Introduction Functional programming is a programming paradigm where programs are constructed by applying and composing functions and it quite popular in the data science application because of some of its useful properties that can help for example...

Introduction Automating the execution, testing and deployment of R code is a powerful tool to ensure the reproducibility, quality and overall robustness of the code that we are building. A relatively recent feature in GitHub - GitHub actions - allows us to do just that without using additional tools such ...

Introduction Over the past years, R has been gaining popularity, bringing to life new tools to with ith it. Thanks to the amazing work by contributors implementing the Language Server Protocol for R and writing Visual Studio Code Extensions for R, the most popular development environment amongst developers across the ...

Introduction It is almost the 29th of February 2020! A day that is very interesting for R, because it marks 20 years from the release of R v1.0.0, the first official public release of the R programming language. In this post, we will look back on the 20 years of R with a ...

Introduction Over the past months, we published and refined a series of posts on Using Spark from R for performance with arbitrary code. Since the posts have grown in size and scope the blogposts were no longer the best medium to share the content ...

Introduction It is Christmas time again! And just like last year, what better time than this to write about the great tools that are available to all interested in working with R. This post is meant as a praise to a few selected tools and packages that helped me to ...

Introduction In the previous parts of this series, we have shown how to write functions as both combinations of dplyr verbs, SQL query generators that can be executed by Spark and how to use the lower-level API to invoke methods on Java object references from R. In this fifth part, ...

Introduction In the previous parts of this series, we have shown how to write functions as both combinations of dplyr verbs and SQL query generators that can be executed by Spark, how to execute them with DBI and how to achieve lazy SQL statements that only get executed when needed. ...

Introduction In the previous part of this series, we looked at writing R functions that can be executed directly by Spark without serialization overhead with a focus on writing functions as combinations of dplyr verbs and investigated how the SQL is generated and Spark plans created. In this third part, ...

Introduction In the first part of this series, we looked at how the sparklyr interface communicates with the Spark instance and what this means for performance with regards to arbitrarily defined R functions. We also examined how Apache Arrow can increase the performance of data transfers between the R session ...

Introduction Apache Spark is a popular open-source analytics engine for big data processing and thanks to the sparklyr and SparkR packages, the power of Spark is also available to R users. This series of articles will attempt to provide practical insights into using the sparklyr interface to gain the benefits ...

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Introduction 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, ...

Introduction 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 ...

Introduction Recently I was involved in a task that included reading and writing quite large amounts of data, totaling more than 1 TB worth of csvs without the standard big data infrastructure. After trying multiple approaches, the one that made this possible was using data.table’s reading and writing facilities ...

Introduction As pointed out by a recent read the R source post on the R hub’s website, reading the actual code, not just the documentation is a great way to learn more about programming and implementation details. But there is one more activity to get even more hands-on experience ...

Introduction 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. ...

Introduction 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 ...

Introduction 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 ...

Introduction 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 ...