In this article, you will learn how to easily create a histogram by group in R using the ggplot2 package.
Related Book
GGPlot2 Essentials for Great Data Visualization in RPrerequisites
- Load the ggplot2 package and set the theme function
theme_classic()
as the default theme:
library(ggplot2)
theme_set(
theme_classic() +
theme(legend.position = "top")
)
- Create a demo dataset: Weight data by sex:
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each=200)),
weight = c(rnorm(200, 55), rnorm(200, 58))
)
head(wdata, 4)
## sex weight
## 1 F 53.8
## 2 F 55.3
## 3 F 56.1
## 4 F 52.7
Create histogram by group
# Change line color by sex
ggplot(wdata, aes(x = weight)) +
geom_histogram(aes(color = sex), fill = "white",
position = "identity", bins = 30) +
scale_color_manual(values = c("#00AFBB", "#E7B800"))
# change fill and outline color manually
ggplot(wdata, aes(x = weight)) +
geom_histogram(aes(color = sex, fill = sex),
position = "identity", bins = 30, alpha = 0.4) +
scale_color_manual(values = c("#00AFBB", "#E7B800")) +
scale_fill_manual(values = c("#00AFBB", "#E7B800"))
Recommended for you
This section contains best data science and self-development resources to help you on your path.
Books - Data Science
Our Books
- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)
Others
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet
Version: Français
No Comments