20 Debugging

20.1 Introduction

What happens when something goes wrong with your R code? What do you do? What tools do you have to address the problem? This chapter will teach you how to fix unanticipated problems (debugging), show you how functions can communicate problems and how you can take action based on those communications (condition handling), and teach you how to avoid common problems before they occur (defensive programming).

Debugging is the art and science of fixing unexpected problems in your code. In this section you’ll learn the tools and techniques that help you get to the root cause of an error. You’ll learn general strategies for debugging, useful R functions like traceback() and browser(), and interactive tools in RStudio.

The chapter concludes with a discussion of “defensive” programming: ways to avoid common errors before they occur. In the short run you’ll spend more time writing code, but in the long run you’ll save time because error messages will be more informative and will let you narrow in on the root cause more quickly. The basic principle of defensive programming is to “fail fast”, to raise an error as soon as something goes wrong. In R, this takes three particular forms: checking that inputs are correct, avoiding non-standard evaluation, and avoiding functions that can return different types of output.

Outline

  1. Debugging techniques outlines a general approach for finding and resolving bugs.

  2. Debugging tools introduces you to the R functions and RStudio features that help you locate exactly where an error occurred.

  3. Defensive programming introduces you to some important techniques for defensive programming, techniques that help prevent bugs from occurring in the first place.

20.2 Techniques

“Finding your bug is a process of confirming the many things that you believe are true — until you find one which is not true.”

—Norm Matloff

Debugging code is challenging. Many bugs are subtle and hard to find. Indeed, if a bug was obvious, you probably would’ve been able to avoid it in the first place. While it’s true that with a good technique, you can productively debug a problem with just print(), there are times when additional help would be welcome. In this section, we’ll discuss some useful tools, which R and RStudio provide, and outline a general procedure for debugging.

While the procedure below is by no means foolproof, it will hopefully help you to organise your thoughts when debugging. There are four steps:

  1. Realise that you have a bug

    If you’re reading this chapter, you’ve probably already completed this step. It is a surprisingly important one: you can’t fix a bug until you know it exists. This is one reason why automated test suites are important when producing high-quality code. Unfortunately, automated testing is outside the scope of this book, but you can read more about it at http://r-pkgs.had.co.nz/tests.html.

  2. Make it repeatable

    Once you’ve determined you have a bug, you need to be able to reproduce it on command. Without this, it becomes extremely difficult to isolate its cause and to confirm that you’ve successfully fixed it.

    Generally, you will start with a big block of code that you know causes the error and then slowly whittle it down to get to the smallest possible snippet that still causes the error. Binary search is particularly useful for this. To do a binary search, you repeatedly remove half of the code until you find the bug. This is fast because, with each step, you reduce the amount of code to look through by half.

    If it takes a long time to generate the bug, it’s also worthwhile to figure out how to generate it faster. The quicker you can do this, the quicker you can figure out the cause.

    As you work on creating a minimal example, you’ll also discover similar inputs that don’t trigger the bug. Make note of them: they will be helpful when diagnosing the cause of the bug.

    If you’re using automated testing, this is also a good time to create an automated test case. If your existing test coverage is low, take the opportunity to add some nearby tests to ensure that existing good behaviour is preserved. This reduces the chances of creating a new bug.

  3. Figure out where it is

    If you’re lucky, one of the tools in the following section will help you to quickly identify the line of code that’s causing the bug. Usually, however, you’ll have to think a bit more about the problem. It’s a great idea to adopt the scientific method. Generate hypotheses, design experiments to test them, and record your results. This may seem like a lot of work, but a systematic approach will end up saving you time. I often waste a lot of time relying on my intuition to solve a bug (“oh, it must be an off-by-one error, so I’ll just subtract 1 here”), when I would have been better off taking a systematic approach.

  4. Fix it and test it

    Once you’ve found the bug, you need to figure out how to fix it and to check that the fix actually worked. Again, it’s very useful to have automated tests in place. Not only does this help to ensure that you’ve actually fixed the bug, it also helps to ensure you haven’t introduced any new bugs in the process. In the absence of automated tests, make sure to carefully record the correct output, and check against the inputs that previously failed.

20.3 Tools

To implement a strategy of debugging, you’ll need tools. In this section, you’ll learn about the tools provided by R and the RStudio IDE. RStudio’s integrated debugging support makes life easier by exposing existing R tools in a user friendly way. I’ll show you both the R and RStudio ways so that you can work with whatever environment you use. You may also want to refer to the official RStudio debugging documentation which always reflects the tools in the latest version of RStudio.

There are three key debugging tools:

  • RStudio’s error inspector and traceback() which list the sequence of calls that lead to the error.

  • RStudio’s “Rerun with Debug” tool and options(error = browser) which open an interactive session where the error occurred.

  • RStudio’s breakpoints and browser() which open an interactive session at an arbitrary location in the code.

I’ll explain each tool in more detail below.

You shouldn’t need to use these tools when writing new functions. If you find yourself using them frequently with new code, you may want to reconsider your approach. Instead of trying to write one big function all at once, work interactively on small pieces. If you start small, you can quickly identify why something doesn’t work. But if you start large, you may end up struggling to identify the source of the problem.

20.3.1 Determining the sequence of calls

The first tool is the call stack, the sequence of calls that lead up to an error. Here’s a simple example: you can see that f() calls g() calls h() calls i() which adds together a number and a string creating an error:

When we run this code in RStudio we see:

Two options appear to the right of the error message: “Show Traceback” and “Rerun with Debug”. If you click “Show traceback” you see:

If you’re not using RStudio, you can use traceback() to get the same information:

Read the call stack from bottom to top: the initial call is f(), which calls g(), then h(), then i(), which triggers the error. If you’re calling code that you source()d into R, the traceback will also display the location of the function, in the form filename.r#linenumber. These are clickable in RStudio, and will take you to the corresponding line of code in the editor.

Sometimes this is enough information to let you track down the error and fix it. However, it’s usually not. traceback() shows you where the error occurred, but not why. The next useful tool is the interactive debugger, which allows you to pause execution of a function and interactively explore its state.

20.3.2 Browsing on error

The easiest way to enter the interactive debugger is through RStudio’s “Rerun with Debug” tool. This reruns the command that created the error, pausing execution where the error occurred. You’re now in an interactive state inside the function, and you can interact with any object defined there. You’ll see the corresponding code in the editor (with the statement that will be run next highlighted), objects in the current environment in the “Environment” pane, the call stack in a “Traceback” pane, and you can run arbitrary R code in the console.

As well as any regular R function, there are a few special commands you can use in debug mode. You can access them either with the RStudio toolbar () or with the keyboard:

  • Next, n: executes the next step in the function. Be careful if you have a variable named n; to print it you’ll need to do print(n).

  • Step into, or s: works like next, but if the next step is a function, it will step into that function so you can work through each line.

  • Finish, or f: finishes execution of the current loop or function.

  • Continue, c: leaves interactive debugging and continues regular execution of the function. This is useful if you’ve fixed the bad state and want to check that the function proceeds correctly.

  • Stop, Q: stops debugging, terminates the function, and returns to the global workspace. Use this once you’ve figured out where the problem is, and you’re ready to fix it and reload the code.

There are two other slightly less useful commands that aren’t available in the toolbar:

  • Enter: repeats the previous command. I find this too easy to activate accidentally, so I turn it off using options(browserNLdisabled = TRUE).

  • where: prints stack trace of active calls (the interactive equivalent of traceback).

To enter this style of debugging outside of RStudio, you can use the error option which specifies a function to run when an error occurs. The function most similar to RStudio’s debug is browser(): this will start an interactive console in the environment where the error occurred. Use options(error = browser) to turn it on, re-run the previous command, then use options(error = NULL) to return to the default error behaviour. You could automate this with the browseOnce() function as defined below:

(You’ll learn more about functions that return functions in Functional programming.)

There are two other useful functions that you can use with the error option:

To reset error behaviour to the default, use options(error = NULL). Then errors will print a message and abort function execution.

20.3.3 Browsing arbitrary code

As well as entering an interactive console on error, you can enter it at an arbitrary code location by using either an RStudio breakpoint or browser(). You can set a breakpoint in RStudio by clicking to the left of the line number, or pressing Shift + F9. Equivalently, add browser() where you want execution to pause. Breakpoints behave similarly to browser() but they are easier to set (one click instead of nine key presses), and you don’t run the risk of accidentally including a browser() statement in your source code. There are two small downsides to breakpoints:

  • There are a few unusual situations in which breakpoints will not work: read breakpoint troubleshooting for more details.

  • RStudio currently does not support conditional breakpoints, whereas you can always put browser() inside an if statement.

As well as adding browser() yourself, there are two other functions that will add it to code:

  • debug() inserts a browser statement in the first line of the specified function. undebug() removes it. Alternatively, you can use debugonce() to browse only on the next run.

  • utils::setBreakpoint() works similarly, but instead of taking a function name, it takes a file name and line number and finds the appropriate function for you.

These two functions are both special cases of trace(), which inserts arbitrary code at any position in an existing function. trace() is occasionally useful when you’re debugging code that you don’t have the source for. To remove tracing from a function, use untrace(). You can only perform one trace per function, but that one trace can call multiple functions.

20.3.4 The call stack: traceback(), where, and recover()

Unfortunately, the call stacks printed by traceback(), browser() + where, and recover() are not consistent. The following table shows how the call stacks from a simple nested set of calls are displayed by the three tools.

traceback() where recover()
4: stop("Error") where 1: stop("Error") 1: f()
3: h(x) where 2: h(x) 2: g(x)
2: g(x) where 3: g(x) 3: h(x)
1: f() where 4: f()

Note that numbering is different between traceback() and where, and that recover() displays calls in the opposite order, and omits the call to stop(). RStudio displays calls in the same order as traceback() but omits the numbers.

20.3.5 Other types of failure

There are other ways for a function to fail apart from throwing an error or returning an incorrect result.