python

This is part two of our three part series Part 1: API as a package: Structure Part 2: API as a package: Logging (this post) Part 3: API as a package: Testing (to be released) Part 1 of this series laid out some ideas for how one might structure a {plumber} application as an R package, inspired by solutions such as {golem} and {leprechaun} for {shiny}. In this installment of the series we look at adding some functions to our package that will take care of logging as our application runs.

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. We all benefit from some form of data science every day, even if we don’t realise it - the weather forecast is based on analysis of the weather patterns in the atmosphere and what that has resulted in previously; the placement of road safety equipment (speed cameras, SLOW signs, etc) is based on analysis of previous accidents; online advertising is based on you as a user - how else would they do this without analysis of your data to find out what to show you?

This is part one of our three part series Part 1: API as a package: Structure (this post) Part 2: API as a package: Logging Part 3: API as a package: Testing (to be published) Introduction At Jumping Rivers we were recently tasked with taking a prototype application built in {shiny} to a public facing production environment for a public sector organisation. During the scoping exercise it was determined that a more appropriate solution to fit the requirements was to build the application with a {plumber} API providing the interface to the Bayesian network model and other application tools written in R.

This is the final part of our three part series Part 1: Python API deployment with RStudio Connect: Flask Part 2: Python API deployment with RStudio Connect: FastAPI Part 3: Python API deployment with RStudio Connect: Streamlit (this post) RStudio Connect is a platform which is well known for providing the ability to deploy and share R applications such as Shiny apps and Plumber APIs as well as plots, models and R Markdown reports.

This is part two of our three part series Part 1: Python API deployment with RStudio Connect: Flask Part 2: Python API deployment with RStudio Connect: FastAPI (this post) Part 3: Python API deployment with RStudio Connect: Streamlit RStudio Connect is a platform which is well known for providing the ability to deploy and share R applications such as Shiny apps and Plumber APIs, as well as plots, models and R Markdown reports.

This is part one of our three part series Part 1: Python API deployment with RStudio Connect: Flask (this post) Part 2: Python API deployment with RStudio Connect: FastAPI Part 3: Python API deployment with RStudio Connect: Streamlit RStudio recently announced they are changing to Posit. Their publishing platform RStudio Connect (-> Posit Connect) is well known for providing the ability to deploy and share R applications such as Shiny apps and Plumber APIs as well as plots, models and RMarkdown reports.

We would posit (see what we did there) that R-{shiny} has been a boon for data science practitioners using the R language over the last decade. We know that in our Python work, we have certainly been clamouring for something of the same ilk. And whilst there are other frameworks that we also like, streamlit and dash to name a couple, neither of them has filled us with the same excitement and confidence that shiny did in R to build both simple and complex bespoke web applications.

July 25 - 28 2022 saw thousands of people attend rstudio::conf(2022) both in-person in Washington D.C. and virtually from all over the world, including a few of us from Jumping Rivers. Here’s a recap of the big news, and a few of our personal highlights from the conference! The secrets are out! There were a couple of big announcements during the conference that it would be remiss not to mention.

We’re now three months on from the initial release of Diffify, and what a few months it’s been! We thought now seemed like a good time to give you an overview of the big updates that Diffify has been through since it’s launch. Recognition and user feedback We are delighted to see that our app has been quickly adopted by the R community: R Weekly now displays links to Diffify for updated CRAN packages!

At Jumping Rivers, we love data science! Surprised? Didn’t think so … But, did you know that as well as providing training and consultancy, we also like to get involved with the data science community! If you’re reading this, then you’ve already found our blog, where we release weekly posts giving an insight into the projects we do here at JR, as well as hints, tips and tutorials to help you improve your programming.