Publications by The Jumping Rivers Blog
Visualising R Package Risk Assessments using Litmus
A few years ago, we started working with a global pharma company who brought us a particularly thorny challenge. They wanted to use R for FDA submissions—but every package they introduced had to pass through a slow, resource-intensive process to be risk assessed and approved. They’re sadly unable to be gung-ho about what R tooling they use, nee...
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Should I Use Your R Package?
The answer to this simple, innocuous question is: it depends. It depends on the package in question, of course. Perhaps less obviously, but just as importantly, it depends on who’s asking the question. We’re sure if we asked you about “package quality”, we would all come up with what makes a good package: Documentation Unit tests Author cr...
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Sparklines in Reactable Tables
This is the second blog in a series about the {sparkline} R package for inline data visualisations. You can read the first one here. In this post I will be demonstrating how you can include sparklines inside HTML tables. Reactable {reactable} is an R package for producing HTML tables, commonly used in Shiny. To create a HTML reactable table all we ...
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Shiny in Production 2025: Abstracts Deadline Extension
Call for Abstracts Deadline Extended Good news! We’re extending the deadline for abstract submissions for Shiny in Production 2025 by two weeks. You now have until 11:59 PM BST on 3rd April 2025 to submit your proposal. This extension gives you extra time to refine your ideas and submit a strong proposal for the conference, which will take place ...
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Vetiver: MLOps for Python
This post is the fourth in our series on MLOps with vetiver:Part 1: Vetiver: First steps in MLOpsPart 2: Vetiver: Model DeploymentPart 3: Vetiver: Monitoring Models in ProductionPart 4: Vetiver: MLOps for Python (this post)Parts 1 to 3 introduced the {vetiver} package for R and outlined its far-reaching applications in MLOps. But did you know that ...
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Porting a Shiny App to Observable Framework: Part 1
PreambleThis post, Part 1 in a series of two, looks at porting the functional code of a Shiny app – written in R – into JavaScript code to be used in an Observable Framework application. Part 2 will look at styling and deploying the ported application.Background and MotivationIf you’re interested in interactive data visualisation you’ve pro...
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Porting a Shiny App to Observable Framework: Part 2
PreambleThis post, Part 2 in a series of two, looks at styling and deploying the Observable Framework app we built in Part 1. Codeblocks with burgundy backgrounds refer to specifc tagged commits in the accompanying GitHub repositiory. Join us for the next installment of our Shiny in Production conference! For more details, check out our conference ...
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Shiny in Production 2025: Call for Abstracts
Call for abstracts now openWe are excited to announce the Call for Abstracts for Shiny in Production 2025, to be held on 8th-9th October 2025 in Newcastle upon Tyne, UK. This event brings together industry experts, data scientists, and developers to explore the latest advancements and best practices in deploying Shiny applications in production set...
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Shiny in Production 2025
The fourth instalment of Shiny in Production is back this October, hosted at the Catalyst in Newcastle upon Tyne, with the super early bird deadline on the 31st of January! Set in the heart of Newcastle, this conference dives into the world of {shiny} and other web-focused R packages. Whether you’re a seasoned {shiny} user looking to connect and ...
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Creating an animated Christmas tree in R
With Christmas tomorrow we have decided to create an animated Christmas Tree using {ggplot2}, {sf} and {gganimate}. First we need a tree. To do this we have used an {sf} polygon where we pass in the coordinates of the Christmas tree as a list matrix to st_polygon. We can then use geom_sf to add this layer onto a ggplot object. library(ggplot2) libr...
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