ggpubr: Create Easily Publication Ready Plots

September 14, 2017

(This article was first published on Easy Guides, and kindly contributed to R-bloggers)

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:

ggpubr: publication ready plots

Install and load ggpubr

  • Install from CRAN as follow:
  • Or, install the latest version from GitHub as follow:
# Install
if(!require(devtools)) install.packages("devtools")
  • Load ggpubr:

To leave a comment for the author, please follow the link and comment on their blog: Easy Guides. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Recent popular posts

Most visited articles of the week

  1. How to write the first for loop in R
  2. Installing R packages
  3. How to Make a Histogram with Basic R
  4. Scatterplots
  5. Using apply, sapply, lapply in R
  6. Tutorials for learning R
  7. How to perform a Logistic Regression in R
  8. Online textbook on data visualization with the ggplot2 package
  9. In-depth introduction to machine learning in 15 hours of expert videos


RSS Jobs for R users

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)