My simple first Palette is suppose to:
Append my packages Path to $Path
ActionMenu["test",{"The Simple Packages Path":> AppendTo[$Path, ToFileName[{NotebookDirectory[], "02 Simple Packages"}]]}]
Get my packages
ActionMenu["Load Packages", {"Get my package":> Get["myPackage`"]}]
Place on the selected input cell (or on a new input cell), a given input expression, containing different place holders.
OpenerView[{"my Package", Button["construct", Paste[StandardForm#Defer#construct[Placeholder["description"],Placeholder["another description"]]]]}]
The problem is that I keep getting "shadow" messages when I click on the "get my package" action menu item. And I'm sure I'm not double loading the package intentionally. When I click on "construct", it writes Global`construct["description","another description"]. But I'm sure I didn't define it before getting the package (I killed the kernel on my tests).
Do you know what is wrong?
(I use Get on my packages, instead of Needs, to ensure a clean start of the package context)
Also: do you know of a simpler way of doing the Paste[StandardForm#Defer... that ensures both that the expression being paste isn't evaluated and that it goes into an input cell, even when there's no cell selected?
Ok, it seems that your problem is due to the interplay between parsing and interface creation. What you would like in this case is to delay the parsing of package symbols in your interface - constructing code (package symbols that you use in button action functions), from the interface - creation time, until the "press button" time (assuming that by that time, the package has been loaded). Here is one way to do it:
Column[{ActionMenu["Load Packages",
{"Get my package" :> Get["ANOVA`"]}],
OpenerView[{"ANOVA", Button["construct",
With[{sym = Symbol["ANOVA"]},
Paste[StandardForm#Defer#sym[Placeholder["DATA"]]]]]}]}]
What we did here is to use With to inject the symbol into the code for the button function. But, at the time your interface code is parsed, we prevent the creation of the Global symbol with this name - this is what happens otherwise, and this is what causes your problem.
EDIT
If you know for sure that you only use symbols (functions) from packages, and not from the Global' context, here is a version that will be "protected" from this problem: it will Remove the generated symbol if its context turns out to be Global' - and thus pressing button before the package has been loaded would result merely in a warning message (I use the symbol package to attach the message to - should be replaced by whatever is the name of your interface - making function):
package::noload = "Please load the package containing symbol `1`";
Column[{ActionMenu["Load Packages",
{"Get my package" :> Get["ANOVA`"]}],
OpenerView[{"ANOVA", Button["construct",
With[{sym = Symbol["ANOVA"]},
If[Context[sym] === "Global`",
Message[package::noload, Style[ToString[sym], Red]];
Remove[sym];,
(* else *)
Paste[StandardForm#Defer#sym[Placeholder["DATA"]]]]]]}]}]
Well, I don't have your package, so for testing I changed the action menu to get the ANOVA package:
ActionMenu["Load Packages", {"Get my package" :> Get["ANOVA`"]}]
ANOVA[{{1, 1}, {1, 2}, {2, 4}, {2, 3}}] now works without a hitch. No complaints about shadowing. This suggests that the cause of your shadowing problem lies somewhere else. I noticed, though, that the word ANOVA stays blue. This will be related to the problem you reported here.
Related
When I hit a breakpoint I have "this" in my watch window
this -> 0x2cceb42c
I copy that value into a new line in my watch window (it displays both the name and the value in hex)
0x2cceb42c -> 0x2cceb42c
On a third line I cast my value into a pointer to my class:
(MyClass*)0x2cceb42c -> { members of class... }
The problem is, next time I run the program the address has changed so I have to edit the address on my third line. Only, I'm not just using it in the third line, but also in 5 other watch expressions. Which means the next time I run the program I have to change the address in all 5 watched expressions.
What I would like to do is have my 5 watch expressions refer to the value in line 2 - then I only ever need to change the address in one place and all my watches will update automatically.
Is this possible? Or can anyone suggest a trick to achieve as close to this as possible?
Clarification: I want to see the results of my 5 watch expressions when I'm on a breakpoint elsewhere in the code (where "this" is no longer the value I'm interested in, which is why I'm copying the address out of "this").
Occasionally it would be preferable to have some initialization code for palettes (of buttons). For example, it could define some functions that are used by palette buttons.
What is the easiest and preferable way to define/run initialization code for a palette?
The initialization can run either when the palette is loaded or when any button is pressed for the first time (possible issue: what if the kernel is restarted after the palette was loaded?)
The definitions should be somehow localized (i.e. in their own context -- do unique notebook contexts help here?)
If possible, I'd prefer a minimal effort solution (i.e. extra code at the fewest possible places, self contained palette file with no extra package files, palette creation using the existing convenience tools of palettes menu or CreatePalette, etc.)
(You can assume that the initialization code runs fast, e.g. it consists of definitions only)
You're right to be concerned about the visibility of the Dynamic being an issue. The way to absolutely guarantee a Dynamic expression to be evaluated regardless of the visibility of any of the individual cells is to use NotebookDynamicExpression. Here's an example that illustrates NotebookDynamicExpression working while a Dynamic fails because it's hidden within a closed cell group:
cell1 = First # MakeBoxes[
TextCell["Click to open", "Title",
CellMargins -> 0, System`WholeCellGroupOpener -> True],
StandardForm];
cell2 = First # MakeBoxes[
ExpressionCell[DynamicWrapper["hidden cell", Print["DynamicWrapper"]]],
StandardForm];
CreatePalette[
Notebook[{Cell[CellGroupData[{cell1, cell2}, Closed]]},
NotebookDynamicExpression :>
Dynamic[Refresh[Print["NotebookDynamicExpression"], None]]]]
When you evaluate this, note that the Dynamic in NotebookDynamicExpression evaluates immediately. The DynamicWrapper never evaluates until you open the cell group, which you can do by clicking on the "Click to open" text.
In this example, incidentally, notice that I wrapped the NotebookDynamicExpression with Refresh. The function Refresh[#, None]& will make sure that the code evaluates only once -- when the notebook is first opened. Otherwise, the code would obey the standard properties of Dynamic and evaluate whenever any of the dependencies change.
NotebookDynamicExpression has been around since v6, but was only documented in v8. Also documented are its related cousins, CellDynamicExpression and FrontEndDynamicExpression.
A DynamicBox with Initialization is capable of the basic function. You can size the palette such that the object is not visible, and it will still operate.
Here is code for a small sample palette. It sets a value for var. The active code is offset with whitespace.
(* Beginning of Notebook Content *)
Notebook[{
Cell[BoxData[{
TagBox[GridBox[{
{
ButtonBox["\<\"TSV\"\>",
Appearance->Automatic,
ButtonFunction:>None,
Evaluator->Automatic,
Method->"Preemptive"]},
{
ButtonBox["\<\"CSV\"\>",
Appearance->Automatic,
ButtonFunction:>None,
Evaluator->Automatic,
Method->"Preemptive"]},
{
ButtonBox["\<\"Table\"\>",
Appearance->Automatic,
ButtonFunction:>None,
Evaluator->Automatic,
Method->"Preemptive"]}
},
GridBoxAlignment->{"Columns" -> {{Left}}},
GridBoxItemSize->{"Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}],
"Column"], "\[IndentingNewLine]",
DynamicBox[Null,
Initialization :> ($CellContext`var = "It is done, Master.")
]
}], NotebookDefault,
CellMargins->{{0, 0}, {0, 0}},
CellBracketOptions->{"Color"->RGBColor[0.269993, 0.308507, 0.6]},
CellHorizontalScrolling->True,
PageBreakAbove->True,
PageBreakWithin->False,
ShowAutoStyles->True,
LineSpacing->{1.25, 0},
AutoItalicWords->{},
ScriptMinSize->9,
ShowStringCharacters->False,
FontFamily:>CurrentValue["PanelFontFamily"],
FontSize:>CurrentValue["PanelFontSize"]]
},
WindowSize->{55, 105},
WindowMargins->{{Automatic, 583}, {Automatic, 292}},
WindowFrame->"Palette",
WindowElements->{},
WindowFrameElements->{"CloseBox", "MinimizeBox"},
StyleDefinitions->"Palette.nb"
]
(* End of Notebook Content *)
I get an error when using an R function that I wrote:
Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: algorithm did not converge
What I have done:
Step through the function
Adding print to find out at what line the error occurs suggests two functions that should not use glm.fit. They are window() and save().
My general approaches include adding print and stop commands, and stepping through a function line by line until I can locate the exception.
However, it is not clear to me using those techniques where this error comes from in the code. I am not even certain which functions within the code depend on glm.fit. How do I go about diagnosing this problem?
I'd say that debugging is an art form, so there's no clear silver bullet. There are good strategies for debugging in any language, and they apply here too (e.g. read this nice article). For instance, the first thing is to reproduce the problem...if you can't do that, then you need to get more information (e.g. with logging). Once you can reproduce it, you need to reduce it down to the source.
Rather than a "trick", I would say that I have a favorite debugging routine:
When an error occurs, the first thing that I usually do is look at the stack trace by calling traceback(): that shows you where the error occurred, which is especially useful if you have several nested functions.
Next I will set options(error=recover); this immediately switches into browser mode where the error occurs, so you can browse the workspace from there.
If I still don't have enough information, I usually use the debug() function and step through the script line by line.
The best new trick in R 2.10 (when working with script files) is to use the findLineNum() and setBreakpoint() functions.
As a final comment: depending upon the error, it is also very helpful to set try() or tryCatch() statements around external function calls (especially when dealing with S4 classes). That will sometimes provide even more information, and it also gives you more control over how errors are handled at run time.
These related questions have a lot of suggestions:
Debugging tools for the R language
Debugging lapply/sapply calls
Getting the state of variables after an error occurs in R
R script line numbers at error?
The best walkthrough I've seen so far is:
http://www.biostat.jhsph.edu/%7Erpeng/docs/R-debug-tools.pdf
Anybody agree/disagree?
As was pointed out to me in another question, Rprof() and summaryRprof() are nice tools to find slow parts of your program that might benefit from speeding up or moving to a C/C++ implementation. This probably applies more if you're doing simulation work or other compute- or data-intensive activities. The profr package can help visualizing the results.
I'm on a bit of a learn-about-debugging kick, so another suggestion from another thread:
Set options(warn=2) to treat warnings like errors
You can also use options to drop you right into the heat of the action when an error or warning occurs, using your favorite debugging function of choice. For instance:
Set options(error=recover) to run recover() when an error occurs, as Shane noted (and as is documented in the R debugging guide. Or any other handy function you would find useful to have run.
And another two methods from one of #Shane's links:
Wrap an inner function call with try() to return more information on it.
For *apply functions, use .inform=TRUE (from the plyr package) as an option to the apply command
#JoshuaUlrich also pointed out a neat way of using the conditional abilities of the classic browser() command to turn on/off debugging:
Put inside the function you might want to debug browser(expr=isTRUE(getOption("myDebug")))
And set the global option by options(myDebug=TRUE)
You could even wrap the browser call: myBrowse <- browser(expr=isTRUE(getOption("myDebug"))) and then call with myBrowse() since it uses globals.
Then there are the new functions available in R 2.10:
findLineNum() takes a source file name and line number and returns the function and environment. This seems to be helpful when you source() a .R file and it returns an error at line #n, but you need to know what function is located at line #n.
setBreakpoint() takes a source file name and line number and sets a breakpoint there
The codetools package, and particularly its checkUsage function can be particularly helpful in quickly picking up syntax and stylistic errors that a compiler would typically report (unused locals, undefined global functions and variables, partial argument matching, and so forth).
setBreakpoint() is a more user-friendly front-end to trace(). Details on the internals of how this works are available in a recent R Journal article.
If you are trying to debug someone else's package, once you have located the problem you can over-write their functions with fixInNamespace and assignInNamespace, but do not use this in production code.
None of this should preclude the tried-and-true standard R debugging tools, some of which are above and others of which are not. In particular, the post-mortem debugging tools are handy when you have a time-consuming bunch of code that you'd rather not re-run.
Finally, for tricky problems which don't seem to throw an error message, you can use options(error=dump.frames) as detailed in this question:
Error without an error being thrown
At some point, glm.fit is being called. That means one of the functions you call or one of the functions called by those functions is using either glm, glm.fit.
Also, as I mention in my comment above, that is a warning not an error, which makes a big difference. You can't trigger any of R's debugging tools from a warning (with default options before someone tells me I am wrong ;-).
If we change the options to turn warnings into errors then we can start to use R's debugging tools. From ?options we have:
‘warn’: sets the handling of warning messages. If ‘warn’ is
negative all warnings are ignored. If ‘warn’ is zero (the
default) warnings are stored until the top-level function
returns. If fewer than 10 warnings were signalled they will
be printed otherwise a message saying how many (max 50) were
signalled. An object called ‘last.warning’ is created and
can be printed through the function ‘warnings’. If ‘warn’ is
one, warnings are printed as they occur. If ‘warn’ is two or
larger all warnings are turned into errors.
So if you run
options(warn = 2)
then run your code, R will throw an error. At which point, you could run
traceback()
to see the call stack. Here is an example.
> options(warn = 2)
> foo <- function(x) bar(x + 2)
> bar <- function(y) warning("don't want to use 'y'!")
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
> traceback()
7: doWithOneRestart(return(expr), restart)
6: withOneRestart(expr, restarts[[1L]])
5: withRestarts({
.Internal(.signalCondition(simpleWarning(msg, call), msg,
call))
.Internal(.dfltWarn(msg, call))
}, muffleWarning = function() NULL)
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x +
2)))
3: warning("don't want to use 'y'!")
2: bar(x + 2)
1: foo(1)
Here you can ignore the frames marked 4: and higher. We see that foo called bar and that bar generated the warning. That should show you which functions were calling glm.fit.
If you now want to debug this, we can turn to another option to tell R to enter the debugger when it encounters an error, and as we have made warnings errors we will get a debugger when the original warning is triggered. For that you should run:
options(error = recover)
Here is an example:
> options(error = recover)
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
Enter a frame number, or 0 to exit
1: foo(1)
2: bar(x + 2)
3: warning("don't want to use 'y'!")
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x + 2)))
5: withRestarts({
6: withOneRestart(expr, restarts[[1]])
7: doWithOneRestart(return(expr), restart)
Selection:
You can then step into any of those frames to see what was happening when the warning was thrown.
To reset the above options to their default, enter
options(error = NULL, warn = 0)
As for the specific warning you quote, it is highly likely that you need to allow more iterations in the code. Once you've found out what is calling glm.fit, work out how to pass it the control argument using glm.control - see ?glm.control.
So browser(), traceback() and debug() walk into a bar, but trace() waits outside and keeps the motor running.
By inserting browser somewhere in your function, the execution will halt and wait for your input. You can move forward using n (or Enter), run the entire chunk (iteration) with c, finish the current loop/function with f, or quit with Q; see ?browser.
With debug, you get the same effect as with browser, but this stops the execution of a function at its beginning. Same shortcuts apply. This function will be in a "debug" mode until you turn it off using undebug (that is, after debug(foo), running the function foo will enter "debug" mode every time until you run undebug(foo)).
A more transient alternative is debugonce, which will remove the "debug" mode from the function after the next time it's evaluated.
traceback will give you the flow of execution of functions all the way up to where something went wrong (an actual error).
You can insert code bits (i.e. custom functions) in functions using trace, for example browser. This is useful for functions from packages and you're too lazy to get the nicely folded source code.
My general strategy looks like:
Run traceback() to see look for obvious issues
Set options(warn=2) to treat warnings like errors
Set options(error=recover) to step into the call stack on error
After going through all the steps suggested here I just learned that setting .verbose = TRUE in foreach() also gives me tons of useful information. In particular foreach(.verbose=TRUE) shows exactly where an error occurs inside the foreach loop, while traceback() does not look inside the foreach loop.
Mark Bravington's debugger which is available as the package debug on CRAN is very good and pretty straight forward.
library(debug);
mtrace(myfunction);
myfunction(a,b);
#... debugging, can query objects, step, skip, run, breakpoints etc..
qqq(); # quit the debugger only
mtrace.off(); # turn off debugging
The code pops up in a highlighted Tk window so you can see what's going on and, of course you can call another mtrace() while in a different function.
HTH
I like Gavin's answer: I did not know about options(error = recover). I also like to use the 'debug' package that gives a visual way to step through your code.
require(debug)
mtrace(foo)
foo(1)
At this point it opens up a separate debug window showing your function, with a yellow line showing where you are in the code. In the main window the code enters debug mode, and you can keep hitting enter to step through the code (and there are other commands as well), and examine variable values, etc. The yellow line in the debug window keeps moving to show where you are in the code. When done with debugging, you can turn off tracing with:
mtrace.off()
Based on the answer I received here, you should definitely check out the options(error=recover) setting. When this is set, upon encountering an error, you'll see text on the console similar to the following (traceback output):
> source(<my filename>)
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
Enter a frame number, or 0 to exit
1: source(<my filename>)
2: eval.with.vis(ei, envir)
3: eval.with.vis(expr, envir, enclos)
4: LinearParamSearch(data = dataset, y = data.frame(LGD = dataset$LGD10), data.names = data
5: LinearParamSearch.R#66: plot(x = x, y = y.data, xlab = names(y), ylab = data.names[i])
6: LinearParamSearch.R#66: plot.default(x = x, y = y.data, xlab = names(y), ylab = data.nam
7: LinearParamSearch.R#66: localWindow(xlim, ylim, log, asp, ...)
8: LinearParamSearch.R#66: plot.window(...)
Selection:
At which point you can choose which "frame" to enter. When you make a selection, you'll be placed into browser() mode:
Selection: 4
Called from: stop(gettextf("replacement has %d rows, data has %d", N, n),
domain = NA)
Browse[1]>
And you can examine the environment as it was at the time of the error. When you're done, type c to bring you back to the frame selection menu. When you're done, as it tells you, type 0 to exit.
I gave this answer to a more recent question, but am adding it here for completeness.
Personally I tend not to use functions to debug. I often find that this causes as much trouble as it solves. Also, coming from a Matlab background I like being able to do this in an integrated development environment (IDE) rather than doing this in the code. Using an IDE keeps your code clean and simple.
For R, I use an IDE called "RStudio" (http://www.rstudio.com), which is available for windows, mac, and linux and is pretty easy to use.
Versions of Rstudio since about October 2013 (0.98ish?) have the capability to add breakpoints in scripts and functions: to do this, just click on the left margin of the file to add a breakpoint. You can set a breakpoint and then step through from that point on. You also have access to all of the data in that environment, so you can try out commands.
See http://www.rstudio.com/ide/docs/debugging/overview for details. If you already have Rstudio installed, you may need to upgrade - this is a relatively new (late 2013) feature.
You may also find other IDEs that have similar functionality.
Admittedly, if it's a built-in function you may have to resort to some of the suggestions made by other people in this discussion. But, if it's your own code that needs fixing, an IDE-based solution might be just what you need.
To debug Reference Class methods without instance reference
ClassName$trace(methodName, browser)
I am beginning to think that not printing error line number - a most basic requirement - BY DEFAILT- is some kind of a joke in R/Rstudio. The only reliable method I have found to find where an error occurred is to make the additional effort of calloing traceback() and see the top line.
Whilst debugging in Xcode_3.1.2 I am pretty sure I could see the contents of my NSString arrays. However after upgrading to 3.2 I only see the following ...
I know I can print the object in (gdb) using "po planetArray" or simply click in the debugger and "print description to console" I am just curious, as I am sure it worked prior to upgrading. Anyone know anything about this?
cheers gary
edit: data formatters is on and it shows what you see above ...
This is because GDB acts as if the variable you are viewing is out of scope while it really just is confused about what each part function or method call of the data formatter is returning (the data formatter is the "{(unichar *)Xcode_CFStringSummary($VAR, $ID)}:s" part you are seeing.
When you are debugging and you are in a method where you know a local variable must be in scope right now, open the debugger window and the area where you can see "Variable", "Value" and "Summary" column titles double click the "Summary" row entry for the variable you are interested in and enter the following (for array types like NSArray or NSCFArray):
"{(int)[$VAR count]} objects {(NSString *)[(NSArray *)$VAR description]}:s"
then press return. You have now overwritten the default data formatter provided by Xcode's GDB extension (to be found in various plists at "/Developer/Library/Xcode/CustomDataViews/") with your own data formatter string.
Your own overrides are saved at "~/Library/Application Support/Developer/Shared/Xcode/CustomDataViews/CustomDataViews.plist" and if you want to have the Apple default data formatter back just double click the row for a variable of the same type and delete whatever is there.
The nitty-gritty details: In the custom expression above the "{}" construct tells GDB to execute a command (as if you where executing it from GDB's debugger command line, which means the same restrictions apply: you need to specify the return type in cast parens in front of every function or method which returns something). The ":s" behind the closing curly brace tells Xcode and GDB to reference the "Summary" column. Also valid would be ":v" which references the "Value" column which most of the time is just the pointer value. Everything that is outside of the curly braces is shown verbatim.
Unfortuntely curly braces can't be nested which invalidates ternary operator conditionals.
So with the above data formatter you should see the following for an empty NSArray:
"0 objects (\n)"
If you want to write your own data formatters as GDB extensions (equivalent to specifying a function akin to Xcode_CFStringSummary above) you can do so. Take a look at the following header: "/Developer/Applications/Xcode.app/Contents/PlugIns/GDBMIDebugging.xcplugin/Contents/Headers/DataFormatterPlugin.h"
it will tell you all you need to know. But it can be hard to get it right. It might be easier and less error prone to just define another method on your class and call that from the data formatter string instead of "description".
In the Run > Variables View menu in Xcode, is "Use Data Formatters" enabled?
I am not sure if this helps but if you select the array value to wish to see in the debugger window and the go to the Menu : Run > Variables View > View Variable As
you can change it from "NSCFString *" to "NSString *". You then see the value so "Planet_1" for example.
Cheers,
Kevin
I get an error when using an R function that I wrote:
Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: algorithm did not converge
What I have done:
Step through the function
Adding print to find out at what line the error occurs suggests two functions that should not use glm.fit. They are window() and save().
My general approaches include adding print and stop commands, and stepping through a function line by line until I can locate the exception.
However, it is not clear to me using those techniques where this error comes from in the code. I am not even certain which functions within the code depend on glm.fit. How do I go about diagnosing this problem?
I'd say that debugging is an art form, so there's no clear silver bullet. There are good strategies for debugging in any language, and they apply here too (e.g. read this nice article). For instance, the first thing is to reproduce the problem...if you can't do that, then you need to get more information (e.g. with logging). Once you can reproduce it, you need to reduce it down to the source.
Rather than a "trick", I would say that I have a favorite debugging routine:
When an error occurs, the first thing that I usually do is look at the stack trace by calling traceback(): that shows you where the error occurred, which is especially useful if you have several nested functions.
Next I will set options(error=recover); this immediately switches into browser mode where the error occurs, so you can browse the workspace from there.
If I still don't have enough information, I usually use the debug() function and step through the script line by line.
The best new trick in R 2.10 (when working with script files) is to use the findLineNum() and setBreakpoint() functions.
As a final comment: depending upon the error, it is also very helpful to set try() or tryCatch() statements around external function calls (especially when dealing with S4 classes). That will sometimes provide even more information, and it also gives you more control over how errors are handled at run time.
These related questions have a lot of suggestions:
Debugging tools for the R language
Debugging lapply/sapply calls
Getting the state of variables after an error occurs in R
R script line numbers at error?
The best walkthrough I've seen so far is:
http://www.biostat.jhsph.edu/%7Erpeng/docs/R-debug-tools.pdf
Anybody agree/disagree?
As was pointed out to me in another question, Rprof() and summaryRprof() are nice tools to find slow parts of your program that might benefit from speeding up or moving to a C/C++ implementation. This probably applies more if you're doing simulation work or other compute- or data-intensive activities. The profr package can help visualizing the results.
I'm on a bit of a learn-about-debugging kick, so another suggestion from another thread:
Set options(warn=2) to treat warnings like errors
You can also use options to drop you right into the heat of the action when an error or warning occurs, using your favorite debugging function of choice. For instance:
Set options(error=recover) to run recover() when an error occurs, as Shane noted (and as is documented in the R debugging guide. Or any other handy function you would find useful to have run.
And another two methods from one of #Shane's links:
Wrap an inner function call with try() to return more information on it.
For *apply functions, use .inform=TRUE (from the plyr package) as an option to the apply command
#JoshuaUlrich also pointed out a neat way of using the conditional abilities of the classic browser() command to turn on/off debugging:
Put inside the function you might want to debug browser(expr=isTRUE(getOption("myDebug")))
And set the global option by options(myDebug=TRUE)
You could even wrap the browser call: myBrowse <- browser(expr=isTRUE(getOption("myDebug"))) and then call with myBrowse() since it uses globals.
Then there are the new functions available in R 2.10:
findLineNum() takes a source file name and line number and returns the function and environment. This seems to be helpful when you source() a .R file and it returns an error at line #n, but you need to know what function is located at line #n.
setBreakpoint() takes a source file name and line number and sets a breakpoint there
The codetools package, and particularly its checkUsage function can be particularly helpful in quickly picking up syntax and stylistic errors that a compiler would typically report (unused locals, undefined global functions and variables, partial argument matching, and so forth).
setBreakpoint() is a more user-friendly front-end to trace(). Details on the internals of how this works are available in a recent R Journal article.
If you are trying to debug someone else's package, once you have located the problem you can over-write their functions with fixInNamespace and assignInNamespace, but do not use this in production code.
None of this should preclude the tried-and-true standard R debugging tools, some of which are above and others of which are not. In particular, the post-mortem debugging tools are handy when you have a time-consuming bunch of code that you'd rather not re-run.
Finally, for tricky problems which don't seem to throw an error message, you can use options(error=dump.frames) as detailed in this question:
Error without an error being thrown
At some point, glm.fit is being called. That means one of the functions you call or one of the functions called by those functions is using either glm, glm.fit.
Also, as I mention in my comment above, that is a warning not an error, which makes a big difference. You can't trigger any of R's debugging tools from a warning (with default options before someone tells me I am wrong ;-).
If we change the options to turn warnings into errors then we can start to use R's debugging tools. From ?options we have:
‘warn’: sets the handling of warning messages. If ‘warn’ is
negative all warnings are ignored. If ‘warn’ is zero (the
default) warnings are stored until the top-level function
returns. If fewer than 10 warnings were signalled they will
be printed otherwise a message saying how many (max 50) were
signalled. An object called ‘last.warning’ is created and
can be printed through the function ‘warnings’. If ‘warn’ is
one, warnings are printed as they occur. If ‘warn’ is two or
larger all warnings are turned into errors.
So if you run
options(warn = 2)
then run your code, R will throw an error. At which point, you could run
traceback()
to see the call stack. Here is an example.
> options(warn = 2)
> foo <- function(x) bar(x + 2)
> bar <- function(y) warning("don't want to use 'y'!")
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
> traceback()
7: doWithOneRestart(return(expr), restart)
6: withOneRestart(expr, restarts[[1L]])
5: withRestarts({
.Internal(.signalCondition(simpleWarning(msg, call), msg,
call))
.Internal(.dfltWarn(msg, call))
}, muffleWarning = function() NULL)
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x +
2)))
3: warning("don't want to use 'y'!")
2: bar(x + 2)
1: foo(1)
Here you can ignore the frames marked 4: and higher. We see that foo called bar and that bar generated the warning. That should show you which functions were calling glm.fit.
If you now want to debug this, we can turn to another option to tell R to enter the debugger when it encounters an error, and as we have made warnings errors we will get a debugger when the original warning is triggered. For that you should run:
options(error = recover)
Here is an example:
> options(error = recover)
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
Enter a frame number, or 0 to exit
1: foo(1)
2: bar(x + 2)
3: warning("don't want to use 'y'!")
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x + 2)))
5: withRestarts({
6: withOneRestart(expr, restarts[[1]])
7: doWithOneRestart(return(expr), restart)
Selection:
You can then step into any of those frames to see what was happening when the warning was thrown.
To reset the above options to their default, enter
options(error = NULL, warn = 0)
As for the specific warning you quote, it is highly likely that you need to allow more iterations in the code. Once you've found out what is calling glm.fit, work out how to pass it the control argument using glm.control - see ?glm.control.
So browser(), traceback() and debug() walk into a bar, but trace() waits outside and keeps the motor running.
By inserting browser somewhere in your function, the execution will halt and wait for your input. You can move forward using n (or Enter), run the entire chunk (iteration) with c, finish the current loop/function with f, or quit with Q; see ?browser.
With debug, you get the same effect as with browser, but this stops the execution of a function at its beginning. Same shortcuts apply. This function will be in a "debug" mode until you turn it off using undebug (that is, after debug(foo), running the function foo will enter "debug" mode every time until you run undebug(foo)).
A more transient alternative is debugonce, which will remove the "debug" mode from the function after the next time it's evaluated.
traceback will give you the flow of execution of functions all the way up to where something went wrong (an actual error).
You can insert code bits (i.e. custom functions) in functions using trace, for example browser. This is useful for functions from packages and you're too lazy to get the nicely folded source code.
My general strategy looks like:
Run traceback() to see look for obvious issues
Set options(warn=2) to treat warnings like errors
Set options(error=recover) to step into the call stack on error
After going through all the steps suggested here I just learned that setting .verbose = TRUE in foreach() also gives me tons of useful information. In particular foreach(.verbose=TRUE) shows exactly where an error occurs inside the foreach loop, while traceback() does not look inside the foreach loop.
Mark Bravington's debugger which is available as the package debug on CRAN is very good and pretty straight forward.
library(debug);
mtrace(myfunction);
myfunction(a,b);
#... debugging, can query objects, step, skip, run, breakpoints etc..
qqq(); # quit the debugger only
mtrace.off(); # turn off debugging
The code pops up in a highlighted Tk window so you can see what's going on and, of course you can call another mtrace() while in a different function.
HTH
I like Gavin's answer: I did not know about options(error = recover). I also like to use the 'debug' package that gives a visual way to step through your code.
require(debug)
mtrace(foo)
foo(1)
At this point it opens up a separate debug window showing your function, with a yellow line showing where you are in the code. In the main window the code enters debug mode, and you can keep hitting enter to step through the code (and there are other commands as well), and examine variable values, etc. The yellow line in the debug window keeps moving to show where you are in the code. When done with debugging, you can turn off tracing with:
mtrace.off()
Based on the answer I received here, you should definitely check out the options(error=recover) setting. When this is set, upon encountering an error, you'll see text on the console similar to the following (traceback output):
> source(<my filename>)
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
Enter a frame number, or 0 to exit
1: source(<my filename>)
2: eval.with.vis(ei, envir)
3: eval.with.vis(expr, envir, enclos)
4: LinearParamSearch(data = dataset, y = data.frame(LGD = dataset$LGD10), data.names = data
5: LinearParamSearch.R#66: plot(x = x, y = y.data, xlab = names(y), ylab = data.names[i])
6: LinearParamSearch.R#66: plot.default(x = x, y = y.data, xlab = names(y), ylab = data.nam
7: LinearParamSearch.R#66: localWindow(xlim, ylim, log, asp, ...)
8: LinearParamSearch.R#66: plot.window(...)
Selection:
At which point you can choose which "frame" to enter. When you make a selection, you'll be placed into browser() mode:
Selection: 4
Called from: stop(gettextf("replacement has %d rows, data has %d", N, n),
domain = NA)
Browse[1]>
And you can examine the environment as it was at the time of the error. When you're done, type c to bring you back to the frame selection menu. When you're done, as it tells you, type 0 to exit.
I gave this answer to a more recent question, but am adding it here for completeness.
Personally I tend not to use functions to debug. I often find that this causes as much trouble as it solves. Also, coming from a Matlab background I like being able to do this in an integrated development environment (IDE) rather than doing this in the code. Using an IDE keeps your code clean and simple.
For R, I use an IDE called "RStudio" (http://www.rstudio.com), which is available for windows, mac, and linux and is pretty easy to use.
Versions of Rstudio since about October 2013 (0.98ish?) have the capability to add breakpoints in scripts and functions: to do this, just click on the left margin of the file to add a breakpoint. You can set a breakpoint and then step through from that point on. You also have access to all of the data in that environment, so you can try out commands.
See http://www.rstudio.com/ide/docs/debugging/overview for details. If you already have Rstudio installed, you may need to upgrade - this is a relatively new (late 2013) feature.
You may also find other IDEs that have similar functionality.
Admittedly, if it's a built-in function you may have to resort to some of the suggestions made by other people in this discussion. But, if it's your own code that needs fixing, an IDE-based solution might be just what you need.
To debug Reference Class methods without instance reference
ClassName$trace(methodName, browser)
I am beginning to think that not printing error line number - a most basic requirement - BY DEFAILT- is some kind of a joke in R/Rstudio. The only reliable method I have found to find where an error occurred is to make the additional effort of calloing traceback() and see the top line.