One of the criteria we use for submitted packages to our onboarding repo is how well it fits. See guidelines about fit at https://github.com/ropensci/onboarding/blob/master/policies.md#package-fit
In that link above for our policies about fit we list a number of areas that are considered in scope, or a good fit. In brief bulleted form:
- data retrieval (from APIs, data storage services, journals, and other remote servers). The data retrieved must have a scientific application and merely wrapping an API that serves data does not meet our criteria. (e.g.'s rplos)
- data extraction tools that aid in retrieving data from unstructured sources such as text, images and PDFs. (e.g.'s pdftools)
data visualization (interactive graphics in R that extend beyond base and
ggplot2). (e.g.'s plotly)
- data deposition into research repositories, including metadata generation. (e.g.'s zenodo)
data munging (In the context of the tools described above. Generic tools such as
tidyrdo not fit this criteria). Geospatial tools fall under this category. (e.g.'s geojsonio)
- data packages that aggregate large, heterogenous data sets of scientific value or provide R-specific formats for widely-used data (e.g., shapefiles for geographic boundaries) (e.g.'s rnaturalearth)
- reproducibility (tools that facilitate reproducible research, such as interfacing with git to track provenance or similar). (e.g.'s git2r)
These are rOpenSci’s onboarding policies for fit and scope at this time. This discussion is aimed at revising the scope and deciding what areas to broaden and what others to focus more narrowly.
Let us know what you think. What should remain as is, what should change.