NaN versus NA in R18 Jul 2016
R has two different ways of representing missing data and understanding
each is important for the user.
NaN means “not a number” and it
means there is a result, but it cannot be represented in the computer.
NA, explains that the data is just missing for unknown
reasons. These appear at different times when working with R and
each has different implications.
NaN is distinct from
NaN implies a result that cannot
be calculated for whatever reason, or is not a floating point number.
Some calculations that lead to
NaN, other than [latex]0 / 0[/latex],
are attempting to take a square root of a negative number, or perform
calculations with infinities that lead to undefined results:
However, adding two infinities produces infinity:
NA is different from
NaN in that
NA is not a part of the IEEE
standard for floating point numbers.
NA is a construction of R
used to represent a value that is not known, as a placeholder.
says no result was available or the result is missing. It can be
used in a matrix to fill in a value of a vector:
Any operation with
NA results in
Update: David Smith at Microsoft covered NAs in a blog post today, too.
Image by Torindkflt / Wikimedia Commons.