Your Guide to Quality Data Science Products

In this talk you’ll learn tools and best practices for making clean, reproducible R code in a working environment ready to be shared and productionized. We cover the benefits of version control, plumber, RStudio Connect, and many other tools.

In this presentation we present how to make use of the latest technical advancements in computer vision with an R setup even if they are originally implemented in Python.

Appsilon engineers Krystian Igras, Marcin Dubel, and Jędrzej Świeżewski, PhD will be giving virtual presentations on Friday, June 19th. Learn about xspliner, making production-ready R code, and using R for Machine Learning projects. 

Appsilon created a dashboard in collaboration the International Institute for Applied Systems Analysis to deliver information about COVID-19 to policymakers. We implemented the solution with Python and Svelte.

R Shiny has historically been used for data-driven applications and as a rapid development tool, but it's a highly adaptable technology. Is it possible to build a functioning video game in R Shiny? Let's find out.

Our favorite RStudio shortcuts, tips, and tricks to increase productivity while working with the RStudio IDE. We cover keyboard shortcuts, custom code snippets, addins, and more.

Dynamic Shiny Modules can destabilize the Shiny environment and cause its reactive graph to be rendered multiple times. Here is how to safely remove deleted module leftovers and make sure that your Shiny graph observers are rendered properly.

Distributed data science teams bring unique challenges. Managers may be looking for new tools. In this article we’ll explain how RStudio Connect helps organizations to properly organize teams and overcome the typical inefficiencies of remote work.

Introduction to object detection and image classification featuring the YOLO algorithm and its Darknet implementation

In this article, we focus on the technical aspects of the machine learning solution we implemented for the xView2 competition. We used PyTorch to build our models for satellite image analysis and fast.ai to develop their critical parts.

Our submission to the Pandemic Response Hackathon (CoronaRank) is inspired by Google’s PageRank and utilizes geolocation data in the Apache Parquet format from Veraset for effective exposure risk assessment using R and Markov Chain modeling.

This is the first of two blog posts about our recent participation in the Pandemic Response Hackathon. Our project (CoronaRank) was one of only 5 projects out of 230 submissions chosen to present at the closing ceremony.