R/LinkedCharts is a powerful novel way to perform exploratory data analysis in R. With only very few lines of code, you can create interactive data visualization apps that allow you to simultaneously get an overview of your data and dive deeply into details.

See the examples above and the Tutorials section in the navigation menu for simple (and more complex) usage examples.


R/LinkedCharts is available on CRAN as the rlc package. To install R/LinkedCharts, start R and type


A more recent version can be installed from our GitHub repo.

install.packages( "devtools" )
devtools::install_github( "anders-biostat/rlc" )

Examples and Tutorials

Understanding RNA-Seq data

This simple tutorial shows how R/LinkedCharts can help you with a standard bioinformatics task: analysing an RNA-Seq data set, here of tissue samples from patients with oral cancer. The tutorial shows how scatter plots and heatmaps can easily be interactively combined. This is a good start into R/LinkedCharts

Exploring single-cell data

This tutorial uses the “CiteSeq” data set by Stoecklin et al. to demonstrate how LinkedCharts can help you to understand your single-cell transcriptomics data. It introduces lc_scatter, the workhorse of R/LinkedCharts, which produces interactively linked scatter plots. Like the previous one, this tutorial is a good way to get started and get a quick idea of what you can do with R/LinkedCharts.

A multicoloured t-SNE plot

This tutorial continues from the previous one and demonstrates how you can leverage standard HTML5 and JavaScript techniques and make them interact smoothly with R/LinkedCharts functions.

User input

Here you can learn how to add forms for user input and use this input to change your charts.

Customise your chart

Here you can find use cases of all the adjustable parameters in R/LinkedCharts. This tutorial demonstrates how to youse colours, change the shape of elements, add titles, use all built-in parameters to control interactivity, etc.


If you have any questions, send us an e-mail, or, even better, ask them at the R/LinkedCharts Issue Tracker.


LinkedChart and R/LinkedChart are being developed by Svetlana Ovchinnikova (s.ovchinnikova@zmbh.uni-heidelberg.de, kloivenn on GitHub) and Simon Anders (s.anders@zmbh.uni-heidelberg.de, simon-anders on GitHub).

We are researchers at the Center for Molecular Biology of the University of Heidelberg.