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Contributing to twidlr

This doc should help guide you should you wish to contribute to twidlr

Adding a new model function

Contributing to twidlr will mostly involve adding a new model function. The aim is to provide users with two functions:

  • model(data, formula, ...) which fits a model based on a data.frame, formula, and additional arguments.
  • predict(fit, data, ...) which produces predicted values (typically a vector) given a fitted model and a data.frame

To achieve this in a consistent manner, follow these steps:

The quick version

  • Add package to Suggests
  • Add code in "R/twidlr-package_name.R"
  • Add tests in "tests/testthat/test-package_name.R"
  • Add function to README and NEWS

The long version

To add the model function mod() from the package pkg:

  1. If not already done, add pkg to "Suggests" in the DESCRIPTION file
    • Can be done manually or by running devtools::use_package("pkg", "Suggests"). For example, devtools::use_package("randomForest", "Suggests")
  2. If it doesn't exist, create a new file in "R/" named "twidlr-pkg.R", in which you'll add your code
    • For example, code for lm() from the stats package resides in "R/twidlr-stats.R"
  3. In this file, add three functions: mod, mod.default, predict.class_of_fitted_mod. See any "R/twidlr_*.R" file for examples.
    • all mod* functions are defined as function(data, formula, ...)
      • For unsupervised models (e.g., kmeans), formula may be set to ~. by default.
    • mod checks for necessary package via check_pkg("pkg") and calls the appropriate S3 method.
      • Be sure to document this function appropriately. For example, the document title should be "data.frame-first formula-second method for \code{\link[pkg]{mod}}"
    • mod.default does necessary data processing and returns an appropriate call to pkg::mod()
      • This should always start by coercing data to a data.frame (thus allowing other square structures) and formula to a formula (thus allowing string-based formulas) by way of coerce_args.
      • Calling the proper model function can involve passing arguments "as is" (e.g., lm and glm), or data munging to be done first (e.g., xgboost and glmnet). When data munging is required (or formula cannot be passed), be sure to add formula as an attribute to the fitted object (again, see xgboost and glmnet for examples). This is important so that the same data munging can be done by predict.
    • predict.class_of_fitted_mod is defined as function(object, data, ...), and returns an appropriate call to pkg::predict.class_of_mod (or valid alternative).
      • It must begin with data <- predict_checks(data = data, ...), which runs generic checks and coerces data to a data.frame.
  4. It if doesn't exist already, create a new file in "tests/testthat/" named "test-pkg.R", in which you'll add your tests.
    • Can be done manually or by running devtools::use_test("pkg"). For example, devtools::use_test("stats").
  5. In this test file, add a test laid out as test_that("mod", {...})
    • The test should compare the results of a twidlr-created model (named twidlr_fit) and a model fitted by the original package (named origina_fit). See any of the test files for examples. See Hadley Wickham's R Packages for advice about creating tests.
    • The test should evaluate at least three expectations:
      1. Do the models match (e.g., coefficients)?
      2. Does predict(twidlr_fit) throw an error because data is missing?
      3. Does predict() on each fitted object return the same results?
  6. Let everyone know about you addition and credit yourself!
    • Add pkg and mod to the data frame (converted to a table) under "twidlr models" in the README.Rmd file, and knit this file.
    • Add the package and function to a list in the NEWS.md file followed by "(thanks to YOUR NAME)", and link "YOUR NAME" to your GitHub profile.
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