Software projects

Most of my (open-source) software projects are made available as R package(s). R’s packaging system provides a convenient way not only to share code, but also data, and dynamic/reproducible reports. You can find all my projects via my GitHub profile, but here is a list of selected projects:


The R package plotly makes it easy to create highly interactive web-based graphics from R. It is arguably the most widely used R package for web-based data visualization. I’ve been maintaining plotly since early-2015, then started the plotly book in 2016, and received the 2017 John Chambers Statistical Software Award for my contributions.


The R package bcviz provides a (shiny) web application for exploring housing and census data in British Columbia. The video below shows how to navigate the application.


The R package LDAvis provides an interactive web-based visualization of any topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). Given the estimated parameters of the topic model, it computes various summary statistics as input to an interactive visualization built with D3.js. The goal is to enhance interpretation of topics and the video below shows how to leverage the visualization to do so.


The R package zikar provides a (shiny) web application for exploring publicly available Zika virus data. The video below shows how the app may be used to compare the distribution of confirmed/suspected cases overall versus inside the map.


The R package plotdap provides a programming interface that makes it easy to visualize spatio-temporal data from NOAA servers using various mapping projections.


The R package pedestrians provides an interface to the City of Melbourne’s pedestrian data. The video below shows how to navigate one of the numerous visualizations available through this site.


The R package eechidna provides a (shiny) web application for exploring Australian election and census data. The video below shows how the app may be used to compare the demographics (and geographic location) of electorates that elected a liberal party representative (in green) versus a labour representive (orange).


The R package pitchRx provides a programming interface for acquiring and visualizing MLBAM’s PITCHf/x data. This publication describes the project and its use cases.


The 2016election repository provides some R scripts to collect and visualize outcomes from the 2016 US presidential election. The video below shows some insights gleaned from this data: