If there’s one thing that can make your dashboards more intuitive to use, it’s icons. High-quality SVG icons can further explain, or sometimes replace long sections of text, and give...continue reading.
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...continue reading.
R Quarto is a next-gen version of R Markdown. The best thing is – it’s not limited to R programming language. It’s also available in Python, Julia, and Observable. In...continue reading.
It’s crucial you address R Shiny accessibility early on. Why? Because you want to make sure your data products can be used by all users – inclusivity matters. In a...continue reading.
Are your Shiny dashboards getting slow? Maybe it’s time to explore some R Shiny caching options. We’ve been there – a client wants dozens of charts on a single dashboard...continue reading.
Being able to monitor user behavior lets you know why something works (or doesn’t) in your dashboard. It helps build your user adoption strategy with user behavior analytics. So what’s...continue reading.
If you want to talk about tried and tested programming paradigms, look no further than Object-Oriented Programming (OOP). The term was coined by Alan Kay way back in 1966, which...continue reading.
R Shiny is a no-brainer dashboarding choice for companies and individuals. There are thousands of Shiny dashboards in production today in every sector you can imagine – agriculture being no...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
Today we bring you 7 dashboard examples of R Shiny in life sciences. R Shiny is one of the easiest ways for developers to make production-ready dashboards when speed and...continue reading.
Data science is vastly different than programming. We use only four languages – R, Python, Julia, and SQL. Now, SQL is non-negotiable, as every data scientist must be proficient in...continue reading.
By far the easiest way to detect and interpret the interaction between two-factor variables is by drawing an interaction plot in R. It displays the fitted values of the response...continue reading.
Let’s face the facts – you want your amazing dashboards seen by the world. To do so, you have to find a way to share R Shiny apps. There are...continue reading.
PowerPoint is the most recognized presentation-making software, but it isn’t for everyone. Some may find it packed with unnecessary features, and to some extent that’s true. Microsoft updates it regularly...continue reading.
Streamlit Tutorial: How to Deploy Streamlit Apps on RStudio Connect
February 25, 2022
