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.
Want to obtain a specific dataset from a website which does not have an API? In this post, I explain how to do this by scraping data using Python, how you determine whether it is allowed to scrape a specific page and more.
In this blog article, you will learn you how to set up a dashboard with the flexdashboard package, how to integrate interactive widgets and how to deploy the app on shinyapps.io.
Never heard of non-standard evaluation? Then our colleague Markus has the perfect answer for you: Bang Bang! In this blog post, Markus introduces meta-programming when using dplyr.