Hello,
While assisting a fellow R-helper off list, I narrowed down an issue he
was having to the following behavior of type.convert, called through
read.table. This is using R 2.10.1, if newer versions don't exhibit
this behavior, apologies.
# generates numeric vector
> type.convert(c("123.42", "NA"))
[1] 123.42 NA
# generates a numeric vector, notice the space before 123.42
> type.convert(c(" 123.42 ", "NA"))
[1] 123.42 NA
# generates factor, notice the space before NA
# note that the 2nd element is actually " NA", not a true NA value
> type.convert(c("123.42", " NA"))
[1] 123.42 NA
Levels: 123.42 NA
How can this affect read.table/read.csv use 'in the wild'?
This gentleman had a data file that was
1) delimited by something other than white space, CSV in his case
2) contained missing values, designated by NA in his case
3) contained white space between delimiters and data values, e.g.,
NA, NA, 4.5, NA
as opposed to
NA,NA,4.5,NA
With these 3 conditions met, read.table gives type.convert a character
vector like my third example above, and ultimately he got a data.frame
consisting of only factors when we were expecting numeric columns. This
was easily fixed either by modifying the read.csv function call to
specify colClasses directly, or in his case, strip.white = TRUE did the
job just fine.
I believe the confusion stems from the fact that with no NA values in
our data file, this would work as we would expect. The introduction of
what we thought were NA values changed the behavior. In reality, these
were not being treated as NA values by read.table/type.convert. The
question is, should they be in this case?
This behavior of read.table/type.convert may very well be what is
expected/needed. If so, this note could still be of use to someone in
the future if they stumble upon similar behavior. The fact I wasn't
able to uncover anyone who asked about it on list before probably means
the situation is rare.
Best Regards,
Erik Iverson
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