All posts (Page 58 of 122)
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 & A.
As someone with a background in science, not software engineering, Steffi LaZerte will share her experiences using automated testing in R to ensure that packages do what they’re supposed to do, on all the operating systems they’re supposed to do it on, and that they handle weird stuff gracefully.
...Today we are pleased to announce that we have received new funding from the Gordon and Betty Moore Foundation. The $894k grant will help us improve infrastructure for R packages and enable us to move towards a science first package ecosystem for the R community. You may have already noticed some developments on this front when we announced our automated documentation server back in June. Over the coming months we plan to roll out more tools and services to make it easier to maintain and distribute packages while capturing the impact of such work....
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. The tidync package is focussed on efficient data extraction for developing your own software, and this somewhat long post takes the time to explain the concepts in detail....
We announced the testing version of skimr v2 on June 19, 2018. After more than a year of (admittedly intermittent) work, we’re thrilled to be able to say that the package is ready to go to CRAN. So, what happened over the last year? And why are we so excited for v2?
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Wait, what is a “skimr”?
skimr is an R package for summarizing your data. It extends tidyverse packages, and dplyr in particular, so that you can get a broad set of summary statistics with a single function call. You can install a pre-release version from the package’s GitHub repo.
...In early September, the version 2.0.0 of rmangal was approved by rOpenSci, four weeks later it made it to CRAN. Following-up on our experience we detail below the reasons why we wrote rmangal, why we submitted our package to rOpenSci and how the peer review improved our package.
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Mangal, a database for ecological networks
Ecological networks are defined as a set of species populations (the nodes of the network) connected through ecological interactions (the edges). Interactions are ecological processes in which one species affects another. Although predation is probably the most known and documented interaction, other less noticeable associations are just as essential to ecosystem functioning. For instance, a mammal that unintentionally disperses viable seeds attached to its fur might help plants to thrive. All of these interactions occur simultaneously, shaping ecosystem functioning and making them as complex as they are fascinating.
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