Logging is crucial to understanding what's going on with an application as it runs. There are some good solutions to logging of R shiny applications but less so for plumber applications. Continuing the API as a package series we examine how you might add some automatic logging to your API service written in R using plumber and the logger package.
Read more about API as a package: Logging
It will come as no great surprise that here at Jumping Rivers, we are huge advocates for learning data skills. There are many benefits to learning at least some basic data skills, even if you don't work explicitly with data.
Read more about The Benefits of Learning Data Skills
Inspired by opinionated packages on {shiny} app development such as {golem} and {leprechaun}, Jumping Rivers adopted a similar pattern for producing a {plumber} API as a package for one of our client projects. This is one of a series of blog posts discussing the general structure of said package.
Read more about API as a package: Structure
In this installment of our series on deploying to RStudio Connect we take a look at Streamlit. Streamlit enables quick and easy creation of web applications for machine learning and data science. We will go through a simple app and how to deploy it.
Read more about Python app deployment with RStudio Connect: Streamlit
Jumping Rivers are exhibiting at the RSS International Conference 2022, and we'll delivering workshops and talks as well. These are a couple of the sessions we're looking forward to the most!
Read more about Talks to watch at the RSS International Conference
Next up in our blog post series on deploying Python APIs to RStudio Connect is FastAPI. FastAPI is a high-performance web framework for building APIs with Python. Read on to see how you can deploy your FastAPI apps with RStudio Connect!
Read more about Python API deployment with RStudio Connect: FastAPI
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