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[](https://www.bestpractices.dev/projects/4310)
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The pointblank R package lets people easily validate data in data frames, 'tibble' objects, Spark DataFrames, and database tables (e.g., 'PostgreSQL' and 'MySQL'). Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. The package also facilitates an information management workflow, where the aim is to record, collect, and generate useful information on data tables.
There are currently two authors involved in the project (https://github.com/rich-iannone/pointblank/blob/18688d816c23651682bb83e5e41d0591880d16c1/DESCRIPTION#L13).
There are currently two unassociated significant contributors involved in the project (https://github.com/rich-iannone/pointblank/blob/18688d816c23651682bb83e5e41d0591880d16c1/DESCRIPTION#L13).
Each source file contains information on the copyright holder including contact info. An example is available at: https://github.com/rich-iannone/pointblank/blob/master/R/interrogate.R
Each source file contains information on the copyright holder and also a brief license statement (with a link to the full license information). An example is available at: https://github.com/rich-iannone/pointblank/blob/master/R/interrogate.R
Repository on GitHub, which uses git. git is distributed.
Each issue in the Issues page is labeled and certain issue may be labeled with ‘Help Wanted’. Further to this, the predicted difficulty of working on an issue (which could be a big fix, a new feature, documentation, etc.) and the predicted effort level are given as separate labels. In this way, potential contributors could filter the issues list to those that require help by difficulty and effort level. An example of an issue with these labels is https://github.com/rich-iannone/pointblank/issues/16.
R packages are built during installation and reproducibility for this (along with quality checks) is done through GitHub actions (https://github.com/rich-iannone/pointblank/actions?query=workflow%3AR-CMD-check) for every commit and through CRAN’s automated build/check system (https://cran.r-project.org/web/checks/check_results_pointblank.html) for every release.
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.
testthat::test_package()
devtools::test()
The pointblank package uses GitHub Actions to run R CMD check (a comprehensive set of tests for the package) with each commit and pull request (https://github.com/rich-iannone/pointblank/actions?query=workflow%3AR-CMD-check). Merging doesn't typically occur unless all CI checks pass.
One of the GitHub Actions workflows checks for code coverage from the test suite and sends the report to a third-party for further reporting and badge generation. We display the code coverage badge on the project README. Code coverage through this test suite does provide at least 90% coverage and it is a policy to have code coverage be above 90% at all times.
This is out of scope for pointblank and other R packages that do not explicitly focus on privacy and security.
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The lintr package (https://github.com/jimhester/lintr) is invoked by a GitHub Actions workflow (https://github.com/rich-iannone/pointblank/blob/main/.github/workflows/lint.yaml) for every commit and pull request. If any lints are found, the maintainer is notified via email and CI correspondingly fails. This status is also transparent in the 'lint' badge shown in the project repository README.md (on GitHub) and in the project website (https://rich-iannone.github.io/pointblank/).
README.md
Dynamic analysis is not required for pointblank. This is true for all R packages that are implemented entirely in R (without uses of C, C++, etc.).
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