ggpubr: Create Easily Publication Ready Plots

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: https://www.sthda.com/english/rpkgs/ggpubr.

ggpubr: publication ready plots

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")

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

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