rOpenSci accepts packages that meet our guidelines via a streamlined Software Peer Review process. To ensure a consistent style across all of our tools we have written this chapter highlighting our guidelines for package development. Please also read and apply our chapter about continuous integration (CI). Further guidance for after the review process is provided in the third section of this book starting with a chapter about collaboration.
We recommend that package developers read Hadley Wickham and Jenny Bryan’s thorough book on package development which is available for free online. Our guide is partially redundant with other resources but highlights rOpenSci’s guidelines.
To read why submitting a package to rOpenSci is worth the effort to meet guidelines, have a look at reasons to submit.
1.1 Package name and metadata
1.1.1 Naming your package
We strongly recommend short, descriptive names in lower case. If your package deals with one or more commercial services, please make sure the name does not violate branding guidelines. You can check if your package name is available, informative and not offensive by using the pak::pkg_name_check() function; also use a search engine as you’d thus see if it’s offensive in a language other than English. In particular, do not choose a package name that’s already used on CRAN or Bioconductor.
There is a trade-off between the advantages of a unique package name and a less original package name.
A more unique package name might be easier to track (for you and us to assess package use for instance, less false positives when typing its name in GitHub code search) and search (for users to ask “how to use package blah” in a search engine).
On the other hand a too unique package name might make the package less discoverable (that is to say, to find it by searching “how to do this-thing in R”). It might be an argument for naming your package something very close to its topic such as geojson).
We recommend you to use the codemetar package for creating and updating a JSON CodeMeta metadata file for your package via codemetar::write_codemeta(). It will automatically include all useful information, including GitHub topics. CodeMeta uses Schema.org terms so as it gains popularity the JSON metadata of your package might be used by third-party services, maybe even search engines.
1.2 Platforms
Packages should run on all major platforms (Windows, macOS, Linux). Exceptions may be granted packages that interact with system-specific functions, or wrappers for utilities that only operate on limited platforms, but authors should make every effort for cross-platform compatibility, including system-specific compilation, or containerization of external utilities.
1.3 Package API
1.3.1 Function and argument naming
Functions and arguments naming should be chosen to work together to form a common, logical programming API that is easy to read, and auto-complete.
Consider an object_verb() naming scheme for functions in your package that take a common data type or interact with a common API. object refers to the data/API and verb the primary action. This scheme helps avoid namespace conflicts with packages that may have similar verbs, and makes code readable and easy to auto-complete. For instance, in stringi, functions starting with stri_ manipulate strings (stri_join(), stri_sort(), and in googlesheets functions starting with gs_ are calls to the Google Sheets API (gs_auth(), gs_user(), gs_download()).
For functions that manipulate an object/data and return an object/data of the same type, make the object/data the first argument of the function so as to enhance compatibility with the pipe operators (base R’s |>, magrittr’s %>%).
We strongly recommend snake_case over all other styles unless you are porting over a package that is already in wide use.
Avoid function name conflicts with base packages or other popular ones (e.g. ggplot2, dplyr, magrittr, data.table)
Argument naming and order should be consistent across functions that use similar inputs.
Package functions importing data should not import data to the global environment, but instead must return objects. Assignments to the global environment are to be avoided in general.
1.3.2 Console messages
Use either the cli package, or base R’s tools (message() and warning()) to communicate with the user in your functions.
Please do not use print() or cat() unless it’s for a print.*() or str.*() methods, as these methods of printing messages are harder for users to suppress.
Provide a way for users to opt out of verbosity, preferably at the package level: make message creation dependent on an environment variable or option (like “usethis.quiet” in the usethis package), rather than on a function parameter. The control of messages could be on several levels (“none,”inform”, “debug”) rather than logical (no messages at all / all messages). Control of verbosity is useful for end users but also in tests. More interesting comments can be found in an issue of the tidyverse design guide.
1.3.3 Interactive/Graphical Interfaces
If providing graphical user interface (GUI) (such as a Shiny app), to facilitate workflow, include a mechanism to automatically reproduce steps taken in the GUI. This could include auto-generation of code to reproduce the same outcomes, output of intermediate values produced in the interactive tool, or simply clear and well-documented mapping between GUI actions and scripted functions. (See also “Testing” below.)
We recommend your package use a consistent method of your choice for checking inputs – either base R, an R package, or custom helpers.
1.3.5 Packages wrapping web resources (API clients)
If your package accesses a web API or another web resource,
Make sure requests send an user agent, that is, a way to identify what (your package) or who sent the request. The users should be able to override the package’s default user agent. Ideally the user agent should be different on continuous integration services, and in development (based on, for instance, the GitHub usernames of the developers).
You might choose different (better) defaults than the API, in which case you should document them.
Your package should help with pagination, by allowing the users to not worry about it at all since your package does all necessary requests.
Your package should help with rate limiting according to the API rules.
Your package should reproduce API errors, and possibly explain them in informative error messages.
Your package could export high-level functions and low-level functions, the latter allowing users to call API endpoints directly with more control (like gh::gh()).
For more information on how to style your code, name functions, and R scripts inside the R/ folder, we recommend reading the code chapter in The R Packages book. We recommend the styler package for automating part of the code styling. We suggest reading the Tidyverse style guide.
You can choose to use = over <- as long you are consistent with one choice within your package. We recommend avoiding the use of -> for assignment within a package. If you do use <- throughout your package, and you also use R6 in that package, you’ll be forced to use = for assignment within your R6Class construction - this is not considered an inconsistency because you can’t use <- in this case.
1.5 CITATION file
If your package does not yet have a CITATION file, you can create one with usethis::use_citation(), and populate it with values generated by the citation() function.
CRAN requires CITATION files to be declared as bibentry items, and not in the previously-accepted form of citEntry().
If you archive each release of your GitHub repo on Zenodo, add the Zenodo top-level DOI to the CITATION file.
If one day after review at rOpenSci you publish a software publication about your package, add it to the CITATION file.
Less related to your package itself but to what supports it: if your package wraps a particular resource such as data source or, say, statistical algorithm, remind users of how to cite that resource via e.g. citHeader(). Maybe even add the reference for the resource.
As an example see the dynamite CITATION file which refers to the R manual as well as other associated publications.
citHeader("To cite dynamite in publications use:")bibentry(key ="dynamitepaper",bibtype ="Misc",doi ="10.48550/ARXIV.2302.01607",url ="https://arxiv.org/abs/2302.01607",author =c(person("Santtu", "Tikka"), person("Jouni", "Helske")),title ="dynamite: An R Package for Dynamic Multivariate Panel Models",publisher ="arXiv",year ="2023")bibentry(key ="dmpmpaper",bibtype ="Misc",title ="Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models",author =c(person("Santtu", "Tikka"), person("Jouni", "Helske")),publisher ="SocArxiv",year ="2022",url ="https://osf.io/preprints/socarxiv/mdwu5/")bibentry(key ="dynamite",bibtype ="Manual",title ="Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data",author =c(person("Santtu", "Tikka"), person("Jouni", "Helske")),note ="R package version 1.0.0",year ="2022",url ="https://github.com/ropensci/dynamite")
You could also create and store a CITATION.cff thanks to the cffr package. It also provides a GitHub Action workflow to keep the CITATION.cff file up-to-date.
1.6 README
All packages should have a README file, named README.md, in the root of the repository. The README should include, from top to bottom:
The package name.
Badges for continuous integration and test coverage, the badge for rOpenSci peer-review once it has started (see below), a repostatus.org badge, and any other badges (e.g. R-universe).
Short description of goals of package (what does it do? why should a potential user care?), with descriptive links to all vignettes unless the package is small and there’s only one vignette repeating the README. Please also ensure the vignettes are rendered and readable, see the “documentation website” section).
Any additional setup required (authentication tokens, etc).
Brief demonstration usage.
If applicable, how the package compares to other similar packages and/or how it relates to other packages.
Citation information i.e. Direct users to the preferred citation in the README by adding boilerplate text “here’s how to cite my package”. See e.g. ecmwfr README.
where issue_id is the number of the issue in the software-review repository. For instance, the badge for rtimicropem review uses the number 126 since it’s the review issue number. The badge will first indicated “under review” and then “peer-reviewed” once your package has been onboarded (issue labelled “approved” and closed), and will link to the review issue.
If your README has many badges consider ordering them in an html table to make it easier for newcomers to gather information at a glance. See examples in drake repo and in qualtRics repo. Possible sections are
Development (CI statuses cf CI chapter, Slack channel for discussion, repostatus)
Stats/Usage (downloads e.g. download badges from r-hub/cranlogs) The table should be more wide than it is long in order to mask the rest of the README.
If your package connects to a data source or online service, or wraps other software, consider that your package README may be the first point of entry for users. It should provide enough information for users to understand the nature of the data, service, or software, and provide links to other relevant data and documentation. For instance, a README should not merely read, “Provides access to GooberDB,” but also include, “…, an online repository of Goober sightings in South America. More information about GooberDB, and documentation of database structure and metadata can be found at link”.
We recommend not creating README.md directly, but from a README.Rmd file (an R Markdown file) if you have any demonstration code. The advantage of the .Rmd file is you can combine text with code that can be easily updated whenever your package is updated.
Consider using usethis::use_readme_rmd() to get a template for a README.Rmd file and to automatically set up a pre-commit hook to ensure that README.md is always newer than README.Rmd.
Extensive examples should be kept for a vignette. If you want to make the vignettes more accessible before installing the package, we suggest creating a website for your package.
See the gistr README for a good example README to follow for a small package, and bowerbird README for a good example README for a larger package.
1.7 Documentation
1.7.1 General
All exported package functions should be fully documented with examples.
If there is potential overlap or confusion with other packages providing similar functionality or having a similar name, add a note in the README, main vignette and potentially the Description field of DESCRIPTION. Examples in rtweet README, rebird README, and the non-rOpensci package slurmR.
The package should contain top-level documentation for ?foobar, (or ?`foobar-package` if there is a naming conflict). Optionally, you can use both ?foobar and ?`foobar-package` for the package level manual file, using @aliases roxygen tag. usethis::use_package_doc() adds the template for the top-level documentation.
The package should contain at least one HTML vignette providing a substantial coverage of package functions, illustrating realistic use cases and how functions are intended to interact. If the package is small, the vignette and the README may have very similar content.
As is the case for a README, top-level documentation or vignettes may be the first point of entry for users. If your package connects to a data source or online service, or wraps other software, it should provide enough information for users to understand the nature of the data, service, or software, and provide links to other relevant data and documentation. For instance, a vignette intro or documentation should not merely read, “Provides access to GooberDB,” but also include, “…, an online repository of Goober sightings in South America. More information about GooberDB, and documentation of database structure and metadata can be found at link”. Any vignette should outline prerequisite knowledge to be able to understand the vignette upfront.
The general vignette should present a series of examples progressing in complexity from basic to advanced usage.
Functionality likely to be used by only more advanced users or developers might be better put in a separate vignette (e.g. programming/NSE with dplyr).
The README, the top-level package docs, vignettes, websites, etc., should all have enough information at the beginning to get a high-level overview of the package and the services/data it connects to, and provide navigation to other relevant pieces of documentation. This is to follow the principle of multiple points of entry i.e. to take into account the fact that any piece of documentation may be the first encounter the user has with the package and/or the tool/data it wraps.
The vignette(s) should include citations to software and papers where appropriate.
If your package provides access to a data source, we require that DESCRIPTION contains both (1) A brief identification and/or description of the organisation responsible for issuing data; and (2) The URL linking to public-facing page providing, describing, or enabling data access (which may often differ from URL leading directly to data source).
Only use package startup messages when necessary (function masking for instance). Avoid package startup messages like “This is foobar 2.4-0” or citation guidance because they can be annoying to the user. Rely on documentation for such guidance.
You can choose to have a README section about use cases of your package (other packages, blog posts, etc.), example.
1.7.2 roxygen2 use
We request all submissions to use roxygen2 for documentation. roxygen2 is an R package that compiles .Rd files to your man folder in your package from tags written above each function. roxygen2 has support for Markdown syntax. One key advantage of using roxygen2 is that your NAMESPACE will always be automatically generated and up to date.
If you were writing Rd directly without roxygen2, the Rd2roxygen package contains functions to convert Rd to roxygen documentation.
All functions should document the type of object returned under the @return heading.
The default value for each parameter should be clearly documented. For example, instead of writing A logical value determining if ..., you should write A logical value (default `TRUE`) determining if .... It is also good practice to indicate the default values directly in your function definition:
You can re-use documentation pieces (e.g. details about authentication, related packages) across the vignettes/README/man pages. Refer to roxygen2 vignette on documentation reuse.
For including examples, you can use the classic @examples tag (plural “examples”) but also the @example <path> tag (singular “example”) for storing the example code in a separate R script (ideally under man/), and the @exampleIf tag for running examples conditionally and avoiding R CMD check failures. Refer to roxygen2 documentation about examples.
Add #' @noRd to internal functions. You might be interested in the devtag experimental package for getting local manual pages when using #' @noRd.
Starting from roxygen2 version 7.0.0, R6 classes are officially supported. See the roxygen2 docs for details on how to document R6 classes.
1.7.3 URLs in documentation
This subsection is particularly relevant to authors wishing to submit their package to CRAN. CRAN will check URLs in your documentation and does not allow redirect status codes such as 301. You can use the urlchecker package to reproduce these checks and, in particular, replace URLs with the URLs they redirect to. Others have used the option to escape some URLs (change <https://ropensci.org/> to https://ropensci.org/, or \url{https://ropensci.org/} to https://ropensci.org/.), but if you do so, you will need to implement some sort of URL checking yourself to prevent them from getting broken without your noticing. Furthermore, links would not be clickable from local docs.
There are a few elements we’d like to underline here.
1.8.1 Automatic deployment of the documentation website
You only need to worry about automatic deployment of your website until approval and transfer of your package repo to the ropensci organization; indeed, after that a pkgdown website will be built for your package after each push to the GitHub repo. You can find the status of these builds at https://dev.ropensci.org/job/package_name, e.g. for magick; and the website at https://docs.ropensci.org/package_name, e.g. for magick. The website build will use your pkgdown config file if you have one, except for the styling that will use the rotemplate package. The resulting website will have a local search bar. Please report bugs, questions and feature requests about the central builds at https://github.com/ropensci/docs/ and about the template at https://github.com/ropensci/rotemplate/.
If your package vignettes need credentials (API keys, tokens, etc.) to knit, you might want to precompute them since credentials cannot be used on the docs server.
Before submission and before transfer, you could use the approach documented by pkgdown or the tic package for automatic deployment of the package’s website. This would save you the hassle of running (and remembering to run) pkgdown::build_site() yourself every time the site needs to be updated. First refer to our chapter on continuous integration if you’re not familiar with continuous integration. In any case, do not forget to update all occurrences of the website URL after transfer to the ropensci organization.
1.8.2 Grouping functions in the reference
When your package has many functions, use grouping in the reference, which you can do more or less automatically.
If you use roxygen2 above version 6.1.1, you should use the @family tag in your functions documentation to indicate grouping. This will give you links between functions in the local documentation of the installed package (“See also” section) and allow you to use the pkgdownhas_concept function in the config file of your website. Non-rOpenSci example courtesy of optiRum: family tag, pkgdown config file and resulting reference section. To customize the text of the cross-reference title created by roxygen2 (Other {family}:), refer to roxygen2 docs regarding how to provide a rd_family_title list in man/roxygen/meta.R.
You can make the names of (some) authors clickable by adding their URL, and you can even replace their names with a logo (think rOpenSci… or your organisation/company!). See pkgdown documentation.
1.8.4 Tweaking the navbar
You can make your website content easier to browse by tweaking the navbar, refer to pkgdown documentation. In particular, note that if you name the main vignette of your package “pkg-name.Rmd”, it’ll be accessible from the navbar as a Get started link instead of via Articles > Vignette Title.
1.8.5 Mathjax
Once your package is transferred and it gets a website using our pkgdown template, if you want to use Mathjax you’ll need to specify it in the pkgdown config file like so:
template:params:mathjax:true
1.8.6 Package logo
To use your package logo in the pkgdown homepage, refer to usethis::use_logo(). If your package doesn’t have any logo, the rOpenSci docs builder will use rOpenSci logo instead.
1.9 Authorship
The DESCRIPTION file of a package should list package authors and contributors to a package, using the Authors@R syntax to indicate their roles (author/creator/contributor etc.) if there is more than one author, and using the comment field to indicate the ORCID ID of each author, if they have one (cf this post). See this section of “Writing R Extensions” for details. If you feel that your reviewers have made a substantial contribution to the development of your package, you may list them in the Authors@R field with a Reviewer contributor type ("rev"), like so:
person("Bea", "Hernández", role = "rev",
comment = "Bea reviewed the package (v. X.X.XX) for rOpenSci, see <https://github.com/ropensci/software-review/issues/116>"),
Only include reviewers after asking for their consent. Read more in this blog post “Thanking Your Reviewers: Gratitude through Semantic Metadata”. Please do not list editors as contributors. Your participation in and contribution to rOpenSci is thanks enough!
1.9.1 Authorship of included code
Many packages include code from other software. Whether entire files or single functions are included from other packages, rOpenSci packages should follow the CRAN Repository Policy:
The ownership of copyright and intellectual property rights of all components of the package must be clear and unambiguous (including from the authors specification in the DESCRIPTION file). Where code is copied (or derived) from the work of others (including from R itself), care must be taken that any copyright/license statements are preserved and authorship is not misrepresented.
Preferably, an ‘Authors@R’ field would be used with ‘ctb’ roles for the authors of such code. Alternatively, the ‘Author’ field should list these authors as contributors.
Where copyrights are held by an entity other than the package authors, this should preferably be indicated via ‘cph’ roles in the ‘Authors@R’ field, or using a ‘Copyright’ field (if necessary referring to an inst/COPYRIGHTS file).
Trademarks must be respected.
1.10 Licence
The package needs to have a CRAN or OSI accepted license. For more explanations around licensing, refer to the R packages book.
1.11 Testing
All packages should pass R CMD check/devtools::check() on all major platforms.
All packages should have a test suite that covers major functionality of the package. The tests should also cover the behavior of the package in case of errors.
It is good practice to write unit tests for all functions, and all package code in general, ensuring key functionality is covered. Test coverage below 75% will likely require additional tests or explanation before being sent for review.
We recommend using testthat for writing tests. Strive to write tests as you write each new function. This serves the obvious need to have proper testing for the package, but allows you to think about various ways in which a function can fail, and to defensively code against those. More information.
Packages with Shiny apps should use a unit-testing framework such as shinytest2 or shinytest to test that interactive interfaces behave as expected.
For testing your functions creating plots, we suggest using vdiffr, an extension of the testthat package that relies on testthat snapshot tests.
If your package interacts with web resources (web APIs and other sources of data on the web) you might find the HTTP testing in R book by Scott Chamberlain and Maëlle Salmon relevant. Packages helping with HTTP testing (corresponding HTTP clients):
testthat has a function skip_on_cran() that you can use to not run tests on CRAN. We recommend using this on all functions that are API calls since they are quite likely to fail on CRAN. These tests should still run on continuous integration. Note that from testthat 3.1.2 skip_if_offline() automatically calls skip_on_cran(). More info on CRAN preparedness for API wrappers.
If your package interacts with a database you might find dittodb useful.
Even if you use continuous integration, we recommend that you run tests locally prior to submitting your package (you might need to set Sys.setenv(NOT_CRAN="true")).
1.12 Examples
Include extensive examples in the documentation. In addition to demonstrating how to use the package, these can act as an easy way to test package functionality before there are proper tests. However, keep in mind we require tests in contributed packages.
You can run examples with devtools::run_examples(). Note that when you run R CMD CHECK or equivalent (e.g., devtools::check()) your examples that are not wrapped in \dontrun{} or \donttest{} are run. Refer to the summary table in roxygen2 docs.
To safe-guard examples (e.g. requiring authentication) to be run on CRAN you need to use \dontrun{}. However, for a first submission CRAN won’t let you have all examples escaped so. In this case you might add some small toy examples, or wrap example code in try(). Also refer to the @exampleIf tag present, at the time of writing, in roxygen2 development version.
In addition to running examples locally on your own computer, we strongly advise that you run examples on one of the continuous integration systems. Again, examples that are not wrapped in \dontrun{} or \donttest{} will be run, but for those that are you can configure your continuous integration builds to run them via R CMD check arguments --run-dontrun and/or --run-donttest.
1.13 Package dependencies
Consider the trade-offs involved in relying on a package as a dependency. On one hand, using dependencies reduces coding effort, and can build on useful functionality developed by others, especially if the dependency performs complex tasks, is high-performance, and/or is well vetted and tested. On the other hand, having many dependencies places a burden on the maintainer to keep up with changes in those packages, at risk to your package’s long-term sustainability. It also increases installation time and size, primarily a consideration on your and others’ development cycle, and in automated build systems. “Heavy” packages - those with many dependencies themselves, and those with large amounts of compiled code - increase this cost. Here are some approaches to reducing dependencies:
Small, simple functions from a dependency package may be better copied into your own package if the dependency if you are using only a few functions in an otherwise large or heavy dependency. (See Authorship section above for how to acknowledge original authors of copied code.) On the other hand, complex functions with many edge cases (e.g. parsers) require considerable testing and vetting.
An common example of this is in returning tidyverse-style “tibbles” from package functions that provide data. One can avoid the modestly heavy tibble package dependency by returning a tibble created by modifying a data frame like so:
Ensure that you are using the package where the function is defined, rather than one where it is re-exported. For instance many functions in devtools can be found in smaller specialty packages such as sessioninfo. The %>% function should be imported from magrittr, where it is defined, rather than the heavier dplyr, which re-exports it.
Some dependencies are preferred because they provide easier to interpret function names and syntax than base R solutions. If this is the primary reason for using a function in a heavy dependency, consider wrapping the base R approach in a nicely-named internal function in your package. See e.g. the rlang R script providing functions with a syntax similar to purrr functions.
If dependencies have overlapping functionality, see if you can rely on only one.
Use Imports instead of Depends for packages providing functions from other packages. Make sure to list packages used for testing (testthat), and documentation (knitr, roxygen2) in your Suggests section of package dependencies (if you use usethis for adding testing infrastructure via usethis::use_testthat() or a vignette via usethis::use_vignette(), the necessary packages will be added to DESCRIPTION). If you use any package in the examples or tests of your package, make sure to list it in Suggests, if not already listed in Imports.
If your (not Bioconductor) package depends on Bioconductor packages, make sure the installation instructions in the README and vignette are clear enough even for an user who is not familiar with the Bioconductor release cycle.
Should the user use BiocManager (recommended)? Document this.
Is the automatic installation of Bioconductor packages by install.packages() enough? In that case, mention that the user needs to run setRepositories() if they haven’t set the necessary Bioconductor repositories yet.
If your package depends on Bioconductor after a certain version, mention it in DESCRIPTION and in the installation instructions.
Specifying minimum dependencies (e.g. glue (>= 1.3.0) instead of just glue) should be a conscious choice. If you know for a fact that your package will break below a certain dependency version, specify it explicitly. But if you don’t, then no need to specify a minimum dependency. In that case when a user reports a bug which is explicitly related to an older version of a dependency then address it then. An example of bad practice would be for a developer to consider the versions of their current state of dependencies to be the minimal version. That would needlessly force everyone to upgrade (causing issues with other packages) when there is no good reason behind that version choice.
For most cases where you must expose functions from dependencies to the user, you should import and re-export those individual functions rather than listing them in the Depends fields. For instance, if functions in your package produce raster objects, you might re-export only printing and plotting functions from the raster package.
If your package uses a system dependency, you should
Check for it in a configure script (example) and give a helpful error message if it cannot be found (example). configure scripts can be challenging as they often require hacky solutions to make diverse system dependencies work across systems. Use examples (more here) as a starting point but note that it is common to encounter bugs and edge cases and often violate CRAN policies. Do not hesitate to ask for help on our forum.
1.14 Recommended scaffolding
For HTTP requests we recommend using httr2, httr, curl, or crul over RCurl. If you like low level clients for HTTP, curl is best, whereas httr2, httr and crul are better for higher level access.
For spatial data, the sp package should be considered deprecated in favor of sf, and the packages rgdal, rgdal, and rgdal will be retired by the end of 2023. We recommend use of the spatial suites developed by the r-spatial and rspatial communities. See this GitHub issue for relevant discussions.
1.15 Version Control
Your package source files have to be under version control, more specifically tracked with Git. You might find the gert package relevant, as well as some of usethis Git/GitHub related functionality; you can however use git as you want.
This is a collection of CRAN gotchas that are worth avoiding at the outset.
Make sure your package title is in Title Case.
Do not put a period on the end of your title.
Do not put ‘in R’ or ‘with R’ in your title as this is obvious from packages hosted on CRAN. If you would like this information to be displayed on your website nonetheless, check the pkgdown documentation to learn how to override this.
Avoid starting the description with the package name or “This package …”.
Make sure you include links to websites if you wrap a web API, scrape data from a site, etc. in the Description field of your DESCRIPTION file. URLs should be enclosed in angle brackets, e.g. <https://www.r-project.org>.
In both the Title and Description fields, the names of packages or other external software must be quoted using single quotes (e.g., ‘Rcpp’ Integration for the ‘Armadillo’ Templated Linear Algebra Library).
Avoid long running tests and examples. Consider testthat::skip_on_cran in tests to skip things that take a long time but still test them locally and on continuous integration.
Include top-level files such as paper.md, continuous integration configuration files, in your .Rbuildignore file.
For further gotchas, refer to the collaborative list maintained by ThinkR, “Prepare for CRAN”.
1.16.1 CRAN checks
Once your package is on CRAN, it will be regularly checked on different platforms. Failures of such checks, when not false positives, can lead to the CRAN team’s reaching out. You can monitor the state of the CRAN checks via
If you are submitting a package to rOpenSci via the software-review repo, you can direct further questions to the rOpenSci team in the issue tracker, or in our discussion forum.