Size of Points in ggplot2 Comparable Across Plots in R

When creating multiple scatter plots or other point-based visualizations in R using ggplot2, it’s important to ensure that the size of points remains consistent across all plots. This consistency is crucial for accurate comparison and interpretation of the data visualizations, especially when these plots are presented together in reports or publications. This article explains how to achieve comparable point sizes across different plots using ggplot2 in R Programming Language.

Understanding Point Size in ggplot2

In ggplot2 the size of points in scatter plots is controlled by the size aesthetic. This aesthetic can be set either globally within a plot or mapped to a variable, allowing for variable point sizes based on data values.

• Global Size Setting: When you set a fixed size for points, all points in the plot will have the same size.
• Variable Size Mapping: When you map the size aesthetic to a variable, the size of the points will vary based on the values of that variable.

library(ggplot2)

# Sample data
df <- data.frame(x = rnorm(100), y = rnorm(100))

# Plot with fixed point size
ggplot(df, aes(x = x, y = y)) +
  geom_point(size = 3) +
  ggtitle("Fixed Point Size")

Output:

1: Inconsistent Point Sizes Across Multiple Plots

When creating multiple plots, it’s common to encounter inconsistencies in point sizes due to the scaling of the size aesthetic relative to the plot dimensions or differing ranges of data. These inconsistencies can lead to misleading comparisons between plots.

df2 <- data.frame(x = rnorm(100), y = rnorm(100))

# Two plots with different size settings
p1 <- ggplot(df, aes(x = x, y = y)) +
  geom_point(size = 3) +
  ggtitle("Plot 1")

p2 <- ggplot(df2, aes(x = x, y = y)) +
  geom_point(size = 1.5) +
  ggtitle("Plot 2")

# Display plots side by side
library(gridExtra)
grid.arrange(p1, p2, nrow = 1)

Output:

In this example, the point sizes differ between the two plots, making visual comparison difficult.

2: Ensuring Consistent Point Sizes

To maintain consistency across plots, you should use a fixed size for the points across all plots. This ensures that points appear the same size regardless of the plot dimensions or data range.

# Fixed size across multiple plots
p1 <- ggplot(df, aes(x = x, y = y)) +
  geom_point(size = 3) +
  ggtitle("Plot 1 with Consistent Size")

p2 <- ggplot(df2, aes(x = x, y = y)) +
  geom_point(size = 3) +
  ggtitle("Plot 2 with Consistent Size")

# Display plots side by side
grid.arrange(p1, p2, nrow = 1)

Output:

3: Consistent Variable-Based Point Sizes When Mapping Point Size to a Variable

When mapping point size to a variable, ggplot2 scales the point sizes according to the range of that variable in each plot. To maintain consistency, you can manually control the range of the size scale using the scale_size_continuous() or scale_size() functions.

# Sample data with size variable
df3 <- data.frame(x = rnorm(100), y = rnorm(100), size_var = runif(100, 1, 5))
df4 <- data.frame(x = rnorm(100), y = rnorm(100), size_var = runif(100, 2, 10))

# Plots with consistent size mapping
p1 <- ggplot(df3, aes(x = x, y = y, size = size_var)) +
  geom_point() +
  scale_size_continuous(range = c(1, 6)) +
  ggtitle("Plot 1 with Consistent Size Mapping")

p2 <- ggplot(df4, aes(x = x, y = y, size = size_var)) +
  geom_point() +
  scale_size_continuous(range = c(1, 6)) +
  ggtitle("Plot 2 with Consistent Size Mapping")

# Display plots side by side
grid.arrange(p1, p2, nrow = 1)

Output:

In this example, the scale_size_continuous(range = c(1, 6)) ensures that the point sizes are comparable across both plots, despite the different ranges of the size_var variable.

Conclusion

Ensuring that point sizes are comparable across different ggplot2 plots is essential for accurate data visualization and comparison. By setting fixed point sizes, controlling the size scale, and using consistent plot dimensions, you can create visualizations that are both aesthetically pleasing and scientifically rigorous. Using these techniques in your R workflows will help you produce more effective and consistent visualizations, enhancing the clarity and interpretability of your data presentations.