For all those, who are struggling with the (kind of weird) Johns Hopkins University COVID-19 case data CSV files, we’ve created a free API that makes it easy to integrate the latest worldwide COVID-19 data into your application.
This blog is a hands-on experience in Dash, presenting core components, how to display figures with callbacks, supplying you with a working web application to play with, and the resources to build your own. Dash is a powerful tool for Python developers. Developed by the team behind Plotly, Dash is an open-source framework built on top of Flask, Plotly.js, and React.js.
In this blog post, Matthias shows you how to write and structure code even faster and more efficiently. Learn how to define keyboard shortcuts in RStudio with his step-by-step tutorial.
Continuing our effort of applying the principles of reactivity to the UI part of a ShinyApp, this blog introduces two ways of conditionally rendering UI-elements in your app. Both presented solutions accomplish the same goal, once from the server part and once from the UI part of your application.
A short overview of the functionalities of the R package gganimate: Learn how to turn your static ggplots in beautiful animations showcasing your data.
In this blog post, Stephan explains how to translate a simple R script, which transforms tables from wide to long format into a REST API with the R package Plumber and how to run it locally or with Docker.
Benchmarking your code is one thing – another thing is to keep and use the gained knowledge for future projects. In this blog, Jakob presents his collection of benchmarks and creates an easy to use a setup for new ones.
Training random forests on time series is one thing, but tuning them? It’s not like you can just apply cross validation and be done with it. Or can you? This post forms part two our mini-series “Time Series Forecasting with Random Forest”. Find out how you can tune the hyperparameters of the random forest algorithm when dealing with time series data. The answers might surprise you!
rBokeh is an interactive plotting library. Since it functions lack some arguments compared to its Python counterpart, plots are sometimes difficult to customize. I will show how to overcome those issues and drill out the plot objects.