gt

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These are the Passing level criteria. You can also view the Silver or Gold level criteria.

        

 Basics 13/13

  • Identification

    Build display tables from tabular data with an easy-to-use set of functions using the R package called gt. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which gt handles things for you means that you don't often have to worry about the fine details.

    What programming language(s) are used to implement the project?
  • Basic project website content


    The project website MUST succinctly describe what the software does (what problem does it solve?). [description_good]

    The page at https://gt.rstudio.com gives an overview of the package, which problems the software is good at addressing, and several introductory-level examples.



    The project website MUST provide information on how to: obtain, provide feedback (as bug reports or enhancements), and contribute to the software. [interact]

    The project website provides information on all of this. In the 'Installation' section (https://gt.rstudio.com/#installation), instructions are provided on how to install the CRAN version of the package and the in-development version. The final sentence invites users to report bugs, ask questions about usage, and share ideas for new features in the GitHub Issues page (link provided in sentence).



    The information on how to contribute MUST explain the contribution process (e.g., are pull requests used?) (URL required) [contribution]

    Projects on GitHub by default use issues and pull requests, as encouraged by documentation such as https://guides.github.com/activities/contributing-to-open-source/.



    The information on how to contribute SHOULD include the requirements for acceptable contributions (e.g., a reference to any required coding standard). (URL required) [contribution_requirements]

    The pull request template at https://github.com/rstudio/gt/blob/master/.github/PULL_REQUEST_TEMPLATE.md has a checklist of requirements for an acceptable contribution.


  • FLOSS license

    What license(s) is the project released under?



    The software produced by the project MUST be released as FLOSS. [floss_license]

    MIT attribution: https://raw.githubusercontent.com/rstudio/gt/master/LICENSE.md. The MIT license is approved by the Open Source Initiative (OSI).



    It is SUGGESTED that any required license(s) for the software produced by the project be approved by the Open Source Initiative (OSI). [floss_license_osi]

    The MIT license his approved by the Open Source Initiative (OSI). The MIT license is approved by the Open Source Initiative (OSI).



    The project MUST post the license(s) of its results in a standard location in their source repository. (URL required) [license_location]

    Non-trivial license location file in repository: https://github.com/rstudio/gt/blob/master/LICENSE.


  • Documentation


    The project MUST provide basic documentation for the software produced by the project. [documentation_basics]

    There is full documentation for the package functions and an introductory article available in the project website (https://gt.rstudio.com/). The Reference section of the project website (https://gt.rstudio.com/reference/index.html) arranges the package functions into families with descriptions of each function family. Additionally, help for each function can be obtained by using help(<function name>) in the R console.



    The project MUST provide reference documentation that describes the external interface (both input and output) of the software produced by the project. [documentation_interface]

    The manual available at (https://gt.rstudio.com/ goes into detail about the interface, and the help files at https://github.com/rstudio/gt/tree/main/man document and demonstrate every exported function in full detail. The man folder is the standard way to document exported functions and data objects in an R package.


  • Other


    The project sites (website, repository, and download URLs) MUST support HTTPS using TLS. [sites_https]

    The project MUST have one or more mechanisms for discussion (including proposed changes and issues) that are searchable, allow messages and topics to be addressed by URL, enable new people to participate in some of the discussions, and do not require client-side installation of proprietary software. [discussion]

    GitHub supports discussions on issues and pull requests.



    The project SHOULD provide documentation in English and be able to accept bug reports and comments about code in English. [english]

    Bug reports and discussion: https://github.com/rstudio/gt/pulls.



    The project MUST be maintained. [maintained]


(Advanced) What other users have additional rights to edit this badge entry? Currently: []



  • Public version-controlled source repository


    The project MUST have a version-controlled source repository that is publicly readable and has a URL. [repo_public]

    Repository on GitHub, which provides public git repositories with URLs.



    The project's source repository MUST track what changes were made, who made the changes, and when the changes were made. [repo_track]

    Repository on GitHub, which uses git. git can track the changes, who made them, and when they were made.



    To enable collaborative review, the project's source repository MUST include interim versions for review between releases; it MUST NOT include only final releases. [repo_interim]

    The GitHub releases at https://github.com/rstudio/gt/releases include multiple interim development releases.



    It is SUGGESTED that common distributed version control software be used (e.g., git) for the project's source repository. [repo_distributed]

    Repository on GitHub, which uses git. git is distributed.


  • Unique version numbering


    The project results MUST have a unique version identifier for each release intended to be used by users. [version_unique]

    The gt package follows the versioning guide at http://r-pkgs.had.co.nz/release.html#release-version, the community accepted variant of semantic versioning for R packages.



    It is SUGGESTED that the Semantic Versioning (SemVer) or Calendar Versioning (CalVer) version numbering format be used for releases. It is SUGGESTED that those who use CalVer include a micro level value. [version_semver]


    It is SUGGESTED that projects identify each release within their version control system. For example, it is SUGGESTED that those using git identify each release using git tags. [version_tags]

    All releases (viewable at https://github.com/rstudio/gt/releases) have a git/GitHub tag.


  • Release notes


    The project MUST provide, in each release, release notes that are a human-readable summary of major changes in that release to help users determine if they should upgrade and what the upgrade impact will be. The release notes MUST NOT be the raw output of a version control log (e.g., the "git log" command results are not release notes). Projects whose results are not intended for reuse in multiple locations (such as the software for a single website or service) AND employ continuous delivery MAY select "N/A". (URL required) [release_notes]

    Non-trivial release notes file in repository: https://github.com/rstudio/gt/blob/master/NEWS.md.



    The release notes MUST identify every publicly known run-time vulnerability fixed in this release that already had a CVE assignment or similar when the release was created. This criterion may be marked as not applicable (N/A) if users typically cannot practically update the software themselves (e.g., as is often true for kernel updates). This criterion applies only to the project results, not to its dependencies. If there are no release notes or there have been no publicly known vulnerabilities, choose N/A. [release_notes_vulns]
  • Bug-reporting process


    The project MUST provide a process for users to submit bug reports (e.g., using an issue tracker or a mailing list). (URL required) [report_process]

    The project SHOULD use an issue tracker for tracking individual issues. [report_tracker]

    The project MUST acknowledge a majority of bug reports submitted in the last 2-12 months (inclusive); the response need not include a fix. [report_responses]

    The Issue tracker contains all bug reports: https://github.com/rstudio/gt/issues



    The project SHOULD respond to a majority (>50%) of enhancement requests in the last 2-12 months (inclusive). [enhancement_responses]

    The issue tracker contains all feature requests: https://github.com/rstudio/gt/issues. Feature requests are tagged as Enhancements and are usually successfully implemented.



    The project MUST have a publicly available archive for reports and responses for later searching. (URL required) [report_archive]

    The issue tracker at https://github.com/rstudio/gt/issues has the complete archive of all the reports and responses. It is easily searchable.


  • Vulnerability report process


    The project MUST publish the process for reporting vulnerabilities on the project site. (URL required) [vulnerability_report_process]

    Vulnerabilities fall under bugs for this project. The place to report them is https://github.com/rstudio/gt/issues, and the contact information of the maintainer is available in https://github.com/rstudio/gt/blob/master/DESCRIPTION.



    If private vulnerability reports are supported, the project MUST include how to send the information in a way that is kept private. (URL required) [vulnerability_report_private]

    The contact information of the maintainer is at https://github.com/rstudio/gt/blob/master/DESCRIPTION.



    The project's initial response time for any vulnerability report received in the last 6 months MUST be less than or equal to 14 days. [vulnerability_report_response]

    All responses to issues are considerably faster than 14 days. Please refer to the discussion logs at https://github.com/rstudio/gt/issues.


  • Working build system


    If the software produced by the project requires building for use, the project MUST provide a working build system that can automatically rebuild the software from source code. [build]

    Using the instructions available at https://gt.rstudio.com/#installation (under "...install the development version"), users can install the package from source.



    It is SUGGESTED that common tools be used for building the software. [build_common_tools]

    See the instructions available at https://gt.rstudio.com/#installation. The two most common tools for installing R packages (install.packages() and devtools::install_github()) can be used for gt.



    The project SHOULD be buildable using only FLOSS tools. [build_floss_tools]

    The gt package only requires formally released R packages on CRAN http://cran.r-project.org/ to install.


  • Automated test suite


    The project MUST use at least one automated test suite that is publicly released as FLOSS (this test suite may be maintained as a separate FLOSS project). The project MUST clearly show or document how to run the test suite(s) (e.g., via a continuous integration (CI) script or via documentation in files such as BUILD.md, README.md, or CONTRIBUTING.md). [test]

    A test suite SHOULD be invocable in a standard way for that language. [test_invocation]

    The tests are in the standard format for R packages. Using the testthat package (http://github.com/r-lib/testthat), it is easy to run the tests. Standard R package quality checks run these tests, as do testthat::test_package() and devtools::test(). This is the de facto standard for R packages.



    It is SUGGESTED that the test suite cover most (or ideally all) the code branches, input fields, and functionality. [test_most]

    The tests run by testthat::test_package()/devtools::test() under the default settings run nearly every line of code in the package. The test results are sent to a 3rd party service for reporting on code coverage, a metric that is taken very seriously for this project.



    It is SUGGESTED that the project implement continuous integration (where new or changed code is frequently integrated into a central code repository and automated tests are run on the result). [test_continuous_integration]

    The gt package uses GitHub Actions to run R CMD check (a comprehensive set of tests for the package) with each commit and pull request. Users can verify the status of recently run checks by inspecting the badge on the project README. Merging doesn't occur unless all CI checks pass (check the workflow file at: https://github.com/rstudio/gt/blob/master/.github/workflows/R-CMD-check.yaml).


  • New functionality testing


    The project MUST have a general policy (formal or not) that as major new functionality is added to the software produced by the project, tests of that functionality should be added to an automated test suite. [test_policy]

    New tests for the functionality are added to the existing unit test suite, and these tests are checked with continuous integration (GitHub Actions) in a pull request before being merged into the project.



    The project MUST have evidence that the test_policy for adding tests has been adhered to in the most recent major changes to the software produced by the project. [tests_are_added]

    Evidence for the testing policy is available in the pull requests page (https://github.com/rstudio/gt/pulls) and also in the commit history (https://github.com/rstudio/gt/commits/master).



    It is SUGGESTED that this policy on adding tests (see test_policy) be documented in the instructions for change proposals. [tests_documented_added]

    In the pull request instructions (https://github.com/rstudio/gt/blob/master/.github/PULL_REQUEST_TEMPLATE.md), there is a checklist item that expresses the requirement for new tests to be added with the proposed changes.


  • Warning flags


    The project MUST enable one or more compiler warning flags, a "safe" language mode, or use a separate "linter" tool to look for code quality errors or common simple mistakes, if there is at least one FLOSS tool that can implement this criterion in the selected language. [warnings]

    The lintr package https://github.com/jimhester/lintr is used to provide guidance on code quality during the development process.



    The project MUST address warnings. [warnings_fixed]

    A GitHub Actions workflow can effectively detect warnings during installation and testing. Notifications are sent to the maintainer's email and to the respective badge shown in the project repository README.md.



    It is SUGGESTED that projects be maximally strict with warnings in the software produced by the project, where practical. [warnings_strict]

    The GitHub Actions workflow for checking the package (with R CMD check) is configured to fail if any warnings are detected.


  • Secure development knowledge


    The project MUST have at least one primary developer who knows how to design secure software. (See ‘details’ for the exact requirements.) [know_secure_design]

    The codebase almost entirely consists of functions. These are uncomplicated and generally divided between exported and non-exported functions. All exported functions (i.e., user-side) are highly documented. The current maintainer has written nearly all functions entirely, and any incoming contributions are reviewed line-by-line via PRs. With this process in place, attempts to vandalize the code and make it less secure can be easily detected.



    At least one of the project's primary developers MUST know of common kinds of errors that lead to vulnerabilities in this kind of software, as well as at least one method to counter or mitigate each of them. [know_common_errors]

    The maintainer of gt is experienced in programming in R, the language of the implementation. Those errors that lead to vulnerabilities in R packages can be easily recognized by the maintainer. That said, security vulnerabilities are likely to come from other components that gt uses (other R packages as dependencies). To circumvent this, there is a policy to only use packages that the maintainer trusts (based on the reputations of the package developers and other factors), and to periodically review the dependency packages for indications of possible security vulnerabilities.


  • Use basic good cryptographic practices

    Note that some software does not need to use cryptographic mechanisms. If your project produces software that (1) includes, activates, or enables encryption functionality, and (2) might be released from the United States (US) to outside the US or to a non-US-citizen, you may be legally required to take a few extra steps. Typically this just involves sending an email. For more information, see the encryption section of Understanding Open Source Technology & US Export Controls.

    The software produced by the project MUST use, by default, only cryptographic protocols and algorithms that are publicly published and reviewed by experts (if cryptographic protocols and algorithms are used). [crypto_published]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    If the software produced by the project is an application or library, and its primary purpose is not to implement cryptography, then it SHOULD only call on software specifically designed to implement cryptographic functions; it SHOULD NOT re-implement its own. [crypto_call]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    All functionality in the software produced by the project that depends on cryptography MUST be implementable using FLOSS. [crypto_floss]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    The security mechanisms within the software produced by the project MUST use default keylengths that at least meet the NIST minimum requirements through the year 2030 (as stated in 2012). It MUST be possible to configure the software so that smaller keylengths are completely disabled. [crypto_keylength]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    The default security mechanisms within the software produced by the project MUST NOT depend on broken cryptographic algorithms (e.g., MD4, MD5, single DES, RC4, Dual_EC_DRBG), or use cipher modes that are inappropriate to the context, unless they are necessary to implement an interoperable protocol (where the protocol implemented is the most recent version of that standard broadly supported by the network ecosystem, that ecosystem requires the use of such an algorithm or mode, and that ecosystem does not offer any more secure alternative). The documentation MUST describe any relevant security risks and any known mitigations if these broken algorithms or modes are necessary for an interoperable protocol. [crypto_working]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    The default security mechanisms within the software produced by the project SHOULD NOT depend on cryptographic algorithms or modes with known serious weaknesses (e.g., the SHA-1 cryptographic hash algorithm or the CBC mode in SSH). [crypto_weaknesses]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    The security mechanisms within the software produced by the project SHOULD implement perfect forward secrecy for key agreement protocols so a session key derived from a set of long-term keys cannot be compromised if one of the long-term keys is compromised in the future. [crypto_pfs]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    If the software produced by the project causes the storing of passwords for authentication of external users, the passwords MUST be stored as iterated hashes with a per-user salt by using a key stretching (iterated) algorithm (e.g., Argon2id, Bcrypt, Scrypt, or PBKDF2). See also OWASP Password Storage Cheat Sheet. [crypto_password_storage]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.



    The security mechanisms within the software produced by the project MUST generate all cryptographic keys and nonces using a cryptographically secure random number generator, and MUST NOT do so using generators that are cryptographically insecure. [crypto_random]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.


  • Secured delivery against man-in-the-middle (MITM) attacks


    The project MUST use a delivery mechanism that counters MITM attacks. Using https or ssh+scp is acceptable. [delivery_mitm]

    The project website (https://gt.rstudio.com/) is protected with https.



    A cryptographic hash (e.g., a sha1sum) MUST NOT be retrieved over http and used without checking for a cryptographic signature. [delivery_unsigned]

    This is out of scope for gt and other R packages that do not explicitly focus on privacy and security.


  • Publicly known vulnerabilities fixed


    There MUST be no unpatched vulnerabilities of medium or higher severity that have been publicly known for more than 60 days. [vulnerabilities_fixed_60_days]

    Please see the issue tracker for details: https://github.com/rstudio/gt/issues.



    Projects SHOULD fix all critical vulnerabilities rapidly after they are reported. [vulnerabilities_critical_fixed]

    Please see the issue tracker for details: https://github.com/rstudio/gt/issues.


  • Other security issues


    The public repositories MUST NOT leak a valid private credential (e.g., a working password or private key) that is intended to limit public access. [no_leaked_credentials]

    https://github.com/rstudio/gt is a public repository. Owing to that, there are no limitations on the access to the package and its source code


  • Static code analysis


    At least one static code analysis tool (beyond compiler warnings and "safe" language modes) MUST be applied to any proposed major production release of the software before its release, if there is at least one FLOSS tool that implements this criterion in the selected language. [static_analysis]

    The lintr package (https://github.com/jimhester/lintr) is used by developers of this project. If any lints are found, the maintainer is required to make the necessary changes such that linting successfully passes.



    It is SUGGESTED that at least one of the static analysis tools used for the static_analysis criterion include rules or approaches to look for common vulnerabilities in the analyzed language or environment. [static_analysis_common_vulnerabilities]

    The lintr package (https://github.com/jimhester/lintr) is used by developers of this project. If any lints are found, the maintainer is required to make the necessary changes such that linting successfully passes. This tool can easily reveal common vulnerabilities in the codebase.



    All medium and higher severity exploitable vulnerabilities discovered with static code analysis MUST be fixed in a timely way after they are confirmed. [static_analysis_fixed]

    All lints from lintr are quickly addressed after they are discovered.



    It is SUGGESTED that static source code analysis occur on every commit or at least daily. [static_analysis_often]

    All linting from lintr is performed regularly and is part of the development process.


  • Dynamic code analysis


    It is SUGGESTED that at least one dynamic analysis tool be applied to any proposed major production release of the software before its release. [dynamic_analysis]

    All linting from lintr is performed regularly and is part of the development process. This process includes releases of the software.



    It is SUGGESTED that if the software produced by the project includes software written using a memory-unsafe language (e.g., C or C++), then at least one dynamic tool (e.g., a fuzzer or web application scanner) be routinely used in combination with a mechanism to detect memory safety problems such as buffer overwrites. If the project does not produce software written in a memory-unsafe language, choose "not applicable" (N/A). [dynamic_analysis_unsafe]

    The gt package is entirely implemented in R and as such it does not use any memory-unsafe language.



    It is SUGGESTED that the project use a configuration for at least some dynamic analysis (such as testing or fuzzing) which enables many assertions. In many cases these assertions should not be enabled in production builds. [dynamic_analysis_enable_assertions]

    Dynamic analysis is not required for gt. This is true for all R packages that are implemented entirely in R (without uses of C, C++, etc.).



    All medium and higher severity exploitable vulnerabilities discovered with dynamic code analysis MUST be fixed in a timely way after they are confirmed. [dynamic_analysis_fixed]

    Dynamic analysis is not required for gt. This is true for all R packages that are implemented entirely in R (without uses of C, C++, etc.). If there are related issues, they are solved soon after their reporting (see https://github.com/rstudio/gt/issues).



This data is available under the Creative Commons Attribution version 3.0 or later license (CC-BY-3.0+). All are free to share and adapt the data, but must give appropriate credit. Please credit Richard Iannone and the OpenSSF Best Practices badge contributors.

Project badge entry owned by: Richard Iannone.
Entry created on 2022-02-02 03:18:55 UTC, last updated on 2023-04-05 04:26:57 UTC. Last achieved passing badge on 2022-02-02 03:52:08 UTC.

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