How to plot distribution plots on margins of Scatter Plot using ggamarginal & ggscatridges packages
Elevate your data visualization skills in R with ridge plots integrated into margins of scatter plots, displaying distributions of the numeric variables using the ggScatRidges package. This comprehensive RStudio tutorial will demonstrate you to plot stunning visualizations that combine scatter plots with marginal plots showing density distributions. Unlock deeper insights into your data and enhance your ability to communicate findings effectively. Whether you're a seasoned data scientist or an RStudio enthusiast, this tutorial will empower you to tell compelling stories with your data.
Script:
Marginal Plots
This script demonstrates how to create scatter plots with marginal distributions
using ggExtra and ggScatRidges packages in RStudio with the iris dataset.
Load required libraries
library(ggExtra) # for ggmarginal() function
library(ggplot2) # for ggplot() function
library(ggScatRidges) # for ggScatRidges() function
Load the iris dataset
data(iris)
Scatter plot with ggplot
p = ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() +
theme_bw() +
theme(legend.position = "bottom")
Display the scatter plot
print(p)
Add marginal plots using ggExtra
ggMarginal(p, type = "density", groupFill = TRUE)
ggMarginal(p, type = "histogram", groupFill = TRUE)
ggMarginal(p, type = "boxplot", groupFill = TRUE)
ggMarginal(p, type = "violin", groupFill = TRUE)
ggMarginal(p, type = "densigram", groupFill = TRUE)
Scatter plot with ridge plots using ggScatRidges
ggScatRidges(iris$Sepal.Length, iris$Sepal.Width, group = iris$Species, density_2d = FALSE)
Scatter plot with ridge plots using ggScatRidges
df = iris[,c(1, 2, 5)] # select columns to make a dataframe suitable for ggScatRidges()
ggScatRidges(df) # plot the marginal ridgline plots
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