Skill: Ridgeline Plot (R)

Category

Distribution

When to Use

A ridgeline plot, also known as a joyplot, visualizes the distribution of multiple numeric variables across different categories. This method is useful for comparing density distributions while preserving an overall view of trends and variations.

Required R Packages

• dplyr
• ggplot2
• ggridges
• hrbrthemes
• readr
• viridis

Minimal Reproducible Code

# Load packages
library(dplyr)
library(ggplot2)
library(ggridges)
library(hrbrthemes)
library(readr)
library(viridis)

# Prepare data
# Load iris dataset
data("iris")

# Load Lung Cancer (Raponi 2006) clinical data
TCGA_clinic <- readr::read_tsv("https://ucsc-public-main-xena-hub.s3.us-east-1.amazonaws.com/download/raponi2006_public%2Fraponi2006_public_clinicalMatrix.gz") %>%
  mutate(T = as.factor(T))
head(TCGA_clinic)

# Create visualization
# Basic Ridgeline plot
p1_1 <- ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
  geom_density_ridges(alpha = 0.5) +
  theme_ridges(font_size = 16, grid = TRUE) +
  theme(legend.position = "right")

p1_1

Key Parameters

• x: Maps OS to the x aesthetic
• y: Maps T to the y aesthetic
• fill: Maps T to the fill aesthetic
• alpha: Controls transparency (0 = fully transparent, 1 = opaque)
• position: Position adjustment (identity, dodge, stack, fill)
• stat: Statistical transformation to use
• theme: Plot theme; tutorial uses theme_ipsum()

Tips

• Customize color scales with scale_fill_manual() or scale_color_brewer()
• Adjust text size with theme(text = element_text(size = 14)) for presentations
• Consider adding geom_jitter() or raw data points alongside distribution plots for small sample sizes

Full Tutorial

https://openbiox.github.io/Bizard/Distribution/Ridgeline.html