Skip to main content

A Certification for R Package Quality

There are more than 12,000 packages for R available on CRAN, and many others available on Github and elsewhere. But how can you be sure that a given R package follows best development practices for high-quality, secure software? Based on a recent survey of R users related to challenges in selecting R packages, the R Consortium now recommends a way for package authors to self-validate that their package follows best practices for development. The CII Best Practices Badge Program, developed by the Linux Foundation's Core Infrastructure Initiative, defines a set of criteria that open-source software projects should follow for quality and security. The criteria relate to open-source license standards, documentation, secure development and delivery practices, version control and bug reporting practices, build and test processes, and much more. R packages that meet these standards are a signal to R users that can be relied upon, and as the standards document notes: There is no set of practices that can guarantee that software will never have defects or vulnerabilities; even formal methods can fail if the specifications or assumptions are wrong. Nor is there any set of practices that can guarantee that a project will sustain a healthy and well-functioning development community. However, following best practices can help improve the results of projects. For example, some practices enable multi-person review before release, which can both help find otherwise hard-to-find technical vulnerabilities and help build trust and a desire for repeated interaction among developers from different organizations. Developers can self-validate their packages (free of charge) using an online tool to check their adherence to the standards and get a "passing", "silver" or "gold" rating. Passing projects are eligible to display the badge (shown above) as a sign of quality and security, and almost 200 projects qualify as of this writing. A number of R packages have already gone through or started the certification process, including ggplot2, R Consortium projects covr and DBI, and the built-in R package Matrix Matrix. For more details on how R packages can get to a passing certification, and the R Consortium survey they led to the recommendation, see the R Consortium blog post at the link below. R Consortium: Should R Consortium Recommend CII Best Practices Badge for R Packages: Latest Survey Results  

from Revolutions https://ift.tt/2OxQumm
via IFTTT
Labels:

Comments

Post a Comment

Popular posts from this blog

Controlling legend appearance in ggplot2 with override.aes

[This article was first published on Very statisticious on Very statisticious , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. In ggplot2 , aesthetics and their scale_*() functions change both the plot appearance and the plot legend appearance simultaneously. The override.aes argument in guide_legend() allows the user to change only the legend appearance without affecting the rest of the plot. This is useful for making the legend more readable or for creating certain types of combined legends. In this post I’ll first introduce override.aes with a basic example and then go through three additional plotting scenarios to how other instances where override.aes comes in handy. Table of Contents R packages Introducing override.aes Adding a guides() layer Using the guide argument in scale_*() Changing multiple aesthetic par...
Post a Comment
Read more

Zooming in on maps with sf and ggplot2

(This article was first published on r-bloggers – WZB Data Science Blog , and kindly contributed to R-bloggers) When working with geo-spatial data in R, I usually use the sf package for manipulating spatial data as Simple Features objects and ggplot2 with geom_sf for visualizing these data. One thing that comes up regularly is “zooming in” on a certain region of interest, i.e. displaying a certain map detail. There are several ways to do so. Three common options are: selecting only certain areas of interest from the spatial dataset (e.g. only certain countries / continent(s) / etc.) cropping the geometries in the spatial dataset using sf_crop() restricting the display window via coord_sf() I will show the advantages and disadvantages of these options and especially focus on how to zoom in on a certain point of interest at a specific “zoom level”. We will see how to calculate the coordinates of the display window or “bounding box” around this zoom point. Loading t...
Post a Comment
Read more