Summary This post is about the surprising uses I’ve noticed and the questionsabout the censo2017 R package, a tool foraccessing the Chilean census 2017 data, I’ve gotten since it was peer-reviewedthrough rOpenSci one year ago. The originalpost about thepackage one year ago didn’t cover the different examples I present here,including a Python port of the R package.

Summary censo2017 is an R package designed toorganize the Redatam ¹ filesprovided by the Chilean National Bureau of Statistics (Instituto Nacional deEstadísticas de Chile in spanish) in DVD format ² . This package was inspiredby citesdb(Noam Ross, 2020) and taxadb(Carl Boettiger et al, 2021).This post is about thispackage, the problem it solves, how to use it, and the fact that the package andits review process were all

Make ¹ -like pipelines enhance the integrity, transparency, shelf life, efficiency, and scale of large analysis projects.With pipelines, data science feels smoother and more rewarding, and the results are worthy of more trust. targets install.packages("targets") The targets ² package is a new pipeline toolkit for R.It recently cleared software review, and it is now on CRAN.

osfr provides a ( hopefully ) convenient R interface to OSF (Open Science Framework, https://www.osf.io), a free service for managing research developed by the Center for Open Science (COS). osfr completed its rOpenSci peer-review earlier this year and has been available on CRAN since February.

To the uninitiated, software testing may seem variously boring, daunting or bogged down in obscure terminology. However, it has the potential to be enormously useful for people developing software at any level of expertise, and can often be put into practice with relatively little effort. Our 1-hour Call will include two speakers and at least 20 minutes for Q &

Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the drake R package.

Our 1-hour Call on Reproducible Research with R will include three speakers and 20 minutes for Q & A. Ben Marwick will introduce you to a research compendium, which accompanies, enhances, or is a scientific publication providing data, code, and documentation for reproducing a scientific workflow.

Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out.

Despite the hype around “big data”, a more immediate problem facing many scientific analyses is that large-scale databases must be assembled from a collection of small independent and heterogeneous fragments – the outputs of many and isolated scientific studies conducted around the globe. Collecting and compiling these fragments is challenging at both political and technical levels.

So what is Docker? Docker is a relatively new opensource applicationand service, which is seeing interest across a number of areas. Ituses recent Linux kernel features (containers, namespaces) to shieldprocesses.