Visualizing Data Variability with Jittered Boxplots in R

Overview

A jitter boxplot is a hybrid data visualization that overlays individual data points with random horizontal displacement (jitter) onto a traditional boxplot. This combination enhances the interpretability of data by showing both summary statistics and the actual distribution of values.

Example of a Jitter Box Plot

Useful: Explore how to create a jitter box plot in Tableau.

Step-by-Step Guide

Load libraries

# load libraries

library(tidyverse)
library(paletteer)

Load data

# load data

superstore<-read.csv("https://raw.githubusercontent.com/bernardkilonzo-rigor/dataviz/main/data/Sample%20-%20Superstore.csv")

Create a simple jitter box plot.

# creating jitter box plot

superstore%>%filter(Region=="East")%>%ggplot(aes(y = State, x = Quantity, color = State))+ 
stat_boxplot(geom = "errorbar",width =0.4)+
geom_boxplot(outlier.color = NA)+#removing the outliers
geom_jitter(size =0.7)

See the resulting view.

Formatting and customizing the plot.

# creating and formatting jitter box plot

superstore%>%filter(Region=="East")%>%ggplot(aes(y = State, x = Quantity, color = State))+
stat_boxplot(geom = "errorbar",width =0.4)+
geom_boxplot(outlier.color = NA)+#removing the outliers
geom_jitter(size =0.7)+
scale_color_paletteer_d("impressionist.colors::irissen")+
labs(title = "Order Distribution by Quantity by State",
caption = "Viz by: Bernard Kilonzo")+
theme(panel.background = element_blank(),
axis.line = element_line(color = "gray30", linewidth = 0.2),
axis.ticks = element_line(color = "gray30", linewidth = 0.2),
axis.title = element_text(family = "serif", size = 10,face = "bold", color = "gray30"),
axis.text = element_text(family = "serif", size = 9, color = "gray30"),
legend.position = "none",
plot.title = element_text(family = "serif", face = "bold", size = 13, color = "gray25"),
plot.caption = element_text(family = "serif", face = "italic", size = 9, color = "gray35"))

See the resulting final view.

Conclusion

Creating a jitter boxplot in R is a powerful way to explore the intricacies of your dataset by merging statistical summaries with raw data visibility. Through the use of ggplot2 and its complementary geom_boxplot() and geom_jitter() layers, analysts gain a clear view of central tendencies, variability, and individual data points - all in one visualization.

This hybrid plot is especially effective in scenarios where overplotting may obscure patterns, such as repeated values or tightly clustered groups. By jittering the points, subtle distributions emerge that standard boxplots might miss.

Whether you're working in biology, sociology, business analytics, or any field involving grouped data, jitter boxplots in R offer both elegance and insight. With just a few lines of code, you can turn raw numbers into a compelling visual narrative.

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