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CONTRIBUTING.rst

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Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at https://github.com/ELToulemonde/dataPreparation/issues.

If you are reporting a bug, please include:

  • Your operating system name and version.
  • Any details about your local setup that might be helpful in troubleshooting.
  • Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.

If you have some new features ideas, you can also open an issue and tag it with "enhancement".

All implementation should - respect code conventions listed bellow, - should be testted (using same testing schema as other tests).

Write Documentation

dataPreparation could always use more documentation, whether as part of the official dataPreparation docs, vignettes, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue at https://github.com/ELToulemonde/dataPreparation/issues.

If you are proposing a feature:

  • Explain in detail how it would work.
  • Keep the scope as narrow as possible, to make it easier to implement.
  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Controlling your developments

  1. Build new functionality in the best R file
  2. Document function. Make sure every param is commented.
  3. Change package version in DESCRIPTION and add what's new in NEWS.md
  1. Build unit tests in teststestthattest_.....R (one test file per R code file). Unit test should make sure that your function works exactly the way you want it.
  2. Generate documentation using devtools::document() and control unit tests using devtools::test()
  3. Build and install package using devtools::build(), devtools::install()
  4. Control that every thing is conform to CRAN requirement using devtools::check(pkg = "dataPreparation"). You should have no error, no warning and no notes.
  5. Control code coverage with cov <- covr::package_coverage() and then covr::zero_coverage(cov). Your new lines of codes shouldn't appear.
  6. Push on github and travis will check if everything is ok. There would also be a code coverage control.
  7. If everything passed: create a Pull Request.
  8. Thank you very much for your contributions! :)

Code conventions

Use convention interpretation
Function names set... Change into ...
which... Identify ...
fast... Perform in an efficient way (col by col or by exponential search)
is.XXX Check if is XXX
generate... Create new columns
Arguments drop Should original columns be dropped
verbose Boolean to handle if algorithm talk
dataSet Input data set
cols A list of columns names
Variables data_sample Slice of data set copied for calcul
result The result that will be returned
..._tmp Partially build object
...s Iteritable (list, array, ...)
n... Number of ...
col A column name
I a list of index.
n_test Number of rows on which we perform test
args Agruments from "..."
start_time From proc.time() to keep track of time