GGPlot2 vs Base R Graphics
In the world of R programming, visualization is a critical component of data analysis. Two primary tools for visualizing data are base R graphics and the ggplot2 package. Both have their unique strengths and weaknesses, appealing to different types of users. This article dives into the nuances of using these visualization tools, focusing on customizing and enhancing plots. As we navigate through setup, adjusting axes, adding legends, and making final tweaks, you’ll gain insights into the flexibility and ease-of-use offered by each tool. By the end, you will have a comprehensive comparison that will help you decide which method best fits your data visualization needs.
Set Up
The start of any visualization journey in R is setting up the necessary libraries and data. For base R graphics, you’re ready to go with a simple plot() function call, whereas with ggplot2, the process begins with loading the package using library(ggplot2). After loading the package, you can construct complex plots using a layered grammar of graphics, starting with ggplot(data, aes(x, y)).
Base R graphics are intuitive for quick visualizations without needing additional packages. Conversely, ggplot2 provides more structure through its aesthetic mappings, which is ideal for building more detailed and elaborate visual compositions. This setup difference subtly impacts the user’s workflow, with ggplot2 offering modularity and reusability through its specific functions and layering system.
Fixing the Axes
Add the Tick Marks
Tick marks play an essential role in helping readers understand scale and relationships in a plot. In base R, adding tick marks involves using functions like axis() with the at parameter for specifying their position. This could require additional calculations if your data is not uniformly distributed.
In contrast, ggplot2 manages tick marks automatically but also allows for customization through the scale_x_continuous() and scale_y_continuous() functions. This gives you control over the breaks and labels, which can be especially useful for plots with dense data points or non-standard data ranges.
Adjusting Ticks and Tick Labels
Base R graphics require manual label adjustments using functions like xlab() and ylab(). These adjustments can be cumbersome as they may involve recalculating increment values for each axis labeling manually.
On the other hand, ggplot2 has this more streamlined. You can adjust tick labels with minor alterations just by adding arguments within the scale_x_continuous() or scale_y_continuous(). It provides a level of precision without the need for extensive custom code, making the process considerably less cumbersome.
Adjusting Tick Label Length
When dealing with long axis labels in base R graphics, truncation or overlapping can occur, which requires additional work adjusting margins or using par() to fix. However, this process may not always be intuitive for beginners.
In ggplot2, handling long labels is more straightforward, as you can use theme(axis.text.x = element_text(angle=45, hjust=1)) to rotate and adjust the position of labels. This feature helps maintain readability without compromising the plotting area or aesthetics.
Moving the X Labels a Bit More
For basic plots in R, directly customizing label positions might involve more trial-and-error adjustments, using parameters like pos within the axis() function to nudge elements into place.
ggplot2 simplifies this with its theme system, where adjustments like theme(axis.title.x = element_text(margin = margin(t = 10))) allow more intuitive control over spacing and positioning relative to the plotting area’s axes.
Fixing the Points
When it comes to plotting points in a scatterplot, the base R plot() function utilizes simple point shapes and colors defined within the function parameters. Flexibility is available but requires additional arguments such as pch for shape and col for color customization.
In ggplot2, altering point appearance is part of the aesthetic mappings, enabling dynamic adjustments with properties like geom_point(size=, color=). This method abstracts some of the complexity away and provides more clarity in applying visual distinctions across categories or datasets.
Adding a Legend
Base R grappling with legends is frequently a manual process, often inserting legend() calls post-plotting to assign colors and shapes to specific data points. Positioning and styling remain manual, which adds layers of complexity for large datasets.
Alternatively, ggplot2 integrates legend creation automatically during the plotting process. Attributes defined in the aes() function guide the legend formation, allowing for seamless incorporation of labels representing different data groupings with minimal extra coding.
Adjusting the Legend
Refining a legend in base R requires changing legend parameters such as lty for line types or adjusting leg <- locator() for placing the legend interactively, which can be tedious and error-prone with non-static data.
The ggplot2 package offers more control over legend aesthetics and position with simple additions like theme(legend.position=”top”) or invoking guides() for additional refinement. This makes modifications more graceful and productive.
Move the Legend Outside of the Plotting Area
Relocating a legend outside the plotting area in base R is a highly manual affair, needing adjustments in the overall plotting region using par() to redefine margins and title spaces, ensuring that there’s enough room.
In ggplot2, moving a legend is trivial with theme(legend.position=”bottom”) or legend.margin = bottom_margin(50), enabling seamless integration into varied form factors and exporting options while maintaining coherence and clarity in presentation.
Some Final Touchups
Tick Label Size
Size adjustments of tick labels in base R graphics involve setting parameters within par() for text size (cex.axis), which can be labor-intensive, especially when tuning plots for publications or presentations.
ggplot2’s theme() options allow for easy adjustments, enabling quick scaling of axis text sizes with axis.text.x = element_text(size=12), fostering consistency in visual styles and facilitating quicker turnaround times.
Plot Element Colors
Customizing plot colors in base R typically involves defining color vectors and ensuring their application across various plot elements. While it offers depth, it asks for more substantial preliminary setting up, especially with multiple datasets.
ggplot2 allows color manipulation through the scale_color_manual() function, supporting palettes and custom color aesthetics succinctly, allowing for visually pleasing outputs that are simple to implement and modify.
Plot Margins
In base R, margin adjustments call for par(mar=…) tweaks, which, while flexible, require manual calculations to ensure elements fit within the visual space adequately without overcrowding or underutilizing the space.
Within ggplot2, themes and margins are more intuitive, with options to nudge boundaries dynamically using plot.margin = unit(c(1, 1, 1, 1), “cm”), streamlining the process of aligning compositions for varied devices or print media.
All the Final Adjustments
The sum of these adjustments can transform a plot from basic to publication-ready. In base R this may demand a granular approach, assessing each plot element individually, sometimes at the cost of iterative trial and error.
With ggplot2, final adjustments are compounded through cohesive theme manipulations and layering, empowering developers to implement global changes with precision and ease, paving the way for professional-grade outputs with considerably less effort.
Wrap Up
Related
Choosing between ggplot2 and base graphics in R often comes down to project requirements and personal preference. With both offering unique advantages—from base R’s minimalist, direct approach to ggplot2’s extensible, design-centric system—data analysts and scientists can tailor their selection to match their data visualization philosophy.
| Aspect | Base R Graphics | ggplot2 |
|---|---|---|
| Setup | Immediate, built-in functions | Requires loading ggplot2 package |
| Axes Adjustment | Manual, using axis parameters | Automated, with overrides using scale functions |
| Legend Customization | Manual, post-plot processes | Automatic, part of aesthetic mappings |
| Element Tweaks | Via par() and plot() adjustments | Through theme and geom functions |
| Accessibility | Minimal setup, fast results | Structured setup for in-depth customization |
