Visualizations grant users the ability to explore, manipulate, and interact with data by employing dynamic charts, changing colors, and shapes based on queries or interactions.
What is interactive visualization?
Interactive visualizations are produced using data visualization tools which allow for direct modification of elements on a graphical plot.
HTML widgets work just like R plots except they produce interactive web visualizations. HTML widgets can be used at the R console as well as embedded in R Markdown reports and Shiny web applications.
This dashboard presents and compares five most popular html widget tools for building interactive plots in Rshiny.
Interactivity: long lasting RShiny apps should be interactive and self-explicit. R packages that support html widgets might help achive this goal pretty easily: plot_ly, ggplotly, highcharter, bokeh or echart?
I have built an Rshiny dashboard that shows and compares five different R packages that help to build interactive plots for Rshiny dashboards and Rmarkdowns.
It could be seen here: https://minijen.shinyapps.io/benchmarking/
Plotly Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST. Plotly is a technical computing company headquartered in Montreal, Quebec. It develops online data analytics and visualization tools. Probably the best free package.
GGPLOTLY ggplotly is a plotly R libraray that builds a wrapper around almost any ggplot object and converting them into interactive plot. It is fast and easy to implement, but not always generates the best result.
HIGHCHARTER Highcharts is very flexible and customizable javascript charting library and it has a great and powerful API. Highcharts offers both a commercial license as well as a free non-commercial license.
BOKEH A native R plotting library that provides a flexible declarative interface for creating interactive web-based graphics, backed by the Bokeh visualization library. The Bokeh library is written and maintained by the Bokeh Core Team consisting of several members of Continuum Analytics and other members of the open source community.
ECHART4R Easily create interactive charts by leveraging the 'Echarts Javascript' library which includes 36 chart types, themes, 'Shiny' proxies and animations.
- DT tables
- plotly and ggplotly
- highcharter
- leaflet (interactive geo plots, scatter geoplots)
- visNetwork (interactive networks)
- wordcloud2
- d3heatmap
- networkD3
- wordcloud2
HTML widgets in R https://www.htmlwidgets.org/showcase_plotly.html
Gallary of widgets http://gallery.htmlwidgets.org/
https://cran.r-project.org/web/packages/highcharter/highcharter.pdf https://www.datacamp.com/community/tutorials/data-visualization-highcharter-r https://www.datacamp.com/tutorial/data-visualization-highcharter-r https://www.kaggle.com/code/nulldata/beginners-guide-to-highchart-visual-in-r/report#scatter-plot
https://www.infoworld.com/article/3607068/plot-in-r-with-echarts4r.html https://echarts4r.john-coene.com/articles/chart_types.html
https://hafen.github.io/rbokeh/articles/rbokeh.html