Skill: Break Plot (R)

Category

Distribution

When to Use

Create a Break Plot visualization in R for biomedical data analysis and research publications.

Required R Packages

• RColorBrewer
• dplyr
• ggbreak
• ggplot2
• ggpubr
• rstatix

Minimal Reproducible Code

# Load packages
library(RColorBrewer)
library(dplyr)
library(ggbreak)
library(ggplot2)
library(ggpubr)
library(rstatix)

# Prepare data
# Data Preparation
df <- ToothGrowth %>%
  group_by(supp, dose) %>%
  summarise(
    mean_len = mean(len),
    sd_len = sd(len),
    n = n(),
    se_len = sd_len/sqrt(n),
    .groups = 'drop')


# Statistical tests (key repair points)
stat.test <- ToothGrowth %>%
  group_by(dose) %>%
  t_test(len ~ supp) %>%
  add_xy_position(x = "dose", dodge = 0.8)

head(df)

# Create visualization
# Basic BarPlot
p1 <- ggplot(df, aes(x=dose, y=mean_len, fill=supp)) +
  geom_col(position=position_dodge(0.4), width=0.2) +
  geom_errorbar(aes(ymin=mean_len-sd_len, ymax=mean_len+sd_len),
                width=0.1, position=position_dodge(0.4)) +
  scale_y_continuous(breaks = seq(0, 30, 5)) +
  scale_y_cut(breaks=c(15), which=1, scales=1.5) +
  labs(x="Dose (mg/day)", y="Tooth Length (mm)") +
  theme_classic()

p1

Key Parameters

• x: Maps dose to the x aesthetic
• y: Maps len to the y aesthetic
• fill: Maps supp to the fill aesthetic
• color: Maps supp to the color aesthetic
• alpha: Controls transparency (0 = fully transparent, 1 = opaque)
• width: Controls element width
• position: Position adjustment (identity, dodge, stack, fill)
• stat: Statistical transformation to use

Tips

• The tutorial includes a '4. More advanced charts' section with advanced styling options
• Use theme_minimal() or theme_bw() for clean, publication-ready plots
• Customize color scales with scale_fill_manual() or scale_color_brewer()
• Consider adding geom_jitter() or raw data points alongside distribution plots for small sample sizes

Full Tutorial

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