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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
The shiny.benchmark package by Appsilon allows you to compare the performance of different versions of R Shiny applications. You can write instructions that will be executed and evaluated on different...continue reading.
Spatial data is always tougher to visualize than it seems at first. Well, not anymore. R ggmap is an amazing package you can start using almost instantly, after some light...continue reading.
R programming language can read all sorts of data, and XML is no exception. There are many ways to read, parse, and manipulate these markup language files in R, and...continue reading.
There’s an optimal way to make R Shiny apps, and the Rhino package is a top choice for software engineers. In short, Rhino allows you to create Shiny apps like...continue reading.
Geospatial data is everywhere around us, and it’s essential for data professionals to know how to work with it. One common way to store this type of data is in...continue reading.
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!...continue reading.
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...continue reading.
R Shiny bslib is a package that makes working with Bootstrap themes a breeze. Let’s face it – Shiny apps look dull and mainstream by default. Not everyone has a...continue reading.
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.
Data Science / Python / R / R News / Statistics
Update to Data Science Software Popularity
June 8, 2023
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