In May 2019 version 0.2.0 of tidync was approved by rOpenSci and accepted to CRAN. Here we provide a quick overview of the typical workflow with some pseudo-code for the main functions in tidync. This overview is enough to read if you just want to try out the package on your own data.
Theme song: PSA by Jay-Z We announced the testing version of skimr v2 onJune 19, 2018. After more than ayear of (admittedly intermittent) work, we’re thrilled to be able to say thatthe package is ready to go to CRAN. So, what happened over the last year? Andwhy are we so excited for v2? Wait, what is a “skimr”? skimr is an R package for summarizing your data.
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.
We want to know how you use rOpenSci packages and resources so we can give them, their developers, and your examples more visibility. It’s valuable to both users and developers of a package to see how it has been used “in the wild”. This goes a long way to encouraging people to keep up development knowing there are others who appreciate and build on their work.
The UCSC Xena platform provides an unprecedented resource for public omics data from big projects like The Cancer Genome Atlas (TCGA), however, it is hardfor users to incorporate multiple datasets or data types, integrate the selected data withpopular analysis tools or homebrewed code, and reproduce analysis procedures.
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.
The grainchanger package provides functionality for data aggregation to a coarser resolution via moving-window or direct methods. Why do we need new methods for data aggregation? As landscape ecologists and macroecologists, we often need to aggregate data in order to harmonise datasets. In doing so, we often lose a lot of information about the spatial structure and environmental heterogeneity of data measured at finer resolution.
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.
Three members of the rOpenSci team - Scott Chamberlain, Jenny Bryan, and Rich FitzJohn - as well as many community members will give talks at useR!2019. Many other package authors, maintainers, reviewers and unconf participants will be there too. Don’t hesitate to ask them about rOpenSci packages, software peer review, community, or just say hello if you’re looking for a friendly face. We’ve listed their talks for you.
rOpenSci’s community is increasingly international and multilingual. While we have operated primarily in English, we now receive submissions of packages from authors whose primary language is not. As we expand our community in this way, we want to learn from the experience of other organizations. How can we manage our peer-review process and open-source projects to be welcoming to non-native English speakers?
