You have the fundamentals of R Highcharts under your belt by now. The next logical step is to introduce a bit more complexity in the code, but for the greater good. And that good is implementing drilldown charts straight in R! This will allow you to click on individual chart elements to open up yet another visualization that displays the data for a selected segment. Think sales for a country (overall) vs sales for a city (drilled down). And sure, working with R Highcharts drilldown involves more code, as you have to prepare two datasets and link them, but the overall benefits outweigh the cos ..read more

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 real users behavior is called end-to-end testing. It’s not some function, runs with some parameters, and we’re happy. It’s simulating real interactions with clicks and typing inputs in a programmatic way. Such testing ensures that your applications (be it R/Shiny or Python or any language for that matter) work as they should, from the user interface down to the backend processes. Overall ..read more

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 of the curve. Dive into workshops, seminars, and sessions that push your abilities. Master foundational concepts or explore advanced topics. Continuous learning and networking are essential for professional growth, and ShinyConf will feature introductions and deep dives into the latest R/Shiny trends in industry, research, and community. ‍ Curious about the ShinyConf 2024 keynotes? Learn more and discover what’s in stor ..read more

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 you leverage Git to its full potential for improved productivity and collaboration.‍ We already introduced you to Git and stated why version control is so important in our blog post, Version Control for Pharma: A Comparison of Gitflow and Trunk-based Development, but how do we get the best out of it? Use Case Scenario Depending on the project you are working on, there will be different appro ..read more

If you’re looking to take your R data visualization skills to the next level, interactivity is the name of the game. There aren’t too many packages that offer it out of the box, but you don’t need quantity if you have quality. Highcharts is among the most popular JavaScript packages for interactive data visualization, and it has a superb R port – Highcharter. It’s the package you’ll want to use to explore R Highcharts capabilities for basic data visualizations, maps, drill-down charts, and even R Shiny. We’ll focus on the first one today, but stay tuned to the Appsilon blog to learn more adva ..read more

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 flexible way to create publication-quality tables in Quarto reports or Shiny apps such as clinical trial reports, research documents, and business analytics reports. However, Python users are searching for a similar tool. This search has led to the rise of great_tables —a promising library designed to bridge this gap and enhance table formatting in Quarto and PyShiny. Discover how to craft professional-g ..read more

In a world where compute time is billed by the second, make every one of them count. There are zero valid reasons to utilize a quarter of your CPU and memory, but achieving complete resource utilization isn’t always a straightforward task. That is if you don’t know about R dtplyr. One option is to use dplyr. It’s simple to use and has intuitive syntax. But it’s slow. The other option is to use data.table. It’s lightning-fast but has a steep learning curve and syntax that’s not too friendly to follow. The third – and your best option – is to combine the simplicity of dplyr with efficiency of d ..read more

Parallelizing R dataframe computation is a guaranteed way to shave minutes or even hours from your data processing pipeline compute time. Sure, it adds more complexity to the code, but it can drastically reduce your computing bills, especially if you’re doing everything in the cloud. R doParallel package provides a significant speed increase to your dataframe calculation while minimizing code changes. It has all you need and more to get your feet wet in the world of dataframe parallelization, and today you’ll learn all about it. After reading, you’ll know what changes you need to make to run ..read more

Data visualization and analytics tools are crucial for businesses and researchers alike. Power BI, Spotfire, and R Shiny have emerged as significant players in the market. This article aims to compare these data visualization tools for businesses across various parameters, helping you make informed decisions based on your specific dashboard needs. See how R Shiny stacks up against Tableau as an Excel alternative. Decide which tool fits your needs in our in-depth comparison: Tableau vs. R Shiny: Which Excel Alternative Is Right For You? TL;DR: This article compares R Shiny, Power BI and Spo ..read more

In the previous article, we learned about the first OOP system in R called S3. In this one, we are going to dive into the S4 OOP system. The S4 system is a more formal OOP system developed by Bell Labs and introduced into the S language in the late 1990s. Today, we will learn about features of S4 and look at example use cases of this system in the community. We will also learn about some recommended practices to consider when using S4 classes and cover general tips on object-oriented programming in R. Read the series from the beginning: Object-Oriented Programming in R (Part 1): An Introduct ..read more