In the previous post, we focused on setting up declarative Jenkins pipelines with emphasis on parametrizing builds and using environment variables across pipeline stages.
In this post, we look at various tips that can be useful when automating R application testing and continuous integration, with regards to orchestrating parallelization, combining sources from multiple git repositories and ensuring proper access right to the Jenkins agent.
Running stages in parallel Parallel computation using R Orchestrating parallelization of R jobs with Jenkins Failing early Cloning multiple git repositories Cloning into a separate subdirectory Cleaning up Changing permissions to allow the Jenkins user to read References Running stages in parallel Parallel computation using R There are numerous way to achieve parallel computation in the context of an R application, those native to R are for example
Jenkins is a popular open-source tool that helps teams with automation and implementation of continuous integration and deployment pipelines, comparable to for example Atlassian’s Bamboo, GitLab CI or to some extent Travis.
In this post, we share some practical lessons learned when integrating R applications via Jenkins for the purpose of continuous integration and regression testing on runner nodes configured using Jenkins via declarative pipelines defined in a Jenkinsfile.
As we wrote in Should you start your R blog now?, blogging has probably never been more accessible to the general population, R users included. Usually, the simplest solution is to host your blog via a service that provides it for free, such as Netlify, GitHub or GitLab Pages. But what if you want to host that awesome blog on your own, HTTPS enabled domain?
In this post, we will look at how to port a Hugo-based website, such as a blogdown blog to our own domain, specifically focusing on GitLab Pages.
In the previous post, we looked at how to easily automate R analysis, modeling, and development work for free using GitLab’s CI/CD. Together with the fantastic R-hub project, we can use GitLab CI/CD to do much more.
In this post, we will take it to the next level by using R-hub to test our development work on many different platforms such as multiple Linux setups, MS Windows and MacOS.
Automating the execution, testing and deployment of R work is a very powerful tool to ensure the reproducibility, quality and overall robustness of the code that we are building, be it for data analysis and modeling purposes, developing R packages or even blogging. Modern tools also provide a free an easy to use way of achieving this goal.
In this post, we will show a quick and simple way to automate R data analysis and package development checking, testing and installation with GitLab CI/CD and provide example files that can be used for testing packages and deploying blogdown-based websites.