Help! What’s wrong with my code???
Before class, you can prepare by reading the following materials:
Material for this lecture was borrowed and adopted from
At the end of this lesson you will:
message
, warning
, error
) and a fatal problem (error
)traceback
, debug
, recover
, browser
, and trace
can be used to find problematic code in functionsFinding the root cause of a problem is always challenging. Most bugs are subtle and hard to find because if they were obvious, you would have avoided them in the first place. A good strategy helps. Below I outline a four step process that I have found useful:
Whenever you see an error message, start by googling it. If you are lucky, you will discover that it’s a common error with a known solution. When googling, improve your chances of a good match by removing any variable names or values that are specific to your problem.
To find the root cause of an error, you are going to need to execute the code many times as you consider and reject hypotheses. To make that iteration as quick possible, it’s worth some upfront investment to make the problem both easy and fast to reproduce.
Start by creating a reproducible example (reprex). This will help others help you, and often leads to a solution without asking others, because in the course of making the problem reproducible you often figure out the root cause. Next, make the example minimal by removing code and simplifying data. As you do this, you may discover inputs that do not trigger the error. Make note of them: they will be helpful when diagnosing the root cause.
It’s a great idea to adopt the scientific method here. 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. Often a lot of time can be wasted 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.
If this fails, you might need to ask help from someone else. If you have followed the previous step, you will have a small example that is easy to share with others. That makes it much easier for other people to look at the problem, and more likely to help you find a solution.
Once you have found the bug, you need to figure out how to fix it and to check that the fix actually worked. Again, it is very useful to have automated tests in place. Not only does this help to ensure that you have actually fixed the bug, it also helps to ensure you have not 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.
Once you have made the error repeatable, the next step is to figure out where it comes from.
R has a number of ways to indicate to you that something is not right. There are different levels of indication that can be used, ranging from mere notification to fatal error. Executing any function in R may result in the following conditions.
message
: A generic notification/diagnostic message produced by the message()
function; execution of the function continueswarning
: An indication that something is wrong but not necessarily fatal; execution of the function continues. Warnings are generated by the warning()
functionerror
: An indication that a fatal problem has occurred and execution of the function stops. Errors are produced by the stop()
function.condition
: A generic concept for indicating that something unexpected has occurred; programmers can create their own custom conditions if they want.Here is an example of a warning that you might receive in the course of using R.
log(-1)
Warning in log(-1): NaNs produced
[1] NaN
This warning lets you know that taking the log of a negative number results in a NaN
value because you can’t take the log of negative numbers. Nevertheless, R doesn’t give an error, because it has a useful value that it can return, the NaN
value. The warning is just there to let you know that something unexpected happen. Depending on what you are programming, you may have intentionally taken the log of a negative number in order to move on to another section of code.
Here is another function that is designed to print a message to the console depending on the nature of its input.
This function is simple—it prints a message telling you whether x
is greater than zero or less than or equal to zero. It also returns its input invisibly, which is a common practice with “print” functions. Returning an object invisibly means that the return value does not get auto-printed when the function is called.
Take a hard look at the function above and see if you can identify any bugs or problems.
We can execute the function as follows.
printmessage(1)
[1] "x is greater than zero"
The function seems to work fine at this point. No errors, warnings, or messages.
printmessage(NA)
Error in if (x > 0) print("x is greater than zero") else print("x is less than or equal to zero"): missing value where TRUE/FALSE needed
What happened?
Well, the first thing the function does is test if x > 0
. But you can’t do that test if x
is a NA
or NaN
value. R doesn’t know what to do in this case so it stops with a fatal error.
We can fix this problem by anticipating the possibility of NA
values and checking to see if the input is NA
with the is.na()
function.
Now we can run the following.
printmessage2(NA)
[1] "x is a missing value!"
And all is fine.
Now what about the following situation.
Warning in log(c(-1, 2)): NaNs produced
printmessage2(x)
Warning in if (is.na(x)) print("x is a missing value!") else if (x >
0) print("x is greater than zero") else print("x is less than or equal
to zero"): the condition has length > 1 and only the first element
will be used
[1] "x is a missing value!"
Now what?? Why are we getting this warning? The warning says “the condition has length > 1 and only the first element will be used”.
The problem here is that I passed printmessage2()
a vector x
that was of length 2 rather then length 1. Inside the body of printmessage2()
the expression is.na(x)
returns a vector that is tested in the if
statement. However, if
cannot take vector arguments so you get a warning. The fundamental problem here is that printmessage2()
is not vectorized.
We can solve this problem two ways. One is by simply not allowing vector arguments. The other way is to vectorize the printmessage2()
function to allow it to take vector arguments.
For the first way, we simply need to check the length of the input.
Now when we pass printmessage3()
a vector we should get an error.
printmessage3(1:2)
Error in printmessage3(1:2): 'x' has length > 1
Vectorizing the function can be accomplished easily with the Vectorize()
function.
[1] "x is less than or equal to zero"
[1] "x is greater than zero"
You can see now that the correct messages are printed without any warning or error. Note that I stored the return value of printmessage4()
in a separate R object called out
. This is because when I use the Vectorize()
function it no longer preserves the invisibility of the return value.
Helpful tips
The primary task of debugging any R code is correctly diagnosing what the problem is. When diagnosing a problem with your code (or somebody else’s), it’s important first understand what you were expecting to occur. Then you need to idenfity what did occur and how did it deviate from your expectations. Some basic questions you need to ask are
Being able to answer these questions is important not just for your own sake, but in situations where you may need to ask someone else for help with debugging the problem. Seasoned programmers will be asking you these exact questions.
R provides a number of tools to help you with debugging your code. The primary tools for debugging functions in R are
traceback()
: prints out the function call stack after an error occurs; does nothing if there’s no errordebug()
: flags a function for “debug” mode which allows you to step through execution of a function one line at a timebrowser()
: suspends the execution of a function wherever it is called and puts the function in debug modetrace()
: allows you to insert debugging code into a function at specific placesrecover()
: allows you to modify the error behavior so that you can browse the function call stackThese functions are interactive tools specifically designed to allow you to pick through a function. There is also the more blunt technique of inserting print()
or cat()
statements in the function.
traceback()
The traceback()
function prints out the function call stack after an error has occurred. The function call stack is the sequence of functions that was called before the error occurred.
For example, you may have a function a()
which subsequently calls function b()
which calls c()
and then d()
. If an error occurs, it may not be immediately clear in which function the error occurred. The traceback()
function shows you how many levels deep you were when the error occurred.
> mean(x)
in mean(x) : object 'x' not found
Error > traceback()
1: mean(x)
Here, it’s clear that the error occurred inside the mean()
function because the object x
does not exist.
The traceback()
function must be called immediately after an error occurs. Once another function is called, you lose the traceback.
Here is a slightly more complicated example using the lm()
function for linear modeling.
> lm(y ~ x)
in eval(expr, envir, enclos) : object ’y’ not found
Error > traceback()
7: eval(expr, envir, enclos)
6: eval(predvars, data, env)
5: model.frame.default(formula = y ~ x, drop.unused.levels = TRUE)
4: model.frame(formula = y ~ x, drop.unused.levels = TRUE)
3: eval(expr, envir, enclos)
2: eval(mf, parent.frame())
1: lm(y ~ x)
You can see now that the error did not get thrown until the 7th level of the function call stack, in which case the eval()
function tried to evaluate the formula y ~ x
and realized the object y
did not exist.
Looking at the traceback is useful for figuring out roughly where an error occurred but it’s not useful for more detailed debugging. For that you might turn to the debug()
function.
debug()
debug()
with an interactive browser.
The debug()
function initiates an interactive debugger (also known as the “browser” in R) for a function. With the debugger, you can step through an R function one expression at a time to pinpoint exactly where an error occurs.
The debug()
function takes a function as its first argument. Here is an example of debugging the lm()
function.
> debug(lm) ## Flag the 'lm()' function for interactive debugging
> lm(y ~ x)
in: lm(y ~ x)
debugging : {
debug<- x
ret.x <- y
ret.y <- match.call()
cl
...if (!qr)
$qr <- NULL
z
z
} 2]> Browse[
Now, every time you call the lm()
function it will launch the interactive debugger. To turn this behavior off you need to call the undebug()
function.
The debugger calls the browser at the very top level of the function body. From there you can step through each expression in the body. There are a few special commands you can call in the browser:
n
executes the current expression and moves to the next expressionc
continues execution of the function and does not stop until either an error or the function exitsQ
quits the browserHere’s an example of a browser session with the lm()
function.
2]> n ## Evalute this expression and move to the next one
Browse[: ret.x <- x
debug2]> n
Browse[: ret.y <- y
debug2]> n
Browse[: cl <- match.call()
debug2]> n
Browse[: mf <- match.call(expand.dots = FALSE)
debug2]> n
Browse[: m <- match(c("formula", "data", "subset", "weights", "na.action",
debug"offset"), names(mf), 0L)
While you are in the browser you can execute any other R function that might be available to you in a regular session. In particular, you can use ls()
to see what is in your current environment (the function environment) and print()
to print out the values of R objects in the function environment.
You can turn off interactive debugging with the undebug()
function.
undebug(lm) ## Unflag the 'lm()' function for debugging
recover()
recover()
with an interactive browser.
The recover()
function can be used to modify the error behavior of R when an error occurs. Normally, when an error occurs in a function, R will print out an error message, exit out of the function, and return you to your workspace to await further commands.
With recover()
you can tell R that when an error occurs, it should halt execution at the exact point at which the error occurred. That can give you the opportunity to poke around in the environment in which the error occurred. This can be useful to see if there are any R objects or data that have been corrupted or mistakenly modified.
> options(error = recover) ## Change default R error behavior
> read.csv("nosuchfile") ## This code doesn't work
in file(file, "rt") : cannot open the connection
Error : Warning message:
In additionfile(file, "rt") :
In : No such file or directory
cannot open file ’nosuchfile’
0 to exit
Enter a frame number, or
1: read.csv("nosuchfile")
2: read.table(file = file, header = header, sep = sep, quote = quote, dec =
3: file(file, "rt")
: Selection
The recover()
function will first print out the function call stack when an error occurrs. Then, you can choose to jump around the call stack and investigate the problem. When you choose a frame number, you will be put in the browser (just like the interactive debugger triggered with debug()
) and will have the ability to poke around.
message
, warning
, error
; only an error
is fataltraceback
, debug
, recover
, browser
, and trace
can be used to find problematic code in functionsHere are some post-lecture questions to help you think about the material discussed.
Questions:
traceback()
to debug this piece of code:f <- function(a) g(a)
g <- function(b) h(b)
h <- function(c) i(c)
i <- function(d) {
if (!is.numeric(d)) {
stop("`d` must be numeric", call. = FALSE)
}
d + 10
}
f("a")
Error: `d` must be numeric
Describe in words what is happening above?
Text and figures are licensed under Creative Commons Attribution CC BY-NC-SA 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Hicks (2021, Sept. 23). Statistical Computing: Debugging R Code. Retrieved from https://stephaniehicks.com/jhustatcomputing2021/posts/2021-09-23-debugging-r-code/
BibTeX citation
@misc{hicks2021debugging, author = {Hicks, Stephanie}, title = {Statistical Computing: Debugging R Code}, url = {https://stephaniehicks.com/jhustatcomputing2021/posts/2021-09-23-debugging-r-code/}, year = {2021} }