Our blog-posts
Shiny-App – Best practice: Inline Documentation
Shiny App – Best practice: Inline Documentation If you are thinking: “I already know that”, these posts might be something for you as it is intended for users with pre-existing knowledge in R and Shiny. However, it assumes that you are already familiar with writing code in R, know the most widely used packages and […]
10 Tidyverse functions that might save your day
In this blog post, we will present 10 Tidyverse functions that are often overlooked by beginners but have proven to be very useful in the right context. We will first describe a problem that we faced in practice in a similar form and then explain how Tidyverse helps us to solve this problem.
It’s official – Santa Claus is Data Scientist
Die Familie hat sich versammelt, besinnliche Musik erklingt und die Kinderaugen strahlen mit dem beleuchteten Baum um die Wette. Es ist Heiligabend und der große Auftritt des Weihnachtsmanns steht kurz…
Automating AWS Organizations with Terraform
Automating AWS Organizations with Terraform n this part we will implement a real-world scenario: We will configure our AWS account in a way that allows multiple teams to access a wide variety of AWS services, but still isolate their resources from another. Terraform allows us to automate this process, so we will be able to […]
Shiny: Performance Tuning with future & promises – The practice
Shiny: Performance Tuning with future & promises The practice Let’s talk The second part of our blog series on Shiny dealt with the optimization within Shiny applications. We looked at the theoretical part on how Shiny and the packages future & promises work. In the context of these two packages it was presented how they […]
Shiny: Performance Tuning with future & promises – Theoretical
Shiny: Performance-Tuning mit future & promises Theoretical In our previous article about Shiny we shared our experiences with load testing and horizontal scaling of apps. We showed the design of a process from a proof of concept to a company-wide application. Let’s talk The second part of the blog series focuses on the R packages […]
Shiny: Load testing and horizontal scaling
Shiny: Load testing and horizontal scaling “Money can’t buy you happiness, but it can buy you more EC2 Instances…” – With this quote Sean Lopp, Product Manager at RStudio, PBC, rang in his “Scaling Shiny” showcase. In this showcase, he uses a load-testing approach to show how a Shiny application can be scaled for 10,000 […]
Ansible: Infrastructure as code (IaC)
“Infrastructure as code” has become an important key term in the world of system administration for the development and provision of IT systems – also in a data science context, test and production systems can be set up quickly and easily. The term “as code” refers to the fact that systems are no longer set up and configured manually but are developed using a scripting language. In our article we will show you how configuration processes can be automated by using Ansible.
Kubernetes: Horizontal scaling of data science applications in the cloud
Prediction models, machine learning algorithms and scripts for data storage: The modern data science application not only shows more and more complexity, but also puts the existing infrastructure to the test by temporary resource peaks. In this article, we will show how tools such as the RStudio Job Launcher in conjunction with a Kubernetes cluster can be used to outsource the execution of arbitrary analysis scripts to the cloud, scale them and return them to the local infrastructure.
R, Python & Julia in data science: A comparison
“Which programming language should be used for development?” Data scientists now have a selection of programming languages at their disposal. Each one has different properties. For this reason, the individual languages are also suitable for different areas. Data science languages also play a decisive role in implementing the right IT infrastructure. Based on the assessment, it will be identified which programming language is best suited for the requirements in your individual analysis scenario. In order to simplify the answer to the question posed in advance, this article briefly introduces and evaluates the current and most common languages.
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