In this blog post, our colleague Lukas gives you a high-level introduction to RDS files (vs. RData files) and serialization. The function checks whether there is already an RDS with the same name in the path, renames it if required, and provides it with a timestamp and a reference ARCHIVED_ON_xx. The function then saves the new RDS under the specified name.
As data scientists, getting our hands on the data we need is often the most challenging part of a project. In practice, we tend to make life hard on ourselves because we don’t use the best tools for the job. Well no longer! Read on to learn how can you can harness Airflow to orchestrate your own ETL processes like a pro!
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!