![]() Usethis::use_coverage() reports test coverage. Usethis::use_cran_badge() reports the current version of your package on CRAN. Examples of functions that insert development badges: It sets up a place for future badges, such as results from automatic continuous integration checks ( Section 21.2). ![]() This is important so that your README works when it’s displayed by CRAN. It sets up our recommended knitr options, including saving images to man/figures/README- which ensures that they’re included in your built package. It includes a comment to remind you to edit README.Rmd, not README.md. output: github_document - ``` plot(pressure) ``` In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.Ī few things to note about this starter README.Rmd: Rbuildignore, since only README.md should be included in the package bundle. 1 This creates a template README.Rmd and adds it to. The easiest way to get started is to use usethis::use_readme_rmd(). That is, it works well to have README.Rmd as the main source file, which you then render to README.md. Given that it’s best to include a couple of examples in README.md, ideally you would generate it with R Markdown. Notice the hyperlinked “README” under “Materials”.Īs the home page of your pkgdown site, if you have one. On CRAN, if you release your package there. The repository home page, if you maintain your package on GitHub (or a similar host). This will be rendered as HTML and displayed in several important contexts: This is also a good place to describe how your package fits into the ecosystem of its target domain.Īs mentioned above, we prefer to write README in Markdown, i.e. to have README.md. For more complex packages, this will point to vignettes for more details. Installation instructions, giving code that can be copied and pasted into R.Īn overview that describes the main components of the package. Here’s a good template for README:Ī paragraph that describes the high-level purpose of the package.Īn example that shows how to use the package to solve a simple problem. If they decide that your package looks promising, the README should also show them how to install it and how to do one or two basic tasks. When you write your README, try to put yourself in the shoes of someone who’s come across your package and is trying to figure out if it solves a problem they have. Some of its traditional content is found elsewhere in an R package, for example, we use the DESCRIPTION file to document authorship and licensing. The README file is a long-established convention in software, going back decades. The goal of the README is to answer the following questions about your package: 19.1 READMEįirst, we’ll talk about the role of the README file and we leave off the file extension, until we’re ready to talk about mechanics. In keeping with our practices for help topics and vignettes, it’s our strong recommendation and it’s what we describe here. These two files don’t have to be written in Markdown, but they can be. NEWS.md, which describes how the package has changed over time ( Section 19.2).Įven if your package is intended for a very limited audience and might not ever be released on CRAN, these files can be very useful. The README plays an especially important role on GitHub or similar platforms. ![]() README.md, which describes what the package does ( Section 19.1). These two are important, because they are featured on both the CRAN landing page and the pkgdown site for a package: To get the images to render you then also need to push that image folder into Github.In this chapter we highlight two files that are conventionally used to provide some package-level documentation. ![]() If you have matplotlib images, it saves them in a folder named README_files (not sure if there is a flag to change this option). To compile the notebook to markdown is quite simple: jupyter nbconvert -execute -to markdown README.ipynb You might typically want to add in README.ipynb to your gitignore file, but here I included it in the github package so you can see what this notebook looks like. But you can also include matplotlib images as well: (Note that the pip install code is not run in a %sh cell, it is just code formatting inside of a markdown cell.) I do like the way R markdown renders the output a bit nicer than here (I also haven’t tried with pretty pandas tables). So we have nice syntax highlighting even. Here is the update, where I use jupyter to render the markdown nicely: You can see I have an example code snippet, but it does not actually output the results. So first, here is the old readme in its entirety, rendered on Github: So here is a quick example of that for my retenmod python package. I thought to myself that you could functionally do the same thing with juypter notebooks for python. Working on my R package ptools, the devtools folks have you make a readme R markdown file to compile to a nice readme markdown file for github.
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