Inclusion of our shiny.gosling package on Bioconductor is a three-fold milestone for us. First, it gives our developers the recognition they deserve. Second, it strengthens our position as a leading voice in bioinformatics software development. And finally, it amplifies our visibility and credibility within the scientific community. For you, ...

Note: Thank you Kamil Żyła for providing guidance and expertise in writing R code for this article. R, being single-threaded in nature, isn’t the fastest programming language out there. You have options when it comes to parallelism, but these often don’t reduce the runtime as much as ...

If there’s one thing with a certain downward trend, it’s got to be people’s attention span. Even Google reports that pages with a load time of 5 seconds increase their probability of bounce by 90%! And that was in 2017! As an R Shiny developer, you must do everything in ...

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 ways (plural) to share Shiny apps. Today you’ll learn a bunch of them. By the end, you’ll ...

R Shiny needs no introduction, as it’s one of the go-to frameworks for building amazing applications and dashboards. But most of them lack one thing – interactivity and motion. Truth be told, R’s standard visualization packages such as ggplot2 aren’t interactive, so that’s the main reason why. ...

Welcome to the third (and final) part of our R Highcharts article series. So far, you’ve learned how to make basic interactive charts, and how to make drilldown charts. These two articles are a must-read before going over this one, since today the focus will be on code rather ...

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 ...

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 ...

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 ...

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 ...