ggplot2 title : main, axis and legend titles
The aim of this tutorial is to describe how to modify plot titles (main title, axis labels and legend titles) using R software and ggplot2 package.
The functions below can be used :
ggtitle(label) # for the main title
xlab(label) # for the x axis label
ylab(label) # for the y axis label
labs(...) # for the main title, axis labels and legend titles
The argument label is the text to be used for the main title or for the axis labels.
Prepare the data
ToothGrowth data is used in the following examples.
# convert dose column from a numeric to a factor variable
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
head(ToothGrowth)
## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5
Make sure that the variable dose is converted as a factor using the above R script.
Example of plot
library(ggplot2)
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot()
p
Change the main title and axis labels
Change plot titles by using the functions ggtitle(), xlab() and ylab() :
p + ggtitle("Plot of length \n by dose") +
xlab("Dose (mg)") + ylab("Teeth length")
Note that, you can use \n to split long title into multiple lines.
Change plot titles using the function labs() as follow :
p +labs(title="Plot of length \n by dose",
x ="Dose (mg)", y = "Teeth length")
It is also possible to change legend titles using the function labs():
# Default plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose))+
geom_boxplot()
p
# Modify legend titles
p + labs(fill = "Dose (mg)")
Change the appearance of the main title and axis labels
Main title and, x and y axis labels can be customized using the functions theme() and element_text() as follow :
# main title
p + theme(plot.title = element_text(family, face, colour, size))
# x axis title
p + theme(axis.title.x = element_text(family, face, colour, size))
# y axis title
p + theme(axis.title.y = element_text(family, face, colour, size))
The arguments below can be used for the function element_text() to change the appearance of the text :
- family : font family
- face : font face. Possible values are “plain”, “italic”, “bold” and “bold.italic”
- colour : text color
- size : text size in pts
- hjust : horizontal justification (in [0, 1])
- vjust : vertical justification (in [0, 1])
- lineheight : line height. In multi-line text, the lineheight argument is used to change the spacing between lines.
- color : an alias for colour
# Default plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot() +
ggtitle("Plot of length \n by dose") +
xlab("Dose (mg)") + ylab("Teeth length")
p
# Change the color, the size and the face of
# the main title, x and y axis labels
p + theme(
plot.title = element_text(color="red", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)
Remove x and y axis labels
It’s possible to hide the main title and axis labels using the function element_blank() as follow :
# Hide the main title and axis titles
p + theme(
plot.title = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
Infos
This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. )
Show me some love with the like buttons below... Thank you and please don't forget to share and comment below!!
Montrez-moi un peu d'amour avec les like ci-dessous ... Merci et n'oubliez pas, s'il vous plaît, de partager et de commenter ci-dessous!
Recommended for You!
Recommended for you
This section contains best data science and self-development resources to help you on your path.
Coursera - Online Courses and Specialization
Data science
- Course: Machine Learning: Master the Fundamentals by Standford
- Specialization: Data Science by Johns Hopkins University
- Specialization: Python for Everybody by University of Michigan
- Courses: Build Skills for a Top Job in any Industry by Coursera
- Specialization: Master Machine Learning Fundamentals by University of Washington
- Specialization: Statistics with R by Duke University
- Specialization: Software Development in R by Johns Hopkins University
- Specialization: Genomic Data Science by Johns Hopkins University
Popular Courses Launched in 2020
- Google IT Automation with Python by Google
- AI for Medicine by deeplearning.ai
- Epidemiology in Public Health Practice by Johns Hopkins University
- AWS Fundamentals by Amazon Web Services
Trending Courses
- The Science of Well-Being by Yale University
- Google IT Support Professional by Google
- Python for Everybody by University of Michigan
- IBM Data Science Professional Certificate by IBM
- Business Foundations by University of Pennsylvania
- Introduction to Psychology by Yale University
- Excel Skills for Business by Macquarie University
- Psychological First Aid by Johns Hopkins University
- Graphic Design by Cal Arts
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