Publications by Dario Radečić
Matplotlib vs. ggplot: How to Use Both in R Shiny Apps
Data Science has (unnecessarily) divided the world into two halves – R users and Python users. Irrelevant of the group you belong to, there’s one thing you have to admit – each language individually has libraries far superior to anything available in the alternative. For example, R Shiny is much easier for beginners than anything Python off...
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Observe Function in R Shiny – How to Implement a Reactive Observer
It’s easy to get down the basics of R and Shiny, but the learning curve becomes significantly steeper after a point. Reactive values? Reactive events? Reactive observers? Let’s face it – it’s easy to get lost. We’re here to help. Today you’ll learn all about the Observe function in R Shiny – what it is, what it does, and how to use ...
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R Shiny vs Shiny for Python: What are the Key Differences
If you haven’t been living under a rock for the past couple of weeks, you’ve likely noticed some groundbreaking news in the Shiny department. Yes, it’s finally available for Python! But how is the current Shiny for Python version? How does R Shiny compare vs Shiny for Python (PyShiny)? We have the answers, so continue reading. Shiny for Pyt...
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R Shiny in Government – Top 7 Dashboards You Should See
R Shiny allows developers to write production-ready dashboards in no time, and for any use case. It’s not rare to find a vast number of dashboards in business-related domains, such as sales and marketing, but the possibilities don’t end there. Today we bring you seven curated examples of R Shiny in government. These dashboards are used for an...
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R and Excel: How to Combine the Best of Both Worlds
R and Excel go together like macaroni and cheese. There’s no need to choose one over the other, as there are numerous packages and extensions that allow them to work together. Just because you have an xlsx file doesn’t mean you can’t analyze it with R. Likewise, you can write R code to populate and manipulate Excel spreadsheets. Today you�...
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Data Cleaning in R: 2 R Packages to Clean and Validate Datasets
Real-world datasets are messy. Unless the dataset was created for teaching purposes, it’s likely you’ll have to spend hours or even tens of hours cleaning it before you can show it on a dashboard. That’s where two packages for data cleaning in R come into play – janitor and data.validator. And today you’ll learn how to use them togeth...
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5 Key Data Visualization Principles Explained – Examples in R
Data visualization can be tricky to do right. There are a ton of key principles you need to be aware of. Today we bring you 5 best practices for visualizing data with examples in R programming language. Incorporate these key R data visualization principles into your toolset to improve your data storytelling. After reading, you’ll know how to pr...
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Top 5 Data Science Take-Home Challenges in R Programming Language
Data Science take-home challenges will definitely get you out of your comfort zone. But you know what? That’s a good thing – it’s the only zone in which you’ll learn something, after all. Taming the take-home challenges is necessary both for beginner and advanced R programmers, but the challenges you’ll work on vary significantly depend...
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Top 3 Tools to Monitor User Adoption in R Shiny
Can you monitor user adoption for R Shiny apps? What is user adoption anyway? We’ll answer these questions and show you how to do it yourself in this article. Put simply, user adoption is the process by which new users become familiar with your product and/or service and decide to keep using it. User adoption rate is critical for your R Shiny d...
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How to Connect Google Earth Engine with R Shiny
Visualizing spatial data is always more challenging than it seems at first. There are too many moving parts, and the whole thing can quickly slow down your dashboard. Today you’ll learn how to connect Google Earth Engine with R Shiny. We’ll start by signing up for a free GEE account and configuring everything on the R end. Want to integrate p...
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