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The ggpubr R package facilitates the creation of beautiful ggplot2-based graphs for researcher with non-advanced programming backgrounds.
The current material presents a collection of articles for simply creating and customizing publication-ready plots using ggpubr. To see some examples of plots created with ggpubr click the following link: ggpubr examples.
ggpubr Key features:
- Wrapper around the ggplot2 package with a less opaque syntax for beginners in R programming.
- Helps researchers, with non-advanced R programming skills, to create easily publication-ready plots.
- Makes it possible to automatically add p-values and significance levels to box plots, bar plots, line plots, and more.
- Makes it easy to arrange and annotate multiple plots on the same page.
- Makes it easy to change grahical parameters such as colors and labels.
Official online documentation: http://www.sthda.com/english/rpkgs/ggpubr.
Install and load ggpubr
- Install from CRAN as follow:
install.packages("ggpubr")
- Or, install the latest version from GitHub as follow:
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
- Load ggpubr:
library("ggpubr")
Related articles
Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data
Description: Plot one or a list of variables at once.
Contents:
- Gene expression data
- Box plots
- Violin plots
- Stripcharts and dot plots
- Density plots
- Histogram plots
- Empirical cumulative density function
- Quantile – Quantile plot
Add P-values and Significance Levels to ggplots
Description: Compute and add automatically p-values and significance levels to ggplots.
Contents:
- Methods for comparing means
- R functions to add p-values
- compare_means()
- stat_compare_means()
- Compare two independent groups
- Compare two paired samples
- Compare more than two groups
- Multiple grouping variables
Perfect Scatter Plots with Correlation and Marginal Histograms
Description: Create beautiful scatter plots with correlation coefficients and marginal histograms/density.
Contents:
- Basic plots
- Color by groups
- Add concentration ellipses
- Add point labels
- Bubble chart
- Color by a continuous variable
- Add marginal plots
- Add 2d density estimation
- Application to gene expression data
Plot Means/Medians and Error Bars
Description: Plot easily means or medians with error bars.
Contents:
- Error plots
- Line plots
- Bar plots
- Add labels
- Application to gene expression data
Bar Plots and Modern Alternatives
Description: Create easily basic and ordered bar plots, as well as, some modern alternatives to bar plots, including lollipop charts and cleveland’s dot plots.
Contents:
- Basic bar plots
- Multiple grouping variables
- Ordered bar plots
- Deviation graphs
- Alternatives to bar plots
- Lollipop chart
- Cleveland’s dot plot
Add Text Labels to Histogram and Density Plots
Description: Create histograms/density plots and highlight some key elements on the plot.
ggplot2 – Easy Way to Mix Multiple Graphs on The Same Page
Description: Step by step guide to combine multiple ggplots on the same page, as well as, over multiple pages.
Contents:
- Create some plots
- Arrange on one page
- Annotate the arranged figure
- Align plot panels
- Change column/row span of a plot
- Use common legend for combined ggplots
- Scatter plot with marginal density plots
- Mix table, text and ggplot2 graphs
- Insert a graphical element inside a ggplot
- Place a table within a ggplot
- Place a box plot within a ggplot
- Add background image to ggplot2 graphs
- Arrange over multiple pages
- Nested layout with ggarrange()
- Export plots
ggplot2 – Easy Way to Change Graphical Parameters
Description: Describe the function ggpar() [in ggpubr], which can be used to simply and easily customize any ggplot2-based graphs.
Contents:
- Change titles and axis labels
- Change legend position & appearance
- Change color palettes
- Group colors
- Gradient colors
- Change axis limits and scales
- Customize axis text and ticks
- Rotate a plot
- Change themes
- Remove ggplot components
Create and Customize Multi-panel ggplots: Easy Guide to Facet
Description: split up your data by one or more variables and to visualize the subsets of the data together.
Contents:
- Facet by one grouping variables
- Facet by two grouping variables
- Modifying panel label appearance