The COVID-19 pandemic has dramatically impacted all of our lives in a very short period of time.Spring and summer are usually very busy as students prepare to go the field to engage in various data collection efforts.The pandemic has also disrupted these carefully planned activities as travel is suspended and local and remote field stations have closed indefinitely.A lost field season can be a major setback for a dissertation timeline and

In early September, the version 2.0.0 of rmangal was approved byrOpenSci, four weeks later it made it to CRAN. Following-up on our experience wedetail below the reasons why we wrote rmangal, why we submitted our package torOpenSci and how the peer review improved our package.

NASA generates and provides heaps of data to the scientific community. Not allof it is looking out at the stars. Some of it is looking back at us here onEarth. NASA’s Earth science program observes, understands and models theEarth system ¹ . We can use these data to discover how our Earth is changing,to better predict change, and to understand the consequences for life on Earth.

We’ve just released a new version of rnoaa with A LOT of changes. Check outthe release notesfor a complete list of changes.

Every R package has its story. Some packages are written by experts, some bynovices. Some are developed quickly, others were long in the making. This is thestory of jstor, a package which I developed during my time as a student ofsociology, working in a research project on the scientific elite withinsociology.

Introduction I never thought that I’d be programming software in my career. I startedusing R a little over 2 years now and it’s been one of the most importantdecisions in my career. Secluded in a small academic office with no oneto discuss/interact about my new hobby, I started searching the web fortutorials and packages. After getting to know how amazing and nurturingthe R community is, it made me want to become a data scientist.

The problem Text-mining - the art of answering questions by extracting patterns, data, etc. out of the published literature - is not easy. It’s made incredibly difficult because of publishers. It is a fact that the vast majority of publicly funded research across the globe is published in paywall journals.

Nearly 4 years ago I wrote on this blog about an R package solr for working with the database Solr. Since then we’ve created a refresh of that package in the solrium package. Since solrium first hit CRAN about two years ago, users have raised a number of issues that required breaking changes. Thus, this blog post is about a major version bump in solrium. What is Solr?

elastic is an R client for Elasticsearch elastic has been around since 2013, with the first commit in November, 2013. What is Elasticsearch? If you aren’t familiar with Elasticsearch, it is a distributed, RESTful search and analytics engine.It’s similar to Solr. It falls in the NoSQL bin of databases, holding data in JSON documents, insteadof rows and columns.

Excited to annonunce a new package called charlatan. While perusingpackages from other programming languages, I saw a neat Python librarycalled faker. charlatan is inspired from and ports many things from Python’shttps://github.com/joke2k/faker library. In turn, faker was inspired fromPHP’s faker,Perl’s Faker, andRuby’s faker. It appears that the PHPlibrary was the original - nice work PHP. Use cases What could you do with this package?