If your data uses a special marker such as "--" or "n/a" to indicate not
available then once you have identified those markers (using any method, though
Don's procedure below is what I use) then you can specify them with the
na.strings parameter to read.csv. (See the help for read.table for many other
parameters you can also give to read.csv.) Once those special values are
specified, then you should get numeric columns just fine.
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On November 14, 2014 11:11:20 AM PST, "MacQueen, Don" <macque...@llnl.gov>
wrote:
>Petr is almost certainly correct. A further suggestion:
>
>Continue to import using stringsAsFactors = FALSE
>
>On one of the columns that should be numeric, use as.numeric(), find
>the
>NA's in the result of that, and then look at those rows of the data.
>There
>will be something there that is non-numeric. By any chance, does your
>data
>use a comma instead of a decimal point (see the "dec" argument to
>read.table)?
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