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Debugging in {rtables} and Beyond

Davide Garolini

2023-12-08

Source: vignettes/dev-guide/dg_debug_rtables.Rmd
dg_debug_rtables.Rmd

Debugging

This is a short and non-comprehensive guide to debugging rtables. Regardless, it is to be considered valid for personal use at your discretion.

Coding in Practice

  • It is easy to read and find problems
  • It is not clever if it is impossible to debug

Some Definitions

  • Coding Error - Code does not do what you intended -> Bug in the punch card
  • Unexpected Input - Defensive programming FAIL FAST FAIL LOUD (FFFL) -> useful and not too time consuming
  • Bug in Dependency -> never use dependencies if you can!

Considerations About FFFL

Errors should be as close as possible to the source. For example, bad inputs should be found very early. The worst possible example is a software that is silently giving incorrect results. Common things that we can catch early are missing values, column length == 0, or length > 1.

General Suggestions

  • Robust code base does not attempt doing possibly problematic operations.
  • Read Error Messages
  • debugcall you can add the signature (formals)
  • trace is powerful because you can add the reaction
  • tracer is very good and precise to find where it happens

options(error = recover) is one of the best tools to debug at it is a core tool when developing that allows you to step into any point of the function call sequence.

dump.frames and debugger: it saves it to a file or an object and then you call debugger to step in it as you did recover.

warn Global Option

  • <0 ignored
  • 0 top level function call
  • 1 immediately as they occur
  • >=2 throws errors

<<- for recover or debugger gives it to the global environment

lo-fi debugging

  • PRINT / CAT is always a low level debugging that can be used. It is helpful for server jobs where maybe only terminal or console output is available and no browser() can be used. For example, you can print the position or state of a function at a certain point untill you find the break point.
  • comment blocks -> does not work with pipes (you can use identity() it is a step that does nothing but does not break the pipes)
  • browser() bombing

Regression Tests

Almost every bug should become a regression test.

Debugging with Pipes

  • Pipes are better to write code but horrible to debug
  • T in pipe %T>% does print it midway
  • debug_pipe() -> it is like the T pipe going into browser()

Shiny Debugging

More difficult due to reactivity.

General Suggestion

DO NOT BE CLEVER WITH CODE - ONLY IF YOU HAVE TO, CLEVER IS ALSO SUBJECTIVE AND IT WILL CHANGE WITH TIME.

Debugging in rtables

We invite the smart developer to use the provided examples as a way to get an “interactive” and dynamic view of the internal algorithms as they are routinely executed when constructing tables with rtables. This is achieved by using browser() and debugonce() on internal and exported functions (rtables::: or rtables::), as we will see in a moment. We invite you to continuously and autonomously explore the multiple S3 and S4 objects that constitute the complexity and power of rtables. To do so, we will use the following functions:

  • methods(generic_function): This function lists the methods that are available for a generic function. Specifically for S4 generic functions, showMethods(generic_function) gives more detailed information about each method (e.g. inheritance).
  • class(object): This function returns the class of an object. If the class is not one of the built-in classes in R, you can use this information to search for its documentation and examples. help(class) may be informative as it will call the documentation of the specific class. Similarly, the ? operator will bring up the documentation page for different S4 methods. For S3 methods it is necessary to postfix the class name with a dot (e.g. ?summary.lm).
  • getClass(class): This describes the type of class in a compact way, the slots that it has, and the relationships that it may have with the other classes that may inherit from or be inherited by it. With getClass(object) we can see to which values the slots of the object are assigned. It is possible to use str(object, max.level = 2) to see less formal and more compact descriptions of the slots, but it may be problematic when there are one or more objects in the class slots. Hence, the maximum number of levels should always be limited to 2 or 3 (max.level = 2). Similarly, attributes() can be used to retrieve some information, but we need to remember that storing important variables in this way is not encouraged. Information regarding the type of class can be retrieved with mode() and indirectly by summary() and is.S4(). *getAnywhere(function) is very useful to get the source code of internal functions and specific generics. It works very well with S3 methods, and will display the relevant namespace for each of the methods found. Similarly, getMethod(S4_generic, S4_class) can retrieve the source code of class-specific S4 methods.
  • eval(debugcall(generic_function(obj))): this is a very useful way to browse a S4 method, specifically for a defined object, without having to manually insert browser() into the code. It is also possible to do similarly with R > 3.4.0 where debug*() calls can have the triggering signature (class) specified. Both of these are modern and simplified wrappers of the tracing function trace().