R Debugging

RStudio integrates a set of debugging tools that  R interpreter has. In comparison with Perl R debugger is much less powerful but has some interesting ideas implemented more cleanly, such as browse() function which is equivalent to Perl's debugger $DB::single = 1; 

To debug a function you need to call it via special function, that as a side effect activates debug mode of interpreter.  Normally, debug mode in Rstudio is entered by calling a function using  debug(f) or debugonce(f). Rís debugging can't work outside function, but you can always convert this part of your program into main function and call it using debug.

Debugger works only within functions. Before a function can be debugged, it requires to be saved to a file and the file itself need to sourced using source statement.

In case you need to debug a function not yet saved nor sourced, a simple function like:

myDebug = function(f)
{ 
fname = deparse(substitute(f)) 
dump(fname, file = "tmp.R") 
source("tmp.R") 
do.call("debugonce", args = list(fname), envir = globalenv()) 
invisible(NULL) 
}
allows to start the visual debugger immediately.

Readers who have used Perl debugger, or GDB (the GNU debugger) will find similarities, but some aspects will come as a surprise. One unpleasant surprise is that  debug() is called on the function level, not on the overall program level. If you believe you have bugs in several of your functions, youíll need to call debug() on each one.

It you want to execute step by step function only once use debugonce() instead; calling debugonce(f) puts f() into debugging status the first time you execute it, but that status is reversed immediately upon exit from the function.

Browser function

The core of Rís debugging facility consists of the browser. It allows you to single-step through your code, line by line from the point of its invocation.. You need to place the call into your code and resave this function.

When control reaches this point, you will automatically enter the debugging mode. So you can start debugging from a specific point instead from a beginning of the functions as with debug.

Calling undebug(f) will unset the debug status of the function so that entry to the function will no longer invoke the browser.

You can move the point of invocation of browser function as you progress and have several points of invocation. 

When you pause in the middle of running a function and get control back to you with browser, you can  enter new commands at the command line. The active environment for these commands will not be the global environment (as usual); it will be the runtime environment of the function that you have paused. As a result, you can look at the objects that the function is using, look up their values with the same scoping rules that the function would use, and run code under the same conditions that the function would run it in. This arrangement provides the best chance for spotting the source of bugs in a function.

You can make invoking the browser conditional so that it is entered only in specified situations. Use the expr argument to define those situations. For instance, suppose you suspect that your bug arises only when a certain variable s is larger than 1. You could use this code:

browser(s > 1)

The browser will be invoked only if s is larger than 1. The following would have the same effect:

if (s > 1) browser()

Calling the browser directly, rather than entering the debugger via debug() is very useful in situations in which you have a loop with many iterations and the bug surfaces only after, say, the 50th iteration. If the loop index is i, then you could write this:

if (i > 49) browser()

That way, you would avoid the tedium of stepping through the first 49 iterations!

To use browser, add the call browser() to the body of a function and then resave the function. Press 'n' to go step by step.

Inserting browser command with the trace() Function

The trace() in its simples form cn be invokes as following: 

> trace(f,t)

This call instructs R to call the function t() every time we enter the function f(). For instance, say we wish to set a breakpoint at the beginning of the function main(). We could use this command:

> trace(main,browser)

This has the same effect as placing the command browser() in our source code for gy(), but itís quicker and more convenient than inserting such a line, saving the file, and rerunning source() to load in the new version of the file. Calling trace() does not change your source file, though it does change a temporary version of your file maintained by R. It would also be quicker and more convenient to undo, by simply running untrace:

> untrace(gy)

You can also turn tracing on or off globally by calling tracingState(), using the argument TRUE to turn it on or FALSE to turn it off.

Ctrl-R

In Rstudio, pressing Ctrl+R without any text highlighted will run whatever code is on the same line as your cursor. Then your cursor will automatically advance to the next line. You can just keep pressing Ctrl+R to run through your code line by line.

  
While you are in the browser, the prompt changes from > to Browse[d]>. (Here, d is the depth of the call chain.)

Debugger Commands

 You may submit any of the following commands at that prompt:

Setting Breakpoints

Calling debug(f) places a call to browser() at the beginning of f(). However, this may be too coarse a tool in some cases. If you suspect that the bug is in the middle of the function, itís wasteful to trudge through all the intervening code.

The solution is to set breakpoints at certain key locations of your codeóplaces where you want execution to be paused. How can this be done in R? You can call browser directly or use the setBreakpoint() function. 

setBreakpoint() Function

Starting with R 2.10, you can use setBreakpoint() in the format

setBreakpoint((*@filename,linenumber@*))

This will result in browser() being called at line linenumber of our source file filename.

This is especially useful when you are in the midst of using the debugger, single-stepping through code. Say you are currently at line 12 of your source file x.R and want to have a breakpoint at line 28. Instead of exiting the debugger, adding a call to browser() at line 28, and then re-entering the function, you could simply type this:

> setBreakpoint("x.R",28)

You could then resume execution within the debugger, say by issuing the c command.

The setBreakpoint() function works by calling the trace() function, discussed in the next section. Thus, to cancel the breakpoint, you cancel the trace. For instance, if we had called setBreakpoint() at a line in the function g(), we would cancel the breakpoint by typing the following:

> untrace(g)

You can call setBreakpoint() whether or not you are currently in the debugger. If you are not currently running the debugger and you execute the affected function and hit the breakpoint during that execution, you will be put into the browser automatically. This is similar to the case of browser(), but using this approach, you save yourself the trouble of changing your code via your text editor.

Performing Checks After a Crash 

Say your R code crashes when you are not running the debugger. There is still a debugging tool available to you after the fact. You can do a ďpostmortemĒ by simply calling traceback(). It will tell you in which function the problem occurred and the call chain that led to that function.

You can get a lot more information if you set up R to dump frames in the event of a crash:

> options(error=dump.frames)

If youíve done this, then after a crash, run this command:

> debugger()

You will then be presented with a choice of levels of function calls to view. For each one that you choose, you can take a look at the values of the variables there. After browsing through one level, you can return to the debugger() main menu by hitting N.

You can arrange to automatically enter the debugger by writing this code:

> options(error=recover)

Note, though, that if you do choose this automatic route, it will whisk you into the debugger, even if you simply have a syntax error (not a useful time to enter the debugger).

To turn off any of this behavior, type the following:

> options(error=NULL)

Youíll see a demonstration of this approach in the next section.

Dr Nikolai Bezroukov


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Debugging Finding Runs of Ones

First recall our extended example of finding runs of 1s in Chapter 2. Here is a buggy version of the code:

1    findruns <- function(x,k) {
2       n <- length(x)
3       runs <- NULL
4       for (i in 1:(n-k)) {
5          if (all(x[i:i+k-1]==1)) runs <- c(runs,i)
6       }
7       return(runs)
8    }

Let's try it on a small test case:

> source("findruns.R")
> findruns(c(1,0,0,1,1,0,1,1,1),2)
[1] 3 4 6 7

The function was supposed to report runs at indices 4, 7, and 8, but it found some indices that it shouldn't have and missed some as well. Something is wrong. Let's enter the debugger and take a look around.

> debug(findruns)
> findruns(c(1,0,0,1,1,0,1,1,1),2)
debugging in: findruns(c(1, 0, 0, 1, 1, 0, 1, 1, 1), 2)
debug at findruns.R#1: {
    n <- length(x)
    runs <- NULL
    for (i in 1:(n - k)) {
        if (all(x[i:i + k - 1] == 1))
            runs <- c(runs, i)
    }
    return(runs)
}
attr(,"srcfile")
findruns.R

So, according to the principle of confirmation, let's first make sure our test vector was received correctly:

Browse[2]> x
[1] 1 0 0 1 1 0 1 1 1

So far, so good. Let's step through the code a bit. We hit n a couple of times to single-step through the code.

Browse[2]> n
debug at findruns.R#2: n <- length(x)
Browse[2]> n
debug at findruns.R#3: runs <- NULL
Browse[2]> print(n)
[1] 9

Note that after each single step, R tells us which statement would be the next one to execute. In other words, at the time we executed print(n), we had not yet executed the assignment of NULL to runs.

Note, too, that although normally you can print out the value of a variable by simply typing its name, we could not do so here for our variable n, because n is also the abbreviation for the debugger's next command. Thus, we needed print().

At any rate, we found that the length of our test vector was 9, confirming what we knew. Now, let's single-step some more, getting into the loop.

Browse[2]> n
debug at findruns.R#4: for (i in 1:(n - k + 1)) {
    if (all(x[i:i + k - 1] == 1))
        runs <- c(runs, i)
}
Browse[2]> n
debug at findruns.R#4: i
Browse[2]> n
debug at findruns.R#5: if (all(x[i:i + k - 1] == 1)) runs <- c(runs, i)

Since k is 2-that is, we are checking for runs of length 2- the if() statement should be checking the first two elements of x, which are (1,0). Let's confirm:

Browse[2]> x[i:i + k - 1]
[1] 0

So, it did not confirm. Let's check that we have the correct subscript range, which should be 1:2. Is it?

Browse[2]> i:i + k - 1
[1] 2

Also wrong. Well, how about i and k? They should be 1 and 2, respectively. Are they?

Browse[2]> i
[1] 1
Browse[2]> k
[1] 2

Well, those do confirm. Thus, our problem must be with the expression i:i + k - 1. After some thought, we realize there is an operator precedence problem there, and we correct it to i:(i + k - 1).

Is it okay now?

> source("findruns.R")
> findruns(c(1,0,0,1,1,0,1,1,1),2)
[1] 4 7

No, as mentioned, it should be (4,7,8).

Let's set a breakpoint inside the loop and take a closer look.

> setBreakpoint("findruns.R",5)
/home/nm/findruns.R#5:
 findruns step 4,4,2 in <environment: R_GlobalEnv>
> findruns(c(1,0,0,1,1,0,1,1,1),2)
findruns.R#5
Called from: eval(expr, envir, enclos)
Browse[1]> x[i:(i+k-1)]
[1] 1 0

Good, we're dealing with the first two elements of the vector, so our bug fix is working so far. Let's look at the second iteration of the loop.

Browse[1]> c
findruns.R#5
Called from: eval(expr, envir, enclos)
Browse[1]> i
[1] 2
Browse[1]> x[i:(i+k-1)]
[1] 0 0

That's right, too. We could go another iteration, but instead, let's look at the last iteration, a place where bugs frequently arise in loops. So, let's add a conditional breakpoint, as follows:

findruns <- function(x,k) {
   n <- length(x)
   runs <- NULL
   for (i in 1:(n-k)) {
      if (all(x[i:(i+k-1)]==1)) runs <- c(runs,i)
      if (i == n-k) browser()  # break in last iteration of loop
   }
   return(runs)
}

And now run it again.

> source("findruns.R")
> findruns(c(1,0,0,1,1,0,1,1,1),2)
Called from: findruns(c(1, 0, 0, 1, 1, 0, 1, 1, 1), 2)
Browse[1]> i
[1] 7

This shows the last iteration was for i = 7. But the vector is nine elements long, and k = 2, so our last iteration should be i = 8. Some thought then reveals that the range in the loop should have been written as follows:

for (i in 1:(n-k+1)) {

By the way, note that the breakpoint that we set using setBreakpoint() is no longer valid, now that we've replaced the old version of the object findruns.

Subsequent testing (not shown here) indicates the code now works. Let's move on to a more complex example.

Debugging Finding City Pairs

Recall our code in Section 3.4.2, which found the pair of cities with the closest distance between them. Here is a buggy version of that code:

1    returns the minimum value of d[i,j], i != j, and the row/col attaining
2    that minimum, for square symmetric matrix d; no special policy on
3    ties;
4    motivated by distance matrices
5    mind <- function(d) {
6       n <- nrow(d)
7       add a column to identify row number for apply()
8       dd <- cbind(d,1:n)
9       wmins <- apply(dd[-n,],1,imin)
10       wmins will be 2xn, 1st row being indices and 2nd being values
11       i <- which.min(wmins[1,])
12       j <- wmins[2,i]
13       return(c(d[i,j],i,j))
14    }
15
16    finds the location, value of the minimum in a row x
17    imin <- function(x) {
18       n <- length(x)
19       i <- x[n]
20       j <- which.min(x[(i+1):(n-1)])
21       return(c(j,x[j]))
22    }

Let's use R's debugging tools to find and fix the problems.

We'll run it first on a small test case:

> source("cities.R")
> m <- rbind(c(0,12,5),c(12,0,8),c(5,8,0))
> m
     [,1] [,2] [,3]
[1,]    0   12    5
[2,]   12    0    8
[3,]    5    8    0
> mind(m)
Error in mind(m) : subscript out of bounds

Not an auspicious start! Unfortunately, the error message doesn't tell us where the code blew up. But the debugger will give us that information:

> options(error=recover)
> mind(m)
Error in mind(m) : subscript out of bounds

Enter a frame number, or 0 to exit

1: mind(m)

Selection: 1
Called from: eval(expr, envir, enclos)


Browse[1]> where
where 1: eval(expr, envir, enclos)
where 2: eval(quote(browser()), envir = sys.frame(which))
where 3 at cities.R#13: function ()
{
    if (.isMethodsDispatchOn()) {
        tState <- tracingState(FALSE)
...

Okay, so the problem occurred in mind() rather than imin() and in particular at line 13. It still could be the fault of imin(), but for now, let's deal with the former.

Note

There is another way we could have determined that the blowup occurred on line 13. We would enter the debugger as before but probe the local variables. We could reason that if the subscript bounds error had occurred at line 9, then the variable wmins would not have been set, so querying it would give us an error message like Error: object 'wmins' not found. On the other hand, if the blowup occurred on line 13, even j would have been set.

Since the error occurred with d[i,j], let's look at those variables:

Browse[1]> d
     [,1] [,2] [,3]
[1,]    0   12    5
[2,]   12    0    8
[3,]    5    8    0
Browse[1]> i
[1] 2
Browse[1]> j
[1] 12

This is indeed a problem-d only has three columns, yet j, a column subscript, is 12.

Let's look at the variable from which we gleaned j, wmins:

Browse[1]> wmins
     [,1] [,2]
[1,]    2    1
[2,]   12   12

If you recall how the code was designed, column k of wmins is supposed to contain information about the minimum value in row k of d. So here wmins is saying that in the first row (k = 1) of d,(0,12,5), the minimum value is 12, occurring at index 2. But it should be 5 at index 3. So, something went wrong with this line:

wmins <- apply(dd[-n, ], 1, imin)

There are several possibilities here. But since ultimately imin() is called, we can check them all from within that function. So, let's set the debug status of imin(), quit the debugger, and rerun the code.

Browse[1]> Q
> debug(imin)
> mind(m)
debugging in: FUN(newX[, i], ...)
debug at cities.R#17: {
    n <- length(x)
    i <- x[n]
    j <- which.min(x[(i + 1):(n - 1)])
    return(c(j, x[j]))
}
...

So, we're in imin(). Let's see if it properly received the first row of dd, which should be (0,12,5,1).

Browse[4]> x
[1]  0 12  5  1

It's confirmed. This seems to indicate that the first two arguments to apply() were correct and that the problem is instead within imin(), though that remains to be seen.

Let's single-step through, occasionally typing confirmational queries:

Browse[2]> n
debug at cities.r#17: n <- length(x)
Browse[2]> n
debug at cities.r#18: i <- x[n]
Browse[2]> n
debug at cities.r#19: j <- which.min(x[(i + 1):(n - 1)])
Browse[2]> n
debug at cities.r#20: return(c(j, x[j]))
Browse[2]> print(n)
[1] 4
Browse[2]> i
[1] 1
Browse[2]> j
[1] 2

Recall that we designed our call which.min(x[(i + 1):(n - 1)] to look only at the above-diagonal portion of this row. This is because the matrix is symmetric and because we don't want to consider the distance between a city and itself.

But the value j = 2 does not confirm. The minimum value in (0,12,5) is 5, which occurs at index 3 of that vector, not index 2. Thus, the problem is in this line:

j <- which.min(x[(i + 1):(n - 1)])

What could be wrong?

After taking a break, we realize that although the minimum value of (0,12,5) occurs at index 3 of that vector, that is not what we asked which.min() to find for us. Instead, that i + 1 term means we asked for the index of the minimum in (12,5), which is 2.

We did ask which.min() for the correct information, but we failed to use it correctly, because we do want the index of the minimum in (0,12,5). We need to adjust the output of which.min() accordingly, as follows:

j <- which.min(x[(i+1):(n-1)])
k <- i + j
return(c(k,x[k]))

We make the fix and try again.

> mind(m)
Error in mind(m) : subscript out of bounds

Enter a frame number, or 0 to exit

1: mind(m)

Selection:

Oh no, another bounds error! To see where the blowup occurred this time, we issue the where command as before, and we find it was at line 13 again. What about i and j now?

Browse[1]> i
[1] 1
Browse[1]> j
[1] 5

The value of j is still wrong; it cannot be larger than 3, as we have only three columns in this matrix. On the other hand, i is correct. The overall minimum value in dd is 5, occurring in row 1, column 3.

So, let's check the source of j again, the matrix wmins:

Browse[1]> wmins
     [,1] [,2]
[1,]    3    3
[2,]    5    8

Well, there are the 3 and 5 in column 1, just as should be the case. Remember, column 1 here contains the information for row 1 in d, so wmins is saying that the minimum value in row 1 is 5, occurring at index 3 of that row, which is correct.

After taking another break, though, we realize that while wmins is correct, our use of it isn't. We have the rows and columns of that matrix mixed up. This code:

i <- which.min(wmins[1,])
j <- wmins[2,i]

should be like this:

i <- which.min(wmins[2,])
j <- wmins[1,i]

After making that change and resourcing our file, we try it out.

> mind(m)
[1] 5 1 3

This is correct, and subsequent tests with larger matrices worked, too.

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