The goal of this guide is to help contributors to ordr plan their contributions and navigate the process. The sections below cover different types of contributions, usually with increasing degrees of proposed change.
Note that ordr is released with a Contributor Code of Conduct. By contributing, you agree to its terms.
This guide was copied from that of ggalluvial, which was loosely adapted from the contributing guides for ggplot2 and from ropensci. If you have questions about or suggestions for this guide, please feel free to contact a package maintainer.
Small edits to comments, documentation, and other non-code files may be made using the GitHub file editor. To edit roxygen2 documentation, make changes to a source file R/<topic>.r
, on a line that begins with #'
, rather than to a documentation file man/<topic>.Rd
.
For more guidance on roxygen2 documentation, see this chapter in the online book R packages.
Questions, possible bugs, feature requests, other open-ended issues may be raised as issues on GitHub. In most cases, the issue should consist of two sections: a description of the issue, and a reproducible example of it. A reproducible example should attach packages, include data, and execute code:
Attach all relevant packages at the top of the example.
Use as small a data set as you can conveniently get that illustrates the issue. Once you have such a data set, the best way to include it in the example is to re-create it using data.frame()
or some other constructor function. Another way is as follows:
dput(data)
in R, where data
is the data set.Use as few lines or steps of code as feasible to illustrate the issue. Add comments at key steps.
For more guidance on reproducible examples, see this vignette for the reprex package.
Bug fixes, feature upgrades, and other substantive, self-contained changes may be contributed via pull request. Unless previously discussed with a maintainer, please follow these steps:
corybrunson/ordr
).For more guidance on the pull request process, see the contributing guidelines for ggplot2.