[Rcpp-devel] Missing values

Hadley Wickham h.wickham at gmail.com
Thu Nov 15 23:36:30 CET 2012

Hi all,

I'm working on a description of how missing values work in Rcpp
(expanding on FAQ 3.4).  I'd really appreciate any comments,
corrections or suggestions on the text below.



# Missing values

If you're working with missing values, you need to know two things:

* what happens when you put missing values in scalars (e.g. `double`)
* how to get and set missing values in vectors (e.g. `NumericVector`)

## Scalars

The following code explores what happens when you coerce the first
element of a vector into the corresponding scalar:

    cppFunction('int first_int(IntegerVector x) {
    cppFunction('double first_num(NumericVector x) {
    cppFunction('std::string first_char(CharacterVector x) {
      return((std::string) x[0]);
    cppFunction('bool first_log(LogicalVector x) {



* `NumericVector` -> `double`: NAN

* `IntegerVector` -> `int`: NAN (not sure how this works given that
integer types don't usually have a missing value)

* `CharacterVector` -> `std::string`: the string "NA"

* `LogicalVector` -> `bool`: TRUE

If you're working with doubles, depending on your problem, you may be
able to get away with ignoring missing values and working with NaNs.
R's missing values are a special type of the IEEE 754 floating point
number NaN (not a number). That means if you coerce them to `double`
or `int` in your C++ code, they will behave like regular NaN's.

In a logical context they always evaluate to FALSE:

    evalCpp("NAN == 1")
    evalCpp("NAN < 1")
    evalCpp("NAN > 1")
    evalCpp("NAN == NAN")

But be careful when combining then with boolean values:

    evalCpp("NAN && TRUE")
    evalCpp("NAN || FALSE")

In numeric contexts, they propagate similarly to NA in R:

    evalCpp("NAN + 1")
    evalCpp("NAN - 1")
    evalCpp("NAN / 1")
    evalCpp("NAN * 1")

## Vectors

To set a missing value in a vector, you need to use a missing value
specific to the type of vector. Unfortunately these are not named
terribly consistently:

      List missing_sampler() {

        NumericVector num(1);
        num[0] = NA_REAL;

        IntegerVector intv(1);
        intv[0] = NA_INTEGER;

        LogicalVector lgl(1);
        lgl[0] = NA_LOGICAL;

        CharacterVector chr(1);
        chr[0] = NA_STRING;

        List out(4);
        out[0] = num;
        out[1] = intv;
        out[2] = lgl;
        out[3] = chr;

To check if a value in a vector is missing, use `ISNA`:

      LogicalVector is_na2(NumericVector x) {
        LogicalVector out(x.size());

        NumericVector::iterator x_it;
        LogicalVector::iterator out_it;
        for (x_it = x.begin(), out_it = out.begin(); x_it != x.end();
x_it++, out_it++) {
          *out_it = ISNA(*x_it);
    is_na2(c(NA, 5.4, 3.2, NA))

Rcpp provides a helper function called `is_na` that works similarly to
`is_na2` above, producing a logical vector that's true where the value
in the vector was missing.

RStudio / Rice University

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