ggVennDiagram: An Intuitive, Easy-to-Use, and Highly Customizable R Package to Generate Venn Diagram

By Chunhui Gao, Guangchuang Yu, Peng Cai. Frontiers in Genetics, 2021

391 citations recorded by the source corpus.

Venn diagrams are widely used diagrams to show the set relationships in biomedical studies. In this study, we developed ggVennDiagram, an R package that could automatically generate high-quality Venn diagrams with two to seven sets. The ggVennDiagram is built based on ggplot2, and it integrates the advantages of existing packages, such as venn, RVenn, VennDiagram, and sf. Satisfactory results can be obtained with minimal configurations. Furthermore, we designed comprehensive objects to store the entire data of the Venn diagram, which allowed free access to both intersection values and Venn plot sub-elements, such as set label/edge and region label/filling. Therefore, high customization of every Venn plot sub-element can be fulfilled without increasing the cost of learning when the user is familiar with ggplot2 methods. To date, ggVennDiagram has been cited in more than 10 publications, and its source code repository has been starred by more than 140 GitHub users, suggesting a great potential in applications. The package is an open-source software released under the GPL-3 license, and it is freely available through CRAN (https://cran.r-project.org/package=ggVennDiagram).

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/35500f0153ea2f2ad5b722e473895978c4d526a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

  1. Li Zhan
  2. Tianzhi Wu
  3. Erqiang Hu
  4. Meijun Chen
  5. Shuangbing Xu
  6. Guangchuang Yu

In Molecular Biology

  1. Li Zhan
  2. Meijun Chen
  3. Tianzhi Wu
  4. Erqiang Hu
  5. Shuangbing Xu
  6. Lin Li
  7. Zijing Xie
  8. Qianwen Wang

Citations in the corpus