| NA | R Documentation |
NA is a logical constant of length 1 which contains a missing
value indicator. NA can be coerced to any other vector
type except raw. There are also constants NA_integer_,
NA_real_, NA_complex_ and NA_character_ of the
other atomic vector types which support missing values: all of these
are reserved words in the R language.
The generic function is.na indicates which elements are missing.
The generic function is.na<- sets elements to NA.
The generic function anyNA implements any(is.na(x)) in a
possibly faster way (especially for atomic vectors).
NA is.na(x) anyNA(x, recursive = FALSE) ## S3 method for class 'data.frame' is.na(x) is.na(x) <- value
x |
an R object to be tested: the default method for
|
recursive |
logical: should |
value |
a suitable index vector for use with |
The NA of character type is distinct from the string
"NA". Programmers who need to specify an explicit missing
string should use NA_character_ (rather than "NA") or set
elements to NA using is.na<-.
is.na and anyNA are generic: you can write
methods to handle specific classes of objects, see
InternalMethods.
Function is.na<- may provide a safer way to set missingness.
It behaves differently for factors, for example.
Numerical computations using NA will normally result in
NA: a possible exception is where NaN is also
involved, in which case either might result (which may depend on
the R platform). However, this is not guaranteed and future CPUs
and/or compilers may behave differently. Dynamic binary translation may
also impact this behavior (with valgrind, computations using NA
may result in NaN even when no NaN is involved).
Logical computations treat NA as a missing TRUE/FALSE
value, and so may return TRUE or FALSE if the expression
does not depend on the NA operand.
The default method for anyNA handles atomic vectors without a
class and NULL. It calls any(is.na(x)) on objects with
classes and for recursive = FALSE, on lists and pairlists.
The default method for is.na applied to an atomic vector
returns a logical vector of the same length as its argument x,
containing TRUE for those elements marked NA or, for
numeric or complex vectors, NaN, and FALSE
otherwise. (A complex value is regarded as NA if either its
real or imaginary part is NA or NaN.)
dim, dimnames and names attributes are copied to
the result.
The default methods also work for lists and pairlists:
For is.na, elementwise the result is false unless that element
is a length-one atomic vector and the single element of that vector is
regarded as NA or NaN (note that any is.na
method for the class of the element is ignored).
anyNA(recursive = FALSE) works the same way as is.na;
anyNA(recursive = TRUE) applies anyNA (with method
dispatch) to each element.
The data frame method for is.na returns a logical matrix
with the same dimensions as the data frame, and with dimnames taken
from the row and column names of the data frame.
anyNA(NULL) is false; is.na(NULL) is logical(0)
(no longer warning since R version 3.5.0).
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
Chambers, J. M. (1998) Programming with Data. A Guide to the S Language. Springer.
NaN, is.nan, etc.,
and the utility function complete.cases.
na.action, na.omit, na.fail
on how methods can be tuned to deal with missing values.
is.na(c(1, NA)) #> FALSE TRUE
is.na(paste(c(1, NA))) #> FALSE FALSE
(xx <- c(0:4))
is.na(xx) <- c(2, 4)
xx #> 0 NA 2 NA 4
anyNA(xx) # TRUE
# Some logical operations do not return NA
c(TRUE, FALSE) & NA
c(TRUE, FALSE) | NA
## Measure speed difference in a favourable case:
## the difference depends on the platform, on most ca 3x.
x <- 1:10000; x[5000] <- NaN # coerces x to be double
if(require("microbenchmark")) { # does not work reliably on all platforms
print(microbenchmark(any(is.na(x)), anyNA(x)))
} else {
nSim <- 2^13
print(rbind(is.na = system.time(replicate(nSim, any(is.na(x)))),
anyNA = system.time(replicate(nSim, anyNA(x)))))
}
## anyNA() can work recursively with list()s:
LL <- list(1:5, c(NA, 5:8), c("A","NA"), c("a", NA_character_))
L2 <- LL[c(1,3)]
sapply(LL, anyNA); c(anyNA(LL), anyNA(LL, TRUE))
sapply(L2, anyNA); c(anyNA(L2), anyNA(L2, TRUE))
## ... lists, and hence data frames, too:
dN <- dd <- USJudgeRatings; dN[3,6] <- NA
anyNA(dd) # FALSE
anyNA(dN) # TRUE
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