After having successfully run a simple R-script inside a Docker container before, we next attempt to repeat this process for entire apps built within the RShiny framework. Join me on the next step toward deploying your work done in R with the help of neat Docker containers!
How can you frame a data science question according to your client’s needs? In this blog post, our colleague Dominique explains how important it is to think about the business question in a different way – the data science way.
To increase revenue, customers should be offered products they may need or films they might like. In this blog post, our colleague Andreas explains how to train your own movie recommender with R and provides it in a Shiny App.
As data scientists, getting our hands on the data we need is often the most challenging part of a project. In practice, we tend to make life hard on ourselves because we don’t use the best tools for the job. Well no longer! Read on to learn how can you can harness Airflow to orchestrate your own ETL processes like a pro!
Have a look at what my team and I worked on during the Permafrost Hackathon in Zurich. The goal was to detect movements from multitemporal images. Since the images didn’t have any labels, we used unsupervised learning methods. Check it out, yo!
This blog is a hands-on experience in Dash, presenting core components, how to display figures with callbacks, supplying you with a working web application to play with, and the resources to build your own. Dash is a powerful tool for Python developers. Developed by the team behind Plotly, Dash is an open-source framework built on top of Flask, Plotly.js, and React.js.
In this blog post, Matthias shows you how to write and structure code even faster and more efficiently. Learn how to define keyboard shortcuts in RStudio with his step-by-step tutorial.
The number of different products and customers in any business area are practically infinite.
But how can you find interactions between them like joint purchases and define groups? One solution is the so-called Community Detection. In this blog post, I want to show you the magic behind Community Detection and give you a theoretical introduction into the Louvain and Infomap algorithm.
Newman and Postman form a great team to test your REST API. I will give you a quick roundtrip through both tools and their interplay: define requests and tests, export them, and let them run with CLI and within Jenkins.
Continuing our effort of applying the principles of reactivity to the UI part of a ShinyApp, this blog introduces two ways of conditionally rendering UI-elements in your app. Both presented solutions accomplish the same goal, once from the server part and once from the UI part of your application.