This Comparison of ggplot with Base R Graphics will help us in understanding the differences between ggplot2 and Base R graphics is crucial for choosing the right tool for your data visualization needs. Both have their strengths and ideal use cases.
Key Insight: Base R graphics are procedural (tell the computer what to draw), while ggplot2 is declarative (describe the relationships in your data).
Philosophical Differences
Imperative Approach
“Draw this point here, then draw a line there” – you explicitly specify every element.
Function-Based
Different functions for different plot types: plot(), hist(), barplot()
Immediate Mode
Plots are drawn immediately and modifications overwrite previous elements.
Declarative Approach
“Show the relationship between these variables” – you describe what you want to see.
Grammar-Based
Consistent grammar for all plots: ggplot() + geom_*() + ...
Layered Mode
Plots are built by adding layers, creating an object that can be modified.
Syntax Comparison
Simple Scatter Plot
plot(mtcars$wt, mtcars$mpg,
main = “Car Weight vs MPG”,
xlab = “Weight”,
ylab = “Miles per Gallon”,
col = “blue”,
pch = 16)
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = “blue”) +
labs(
title = “Car Weight vs MPG”,
x = “Weight”,
y = “Miles per Gallon”
)
Adding Multiple Elements
plot(mtcars$wt, mtcars$mpg)
abline(lm(mpg ~ wt, data = mtcars),
col = “red”, lwd = 2)
Note: Two separate commands needed
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = “lm”,
color = “red”, se = FALSE)
Note: Single layered approach
Feature Comparison
| Feature | Base R Graphics | ggplot2 | Advantage |
|---|---|---|---|
| Learning Curve | Gentle for beginners, simple plots | Steeper initially, consistent once learned | Base R |
| Syntax Consistency | Different functions for different plots | Consistent grammar across all plots | ggplot2 |
| Customization | Fine control but can be complex | Systematic, layered customization | ggplot2 |
| Publication Quality | Requires significant tweaking | Beautiful defaults, easy refinement | ggplot2 |
| Complex Plots | Can become unwieldy | Easier to build and maintain | ggplot2 |
| Speed | Generally faster for simple plots | Slower for very simple plots | Base R |
| Faceting | Manual setup required | Built-in faceting system | ggplot2 |
| Themes | Limited built-in options | Extensive theming system | ggplot2 |
| Interactive Use | Immediate feedback | Object-oriented, store and modify | Both |
When to Use Each
Quick Exploratory Analysis
When you need to quickly visualize data during analysis without worrying about aesthetics.
plot(x, y)
hist(data)
boxplot(values ~ groups)
Publication Graphics
When creating figures for papers, reports, or presentations that require polished appearance.
ggplot(data, aes(x, y)) +
geom_point() +
theme_classic() +
labs(title = “Professional Plot”)
Complex Multi-layer Plots
When building visualizations with multiple data representations or complex layouts.
ggplot(data, aes(x, y)) +
geom_point() +
geom_smooth() +
facet_wrap(~group) +
theme_bw()
Minimal Dependencies
When working in environments where installing additional packages is problematic.
# No package installation needed
plot(iris$Sepal.Length,
iris$Sepal.Width)
Advanced Comparison Examples
Grouped Visualization
colors <- c(“red”, “blue”, “green”)
plot(mtcars$wt, mtcars$mpg,
col = colors[as.numeric(mtcars$cyl)],
pch = 16)
legend(“topright”,
legend = levels(factor(mtcars$cyl)),
col = colors, pch = 16)
Manual color and legend management
ggplot(mtcars,
aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point() +
labs(color = “Cylinders”)
Automatic color scaling and legend
Performance Considerations
Base R Advantages
- Faster for simple plots – Less overhead
- No dependencies – Works in any R environment
- Immediate feedback – Good for interactive exploration
- Small memory footprint – No package loading required
ggplot2 Advantages
- Better for complex plots – Layered approach scales well
- Reproducible code – Systematic approach is more maintainable
- Consistent output – Same code produces same results
- Extensible – Easy to create custom geoms and themes
Modern Recommendation: For most data analysis workflows, ggplot2 is recommended due to its consistency, reproducibility, and beautiful defaults. However, Base R graphics remain valuable for quick exploratory analysis and environments with minimal dependencies.
Note: Many professional R users employ both systems – using Base R for quick data exploration and ggplot2 for final publication-quality graphics.
In our next tutorial, we’ll dive into creating your first ggplot2 visualization and explore the fundamental building blocks of the grammar of graphics.