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 ...

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 ...

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, ...

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 ...

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. ...

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 ...

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 ...

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. ...

Let’s be honest – all of your R Shiny app logic is contained in a single app.R file, and it’s probably several hundreds of lines long. No judgment – we’ve all been there. But there’s a better way, and today you’ll learn all about it. R ...

R Shiny is all about reactivity. It’s a paradigm that allows you to build responsive and interactive applications by implementing data-driven interfaces. Changes triggered by the user will be immediately reflected in the application. Long story short, master R Shiny reactivity and you’ll master Shiny. But where do ...

So, you’ve trained a classification machine learning model. Now what? How do you evaluate it? That’s where machine learning evaluation metrics for classification come in. This article brings you the top 10 metrics you must know, implemented primarily for binary classification problems. Multi-class classification datasets might require you to ...

R Esquisse is an R package that provides an intuitive GUI for creating data visualizations using ggplot2. This package is particularly valuable for stakeholders seeking quick interactive analysis and insights gathering from data sources, whether they are new to R or prefer a more visual approach to creating plots. With ...

We live in a constantly and rapidly evolving environment, which means that effective planning management is crucial for companies. This is particularly true in the pharmaceutical sector, which involves stringent regulations, research and development timelines, and intricate supply chains. Traditional planning methods often fall short in providing a comprehensive overview ...

The R {targets} package is a pipeline tool for statistics, data science, and machine learning in R. The package allows you to write and maintain reproducible R workflows in pipelines that run only when necessary (e.g., either data or code has changed). The best part is – you’ll learn ...

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 ...

What happens when there’s no data to show to the user? This can occur in Shiny apps, where users must upload their own data. Well, one solution is to show an empty screen (an empty state). But this negatively impacts the user experience. A better approach would be to ...

Is your R Shiny app slow, but you doubt Shiny itself is the bottleneck? This is a likely scenario, especially if you’re given a slow database with no indexes or normalization. Data retrieval and database communication in general become an issue, and it has nothing to do with R ...

How many times have you encountered a file path issue when running your code on a different environment? Probably far too many. It doesn’t need to be the case anymore – The R config package is here to allow you to manage environment-specific configuration files. The best part? You’ll ...

How many times has your code unexpectedly stopped working? Even better, how many times did the code work on your machine, but your coworkers couldn’t run it? Both questions share the same underlying problem – R environment. That’s where R renv comes in – a dependency management package that will ...

Every data science project needs a data validation step. It’s a crucial part, especially when feeding data into machine learning models. You don’t want errors or unexpected behaviors in a production environment. Data validation is a way you can check the data before it touches the model and ...