Our Experience at posit::conf 2024
posit::conf 2024 was nothing short of amazing! While Posit has already shared their top highlights, we wanted to offer our own take on the experience—what really stood out to us,...continue reading.
Category: Python
GxP Validation in Software Development starts from the Definition of Done
GxP validation can be a complex and often misunderstood process, with each company implementing their own unique approach. During a recent roundtable I attended at the R/Pharma Summit in Seattle,...continue reading.
Simplifying Clinical Data Dashboards with {teal} and {pharmaverseadam}
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...continue reading.
Unveiling Bottlenecks (Part 2): A Deep Dive into Profiling Tools
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,...continue reading.
Exploring the Top 5 pharmaverse Packages
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...continue reading.
A Day in the Life of a Delivery Manager
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...continue reading.
R tidymodels: A tidyverse Like Ecosystem for Efficient Machine Learning in R
Have you ever thought R’s approach to machine learning is outdated? Like, data analysis and visualization tools are superb. Everything feels intuitive and every following step of your workflow integrates...continue reading.
Applications of Machine Learning in Pharma: From Drug Design to Clinical Trials
Machine Learning is transforming how we design drugs, model diseases, develop treatments, and conduct clinical trials. We recently collaborated with IIMCB to carry out augmented RNA-Ligand binding prediction with machine...continue reading.
Augmenting RNA-Ligand Binding Prediction With Machine Learning: A Leap Towards Enhanced Drug Discovery
In pharmaceutical research, the exploration of RNA-ligand interactions is a significant challenge, marking a stark contrast to the more developed understanding of protein-ligand interactions. Our AI model reduces the number...continue reading.
Introducing Positron: A New, Yet Familiar IDE For R And Python
Nowadays, most data professionals choose either R or Python when it comes to a programming language of choice. But what if you need both? Do you have to constantly switch...continue reading.
Beyond R Shiny: PyShiny’s Clean Design For Dynamic Plot Management
PyShiny (Shiny for Python) represents a significant advancement in the field of data dashboarding, setting new standards for design, maintainability, and scalability. This framework is not just an extension of...continue reading.
Open Source Spotlight Recap: Tapyr (Deployment-Ready PyShiny Template)
Following the release and an introductory blog post on Tapyr, our Shiny for Python framework, we recently hosted a Shiny Gathering to further explore its features and capabilities. Ready to...continue reading.
Shiny for Python Shinylive: How to Run Shiny for Python Apps Without a Python Server
Writing Shiny for Python applications is fun and games, but what about deploying them? Well, it’s the same story. Shiny for Python Shinylive saves the day by offering a beginner-friendly...continue reading.
Introducing Tapyr: Create and Deploy Enterprise-Ready PyShiny Dashboards with Ease
Are you an R/Shiny user looking to leverage the incredible capabilities of Shiny for Python without sacrificing the familiarity and comfort of your existing tools? Introducing Tapyr—our Shiny for Python...continue reading.
How to prevent data leakage in pandas & scikit-learn ☔
What is data leakage, why is it problematic, and how can you prevent it when working on a supervised Machine Learning problem in Python?continue reading.
Introduction to Quality Assurance for Shiny for Python Dashboards with Playwright
Business needs to have dashboards validated. Many companies have dedicated Quality Assurance teams. As software engineers, we call the validation process testing. The process of validating an app by mimicking...continue reading.
Counting Down to ShinyConf 2024: Why Attending Can Be a Game Changer for Your Career
Ignite your passion and propel your career at ShinyConf 2024, online from the 17th to the 19th of April! This isn’t just another conference; it’s an opportunity to stay ahead...continue reading.
Should you discretize continuous features for Machine Learning? 🤖
Learn how to “discretize” or “bin” your continuous features using Python’s scikit-learn, and find out why I usually don’t recommend doing so.continue reading.
Git Gud: Version Control Best Practices
Git best practices are essential for developers looking to manage their projects efficiently. In this article, we’ll dive into the key techniques that can transform your version control workflow, ensuring...continue reading.
Discover great_tables: The Python Answer to R’s {gt} Package for Table Formatting in Quarto and PyShiny
Crafting compelling narratives often depends on presenting insights clearly and effectively. This skill is key for data science. For users of R’s {gt} package, it has provided a powerful and...continue reading.
