Publications by Dario Radečić
R doParallel: A Brain-Friendly Introduction to Parallelism in R
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 speed, but on the other, you’re sacrificing the simplicity of single-threaded programs. Luckily, parallel processing in R is extremely developer-friendly. You don’t have ...
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R Docker: How to Run Your R Scripts in a Docker Container
So, you’ve written this amazing R script, but your coworkers can’t run it? It works on your machine, so they have to be doing something wrong, right? Wrong. It’s all about isolating and managing R environments. And that’s where R Docker comes in. Think of Docker as a program that allows you to run multiple operating systems (containers) on ...
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Renv with Docker: How to Dockerize a Shiny Application with an Reproducible Environment
You might have seen our previous articles on Dockerizing R scripts and R Shiny applications and were left wondering: Are all of my dependency versions really fixed right now? It’s a good question, and the answer is both yes and no at the same time. Your dependencies are fixed, but their version might change in the future, which might cause unwant...
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FastAI in R: How to Train Deep Learning Models with FastAI
It seems like it’s getting easier and easier to get into deep learning, at least as a practitioner. Packages like FastAI are available to masses for both Python and R, and in some simple scenarios they seem to provide a no-brainer solution for training deep learning models with as few lines of code as possible. Today we’ll dive into FastAI in R...
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R Data Processing Frameworks: How To Speed Up Your Data Processing Pipelines up to 20 Times
Picture this – the data science team you manage primarily uses R and heavily relies on dplyr for implementing data processing pipelines. All is good, but then out of the blue you’re working with a client that has a massive dataset, and all of a sudden dplyr becomes the bottleneck. You want a faster way to process data with minimum to no changes...
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R Shiny Docker: How To Run Shiny Apps in a Docker Container
It doesn’t matter if you’ve created the world’s best R Shiny application if you can’t share it with others. Reproducibility and portability are two major key points in software development, and they basically stand for the idea that the code running on your machine should be easily reproduced on another machine. That’s where Docker comes ...
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R lubridate: How To Efficiently Work With Dates and Times in R
Data analytics and programming don’t often have a lot of things in common. But where they’re almost indistinguishable is the topic of dates. The skill of working with datetimes is an integral part of both areas and is something you’ll want to master sooner rather than later. Newcomers often find this area challenging due to the sheer amount o...
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Time Series Analysis in R: How to Read and Understand Time Series Data
If there’s one type of data no company has a shortage of, it has to be time series data. Yet, many beginner and intermediate R developers struggle to grasp their heads around basic R time series concepts, such as manipulating datetime values, visualizing time data over time, and handling missing date values. Lucky for you, that will all be a thin...
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Shiny for Python Deploy: How to Host Your App For Free on GitHub Pages
When it comes to deploying Shiny for Python applications, you have options – some of which are entirely free. If you have been a developer for some time, you know free deployment isn’t always a given, especially when working with newer, more demanding tech stacks. Luckily, GitHub pages allow you to handle Shiny for Python deploy with a couple o...
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5 Ways R Programming and R Shiny Can Improve Your Business Workflows
It doesn’t take a data scientist to use R – that’s the point we’ll prove in today’s article. If you’re an experienced Excel user, you’ve likely run into some hard limitations, or find the tool confusing and sluggish when working with large datasets. That’s not the case with programming languages, as the recommended next step from Ex...
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