Publications by Dario Radečić
shiny.emptystate – How To Add an Empty State Component to R Shiny
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 have a dedicated empty state screen, and today you’ll implement a Shiny empty ...
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RStudio Package Tests – From Theory to Implementation in R and Python
Let’s face it – the technical aspect of writing R and Python packages from scratch isn’t complicated. However, the challenging part lies in proper testing. The goals with package tests are to ensure the package works properly and without any bugs on the client’s hardware and that the correct dependency versions are used. To do this, we use ...
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Is Your Company Giving You What You Need to Build Great R Shiny Apps?
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 Shiny. Today you’ll learn how to build great R Shiny apps by solving...
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R config: How to Manage Environment-Specific Configuration Files
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 learn all about it today. In this article, we’ll go throu...
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R renv: How to Manage Dependencies in R Projects Easily
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 ensure issues like these won’t happen ever ...
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R data.validator – How to Create Automated Data Quality Reports in R and Shiny
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 ensures it’s not corrupted. And yes, you can automat...
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R Shiny Shipping Logistics: How Shiny Provides Value to Your Business
R Shiny is a go-to technology when making apps and dashboards for companies using the R programming language. Shiny helps solve problems in complex business areas with large datasets, such as agriculture and life sciences. It’s also used extensively in business workflows from supply chain management to improving the process of shipping logisti...
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Shiny for Python: How to Work With MySQL and Postgres Databases
Most of the real-world data is stored in databases, so knowing how to access different database systems from Shiny is a mandatory skill. With the rise of Shiny for Python, databases and their access got a whole new meaning, as Shiny developers are no longer limited to R.That being said, Shiny developers must learn how to connect to databases from...
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Imputation in R: Top 3 Ways for Imputing Missing Data
Real-world data is often messy and full of missing values. As a result, data scientists spend the majority of their time cleaning and preparing the data, and have less time to focus on predictive modeling and machine learning. If there’s one thing all data preparation steps share, then it’s dealing with missing data. Today we’ll make this p...
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Python Pipreqs – How to Create requirements.txt File Like a Sane Person
Want to include only the libraries you use in requirements.txt? Try pipreqs, a Python module for creating leaner requirements files.Every Python project should have a requirements.txt file. It stores the information of all libraries needed for a project to run, and is essential when deploying Python projects. This is traditionally done via the pi...
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