Keywords: R | plot | transparency | alpha | scales | rgb
Abstract: This article discusses how to solve the issue of color masking in scatter plots in R by setting point transparency. It focuses on the use of the alpha function from the scales package and the alternative rgb method, with practical code examples and explanations to enhance data visualization.
Problem Description
When plotting scatter plots in R, darker colors such as purple can mask lighter-colored points when data points are colored based on different groups. For example, a user uses the cut function to divide data into 6 groups and assign colors, with group 6 being purple, causing other groups to be covered in the plot.
Main Solution
To address this issue, transparency (alpha) can be used to make points semi-transparent. The scales package in R provides the alpha function, which can be directly applied to color vectors. For instance, the original code col = as.character(cols) can be modified to col = alpha(cols, 0.4), where 0.4 indicates 40% opacity, thereby increasing point transparency and improving visualization.
Alternative Method
Another approach is to use the rgb function to set the alpha parameter for defining color transparency. For example, rgb(red = 1, green = 0, blue = 0, alpha = 0.5) creates a semi-transparent red. This method is suitable for custom colors but is less convenient than the alpha function for directly applying to existing color vectors.
Code Example
library(scales)
s <- read.table("/.../parse-output.txt", sep="\t")
x <- s[,1]
y <- s[,2]
z <- s[,3]
cols <- cut(z, 6, labels = c("pink", "red", "yellow", "blue", "green", "purple"))
plot(x, y, main= "Fragment recruitment plot - FR-HIT", ylab = "Percent identity", xlab = "Base pair position", col = alpha(cols, 0.4), pch=16)In this example, alpha(cols, 0.4) converts the color vector to a version with 40% transparency, effectively solving the color masking problem.
Conclusion
By using the alpha function from the scales package or the rgb function, point transparency can be easily implemented in R scatter plots, enhancing chart readability and data presentation clarity, especially for handling large amounts of overlapping data points.