(Title image from Computer World)

Although it’s normally editor’s responsibility, we do benifit a lot if we can use them.

Interactive graphs are normally archived through JavaScript libraries, such as D3, Poltly, Highcharts, etc, and render to HTML pages.

Use Javascript

For charts like bar plot, line chart, or even volcano plot, we can use the data directly in JS script. For PCA plot, we can export the pca matrix first, e.g. from DESeq2

pca_data <- plotPCA(data, intgroup=c("conditions"), returnData = TRUE)

or to export Heatmap matrix data, then make a chart.

From R

• Plotly has it’s own R package to make interactive charts
• If you prefers to use ggplot2, there is also Plotly ggplot2 Library
• R package for Highcharts is also developed with easy usage
• recharts: an R interface to ECharts
• rbokeh

Using Python

• Explore data with Pandas
• Popular plotting module matplotlib
• or Seaborn
• or Bokeh

• THE GLOBAL FLOW OF PEOPLE published in Science

Related discussions:

• From Static to Interactive: Transforming Data Visualization to Improve Transparency
• Interactive Plot Viewer - Elsevier