> This is all application specific and
> sort of beyond the scope of type.convert(), which now behaves as it
> has been documented to behave.
That's only a true statement because the documentation was changed to reflect
the new behavior! The new feature in type.convert certainly does not behave
according to the documentation as of R 3.0.3. Here's a snippit:
The first type that can accept all the
non-missing values is chosen (numeric and complex return values
will represented approximately, of course).
The key phrase is in parentheses, which reminds the user to expect a possible
loss of precision. That important parenthetical was removed from the
documentation in R 3.1.0 (among other changes).
Putting aside the fact that this introduces a large amount of unnecessary work
rewriting SQL / data import code, SQL packages, my biggest conceptual problem
is that I can no longer rely on a particular function call returning a
particular class. In my example querying stock prices, about 5% of prices came
back as factors and the remaining 95% as numeric, so we had random errors
popping in throughout the morning.
Here's a short example showing us how the new behavior can be unreliable. I
pass a character representation of a uniformly distributed random variable to
type.convert. 90% of the time it is converted to "numeric" and 10% it is a
"factor" (in R 3.1.0). In the 10% of cases in which type.convert converts to a
factor the leading non-zero digit is always a 9. So if you were expecting a
numeric value, then 1 in 10 times you may have a bug in your code that didn't
exist before.
> options(digits=16)
> cl <- NULL; for (i in 1:10000) cl[i] <- class(type.convert(format(runif(1))))
> table(cl)
cl
factor numeric
990 9010
Cheers, Robert
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