Is there a compelling reason to have strip.white default to FALSE? It seems to me that it would be more common to want the TRUE case. Having said that, I must confess that I've never had the problem Erik describes.
-Peter Ehlers On 2010-06-29 17:14, Matt Shotwell wrote:
The document RFC 4180 (which appears to be the CSV standard used by R, see ?read.table) considers spaces to be part of the fielded value. Some have taken this to mean that all white space characters should be considered part of the fielded value, though the RFC is not explicit here. Hence, this behavior is in compliance with the "standard" for CSV files. It seems that R treats '\t' (and perhaps all?) separated value files the same way by default. The RFC is very short and easy to read if you're interested. http://tools.ietf.org/html/rfc4180 -Matt On Tue, 2010-06-29 at 16:41 -0400, Erik Iverson wrote: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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