant information.

Use cowplot R package

The cowplot package complements ggplot2 by providing plot alignment and annotation tools. Its

plot_grid()

function efficiently lays out multiple plot objects in a grid, sharing titles and legends across them.

One of cowplot’s strengths is its ability to maintain aspect ratios and align plots precisely. This functionality is particularly useful when dealing with plots of differing dimensions, as it ensures a harmonized and professional presentation.

Use gridExtra R package

GridExtra offers another solution to combine plots into a unified visual output. The

grid.arrange()

function aligns plots and maintains consistency in layout. It can also embed mixed content, such as tables and text, alongside plots.

Though powerful, GridExtra might lack ggplot2’s theme management strengths, requiring additional tweaking to achieve uniform aesthetics across combined layouts. However, it remains a versatile tool for plot arrangement in R.

Use grid R package

The grid package is integral to R’s graphics system, underpinning ggplot2’s plotting mechanics. Utilizing functions like

grid.draw()

and

viewport()

, you can micromanage plot positioning and layout intricately.

While it demands more manual input than other packages, mastering grid allows for unparalleled control over plot structures, enabling precise adjustment of legend placements and plot dimensions within composites.

Arrange on one page

Arranging multiple plots on a single page is a common requirement for comprehensive data presentations. By merging several visual representations, viewers can easily interpret the relationships between different data facets.

Using functions from ggpubr, cowplot, or gridExtra, plots can be effectively combined. Each approach ensures the resulting figure is coherent and interprets smoothly, serving both as informative and aesthetically appealing.

Annotate the arranged figure

Annotations add context, clarity, and direction to a plot. With ggplot2, you have the flexibility to insert titles, captions, and even direct annotations within the plot space to guide viewers effectively across the graphical display.

Employ annotations like

geom_text()

in specific plot areas to highlight critical data points or trends. This emphasis ensures important data signals are not lost within the graphical storytelling process.

Align plot panels

To maintain visual consistency, aligning plot panels is essential. Whether dealing with axes labels or grid lines, ensuring these elements match across different plots in an arrangement promotes comparability and cleanliness.

Cowplot’s alignment features come in especially handy here. Using commands like

align_plots()

, seamless alignment across shared properties is achieved, contributing to your figure’s logical flow and overall presentation.

Change column/row span of a plot

Varying the span of individual plots within a multi-plot grid can enhance figure aesthetics and emphasize specific data narratives. This technique is essential in dashboard layouts where space utilization and visual emphasis are critical.

In gridExtra, parameters like

widths

and

heights

can be configured to adjust span distribution. This flexibility lets you retain balance, focusing viewer attention precisely where it’s needed across visual elements.

Scatter plot with marginal density plots

Combining a scatter plot with marginal density plots can provide deeper insight. The main scatter plot shows relationships between two variables, while density plots along the margins hint at the distribution profiles of each variable.

The ggplot2 extension ggExtra simplifies this with functions like

ggMarginal()

, enabling seamless integration of marginal density or box plots. Enhancing scatter plots through this method supports in-depth exploratory data analysis.

Mix table, text and ggplot2 graphs

Place a table within a ggplot

Tables present concise numerical summaries and, when integrated with plots, enrich the data narrative. ggplot2 allows tables to coexist with plots, positioning them accurately through grid and cowplot functionalities.

This approach ensures a continuity of information, enabling readers to access detailed quantitative data alongside visual summaries, thus rounding out the data exploration and presentation capability of ggplot2.

Place a box plot within a ggplot

Embedding a box plot within another plot type can showcase distribution characteristics while illustrating other attributes. This composite view is valuable in highlighting outliers and variation within specific data segments.

By leveraging ggplot2 layering mechanics, add box plots using

geom_boxplot()

, positioning them either alongside or within the main plot. This adds multi-dimensional context to a single graphical representation.

Insert a graphical element inside a ggplot

Add background image to ggplot2 graphs

To enhance the visual appeal of ggplot2 graphs, adding a background image can provide contextual or thematic ambiance. This can be achieved using the gganimate package or custom theme settings.

Background images can emphasize narrative or simply improve engagement. Using

annotation_custom()

, incorporate logos, thematic backgrounds, or other static imagery without interrupting graphical integrity.

Arrange over multiple pages

In certain cases, visualizing expansive datasets necessitates extending plots over multiple pages. This technique is essential for reports or presentations where comprehensive detail is required without compromising readability.

Paginated output can be achieved through packages such as gridExtra, utilizing layouts that accommodate page breaks, ensuring seamless transitions from one page to the next, thereby maintaining narrative continuity.

Nested layout with ggarrange()

For advanced layout customization, nesting plots within plots using ggarrange becomes invaluable. This allows for complex, interrelated data stories to be told within a single coherent visual framework, enhancing comparability and interaction.

Employ ggarrange’s capabilities to develop nested plots by specifying row and column parameters. This creates a sophisticated visual hierarchy, leading to insightful and impactful data storytelling.

Export plots

Once your plots are ready, exporting them in high quality is paramount. ggplot2 provides functions like

ggsave()

, which support various formats including PNG, JPEG, and PDF, ensuring your visuals meet professional standards.

Ensure to set parameters like width, height, and resolution appropriately when exporting. This guarantees your plots translate effectively across different media, from printed reports to online presentations, without loss of detail or clarity.

Acknowledgment

Creating cohesive visual narratives with ggplot2 is a powerful skill for any data practitioner. This guide aimed to arm you with the knowledge needed to exploit ggplot2’s potential. We acknowledge the vast array of community-contributed resources and packages that enhance ggplot2’s functionality and continue to support data visualization enthusiasts worldwide.

Next steps

Section	Content Summary
Prerequisites	Essential R packages and datasets needed for ggplot2 plot creation.
Create some plots	Basics of plot creation using ggplot2 syntax.
Use common legend for combined ggplots	Techniques to share legends using different R packages like ggpubr and cowplot.
Arrange on one page	Arranging multiple plots for comprehensive displays.
Annotate the arranged figure	Adding context and direction through annotations.
Align plot panels	Ensuring visual consistency across multiple plot panels.
Change column/row span of a plot	Adjusting plot spans for better focus and presentation.
Scatter plot with marginal density plots	Combining scatter and density plots for deeper insights.
Mix table, text and ggplot2 graphs	Integrating tables and text within plotted visuals.
Insert a graphical element inside a ggplot	Enhancing plots with background images and elements.
Arrange over multiple pages	Handling expansive datasets across multiple visual pages.
Nested layout with ggarrange()	Creating complex, nested plot layouts using ggarrange.
Export plots	Ensuring high-quality plot exports for various formats.