Author: Dario Radečić

Are you still conducting runtime benchmarks in R through manual calls to `Sys.time()`? We don’t judge, but there’s a way more powerful and automated way to do the same –...continue reading.

When it comes to time series forecasting in R, one thing you don’t lack is options. There are dozens of algorithms and their variations you can choose from, and doing...continue reading.

When it comes to finding an R package capable of making interactive visualizations out of the box while also working flawlessly with R Shiny, you don’t have that many options....continue reading.

So, you’ve mastered the basics of ggplot2 animation and are now looking for a real-world challenge? You’re in the right place. After reading this one, you’ll know how to download...continue reading.

The main criticism people have when it comes to ggplot2 is the static nature of the charts it has to offer. Truth be told, it will never be an interactive...continue reading.

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...continue reading.

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...continue reading.

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...continue reading.

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...continue reading.

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...continue reading.

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...continue reading.

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...continue reading.

Implementing parallel execution in your code can be both a blessing and a curse. On the one hand, you’re leveraging more power your CPU has to offer and increasing execution...continue reading.