Python-bloggers

Every developer must solve two difficult problems when creating a Shiny application (in fact, any application) from the ground up: software architecture and data design. In the world of clinical data analysis, however, much development has been aimed at providing a jump-start approach to creating R/Shiny applications that would ...

In our previous blog post, we introduced the concept of profiling for optimizing Shiny app performance. Today, we’ll take a deep dive into three powerful tools in this arsenal: reactlog, profvis and shiny.tictoc. Imagine your Shiny app – users are interacting seamlessly, data is processing swiftly, and visualizations update ...

The pharmaverse is a collaborative project where leading pharmaceutical companies and passionate individuals come together to create helpful tools for clinical reporting. By using R programming and the open-source community, the pharmaverse makes it easier to gain insights and increase transparency in the pharma industry. In this post, let’s ...

1. Introduction 2. Problem 3. Scenario 4. Notations Used and Coding Guidelines 4.1. Notations Used 4.2. Coding Guidelines 5. Solutions 5.1 Design 5.1.1 Algorithms Steps 5.1.2 Algorithms Steps Visuals 5.1.3 Algorithms Flow Chart 5.1.4 Strategy Design Patterns 5.2 The Algorithms 5.2.1 Algorithms from Scratch 5.2.2 Algorithms from sklearn.cluster package 5.2.3 Complexity of the Algorithms 6.… Read More »UnSupervised Learning, Clustering and K-Means

Ever wondered what it really takes to keep complex tech projects on track? Who even is a Delivery Manager? Meet Aga Rasińska, one of our Delivery Managers at Appsilon, who does just that every day. What can you expect from a project collaboration with Appsilon? Learn more about how ...

Imagine your Shiny app – users are interacting seamlessly, data is processing swiftly, and visualizations update effortlessly. This dream becomes reality with a focus on performance optimization. We created a guide on profiling R/Shiny applications. Check it out to learn the right tweaks to speed up your application.  Here’s ...

Adaptive (online/streaming) learning with uncertainty quantification and explanations using learningmachine in Python and R

In a previous blog post I showed how to get over limitations of creating dummy datasets directly in Microsoft 365 Copilot by asking for a Python script instead, particularly the faker package. Technology moves quickly, and since publishing that post, the Faker package has become available directly within the Python in ...

Geospatial vector data is a way of representing geographic features in a digital format using points, lines, and polygons. Unlike raster data, which represents geographic data as a grid of cells or pixels, vector data represents features more pr...