| try | R Documentation |
try is a wrapper to run an expression that might fail and allow
the user's code to handle error-recovery.
try(expr, silent = FALSE,
outFile = getOption("try.outFile", default = stderr()))
expr |
an R expression to try. |
silent |
logical: should the report of error messages be suppressed? |
outFile |
a connection, or a character string naming the
file to print to (via |
try evaluates an expression and traps any errors that occur
during the evaluation. If an error occurs then the error
message is printed to the stderr connection unless
options("show.error.messages") is false or
the call includes silent = TRUE. The error message is also
stored in a buffer where it can be retrieved by
geterrmessage. (This should not be needed as the value returned
in case of an error contains the error message.)
try is implemented using tryCatch; for
programming, instead of try(expr, silent = TRUE), something like
tryCatch(expr, error = function(e) e) (or other simple
error handler functions) may be more efficient and flexible.
It may be useful to set the default for outFile to
stdout(), i.e.,
options(try.outFile = stdout())
instead of the default stderr(),
notably when try() is used inside a Sweave code
chunk and the error message should appear in the resulting document.
The value of the expression if expr is evaluated without error,
but an invisible object of class "try-error" containing the
error message, and the error condition as the "condition"
attribute, if it fails.
options for setting error handlers and suppressing the
printing of error messages;
geterrmessage for retrieving the last error message.
The underlying tryCatch provides more flexible means of
catching and handling errors.
assertCondition in package tools is related and
useful for testing.
## this example will not work correctly in example(try), but
## it does work correctly if pasted in
options(show.error.messages = FALSE)
try(log("a"))
print(.Last.value)
options(show.error.messages = TRUE)
## alternatively,
print(try(log("a"), TRUE))
## run a simulation, keep only the results that worked.
set.seed(123)
x <- stats::rnorm(50)
doit <- function(x)
{
x <- sample(x, replace = TRUE)
if(length(unique(x)) > 30) mean(x)
else stop("too few unique points")
}
## alternative 1
res <- lapply(1:100, function(i) try(doit(x), TRUE))
## alternative 2
## Not run: res <- vector("list", 100)
for(i in 1:100) res[[i]] <- try(doit(x), TRUE)
## End(Not run)
unlist(res[sapply(res, function(x) !inherits(x, "try-error"))])
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