Appsilon’s open source shiny.info package just received a major update and is now available on CRAN. A great solution for development and testing, shiny.info allows for displaying diagnostic information from inside Shiny apps.

Artificial Intelligence, Machine Learning, and Deep Learning models have demonstrated significant power to grow and improve businesses. This article explains how to properly approach developing an AI model.

As part of our D4G Initiative and with the support of a Google grant, Appsilon Data Science will be contributing to to the work of biodiversity conservationists at the National Parks Agency in Gabon in collaboration with the University of Stirling.

Can you build Shiny apps without any R knowledge? This R Shiny Dashboard Tutorial shows you my experience with learning R creating R Shiny dashboards in 2 days.

Learn how to find and use satellite image datasets for data science and machine learning projects with this beginner-friendly guide.

Have you considered adding AI/ML to your organization's operations? AI models can significantly reduce costs when they are implemented properly, but can end up in "purgatory" with the wrong approach. Here's how to properly implement AI models.

We'll show you how to import the proper libraries, set up a dashboard, add UI components, and more!

Appsilon engineers Paweł and Pedro share their perspective on rstudio::conf 2020, especially news and updates about R packages...

RStudio::conf 2020 is a wrap!

We recently took part in Hakuna-ma Data, a competition organised by DrivenData in partnership with Microsoft’s AI for Earth, which asked participants to build an algorithm for wildlife detection that would generalise well across time and locations...

rstudio::conf 2020 is here! Appsilon Data Science is proud to be a co-sponsor of the conference. Here is some information about the presentations we are doing and others that we hope to attend.

"So we sat down with the project manager on the client side, re-ordered the priorities, estimated additional work and were able to start coding on the very same day."

Appsilon is an RStudio Certified Partner offering R Shiny dashboards, Managed Services, and RStudio product implementations.

Many AI projects languish in pilot purgatory. Filip Stachura points out known risks in deploying and maintaining AI models. The question becomes, do you have the right people in place to respond to potential and likely issues?

Why stick to Excel and risk poorly maintained data or serious miscalculations? Discover efficiency gains brought by R Shiny and Python Dash

Building Deep Learning vision models satellite imagery to assist in identifying and labeling property damage from natural disasters.

Learn to create R apps with this R Shiny Dashboard Tutorial! We'll go from simple R Shiny to Bootstrap, Semantic UI, and then cover the custom version.

The city of Poznań wanted to know why it didn't rate highly with tourists. So they gave loads of data to a gathering of developers to gain some insight. TL;DR: we learned a lot from the hackathon & won the thing. R Shiny gave us a slight edge. :)

The algorithms we build affect lives. We have an ethical obligation not to teach our machines to be prejudiced. Here are some lessons and examples that will help you achieve this in your machine learning model assembly and use.

We need to build a culture where AI is built with a focus on the entire outcome, not just metrics or profit.

We live in a competitive time, filled with regional and global challenges, both physical and digital, and yet we are empowered with access to ever-growing data sets and tools at our fingertips: cyber-security, NewSpace, asymmetry of information

A recap of satRday Cardiff 2019. New R packages, rstats, education, a hackathon for inclusivity, community, fantasy football, live coding and pizza.

Climate change will exacerbate the consequences of natural disasters. Developing countries will be hit the hardest. We helped Dr. Junko Mochizuki build a decision support tool for policymakers in Madagascar to better mitigate this risk.

Explore ways NGOs can use AI for species conservation or find out how AI pros and data scientists can contribute to real-world projects.

How to help an organization turn piles of paper into a data lake - Now easier with Optical Character Recognition (OCR).

I had the honor to meet people who preserve biodiversity and genetic resources at the GenRes Bridge conference in Tuusula, Finland. I represented Appsilon Data Science's Data for Good initiative and shared pathways for incorporating DS and AI.

To achieve IA implementation success, plan for a partnership between humans and machines, not replacement. Buy-in is crucial, of course. So educate your workforce about IA and plan for hero moments. Automate tasks, not jobs and start small.

What are the differences between AI and IA? When is Intelligence Augmentation superior to a pure Artificial Intelligence approach? Which solution makes the most sense for your problem set? Consider IA solutions when a limited amount of data exists

Describes the best practices for setting automated test architecture for R Shiny apps. Automate and test early and often with unit tests, user interface tests, and performance tests.

Intelligence Augmentation is concerned with leveraging machines to enhance human intelligence. It’s a partnership between person and machine in which both parties contribute their strengths. It's AI that partners with humans for decision support

For advanced R Shiny applications, R6 classes help you organize your code. The Hows and Whys of using object-oriented programming (OOP) in Shiny applications...

Artificial Intelligence receives a great deal of hype with good reason but is it appropriate for your enterprise? You might want to consider the same tech with a different approach for your decision support system -- Intelligence Augmentation.

Two methods for keeping your Shiny app organized while avoiding hardcoding. Also, a tip for adding multiple languages (internationalization) to your app.

Catch highlights from the WhyR 2019 Conference in Warsaw, covering NLP, geocomputation, spatial analysis, random forests, and more.

Three methods for using javascript code in Shiny applications to build faster apps, avoid unnecessary re-rendering, and add components beyond Shiny's limits. Part 2 of a 5 part series on super solutions for Shiny architecture. 

EARL is the conference that you don't want to miss if you're an R enthusiast or just getting started with R. I've worked on numerous R Shiny projects with Appsilon Data Science Consulting, and I still learn new things each time I go