preserve old (pre 3.1.0) type.convert behavior
r, read.table
Solution
In version 3.1.1, there is this change listed in the News file:
`type.convert()`, `read.table()` and similar `read.*()` functions get a new `numerals` argument, specifying how numeric input is converted when its conversion to double precision loses accuracy. The default `numerals = "allow.loss"` allows accuracy loss, as in R versions before 3.1.0.
Much of post-release discussion about the original change, including the decisions to revert the default behavior with an additional warning, can be found in a thread on the developers' email list.
For version 3.1.0, code will have to be modified to get the old behavior. Switching to 3.1.1 is another strategy.
The mention of this change for version 3.1.0 (from the same News file) says
`type.convert()` (and hence by default `read.table()`) returns a character vector or factor when representing a numeric input as a double would lose accuracy. Similarly for complex inputs.
If a file contains numeric data with unrepresentable numbers of decimal places that are intended to be read as numeric, specify `colClasses` in `read.table()` to be `"numeric"`.
Note: original answer was written when the applicable version with the fix was 3.1.0 patched. The answer has been updated now that 3.1.1 has been released.
Problem
R 3.1.0 is out and one of the new features is the following: `type.convert()` (and hence by default `read.table()`) returns a character vector or factor when representing a numeric input as a double would lose accuracy. Similarly for complex inputs. To give an example: ``` df <- read.table(text = "num1 num2 1.1 1.1234567890123456 2.2 2.2 3.3 3.3", header = TRUE) sapply(df, class) # num1 num2 # "numeric" "factor" ``` while with previous versions, `read.table` would have returned two numeric columns. For those who like me are a concerned about that change, what can be done to preserve the old behavior? Note: I'd like a general solution that does not make assumptions on the input data, i.e. do not suggest I use `colClasses = "numeric"` in the example above. Thanks.