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

See how an exploratory machine learning approach can inspire new ways to fight climate change and empower global stakeholders.

Learn how to use the session argument as a global list for passing parameters between modules in advanced Shiny apps to simplify the objects’ flow in code., organize app content, simplify objects flow logic, decrease bugs, increase speed

I used a Kaggle database to show how to separate customers into groups based on their purchase behavior.  You can customize your interactions with customers to increase loyalty. For R enthusiasts -- I used ggradar, ggplot2, animation, and factoextra

At Appsilon, we design, develop, and maintain enterprise R Shiny dashboards. These 6 best practices include tips for user adoption, architecture, and testing.

Is R Shiny better than Excel? Explore 8 Shiny alternatives to Excel, both for table data and data visualization.

SASS gives you variables, conditions, and loops to generate CSS. Learn how to use it with this SASS tutorial in R.

Learn how to add custom CSS to your Shiny apps and make them visually stunning by using style sheets in your project’s www folder.

In this article we explore if machines can replace us (R programmers) in writing Shiny code. I'll show you some easy applications of a simple model of a recurrent neural network implemented in an R version of Keras

AI will transform the insurance sector. Two techniques that will have the biggest impact are convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The 3rd key to successful AI implementation: productionization

A short summary on useR conference in Toulouse, France.

How to use R in AWS Lambda With AWS Lambda, small and frequent jobs can be done without setting up an instance and keeping it “alive” waiting for requests

Appsilon Data Science offers R Shiny open source packages that you can use for UI improvements, UI themes, translation, URL routing, and diagnostic display

Data scientists must check if the story behind the data makes sense. This requires business knowledge about the data. It's our responsibility to be curious, to explore and challenge to avoid logical problems in the data to save clients money.

Our CTO used transfer learning to build an object detection model on a small dataset - the model can recognize certain animals in images.

shiny.info is an open-source package that allows developers to display R Shiny diagnostic information - Here's how to use it!

A diversity of panels at the hottest R event in Europe: Shiny dashboards, R implementations, , machine learning, community! + beer and a tour of Old Town Gdansk!

Useful tips to be more productive while working with R Studio. Part 2.

Unveiling secrets of black box models is no longer a novelty but a new business requirement.

Useful tips and shortcuts to be more productive while working with RStudio.

Filip personal experience from Gartner Data & Analytics Summit London 2019

Zipf’s law is an empirical law that states that many different datasets found in nature can be described using Zipf’s distribution.